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• Java EE 7 Roadmap

  - by Linda DeMichiel

  The Java EE 6 Platform, released in December 2009, has seen great uptake from the community with its POJO-based programming model, lightweight Web Profile, and extension points. There are now 13 Java EE 6 compliant appserver implementations today! When we announced the Java EE 7 JSR back in early 2011, our plans were that we would release it by Q4 2012. This target date was slightly over three years after the release of Java EE 6, but at the same time it meant that we had less than two years to complete a fairly comprehensive agenda — to continue to invest in significant enhancements in simplification, usability, and functionality in updated versions of the JSRs that are currently part of the platform; to introduce new JSRs that reflect emerging needs in the community; and to add support for use in cloud environments. We have since announced a minor adjustment in our dates (to the spring of 2013) in order to accommodate the inclusion of JSRs of importance to the community, such as Web Sockets and JSON-P. At this point, however, we have to make a choice. Despite our best intentions, our progress has been slow on the cloud side of our agenda. Partially this has been due to a lack of maturity in the space for provisioning, multi-tenancy, elasticity, and the deployment of applications in the cloud. And partially it is due to our conservative approach in trying to get things "right" in view of limited industry experience in the cloud area when we started this work. Because of this, we believe that providing solid support for standardized PaaS-based programming and multi-tenancy would delay the release of Java EE 7 until the spring of 2014 — that is, two years from now and over a year behind schedule. In our opinion, that is way too long. We have therefore proposed to the Java EE 7 Expert Group that we adjust our course of action — namely, stick to our current target release dates, and defer the remaining aspects of our agenda for PaaS enablement and multi-tenancy support to Java EE 8. Of course, we continue to believe that Java EE is well-suited for use in the cloud, although such use might not be quite ready for full standardization. Even today, without Java EE 7, Java EE vendors such as Oracle, Red Hat, IBM, and CloudBees have begun to offer the ability to run Java EE applications in the cloud. Deferring the remaining cloud-oriented aspects of our agenda has several important advantages: It allows Java EE Platform vendors to gain more experience with their implementations in this area and thus helps us avoid risks entailed by trying to standardize prematurely in an emerging area. It means that the community won't need to wait longer for those features that are ready at the cost of those features that need more time. Because we have already laid some of the infrastructure for cloud support in Java EE 7, including resource definition metadata, improved security configuration, JPA schema generation, etc., it will allow us to expedite a Java EE 8 release. We therefore plan to target the Java EE 8 Platform release for the spring of 2015. This shift in the scope of Java EE 7 allows us to better retain our focus on enhancements in simplification and usability and to deliver on schedule those features that have been most requested by developers. These include the support for HTML 5 in the form of Web Sockets and JSON-P; the simplified JMS 2.0 APIs; improved Managed Bean alignment, including transactional interceptors; the JAX-RS 2.0 client API; support for method-level validation; a much more comprehensive expression language; and more. We feel strongly that this is the right thing to do, and we hope that you will support us in this proposed direction.

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• Tuning Linux IP routing parameters -- secret_interval and tcp_mem

  - by Jeff Atwood

  We had a little failover problem with one of our HAProxy VMs today. When we dug into it, we found this: Jan 26 07:41:45 haproxy2 kernel: [226818.070059] __ratelimit: 10 callbacks suppressed Jan 26 07:41:45 haproxy2 kernel: [226818.070064] Out of socket memory Jan 26 07:41:47 haproxy2 kernel: [226819.560048] Out of socket memory Jan 26 07:41:49 haproxy2 kernel: [226822.030044] Out of socket memory Which, per this link, apparently has to do with low default settings for net.ipv4.tcp_mem. So we increased them by 4x from their defaults (this is Ubuntu Server, not sure if the Linux flavor matters): current values are: 45984 61312 91968 new values are: 183936 245248 367872 After that, we started seeing a bizarre error message: Jan 26 08:18:49 haproxy1 kernel: [ 2291.579726] Route hash chain too long! Jan 26 08:18:49 haproxy1 kernel: [ 2291.579732] Adjust your secret_interval! Shh.. it's a secret!! This apparently has to do with /proc/sys/net/ipv4/route/secret_interval which defaults to 600 and controls periodic flushing of the route cache The secret_interval instructs the kernel how often to blow away ALL route hash entries regardless of how new/old they are. In our environment this is generally bad. The CPU will be busy rebuilding thousands of entries per second every time the cache is cleared. However we set this to run once a day to keep memory leaks at bay (though we've never had one). While we are happy to reduce this, it seems odd to recommend dropping the entire route cache at regular intervals, rather than simply pushing old values out of the route cache faster. After some investigation, we found /proc/sys/net/ipv4/route/gc_elasticity which seems to be a better option for keeping the route table size in check: gc_elasticity can best be described as the average bucket depth the kernel will accept before it starts expiring route hash entries. This will help maintain the upper limit of active routes. We adjusted elasticity from 8 to 4, in the hopes of the route cache pruning itself more aggressively. The secret_interval does not feel correct to us. But there are a bunch of settings and it's unclear which are really the right way to go here. /proc/sys/net/ipv4/route/gc_elasticity (8) /proc/sys/net/ipv4/route/gc_interval (60) /proc/sys/net/ipv4/route/gc_min_interval (0) /proc/sys/net/ipv4/route/gc_timeout (300) /proc/sys/net/ipv4/route/secret_interval (600) /proc/sys/net/ipv4/route/gc_thresh (?) rhash_entries (kernel parameter, default unknown?) We don't want to make the Linux routing worse, so we're kind of afraid to mess with some of these settings. Can anyone advise which routing parameters are best to tune, for a high traffic HAProxy instance?

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• Nginx Retry of Requests ( Nginx - Haproxy Combination )

  - by vaibhav

  I wanted to ask about Nginx Retry of Requests. I have a Nginx running at the backend which then sends the requests to HaProxy which then passes it on the web server and the request is processed. I am reloading my Haproxy config dynamically to provide elasticity. The problem is that the requests are dropped when I reload Haproxy. So I wanted to have a solution where I can just retry that from Nginx. I looked through the proxy_connect_timeout, proxy_next_upstream in http module and max_fails and fail_timeout in server module. I initially only had 1 server in the upstream connections so I just that up twice now and less requests are getting dropped ( only when ) have say the same server twice in upstream , if I have same server 3-4 times drops increase ). So , firstly I wanted to now , that when a request is not able to establish connection from Nginx to Haproxy so while reloading it seems that conneciton is seen as error and straightway the request is dropped . So how can I either specify the time after the failure I want to retry the request from Nginx to upstream or the time before which Nginx treats it as failed request. ( I have tried increaing proxy_connect_timeout - didn't help , mail_retires , fail_timeout and also putting the same upstream server twice ( that gave the best results so far ) Nginx Conf File upstream gae_sleep { server 128.111.55.219:10000; } server { listen 8080; server_name 128.111.55.219; root /var/apps/sleep/app; # Uncomment these lines to enable logging, and comment out the following two #access_log /var/log/nginx/sleep.access.log upstream; error_log /var/log/nginx/sleep.error.log; access_log off; #error_log /dev/null crit; rewrite_log off; error_page 404 = /404.html; set $cache_dir /var/apps/sleep/cache; location / { proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header Host $http_host; proxy_redirect off; proxy_pass http://gae_sleep; client_max_body_size 2G; proxy_connect_timeout 30; client_body_timeout 30; proxy_read_timeout 30; } location /404.html { root /var/apps/sleep; } location /reserved-channel-appscale-path { proxy_buffering off; tcp_nodelay on; keepalive_timeout 55; proxy_pass http://128.111.55.219:5280/http-bind; } }

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• Building a Distributed Commerce Infrastructure in the Cloud using Azure and Commerce Server

  - by Lewis Benge

  One of the biggest questions I routinely get asked is how scalable Commerce Server is. Of course the text book answer is the product has been around for 10 years, powers some of the largest e-Commerce websites in the world, so it scales horizontally extremely well. One argument however though is what if you can't predict the growth of demand required of your Commerce Platform, or need the ability to scale up during busy seasons such as Christmas for a retail environment but are hesitant on maintaining the infrastructure on a year-round basis? The obvious answer is to utilise the many elasticated cloud infrastructure providers that are establishing themselves in the ever-growing market, the problem however is Commerce Server is still product which has a legacy tightly coupled dependency on Windows and IIS components. Commerce Server 2009 codename "R2" however introduced to the concept of an n-tier deployment of Microsoft Commerce Server, meaning you are no longer tied to core objects API but instead have serializable Commerce Entity objects, and business logic allowing for Commerce Server to now be built into a WCF-based SOA architecture. Presentation layers no-longer now need to remain on the same physical machine as the application server, meaning you can now build the user experience into multiple-technologies and host them in multiple places – leveraging the transport benefits that a WCF service may bring, such as message queuing, security, and multiple end-points. All of this logic will still need to remain in your internal infrastructure, for two reasons. Firstly cloud based computing infrastructure does not support PCI security requirements, and secondly even though many of the legacy Commerce Server dependencies have been abstracted away within this version of the application, it is still not a fully supported to be deployed exclusively into the cloud. If you do wish to benefit from the scalability of the cloud however, you can still achieve a great Commerce Server and Azure setup by utilising both the Azure App Fabric in terms of the service bus, and authentication services and Windows Azure to host any online presence you may require. The architecture would be something similar to this: This setup would allow you to construct your Commerce Services as part of your on-site infrastructure. These services would contain all of the channels custom business logic, and provide the overall interface back into the underlying Commerce Server components. It would be recommended that services are constructed around the specific business domain of the application, which based on your business model would usually consist of separate services around Catalogue, Orders, Search, Profiles, and Marketing. The App Fabric service bus is then used to abstract and aggregate further the services, making them available to the cloud and subsequently secured by App Fabrics authentication services. These services are now available for consumption by any client, using any supported technology – not just .NET. Thus meaning you are now able to construct apps for IPhone, integrate with Java based POS Devices, and any many other potential uses. This aggregation is useful, and forms the basis of the further strategy around diversifying and enhancing the e-Commerce experience, but also provides the foundation for the scalability we want to gain from utilising a cloud-based application platform. The Windows Azure application platform is Microsoft solution to benefiting from the true economies of scale in terms of the elasticity of the cloud. Just before the launch of the Azure Platform – Domino's pizza actually managed to run their whole SuperBowl operation from the scalability of Windows Azure, and simply switching back to their traditional operation the next day with no residual infrastructure costs. The platform also natively can subscribe to services and messages exposed within the AppFabric service bus, making it an ideal solution to build and deploy a presentation layer which will need to support of scalable infrastructure – such as a high demand public facing e-Commerce portal, or a promotion element of a brand. Windows Azure has excellent support for ASP.NET, including its own caching providers meaning expensive operations such as catalogue queries can persist in memory on the application server, reducing the demand on internal infrastructure and prioritising it for more business critical operations such as receiving orders and processing payments. Windows Azure also supports other languages too, meaning utilising this approach you can technically build a Commerce Server presentation layer in Java, PHP, or Ruby – or equally in ASP.NET or Silverlight without having to change any of the underlying business or Commerce Server implementation. This SOA-style architecture is one of the primary differentiators for Commerce Server as a product in the e-Commerce market, and now with the introduction of a WCF capability in Commerce Server 2009/2009 R2 the opportunities for extensibility of the both the user experience, and integration into third parties, are drastically increased, all with no effect to the underlying channel logic. So if you are looking at deployment options for your e-Commerce application to help support demand in a cost effective way. I would highly recommend you consider looking at Windows Azure, and if you have any questions in-particular about this style of deployment, please feel free to get in touch!

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• Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Java EE at JavaOne - A Few Picks from a Very Rich Line-up

  - by Janice J. Heiss

  A rich and diverse set of sessions cast a spotlight on Java EE at this year’s JavaOne, ranging from the popular Web Framework Smackdown, to Java EE 6 and Spring, to sessions exploring Java EE 7, and one on the implications of HTML5. Some of the world’s best EE architects and developers will be sharing their insight and expertise. If only I could be at ten places at once!BOF4149 - Web Framework Smackdown 2012    Markus Eisele - Principal IT Architect, msg systems ag    Graeme Rocher - Senior Staff Engineer, VMware    James Ward - Developer Evangelist, Heroku    Ed Burns - Consulting Member of Technical Staff, Oracle    Santiago Pericasgeertsen - Software Engineer, Oracle* Monday, Oct 1, 8:30 PM - 9:15 PM - Parc 55 - Cyril Magnin II/III Much has changed since the first Web framework smackdown, at JavaOne 2005. Or has it? The 2012 edition of this popular panel discussion surveys the current landscape of Web UI frameworks for the Java platform. The 2005 edition featured JSF, Webwork, Struts, Tapestry, and Wicket. The 2012 edition features representatives of the current crop of frameworks, with a special emphasis on frameworks that leverage HTML5 and thin-server architecture. Java Champion Markus Eisele leads the lively discussion with panelists James Ward (Play), Graeme Rocher (Grails), Edward Burns (JSF) and Santiago Pericasgeertsen (Avatar).CON6430 - Java EE and Spring Framework Panel Discussion    Richard Hightower - Developer, InfoQ    Bert Ertman - Fellow, Luminis    Gordon Dickens - Technical Architect, IT101, Inc.    Chris Beams - Senior Technical Staff, VMware    Arun Gupta - Technology Evangelist, Oracle* Tuesday, Oct 2, 10:00 AM - 11:00 AM - Parc 55 - Cyril Magnin II/III In the age of Java EE 6 and Spring 3, enterprise Java developers have many architectural choices, including Java EE 6 and Spring, but which one is right for your project? Many of us have heard the debate and seen the flame wars—it’s a topic with passionate community members, and it’s a vibrant debate. If you are looking for some level-headed discussion, grounded in real experience, by developers who have tried both, then come join this discussion. InfoQ’s Java editors moderate the discussion, and they are joined by independent consultants and representatives from both Java EE and VMWare/SpringSource.BOF4213 - Meet the Java EE 7 Specification Leads   Linda Demichiel - Consulting Member of Technical Staff, Oracle   Bill Shannon - Architect, Oracle* Tuesday, Oct 2, 5:30 PM - 6:15 PM – Parc 55 - Cyril Magnin II/III This is your chance to meet face-to-face with the engineers who are developing the next version of the Java EE platform. In this session, the specification leads for the leading technologies that are part of the Java EE 7 platform discuss new and upcoming features and answer your questions. Come prepared with your questions, your feedback, and your suggestions for new features in Java EE 7 and beyond.CON10656 - JavaEE.Next(): Java EE 7, 8, and Beyond    Ian Robinson - IBM Distinguished Engineer, IBM    Mark Little - JBoss CTO, NA    Scott Ferguson - Developer, Caucho Technology    Cameron Purdy - VP Development, Oracle*Wednesday, Oct 3, 4:30 PM - 5:30 PM - Parc 55 - Cyril Magnin II/IIIIn this session, hear from a distinguished panel of industry and open source luminaries regarding where they believe the Java EE community is headed, starting with Java EE 7. The focus of Java EE 7 and 8 is mostly on the cloud, specifically aiming to bring platform as a service (PaaS) providers and application developers together so that portable applications can be deployed on any cloud infrastructure and reap all its benefits in terms of scalability, elasticity, multitenancy, and so on. Most importantly, Java EE will leverage the modularization work in the underlying Java SE platform. Java EE will, of course, also update itself for trends such as HTML5, caching, NoSQL, ployglot programming, map/reduce, JSON, REST, and improvements to existing core APIs.CON7001 - HTML5 WebSocket and Java    Danny Coward - Java, Oracle*Wednesday, Oct 3, 4:30 PM - 5:30 PM - Parc 55 - Cyril Magnin IThe family of HTML5 technologies has pushed the pendulum away from rich client technologies and toward ever-more-capable Web clients running on today’s browsers. In particular, WebSocket brings new opportunities for efficient peer-to-peer communication, providing the basis for a new generation of interactive and “live” Web applications. This session examines the efforts under way to support WebSocket in the Java programming model, from its base-level integration in the Java Servlet and Java EE containers to a new, easy-to-use API and toolset that are destined to become part of the standard Java platform.

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• Database-as-a-Service on Exadata Cloud

  - by Gagan Chawla

  Note – Oracle Enterprise Manager 12c DBaaS is platform agnostic and is designed to work on Exadata/non-Exadata, physical/virtual, Oracle/non Oracle platforms and it’s not a mandatory requirement to use Exadata as the base platform. Database-as-a-Service (DBaaS) is an important trend these days and the top business drivers motivating customers towards private database cloud model include constant pressure to reduce IT Costs and Complexity, and also to be able to improve Agility and Quality of Service. The first step many enterprises take in their journey towards cloud computing is to move to a consolidated and standardized environment and Exadata being already a proven best-in-class popular consolidation platform, we are seeing now more and more customers starting to evolve from Exadata based platform into an agile self service driven private database cloud using Oracle Enterprise Manager 12c. Together Exadata Database Machine and Enterprise Manager 12c provides industry’s most comprehensive and integrated solution to transform from a typical silo’ed environment into enterprise class database cloud with self service, rapid elasticity and pay-per-use capabilities.   In today’s post, I’ll list down the important steps to enable DBaaS on Exadata using Enterprise Manager 12c. These steps are chalked down based on a recent DBaaS implementation from a real customer engagement - Project Planning - First step involves defining the scope of implementation, mapping functional requirements and objectives to use cases, defining high availability, network, security requirements, and delivering the project plan. In a Cloud project you plan around technology, business and processes all together so ensure you engage your actual end users and stakeholders early on in the project right from the scoping and planning stage. Setup your EM 12c Cloud Control Site – Once the project plan approval and sign off from stakeholders is achieved, refer to EM 12c Install guide and these are some important tips to follow during the site setup phase - Review the new EM 12c Sizing paper before you get started with install Cloud, Chargeback and Trending, Exadata plug ins should be selected to deploy during install Refer to EM 12c Administrator’s guide for High Availability, Security, Network/Firewall best practices and options Your management and managed infrastructure should not be combined i.e. EM 12c repository should not be hosted on same Exadata where target Database Cloud is to be setup Setup Roles and Users – Cloud Administrator (EM_CLOUD_ADMINISTRATOR), Self Service Administrator (EM_SSA_ADMINISTRATOR), Self Service User (EM_SSA_USER) are the important roles required for cloud lifecycle management. Roles and users are managed by Super Administrator via Setup menu –> Security option. For Self Service/SSA users custom role(s) based on EM_SSA_USER should be created and EM_USER, PUBLIC roles should be revoked during SSA user account creation. Configure Software Library – Cloud Administrator logs in and in this step configures software library via Enterprise menu –> provisioning and patching option and the storage location is OMS shared filesystem. Software Library is the centralized repository that stores all software entities and is often termed as ‘local store’. Setup Self Update – Self Update is one of the most innovative and cool new features in EM 12c framework. Self update can be accessed via Setup -> Extensibility option by Super Administrator and is the unified delivery mechanism to get all new and updated entities (Agent software, plug ins, connectors, gold images, provisioning bundles etc) in EM 12c. Deploy Agents on all Compute nodes, and discover Exadata targets – Refer to Exadata discovery cookbook for detailed walkthrough to ensure successful discovery of Exadata targets. Configure Privilege Delegation Settings – This step involves deployment of privilege setting template on all the nodes by Super Administrator via Setup menu -> Security option with the option to define whether to use sudo or powerbroker for all provisioning and patching operations. Provision Grid Infrastructure with RAC Database on Compute Nodes – Software is provisioned in this step via a provisioning profile using EM 12c database provisioning. In case of Exadata, Grid Infrastructure and RAC Database software is already deployed on compute nodes via OneCommand from Oracle, so SSA Administrator just needs to discover Oracle Homes and Listener as EM targets. Databases will be created as and when users request for databases from cloud. Customize Create Database Deployment Procedure – the actual database creation steps are "templatized" in this step by Self Service Administrator and the newly saved deployment procedure will be used during service template creation in next step. This is an important step and make sure you have locked all the required variables marked as locked as ‘Y’ in this table. Setup Self Service Portal – This step involves setting up of zones, user quotas, service templates, chargeback plan. The SSA portal is setup by Self Service Administrator via Setup menu -> Cloud -> Database option and following guided workflow. Refer to DBaaS cookbook for details. You also have an option to customize SSA login page via steps documented in EM 12c Cloud Administrator’s guide Final Checks – Define and document process guidelines for SSA users and administrators. Get your SSA users trained on Self Service Portal features and overall DBaaS model and SSA administrators should be familiar with Self Service Portal setup pieces, EM 12c database lifecycle management capabilities and overall EM 12c monitoring framework. GO LIVE – Announce rollout of Database-as-a-Service to your SSA users. Users can login to the Self Service Portal and request/monitor/view their databases in Exadata based database cloud. Congratulations! You just delivered a successful database cloud implementation project! In future posts, we will cover these additional useful topics around database cloud – DBaaS Implementation tips and tricks – right from setup to self service to managing the cloud lifecycle ‘How to’ enable real production databases copies in DBaaS with rapid provisioning in database cloud Case study of a customer who recently achieved success with their transformational journey from traditional silo’ed environment on to Exadata based database cloud using Enterprise Manager 12c. More Information – Podcast on Database as a Service using Oracle Enterprise Manager 12c Oracle Enterprise Manager 12c Installation and Administration guide, Cloud Administration guide DBaaS Cookbook Exadata Discovery Cookbook Screenwatch: Private Database Cloud: Set Up the Cloud Self-Service Portal Screenwatch: Private Database Cloud: Use the Cloud Self-Service Portal Stay Connected: Twitter |  Face book |  You Tube |  Linked in |  Newsletter

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• Part 1 - Load Testing In The Cloud

  - by Tarun Arora

  Azure is fascinating, but even more fascinating is the marriage of Azure and TFS! Introduction Recently a client I worked for had 2 major business critical applications being delivered, with very little time budgeted for Performance testing, we immediately hit a bottleneck when the performance testing phase started, the in house infrastructure team could not support the hardware requirements in the short notice. It was suggested that the performance testing be performed on one of the QA environments which was a fraction of the production environment. This didn’t seem right, the team decided to turn to the cloud. The team took advantage of the elasticity offered by Azure, starting with a single test agent which was provisioned and ready for use with in 30 minutes the team scaled up to 17 test agents to perform a very comprehensive performance testing cycle. Issues were identified and resolved but the highlight was that the cost of running the ‘test rig’ proved to be less than if hosted on premise by the infrastructure team. Thank you for taking the time out to read this blog post, in the series of posts, I’ll try and cover the start to end of everything you need to know to use Azure to build your Test Rig in the cloud. But Why Azure? I have my own Data Centre… If the environment is provisioned in your own datacentre, - No matter what level of service agreement you may have with your infrastructure team there will be down time when the environment is patched - How fast can you scale up or down the environments (keeping the enterprise processes in mind) Administration, Cost, Flexibility and Scalability are the areas you would want to think around when taking the decision between your own Data Centre and Azure! How is Microsoft's Public Cloud Offering different from Amazon’s Public Cloud Offering? Microsoft's offering of the Cloud is a hybrid of Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) which distinguishes Microsoft's offering from other providers such as Amazon (Amazon only offers IaaS). PaaS – Platform as a Service IaaS – Infrastructure as a Service Fills the needs of those who want to build and run custom applications as services. Similar to traditional hosting, where a business will use the hosted environment as a logical extension of the on-premises datacentre. A service provider offers a pre-configured, virtualized application server environment to which applications can be deployed by the development staff. Since the service providers manage the hardware (patching, upgrades and so forth), as well as application server uptime, the involvement of IT pros is minimized. On-demand scalability combined with hardware and application server management relieves developers from infrastructure concerns and allows them to focus on building applications. The servers (physical and virtual) are rented on an as-needed basis, and the IT professionals who manage the infrastructure have full control of the software configuration. This kind of flexibility increases the complexity of the IT environment, as customer IT professionals need to maintain the servers as though they are on-premises. The maintenance activities may include patching and upgrades of the OS and the application server, load balancing, failover clustering of database servers, backup and restoration, and any other activities that mitigate the risks of hardware and software failures.   The biggest advantage with PaaS is that you do not have to worry about maintaining the environment, you can focus all your time in solving the business problems with your solution rather than worrying about maintaining the environment. If you decide to use a VM Role on Azure, you are asking for IaaS, more on this later. A nice blog post here on the difference between Saas, PaaS and IaaS. Now that we are convinced why we should be turning to the cloud and why in specific Azure, let’s discuss about the Test Rig. The Load Test Rig – Topology Now the moment of truth, Of course a big part of getting value from cloud computing is identifying the most adequate workloads to take to the cloud, so I’ve decided to try to make a Load Testing rig where the Agents are running on Windows Azure.   I’ll talk you through the above Topology, - User: User kick starts the load test run from the developer workstation on premise. This passes the request to the Test Controller. - Test Controller: The Test Controller is on premise connected to the same domain as the developer workstation. As soon as the Test Controller receives the request it makes use of the Windows Azure Connect service to orchestrate the test responsibilities to all the Test Agents. The Windows Azure Connect endpoint software must be active on all Azure instances and on the Controller machine as well. This allows IP connectivity between them and, given that the firewall is properly configured, allows the Controller to send work loads to the agents. In parallel, the Controller will collect the performance data from the agents, using the traditional WMI mechanisms. - Test Agents: The Test Agents are on the Windows Azure Public Cloud, as soon as the test controller issues instructions to the test agents, the test agents start executing the load tests. The HTTP requests are issued against the web server on premise, the results are captured by the test agents. And finally the results are passed over to the controller. - Servers: The Web Server and DB Server are hosted on premise in the datacentre, this is usually the case with business critical applications, you probably want to manage them your self. Recap and What’s next? So, in the introduction in the series of blog posts on Load Testing in the cloud I highlighted why creating a test rig in the cloud is a good idea, what advantages does Windows Azure offer and the Test Rig topology that I will be using. I would also like to mention that i stumbled upon this [Video] on Azure in a nutshell, great watch if you are new to Windows Azure. In the next post I intend to start setting up the Load Test Environment and discuss pricing with respect to test agent machine types that will be used in the test rig. Hope you enjoyed this post, If you have any recommendations on things that I should consider or any questions or feedback, feel free to add to this blog post. Remember to subscribe to http://feeds.feedburner.com/TarunArora.  See you in Part II.   Share this post : CodeProject

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• #OOW 2012 @PARIS...talking Oracle and Clouds, and Optimized Datacenter

  - by Eric Bezille

  For those of you who want to get most out of Oracle technologies to evolve your IT to the Next Wave, I encourage you to register to the up coming Oracle Optimized Datacenter event that will take place in Paris on November 28th. You will get the opportunity to exchange with Oracle experts and customers having successfully evolve their IT by leveraging Oracle technologies. You will also get the latest news on some of the Oracle systems announcements made during OOW 2012. During this event we will make an update about Oracle and Clouds, from private to public and hybrid models. So in preparing this session, I thought it was a good start to make a status of Cloud Computing in France, and CIO requirements in particular. Starting in 2009 with the first Cloud Camp in Paris, the market has evolved, but the basics are still the same : think hybrid. From Traditional IT to Clouds One size doesn't fit all, and for big companies having already an IT in place, there will be parts eligible to external (public) cloud, and parts that would be required to stay inside the firewalls, so ability to integrate both side is key.  None the less, one of the major impact of Cloud Computing trend on IT, reported by Forrester, is the pressure it makes on CIO to evolve towards the same model that end-users are now used to in their day to day life, where self-service and flexibility are paramount. This is what is driving IT to transform itself toward "a Global Service Provider", or for some as "IT "is" the Business" (see : Gartner Identifies Four Futures for IT and CIO), and for both models toward a Private Cloud Service Provider. In this journey, there is still a big difference between most of existing external Cloud and a firm IT : the number of applications that a CIO has to manage. Most cloud providers today are overly specialized, but at the end of the day, there are really few business processes that rely on only one application. So CIOs has to combine everything together external and internal. And for the internal parts that they will have to make them evolve to a Private Cloud, the scope can be very large. This will often require CIOs to evolve from their traditional approach to more disruptive ones, the time has come to introduce new standards and processes, if they want to succeed. So let's have a look at the different Cloud models, what type of users they are addressing, what value they bring and most importantly what needs to be done by the  Cloud Provider, and what is left over to the user. IaaS, PaaS, SaaS : what's provided and what needs to be done First of all the Cloud Provider will have to provide all the infrastructure needed to deliver the service. And the more value IT will want to provide, the more IT will have to deliver and integrate : from disks to applications. As we can see in the above picture, providing pure IaaS, left a lot to cover for the end-user, that’s why the end-user targeted by this Cloud Service is IT people. If you want to bring more value to developers, you need to provide to them a development platform ready to use, which is what PaaS is standing for, by providing not only the processors power, storage and OS, but also the Database and Middleware platform. SaaS being the last mile of the Cloud, providing an application ready to use by business users, the remaining part for the end-users being configuring and specifying the application for their specific usage. In addition to that, there are common challenges encompassing all type of Cloud Services : Security : covering all aspect, not only of users management but also data flows and data privacy Charge back : measuring what is used and by whom Application management : providing capabilities not only to deploy, but also to upgrade, from OS for IaaS, Database, and Middleware for PaaS, to a full Business Application for SaaS. Scalability : ability to evolve ALL the components of the Cloud Provider stack as needed Availability : ability to cover “always on” requirements Efficiency : providing a infrastructure that leverage shared resources in an efficient way and still comply to SLA (performances, availability, scalability, and ability to evolve) Automation : providing the orchestration of ALL the components in all service life-cycle (deployment, growth & shrink (elasticity), upgrades,...) Management : providing monitoring, configuring and self-service up to the end-users Oracle Strategy and Clouds For CIOs to succeed in their Private Cloud implementation, means that they encompass all those aspects for each component life-cycle that they selected to build their Cloud. That’s where a multi-vendors layered approach comes short in terms of efficiency. That’s the reason why Oracle focus on taking care of all those aspects directly at Engineering level, to truly provide efficient Cloud Services solutions for IaaS, PaaS and SaaS. We are going as far as embedding software functions in hardware (storage, processor level,...) to ensure the best SLA with the highest efficiency. The beauty of it, as we rely on standards, is that the Oracle components that you are running today in-house, are exactly the same that we are using to build Clouds, bringing you flexibility, reversibility and fast path to adoption. With Oracle Engineered Systems (Exadata, Exalogic & SPARC SuperCluster, more specifically, when talking about Cloud), we are delivering all those components hardware and software already engineered together at Oracle factory, with a single pane of glace for the management of ALL the components through Oracle Enterprise Manager, and with high-availability, scalability and ability to evolve by design. To give you a feeling of what does that bring in terms just of implementation project timeline, for example with Oracle SPARC SuperCluster, we have a consistent track of record to have the system plug into existing Datacenter and ready in a week. This includes Oracle Database, OS, virtualization, Database Storage (Exadata Storage Cells in this case), Application Storage, and all network configuration. This strategy enable CIOs to very quickly build Cloud Services, taking out not only the complexity of integrating everything together but also taking out the automation and evolution complexity and cost. I invite you to discuss all those aspect in regards of your particular context face2face on November 28th.

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• 7-Eleven Improves the Digital Guest Experience With 10-Minute Application Provisioning

  - by MichaelM-Oracle

  By Vishal Mehra - Director, Cloud Computing, Oracle Consulting Making the Cloud Journey Matter There’s much more to cloud computing than cutting costs and closing data centers. In fact, cloud computing is fast becoming the engine for innovation and productivity in the digital age. Oracle Consulting Services contributes to our customers’ cloud journey by accelerating application provisioning and rapidly deploying enterprise solutions. By blending flexibility with standardization, our Middleware as a Service (MWaaS) offering is ensuring the success of many cloud initiatives. 10-Minute Application Provisioning Times at 7-Eleven As a case in point, 7-Eleven recently highlighted the scope, scale, and results of a cloud-powered environment. The world’s largest convenience store chain is rolling out a Digital Guest Experience (DGE) program across 8,500 stores in the U.S. and Canada. Everyday, 7-Eleven connects with tens of millions of customers through point-of-sale terminals, web sites, and mobile apps. Promoting customer loyalty, targeting promotions, downloading digital coupons, and accepting digital payments are all part of the roadmap for a comprehensive and rewarding customer experience. And what about the time required for deploying successive versions of this mission-critical solution? Ron Clanton, 7-Eleven's DGE Program Manager, Information Technology reported at Oracle Open World, " We are now able to provision new environments in less than 10 minutes. This includes the complete SOA Suite on Exalogic, and Enterprise Manager managing both the SOA Suite, Exalogic, and our Exadata databases ." OCS understands the complex nature of innovative solutions and has processes and expertise to help clients like 7-Eleven rapidly develop technology that enhances the customer experience with little more than the click of a button. OCS understood that the 7-Eleven roadmap required careful planning, agile development, and a cloud-capable environment to move fast and perform at enterprise scale. Business Agility Today’s business-savvy technology leaders face competing priorities as they confront the digital disruptions of the mobile revolution and next-generation enterprise applications. To support an innovation agenda, IT is required to balance competing priorities between development and operations groups. Standardization and consolidation of computing resources are the keys to success. With our operational and technical expertise promoting business agility, Oracle Consulting's deep Middleware as a Service experience can make a significant difference to our clients by empowering enterprise IT organizations with the computing environment they seek to keep up with the pace of change that digitally driven business units expect. Depending on the needs of the organization, this environment runs within a private, public, or hybrid cloud infrastructure. Through on-demand access to a shared pool of configurable computing resources, IT delivers the standard tools and methods for developing, integrating, deploying, and scaling next-generation applications. Gold profiles of predefined configurations eliminate the version mismatches among databases, application servers, and SOA suite components, delivered both by Oracle and other enterprise ISVs. These computing resources are well defined in business terms, enabling users to select what they need from a service catalog. Striking the Balance between Development and Operations As a result, development groups have the flexibility to choose among a menu of available services with descriptions of standard business functions, service level guarantees, and costs. Faced with the consumerization of enterprise IT, they can deliver the innovative customer experiences that seamlessly integrate with underlying enterprise applications and services. This cloud-powered development and testing environment accelerates release cycles to ensure agile development and rapid deployments. At the same time, the operations group is relying on certified stacks and frameworks, tuned to predefined environments and patterns. Operators can maintain a high level of security, and continue best practices for applications/systems monitoring and management. Moreover, faced with the challenges of delivering on service level agreements (SLAs) with the business units, operators can ensure performance, scalability, and reliability of the infrastructure. The elasticity of a cloud-computing environment – the ability to rapidly add virtual machines and storage in response to computing demands -- makes a difference for hardware utilization and efficiency. Contending with Continuous Change What does it take to succeed on the promise of the cloud? As the engine for innovation and productivity in the digital age, IT must face not only the technical transformations but also the organizational challenges of the cloud. Standardizing key technologies, resources, and services through cloud computing is only one part of the cloud journey. Managing relationships among multiple department and projects over time – developing the management, governance, and monitoring capabilities within IT – is an often unmentioned but all too important second part. In fact, IT must have the organizational agility to contend with continuous change. This is where a skilled consulting services partner can play a pivotal role as a trusted advisor in the successful adoption of cloud solutions. With a lifecycle services approach to delivering innovative business solutions, Oracle Consulting Services has expertise and a portfolio of services to help enterprise customers succeed on their cloud journeys as well as other converging mega trends .

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• 3D Ball Physics Theory: collision response on ground and against walls?

  - by David

  I'm really struggling to get a strong grasp on how I should be handling collision response in a game engine I'm building around a 3D ball physics concept. Think Monkey Ball as an example of the type of gameplay. I am currently using sphere-to-sphere broad phase, then AABB to OBB testing (the final test I am using right now is one that checks if one of the 8 OBB points crosses the planes of the object it is testing against). This seems to work pretty well, and I am getting back: Plane that object is colliding against (with a point on the plane, the plane's normal, and the exact point of intersection. I've tried what feels like dozens of different high-level strategies for handling these collisions, without any real success. I think my biggest problem is understanding how to handle collisions against walls in the x-y axes (left/right, front/back), which I want to have elasticity, and the ground (z-axis) where I want an elastic reaction if the ball drops down, but then for it to eventually normalize and be kept "on the ground" (not go into the ground, but also not continue bouncing). Without kluging something together, I'm positive there is a good way to handle this, my theories just aren't getting me all the way there. For physics modeling and movement, I am trying to use a Euler based setup with each object maintaining a position (and destination position prior to collision detection), a velocity (which is added onto the position to determine the destination position), and an acceleration (which I use to store any player input being put on the ball, as well as gravity in the z coord). Starting from when I detect a collision, what is a good way to approach the response to get the expected behavior in all cases? Thanks in advance to anyone taking the time to assist... I am grateful for any pointers, and happy to post any additional info or code if it is useful. UPDATE Based on Steve H's and eBusiness' responses below, I have adapted my collision response to what makes a lot more sense now. It was close to right before, but I didn't have all the right pieces together at the right time! I have one problem left to solve, and that is what is causing the floor collision to hit every frame. Here's the collision response code I have now for the ball, then I'll describe the last bit I'm still struggling to understand. // if we are moving in the direction of the plane (against the normal)... if (m_velocity.dot(intersection.plane.normal) <= 0.0f) { float dampeningForce = 1.8f; // eventually create this value based on mass and acceleration // Calculate the projection velocity PVRTVec3 actingVelocity = m_velocity.project(intersection.plane.normal); m_velocity -= actingVelocity * dampeningForce; } // Clamp z-velocity to zero if we are within a certain threshold // -- NOTE: this was an experimental idea I had to solve the "jitter" bug I'll describe below float diff = 0.2f - abs(m_velocity.z); if (diff > 0.0f && diff <= 0.2f) { m_velocity.z = 0.0f; } // Take this object to its new destination position based on... // -- our pre-collision position + vector to the collision point + our new velocity after collision * time // -- remaining after the collision to finish the movement m_destPosition = m_position + intersection.diff + (m_velocity * intersection.tRemaining * GAMESTATE->dt); The above snippet is run after a collision is detected on the ball (collider) with a collidee (floor in this case). With a dampening force of 1.8f, the ball's reflected "upward" velocity will eventually be overcome by gravity, so the ball will essentially be stuck on the floor. THIS is the problem I have now... the collision code is running every frame (since the ball's z-velocity is constantly pushing it a collision with the floor below it). The ball is not technically stuck, I can move it around still, but the movement is really goofy because the velocity and position keep getting affected adversely by the above snippet. I was experimenting with an idea to clamp the z-velocity to zero if it was "close to zero", but this didn't do what I think... probably because the very next frame the ball gets a new gravity acceleration applied to its velocity regardless (which I think is good, right?). Collisions with walls are as they used to be and work very well. It's just this last bit of "stickiness" to deal with. The camera is constantly jittering up and down by extremely small fractions too when the ball is "at rest". I'll keep playing with it... I like puzzles like this, especially when I think I'm close. Any final ideas on what I could be doing wrong here? UPDATE 2 Good news - I discovered I should be subtracting the intersection.diff from the m_position (position prior to collision). The intersection.diff is my calculation of the difference in the vector of position to destPosition from the intersection point to the position. In this case, adding it was causing my ball to always go "up" just a little bit, causing the jitter. By subtracting it, and moving that clamper for the velocity.z when close to zero to being above the dot product (and changing the test from <= 0 to < 0), I now have the following: // Clamp z-velocity to zero if we are within a certain threshold float diff = 0.2f - abs(m_velocity.z); if (diff > 0.0f && diff <= 0.2f) { m_velocity.z = 0.0f; } // if we are moving in the direction of the plane (against the normal)... float dotprod = m_velocity.dot(intersection.plane.normal); if (dotprod < 0.0f) { float dampeningForce = 1.8f; // eventually create this value based on mass and acceleration? // Calculate the projection velocity PVRTVec3 actingVelocity = m_velocity.project(intersection.plane.normal); m_velocity -= actingVelocity * dampeningForce; } // Take this object to its new destination position based on... // -- our pre-collision position + vector to the collision point + our new velocity after collision * time // -- remaining after the collision to finish the movement m_destPosition = m_position - intersection.diff + (m_velocity * intersection.tRemaining * GAMESTATE->dt); UpdateWorldMatrix(m_destWorldMatrix, m_destOBB, m_destPosition, false); This is MUCH better. No jitter, and the ball now "rests" at the floor, while still bouncing off the floor and walls. The ONLY thing left is that the ball is now virtually "stuck". He can move but at a much slower rate, likely because the else of my dot product test is only letting the ball move at a rate multiplied against the tRemaining... I think this is a better solution than I had previously, but still somehow not the right idea. BTW, I'm trying to journal my progress through this problem for anyone else with a similar situation - hopefully it will serve as some help, as many similar posts have for me over the years.

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• Boost your infrastructure with Coherence into the Cloud

  - by Nino Guarnacci

  Authors: Nino Guarnacci & Francesco Scarano,  at this URL could be found the original article:  http://blogs.oracle.com/slc/coherence_into_the_cloud_boost. Thinking about the enterprise cloud, come to mind many possible configurations and new opportunities in enterprise environments. Various customers needs that serve as guides to this new trend are often very different, but almost always united by two main objectives: Elasticity of infrastructure both Hardware and Software Investments related to the progressive needs of the current infrastructure Characteristics of innovation and economy. A concrete use case that I worked on recently demanded the fulfillment of two basic requirements of economy and innovation.The client had the need to manage a variety of data cache, which can process complex queries and parallel computational operations, maintaining the caches in a consistent state on different server instances, on which the application was installed.In addition, the customer was looking for a solution that would allow him to manage the likely situations in load peak during certain times of the year.For this reason, the customer requires a replication site, on which convey part of the requests during periods of peak; the desire was, however, to prevent the immobilization of investments in owned hardware-software architectures; so, to respond to this need, it was requested to seek a solution based on Cloud technologies and architectures already offered by the market. Coherence can already now address the requirements of large cache between different nodes in the cluster, providing further technology to search and parallel computing, with the simultaneous use of all hardware infrastructure resources. Moreover, thanks to the functionality of "Push Replication", which can replicate and update the information contained in the cache, even to a site hosted in the cloud, it is satisfied the need to make resilient infrastructure that can be based also on nodes temporarily housed in the Cloud architectures. There are different types of configurations that can be realized using the functionality "Push-Replication" of Coherence. Configurations can be either: Active - Passive  Hub and Spoke Active - Active Multi Master Centralized Replication Whereas the architecture of this particular project consists of two sites (Site 1 and Site Cloud), between which only Site 1 is enabled to write into the cache, it was decided to adopt an Active-Passive Configuration type (Hub and Spoke). If, however, the requirement should change over time, it will be particularly easy to change this configuration in an Active-Active configuration type. Although very simple, the small sample in this post, inspired by the specific project is effective, to better understand the features and capabilities of Coherence and its configurations. Let's create two distinct coherence cluster, located at miles apart, on two different domain contexts, one of them "hosted" at home (on-premise) and the other one hosted by any cloud provider on the network (or just the same laptop to test it :)). These two clusters, which we call Site 1 and Site Cloud, will contain the necessary information, so a simple client can insert data only into the Site 1. On both sites will be subscribed a listener, who listens to the variations of specific objects within the various caches. To implement these features, you need 4 simple classes: CachedResponse.java Represents the POJO class that will be inserted into the cache, and fulfills the task of containing useful information about the hypothetical links navigation ResponseSimulatorHelper.java Represents a link simulator, which has the task of randomly creating objects of type CachedResponse that will be added into the caches CacheCommands.java Represents the model of our example, because it is responsible for receiving instructions from the controller and performing basic operations against the cache, such as insert, delete, update, listening, objects within the cache Shell.java It is our controller, which give commands to be executed within the cache of the two Sites So, summarily, we execute the java class "Shell", asking it to put into the cache 100 objects of type "CachedResponse" through the java class "CacheCommands", then the simulator "ResponseSimulatorHelper" will randomly create new instances of objects "CachedResponse ". Finally, the Shell class will listen to for events occurring within the cache on the Site Cloud, while insertions and deletions are performed on Site 1. Now, we realize the two configurations of two respective sites / cluster: Site 1 and Site Cloud.For the Site 1 we define a cache of type "distributed" with features of "read and write", using the cache class store for the "push replication", a functionality offered by the project "incubator" of Oracle Coherence.For the "Site Cloud" we expect even the definition of “distributed” cache type with tcp proxy feature enabled, so it can receive updates from Site 1.  Coherence Cache Config XML file for "storage node" on "Site 1" site1-prod-cache-config.xml Coherence Cache Config XML file for "storage node" on "Site Cloud" site2-prod-cache-config.xml For two clients "Shell" which will connect respectively to the two clusters we have provided two easy access configurations.  Coherence Cache Config XML file for Shell on "Site 1" site1-shell-prod-cache-config.xml Coherence Cache Config XML file for Shell on "Site Cloud" site2-shell-prod-cache-config.xml Now, we just have to get everything and run our tests. To start at least one "storage" node (which holds the data) for the "Cloud Site", we can run the standard class  provided OOTB by Oracle Coherence com.tangosol.net.DefaultCacheServer with the following parameters and values:-Xmx128m-Xms64m-Dcom.sun.management.jmxremote -Dtangosol.coherence.management=all -Dtangosol.coherence.management.remote=true -Dtangosol.coherence.distributed.localstorage=true -Dtangosol.coherence.cacheconfig=config/site2-prod-cache-config.xml-Dtangosol.coherence.clusterport=9002-Dtangosol.coherence.site=SiteCloud To start at least one "storage" node (which holds the data) for the "Site 1", we can perform again the standard class provided by Coherence  com.tangosol.net.DefaultCacheServer with the following parameters and values:-Xmx128m-Xms64m-Dcom.sun.management.jmxremote -Dtangosol.coherence.management=all -Dtangosol.coherence.management.remote=true -Dtangosol.coherence.distributed.localstorage=true -Dtangosol.coherence.cacheconfig=config/site1-prod-cache-config.xml-Dtangosol.coherence.clusterport=9001-Dtangosol.coherence.site=Site1 Then, we start the first client "Shell" for the "Cloud Site", launching the java class it.javac.Shell  using these parameters and values: -Xmx64m-Xms64m-Dcom.sun.management.jmxremote -Dtangosol.coherence.management=all -Dtangosol.coherence.management.remote=true -Dtangosol.coherence.distributed.localstorage=false -Dtangosol.coherence.cacheconfig=config/site2-shell-prod-cache-config.xml-Dtangosol.coherence.clusterport=9002-Dtangosol.coherence.site=SiteCloud Finally, we start the second client "Shell" for the "Site 1", re-launching a new instance of class  it.javac.Shell  using  the following parameters and values: -Xmx64m-Xms64m-Dcom.sun.management.jmxremote -Dtangosol.coherence.management=all -Dtangosol.coherence.management.remote=true -Dtangosol.coherence.distributed.localstorage=false -Dtangosol.coherence.cacheconfig=config/site1-shell-prod-cache-config.xml-Dtangosol.coherence.clusterport=9001-Dtangosol.coherence.site=Site1  And now, let’s execute some tests to validate and better understand our configuration. TEST 1The purpose of this test is to load the objects into the "Site 1" cache and seeing how many objects are cached on the "Site Cloud". Within the "Shell" launched with parameters to access the "Site 1", let’s write and run the command: load test/100 Within the "Shell" launched with parameters to access the "Site Cloud" let’s write and run the command: size passive-cache Expected result If all is OK, the first "Shell" has uploaded 100 objects into a cache named "test"; consequently the "push-replication" functionality has updated the "Site Cloud" by sending the 100 objects to the second cluster where they will have been posted into a respective cache, which we named "passive-cache". TEST 2The purpose of this test is to listen to deleting and adding events happening on the "Site 1" and that are replicated within the cache on "Cloud Site". In the "Shell" launched with parameters to access the "Site Cloud" let’s write and run the command: listen passive-cache/name like '%' or a "cohql" query, with your preferred parameters In the "Shell" launched with parameters to access the "Site 1" let’s write and run the following commands: load test/10 load test2/20 delete test/50 Expected result If all is OK, the "Shell" to Site Cloud let us to listen to all the add and delete events within the cache "cache-passive", whose objects satisfy the query condition "name like '%' " (ie, every objects in the cache; you could change the tests and create different queries).Through the Shell to "Site 1" we launched the commands to add and to delete objects on different caches (test and test2). With the "Shell" running on "Site Cloud" we got the evidence (displayed or printed, or in a log file) that its cache has been filled with events and related objects generated by commands executed from the" Shell "on" Site 1 ", thanks to "push-replication" feature.  Other tests can be performed, such as, for example, the subscription to the events on the "Site 1" too, using different "cohql" queries, changing the cache configuration,  to effectively demonstrate both the potentiality and  the versatility produced by these different configurations, even in the cloud, as in our case. More information on how to configure Coherence "Push Replication" can be found in the Oracle Coherence Incubator project documentation at the following link: http://coherence.oracle.com/display/INC10/Home More information on Oracle Coherence "In Memory Data Grid" can be found at the following link: http://www.oracle.com/technetwork/middleware/coherence/overview/index.html To download and execute the whole sources and configurations of the example explained in the above post,  click here to download them; After download the last available version of the Push-Replication Pattern library implementation from the Oracle Coherence Incubator site, and download also the related and required version of Oracle Coherence. For simplicity the required .jarS to execute the example (that can be found into the Push-Replication-Pattern  download and Coherence Distribution download) are: activemq-core-5.3.1.jar activemq-protobuf-1.0.jar aopalliance-1.0.jar coherence-commandpattern-2.8.4.32329.jar coherence-common-2.2.0.32329.jar coherence-eventdistributionpattern-1.2.0.32329.jar coherence-functorpattern-1.5.4.32329.jar coherence-messagingpattern-2.8.4.32329.jar coherence-processingpattern-1.4.4.32329.jar coherence-pushreplicationpattern-4.0.4.32329.jar coherence-rest.jar coherence.jar commons-logging-1.1.jar commons-logging-api-1.1.jar commons-net-2.0.jar geronimo-j2ee-management_1.0_spec-1.0.jar geronimo-jms_1.1_spec-1.1.1.jar http.jar jackson-all-1.8.1.jar je.jar jersey-core-1.8.jar jersey-json-1.8.jar jersey-server-1.8.jar jl1.0.jar kahadb-5.3.1.jar miglayout-3.6.3.jar org.osgi.core-4.1.0.jar spring-beans-2.5.6.jar spring-context-2.5.6.jar spring-core-2.5.6.jar spring-osgi-core-1.2.1.jar spring-osgi-io-1.2.1.jar At this URL could be found the original article: http://blogs.oracle.com/slc/coherence_into_the_cloud_boost Authors: Nino Guarnacci & Francesco Scarano

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