Network-Attached Memory: Virtualization for Java Environments

By Dennis Barker

October 6, 2008

Easing application scalability across a cluster is a problem being solved in a variety of ways. Terracotta does it with memory – network-attached memory, to be exact. The company’s infrastructure software creates an expandable/retractable pool of shared memory that Java applications can tap to meet increasing demand.

Network-attached memory is analogous to network-attached storage (NAS) in that it provides a service to thousands of connected clients transparently. As NAS is transparent underneath the file system, network-attached memory is transparent underneath the Java language, says Jeff Hartley, vice president of products and marketing at Terracotta. Objects are manipulated and kept consistent in memory like files are in NAS, but in Terracotta’s memory pool, everything can be massively scaled out.

“Another way to think of it is virtualization for the Java environment,” Hartley says. “In the same way that a hypervisor slices a machine into several logical machines, Terracotta takes many physical machines and connects them as one logical machine.”

Developers don’t have to change their applications to get clustering behavior. A clustered application looks the same as a Java application. Essentially, users tell Terracotta what to do in a config file, Hartley says, and the software injects that behavior into the application at runtime. Not only is there is no API required to send messages across the cluster, he says, there is no API at all. Terracotta uses plain old Java objects and plugs into familiar frameworks like Spring, Hibernate and EHcache so developers can continue using the same tools, stacks and development models.

“We provide an open source clustering solution,” says Hartley. “It’s scalability and high availability for Java enterprise apps, without having to change your application code. We actually hook into the Java virtual machine and share data.”

Beside simplified clustering and scalable performance, Hartley says Terracotta’s approach also brings “high availability without tradeoffs” — the possibility of reducing database bottlenecks, achieving better use of hardware, lowering maintenance costs, and, because the software takes care of adding clustering capabilities, focusing on developing new applications rather than retooling old ones.

“We’re working at the level of memory. If you have App Server A die, the user gets sent to App Server B, and all the data is sent from memory to B without the user even realizing it,” Hartley says. “We provide high availability by putting everything in memory instead of adding racks of servers.”

When you add a server to handle demand, it just “joins the group.” “You don’t have to implement anything. Just add the servers and we make them members of the cluster’s shared memory pool. Our server keeps your server’s data in sync,” Hartley says. Changes to one virtual machine are instantaneously reflected to every virtual machine throughout the cluster that needs to know.

Because data can be shared between Java virtual machines and processed at in-memory speeds, some customers use the software to take load off their databases. Transient data like user session info or shopping cart info can be kept and processed in memory, while only critical results are sent to and kept in the database. “Terracotta is used to handle the work-in-progress data while a process is running, and only the completed data goes to the database. As a result, some of our users have been able to reduce database utilization by as much as 70 percent and not have to buy more database licenses to meet increased workload,” Hartley says.

Eliminating mundane work for IT staff is one of Terracotta’s other major selling points. As company CTO Ari Zilka explains in a video tour of the software, “You don’t have to write the plumbing or maintain the plumbing. …You can run an app on two servers at midnight and on 20 servers at noon. It frees IT to run apps the way they need to run them” and focus development time on more important business issues, he says.

Deployment involves two primary components: client nodes and the Terracotta server array. Nodes run on standard Java VMs, and each node corresponds to a Java process in the cluster (e.g., the application server). Terracotta is loaded into each VM at startup. The Terracotta server array provides the intelligence to orchestrate all the nodes in the cluster, synchronize activity between them, replicate data and handle storing data to disk. The array can run in an active-passive pair configuration for high availability that can achieve tens of thousands of requests per second, the company says. Running multiple instances of the Terracotta server in active-passive mode guarantees that a failure won’t compromise the cluster, the company says.

“We’re a very fundamental technology. We’re not a grid solution, we’re not a cloud solution. We’re network-attached memory — distributed memory that sits right below the applications, and can be used for all kinds of things,” explains Hartley.

Demanding Customers

Customers running Terracotta typically have one thing in common: unpredictable numbers of demanding users. (Zilka used to be chief architect at Walmart.com, so he knows about building infrastructure to deal with traffic spikes of epic proportion.) The user list includes big names familiar with meeting heavy online demand, including Adobe, MapQuest, BBC, Electronic Arts, PartyGaming (PartyPoker.com, etc.), and financial services companies like JP Morgan and Mizuho Securities.

An online multiplayer gaming company that wishes to remain unnamed uses clustered servers to not just host games, but also to coordinate and track player activities. It chose Terracotta as its scale-out solution for several reasons: (1) it works behind the scenes, at byte-code level, providing distributed heap memory across multiple Java VMs; (2) developers can use the standard Java semantics for synchronizing access to shared objects; (3) “impressive horizontal scalability” enabled by just adding more server nodes and not having to use databases and caches to manage shared data; (4) no single point of failure; and (5) it’s open source, so the software doesn’t add to the bottom line and doesn’t require a purchase order to get started.

Mark Turansky, a software architect currently working in the health care industry, has written about his experience using Terracotta. “With enabling software like Terracotta, clustering becomes easy. You’ve still got to design your software to take advantage of parallelism, but the act of running programs in parallel is no longer difficult. …It invisibly and magically clusters your Java classes via configuration,” Turansky writes. “Distributing code and running massively parallel programs used to be difficult. It required complex architectures and expensive application servers. This is accidental complexity. Advances in software development — like Terracotta, GridGain, Spring, and other FOSS [free open source] programs — dramatically reduce if not eliminate the accidental complexity of distributing your programs to a cluster of machines.”

Gnip, a service that aggregates and distributes feeds from sites like Twitter and Digg to users of services like Plaxo, runs its system on Amazon’s EC2 but chose Terracotta for node replication at the memory level. A post on the company’s site explains: “The prospect of just writing our app and thinking of it as a single thing, rather than ‘how does all this state get replicated across n number of nodes’ was soooo appealing.”

Terracotta has just released version 2.7, which the company says is more tightly integrated with frameworks like Spring and Glassfish, and adds better management and visualization features, improved garbage collection and the ability to apply hot patches. It is available for download at www. terracotta.org.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurr Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a ne Read more…

By Tiffany Trader

Leading Solution Providers

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which w Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling Read more…

By Steve Campbell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu's Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural networ Read more…

By Tiffany Trader

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of "quantum supremacy," researchers are stretching the limits of today's most advance Read more…

By Tiffany Trader

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" process Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This