Data grid comparison: Oracle Coherence vs Gigaspaces XAP

A short summary of differences between Oracle Coherence and GigaSpaces XAP.

Designing a Scalable Twitter

There were many talks recently about twitter scalability and their specific choice of language such as Scala to address their existing Ruby based scalability. In this post i tried to provide a more methodical approach for handling twitter scalability challenges that is centered around the right choice of architecture patterns rather then the language itself. The architecture pattern are given in a generic fashion that is not specific to twitter itself and can serve anyone who is looking to build a scalable real time web application in the near future.

Click to read more ...

Challanges for Developing Enterprise Application on the Cloud

This post I provided a summary of recent discussions outlining the main challenges that developers face today when deploying their existing JEE application to the cloud such as complexity, database integration, security, standard JEE support etc. In this post i also provided summary of how we managed to handle those challenges with our new Cloud Computing Framework by pointing to an existing production reference of a leading Telco provider.

Click to read more ...

Its time for auto scaling – avoid peak load provisioning for web applications

Many web applications, including eBanking, Trading, eCommerce and Online Gaming, face large, fluctuating loads. In this post will describe how to achieve Right Sizing using virtualization and cloud computing. Will use a standard JEE web application to demonstrate how auto-scaling works on AWS Cloud without changing your application code.

Click to read more ...

Gigaspaces curbs latency outliers with Java Real Time

Today, most banks have migrated their internal software development from C/C++ to the Java language because of well-known advantages in development productivity (Java Platform), robustness & reliability (Garbage Collector) and platform independence (Java Bytecode). They may even have gotten better throughput performance through the use of standard architectures and application servers (Java Enterprise Edition). Among the few banking applications that have not been able to benefit yet from the Java revolution, you find the latency-critical applications connected to the trading floor. Why? Because of the unpredictable pauses introduced by the garbage collector which result in significant jitter (variance of execution time). In this post Frederic Pariente Engineering Manager at Sun Microsystems posted a summary of a case study on how the use of Sun Real Time JVM and GigaSpaces was used in the context of of a customer proof-of-concept this summer to ensure guaranteed latency per message under 10 msec, with no code modification to the matching engine.

Click to read more ...

Private/Public Cloud

Data centers are reshaping themselves by taking ideas from public cloud providers, such as Amazon and Google. The idea is to make the data center more cost-effective by enabling on-demand utility-based computing rather than dedicated machines. At the same time, it is clear that to make IT operations more effective, it doesn't make sense to run all the applications that are currently hosted in a company's data center in the private cloud. This calls for an integration between private and public cloud. In this post i discuss some of the challenges involved in making that happen: 1. How do we design applications to be cloud-agnostic? 2. How do we enable seamless fail-over to a public cloud? 3. Future-proofing: There are many cases in which we can't make a clear decision as to where our application should be running at the time of writing or developing the application. We would like to be in a position to change the decision as to where our application will be running even after our application has been completely developed.

Click to read more ...

Managing application on the cloud using a JMX Fabric

This post describes how you can create a federated management model using JMX standard API. Applications that are already using a standard JMX interface can plug-in the new federated implementation without changing the application code and without introducing additional performance overhead.

Click to read more ...

Alternatives to Google App Engine

One particularly interesting EC2 third party provider is GigaSpaces with their XAP platform that provides in memory transactions backed up to a database. The in memory transactions appear to scale linearly across machines thus providing a distributed in-memory datastore that gets backed up to persistent storage.

Click to read more ...

Is MapReduce going mainstream?

Compares MapReduce to other parallel processing approaches and suggests new paradigm for clouds and grids

Click to read more ...

Economies of Non-Scale

Scalability forces us to think differently. What worked on a small scale doesn't always work on a large scale -- and costs are no different. If 90% of our application is free of contention, and only 10% is spent on a shared resources, we will need to grow our compute resources by a factor of 100 to scale by a factor of 10! Another important thing to note is that 10x, in this case, is the limit of our ability to scale, even if more resources are added. 1. The cost of non-linearly scalable applications grows exponentially with the demand for more scale. 2. Non-linearly scalable applications have an absolute limit of scalability. According to Amdhal's Law, with 10% contention, the maximum scaling limit is 10. With 40% contention, our maximum scaling limit is 2.5 - no matter how many hardware resources we will throw at the problem. This post discuss in further details how to measure the true cost of non linearly scalable systems and suggest a model for reducing that cost significantly.

Click to read more ...