Posts on Cloud Computing for Science

Building a Great Testbed for Cloud Computing Research

Fri, 29 Jul 2016 00:00:00 +0000

We have been silent for a while – not for the lack of something to say but lack of time to say it in ;-). But today is a very special day: yesterday was the first anniversary of the day Chameleon, a cloud computing experimental instrument project that Nimbus team is proud to lead, went public. Considering how busy we are, breaking the silence is a bit of a treat but after all that’s what anniversaries are for!

LANTorrent in Action on FutureGrid

Thu, 09 May 2013 00:00:00 +0000

We just reduced image propagation time on FutureGrid’s Sierra cloud at UCSD from hours to minutes! This magic comes courtesy of Nimbus LANTorrent.

We blogged about LANTorrent before: it can distribute the same file among many nodes using peer-to-peer techniques. It is available in Nimbus since version 2.6 and allows users to efficiently deploy a cluster of virtual machines based on the same image. Installing and configuring LANTorrent on the Nimbus nodes (both service and hypervisor nodes) is easy; it took only a couple of hours on Sierra (all the details are explained in the LANTorrent Configuration section of the Nimbus documentation).

Cloud Highlights from CCGrid 2011

Mon, 13 Jun 2011 00:00:00 +0000

A few weeks ago (May 23rd-26th) I traveled to sunny Newport Beach, California to present a paper, Improving Utilization of Infrastructure Clouds, at CCGrid 2011. Our paper addresses one of the main challenges faced by infrastructure cloud providers: ensuring that resources are utilized efficiently while still providing resources on-demand. To solve this catch-22, we deployed backfill VMs on idle VMM nodes. For evaluation, we deployed Condor in the backfill VMs and demonstrated an increase to 100% utilization of the infrastructure resources. All of the details are in the paper, so I won’t elaborate here. You can also try backfill for yourself with Nimbus 2.7.

Science Cloud 2011

Fri, 10 Jun 2011 00:00:00 +0000

I had the privilege of presenting Cumulus: Open Source Storage Cloud for Science at the Science Cloud 2011 workshop yesterday. While I was focused on our open source S3 implementation ideal for the extensibility and scientific experimentation, many other interesting topics were presented. Shane Canon present a very interesting look at common misconceptions about the cloud in scientific circles. In it he exposed some truths about what ‘on demand’ ultimately means to a data center. He worked to illustrate where on the hype curve the cloud currently is, and what features work for science and what was missing. Elasticity for bursty applications is a clear win but a sighted glaring gap is the lack of a shared file system. A shared file system is an assumed service to most scientific users coming from the grid and most other HPC platforms. This need for a shared file system struck a chord with me and it seemed to be a common theme at the workshop. Lavanya Ramakrishnan gave a talk on Magellan: Experiences from a Science Cloud. In it she mentioned the struggles scientific users had with their applications inside of VMs. One was the difficulty staging in data into the VM’s space. A couple of other talks discussed the huge volumes of data created by scientific applications. All of this discussion made me wonder if a Cloud agnostic shared file system service could be created and if such a thing could solve these problems.

Clouds and TeraGrid: Small is Useful

Tue, 22 Feb 2011 00:00:00 +0000

When we think of science we don’t immediately think of quality assurance and yet… scientists have to run their codes somewhere, they need hardware, the hardware needs software, and the software needs to be operated reliably and efficiently – enter the Quality Assurance (QA) team of TeraGrid: the most powerful open science resource.

Shava Smallen, the co-lead of the TeraGrid QA team, told me recently of their first venture into infrastructure clouds. The TeraGrid Science Gateway projects have been experiencing scalability problems with grid infrastructure. A potential solution came out in the form of GRAM 5; the scientists developed scalability tests to see if it solved their problem — but where could they run them? They tried Ranger, a top-of-the-line TeraGrid resource at Texas Advanced Computing Center (TACC). But Ranger is a powerful resource, very much in demand for large scientific computations that cannot run elsewhere — and thus the QA team found itself with tests all ready to run – but no resources to run them on.

Catch-22 for Infrastructure Clouds

Tue, 15 Feb 2011 00:00:00 +0000

Cloud computing users think on-demand availability is the best thing since sliced bread: it enables elastic computing, outsourcing for applications requiring urgent or interactive response, and reduces wait times in batch queues. But if you are a cloud provider you might not think so… In order to ensure on-demand availability you need to overprovision: keep a lot of nodes idle so that they can be used to service an on-demand request, which could come at any time. This means low utilization. The only way to improve it is to keep fewer nodes idle. But this means rejecting more requests – at which point you’re not really on-demand… a veritable catch-22.

Comparisons: Not so Odious as Once Thought

Thu, 03 Feb 2011 00:00:00 +0000

I often get asked if there is any published work evaluating performance and cost of scientific applications on IaaS clouds and comparing them to using clusters — and I always say LOTS! …and then can’t remember more than a few off the top of my head ;-). So I recently put together a list — included below — of various evaluation and comparison efforts I’ve been able to find. They look all sorts of aspects of performance — from low-level benchmarks to applications of various types, from reliability to cost. They all tend to focus on somewhat different aspects of the issue and collectively paint a picture blessings and challenges of cloud computing for science.

A Nimble Elephant

Tue, 25 Jan 2011 00:00:00 +0000

BaBar from the children’s books is a young elephant who comes to a big city, and brings back the benefits of civilization to other elephants in the jungle. He also happens to be a very apt mascot for a high-energy physics project.

The name BaBar actually derives from the B/B-bar subatomic particles produced at the SLAC collider in Stanford, California during electron-positron collisions. These experiments help us achieve a better understanding of the relationship between matter and anti-matter and ultimately answer questions about the nature of the universe. This groundbreaking research is moving forward at a brisk pace: the BaBar scientists have petabytes of data, a plethora of insight, and a recent Nobel prize to show for it.

Astronomy in the Clouds

Tue, 18 Jan 2011 00:00:00 +0000

HPC in the Cloud posted a nice article describing how scientists from the Canadian CANFAR projects are using cloud computing to deal with their data problem. Quoting from a recent white paper by Nicholas Ball and David Schade:

“in the past two decades, astronomy has gone from being starved for data to being flooded by it. This onslaught has now reached the stage where the exploitation of these data has become a named discipline in its own right”

Science Cloud Workshop

Wed, 12 Jan 2011 00:00:00 +0000

Happy New Year!

To get it off to a new start check out the call for papers for the ScienceCloud 2010 workshop – announced right before Christmas!

The last year’s Science Cloud workshop was a great venue for anybody interested in cloud computing for science. The program covered everything from scientific cloud platforms (and how to set them up), through standards and middleware, to case studies of scientific applications on commercial cloud platforms such as Amazon and Azure. The latter were perhaps the most interesting of the workshop – and in fact one of them, a performance study of a cosmology application on Amazon from Lawrence Berkeley National Lab won the best paper award. The slides and papers can be viewed online – still a great reference to see what’s happening in cloud computing for science. Some of the papers will also appear in the Scientific Programming Journal’s special issue on science-driven cloud computing.

Mohammad and the Mountain

Tue, 07 Dec 2010 00:00:00 +0000

Will the mountain come to the Mohammad or Mohammad go to the mountain?

When we consider whether clouds can provide a suitable platform for high performance computing (HPC) we always talk about how cloud computing needs to evolve to suit the needs of HPC – in other words will the mountain come to Mohammad. But there are signs that there may also be movement in the other direction – transforming HPC so that it may work better in the cloud paradigm. Mohammad may have to go.

Cloud Highlights from Supercomputing 2010

Thu, 25 Nov 2010 00:00:00 +0000

Happy Thanksgiving!

Last week (Nov 13-19) was the annual Supercomputing (SC) conference. This year it was held in New Orleans, Louisiana. Cloud computing was featured by vendors and speakers throughout the conference. There were far too many cool products, talks, and papers to mention in a single post, however, a few of the highlights that we are thankful we caught in person include:

Two representatives from Platform Computing presented a large-scale cloud deployment being tested at the CERN laboratory in “Building the World’s Largest HPC Cloud.” CERN is testing Platform ISF to run scientific jobs in a virtualized environment. Results included reports of launching several thousand VMs and a comparison of image distribution techniques.
In “Virtualization for HPC”, members of the academic (Ohio State University, ORNL) and industrial (VMware, Univa UD, Deopli) communities shared their vision of a future for virtualization technologies in HPC. Topics discussed included pro-active fault tolerance using migration, virtualized access to high-performance interconnects, and new hypervisors technologies designed for exascale computing.
In “Low Latency, High Throughput, RDMA and the Cloud in Between” representatives from Mellanox, Dell, and AMD discussed the advantages of cloud computing and highlighted the importance of reducing latency and increasing throughput for scientific communities. RDMA over Converged Ethernet (RoCE) was emphasized as a specific effort toward reducing latency in virtualized environments.
The work in “Elastic Cloud Caches for Accelerating Service-Oriented Computations” demonstrated a dynamic and fast memory-based cache using IaaS resources, specifically for a geoinformatics cyberinfrastructure. The system responds to changes in demand by dynamically adding or removing IaaS nodes from the cache.

In addition to some great cloud computing talks and sessions, cloud resources were also involved in a handful of demos and tutorials. In particular, Purdue demoed Springboard, a “hub” to work with NSF’s TeraGrid infrastructure. The hub provides a central point for researchers to collaborate and removes the need for researchers to rely strictly on the command line when interacting with the TeraGrid’s resources. Springboard also interfaces with the TeraGrid’s first cloud resource, Wispy, at Purdue. The National Center for Atmospheric Research (NCAR) and the University of Colorado at Boulder used 150 Amazon EC2 instances for the Linux Cluster Construction tutorial. The virtual machines were launched on-demand the morning of the tutorial. They provided participants with a realistic software environment for configuring and deploying a Linux cluster using a variety of open source tools such as OpenMPI, Torque, and Ganglia.

Cumulus: Open Source Storage Cloud for Science

Tue, 16 Nov 2010 00:00:00 +0000

Amazon’s S3 is a great storage cloud service. It provides highly available access to highly reliable storage at a price. It has emerged as the data storage cloud de-facto standard for good reasons. Its REST API has allowed several 3rd party software vendors to make impressive clients, both GUI and command line.

However, it is closed source and unavailable to the numerous data centers actively used for science. Can private data cloud providers that already have substantial amounts of hardware allow their users to take advantage of the known interfaces and existing clients? Can the Scientific community that already has access to vast amounts of computing and storage power, but not access to an expense accounts take advantage of these data storage cloud innovations?

Sky Computing

Wed, 10 Nov 2010 00:00:00 +0000

I’ve been wanting to say a few words about sky computing for a while and eventually iSGTW forestalled me with a very nice article on the topic. It describes a cool work by Nimbus committer Pierre Riteau who created a virtual cluster of over a thousand cores over resources leased from six Nimbus clouds: three provided by Grid’5000 and three by FutureGrid.

“Sky computing” was a name we coined back in 2008 to describe the idea of operating in a multi-cloud environment. It addresses the issues of provider interoperability and comparison between providers (cloud markets) as well as end-user concerns – the abstractions and tools required to provide an integrated and secure environment over resources provisioned in multiple potentially distributed clouds which the original paper focused on.

Get There Faster with Nimbus 2.6

Mon, 08 Nov 2010 00:00:00 +0000

Today we made open source Infrastructure-as-a-Service capabilities for science just a little bit better… The new Nimbus 2.6 makes your images get to the nodes a lot faster and provides capabilities allowing administrators to easily and dynamically shift resources between their tried-and-true batch scheduled cluster and a cloud — depending on where they need them more. The Nimbus Context Broker gets shiny new REST-based interfaces, which opens it up to new applications.

Another Barrier Goes Down

Fri, 16 Jul 2010 00:00:00 +0000

Right on the heels of Amazon’s groundbreaking news on the Cluster Compute instances a couple of days ago, comes this announcement about a partnership between CENIC, Pacific NorthWest GigaPoP (PNWGP), and Amazon: two 10 Gigabit per second (Gbps) connections to Amazon S3 and EC2. This connection will be available to CENIC and PNWGP member institutions (educational and research institutions on the West Coast and in the Pacific North-West) — among others, the many ocean scientists of the Ocean Observatory Initiative (OOI) who we are working with to develop cloud-based scientific infrastructure.

There is a New Supercomputer on the Block

Tue, 13 Jul 2010 00:00:00 +0000

We all woke up to a game-changing announcement today: Amazon announced Cluster Compute instances designed to support the kinds of closely coupled workloads that high performance computing (HPC) relies on. The Cluster Compute instances consist of a pair of quad-core Intel “Nehalem” processors with 23 GB of RAM, and 1690 GB of local instance storage. But by far the best part of the offering is the 10 Gbps network that connects Cluster Compute instances — essential for HPC applications.

Grids versus Clouds

Sat, 19 Dec 2009 00:00:00 +0000

The issue of how exactly cloud computing differs from grid computing was responsible for much controversy in the last year. Here are my two cents on how Infrastructure-as-a-Service (IaaS) cloud computing and grid computing are different (also discussed in the Sky Computing paper)

At some level, both cloud computing and grid computing represent the idea of using remote resources. However, grid computing is built on the assumption that control over the manner in which resources are used stays with the site, reflecting local software and policy choices. These choices are not always useful to remote users who might need a different operating system, or login access instead of a batch scheduler interface to a site. Reconciling those choices between multiple groups of users proved to be complex, time-consuming, and expensive. Looking back, leaving complete control over the resources with the site was a pragmatic choice that enabled very fast adoption of a radically transformative technology. On the other hand, once the technology became successful, this factor made it difficult for it to scale to many user groups with different (and sometimes conflicting) requirements of what the resource should provide.

EC2’s boot from EBS capability

Fri, 04 Dec 2009 00:00:00 +0000

Amazon AWS recently announced that EC2 instances can be configured to launch from EBS volumes instead of bundled disk images.

Science users launching heterogeneous clusters can possibly take advantage of this in order to streamline the bundling of images. Those clusters often share a base image layout. Because these AMIs can now reference any number of EBS volumes in their external description including for the root disk, you can now work on customizing each partition and “mix and match” root disks and partitions more easily to make a cohesive cluster. That’s more convenient than maintaining such a partition organization separately and bundling images for each cluster node type, which is traditionally time consuming.

Cloud Computing and Bioinformatics: Notes from a Workshop

Tue, 01 Dec 2009 00:00:00 +0000

I recently attended an immensely interesting workshop on using cloud computing for systems biology computations. The workshop was co-held with SC09. The agenda and the presentations are available online from the workshop pages and are well worth a look. Here are some impressions from the workshop.

The workshop began with a discussion of current challenges in biosciences. One of the most compelling is personal medicine which helps physicians tailor treatments to individual patients based on feedback obtained on genetic and molecular level. For example, knowledge of genetic variations can now help physicians better assess treatment risks, manage dosing of drugs, better detect diseases in early stages and optimize treatments such as e.g., breast cancer therapy. In his introductory talk, Eugene Kolker said that today there were already hundreds of patients treated based on information obtained from their genetic signatures as part of experimental programs. He also emphasized that the main obstacle to progress in this area is not obtaining the data but the response time and ability to store, process, and analyze it to obtain the right information. And this brings us to cloud computing, in this workshop the “prime suspect” to process, analyze and store on demand.

Welcome to scienceclouds.org

Mon, 16 Nov 2009 00:00:00 +0000

Today we are moving Science Clouds to its own web pages. In addition to enabling quite a few exploratory projects, the Science Clouds to date served as a bit of a “cloud clinic” where various folks interested in using cloud computing for a scientific project would contact us and get advice and help on how to get started using Infrastructure-as-a-Service (IaaS) clouds.

Over time, these efforts resulted in shared images, papers, cloud evaluation projects, and other endeavors of which the following had the most impact: