Testing the Cloud: Assuring Availability

By Joe Barry

August 16, 2011

Cloud computing is changing how IT services are delivered and consumed today. The ability for enterprises large and small to centralize and outsource increasingly complex IT infrastructure, while at the same time consuming cloud-based IT services on an on-demand basis, promises to transform the economics of doing business.

But, note that I state “promises” as even though there are many success stories amongst early adopters, the real test will come when cloud computing becomes the de-facto model for IT service delivery and consumption. By all accounts, mainstream adoption of cloud services is close at hand.

In essence, cloud computing is entering a new phase in its development, where assuring the availability and quality of cloud services will become a major challenge. Preparing for this now will ensure that cloud computing continues to deliver on its “promise”.

From excess to scarce resources

Cloud computing was initially driven by excess computing capacity. Large web companies, such as Amazon and Google, that had to build large data center capacity for their own business, saw an opportunity to provide their excess capacity as a service to others. This has been so successful that these cloud services, such as Amazon Web Services, have become a business in themselves.

Yet, as these services become more popular, demand will tend to outstrip supply, especially as some of the enablers of cloud service adoption, such as higher speed access connections, continue to grow in capacity. Simply adding more servers and higher speed networks is effective, but costly and can undermine one of the main reasons for using cloud services, namely cost reduction.  Cloud service providers will thus face the dilemma of managing demand for scarcer computing resources while at the same time maintaining a low, or at least competitive, cost level.

In other words, how can cloud service providers meet mainstream demand cost-effectively?

Efficiently Assuring Service Availability

Cloud services come in many shapes and sizes, from private clouds to public clouds with software-, platform- and infrastructure-as-a-service. Nevertheless, all these flavors of cloud service have a common need to assure service availability and do so as efficiently and cost effectively as possible.

Many cloud services already provide service availability monitoring tools, but these are often limited to monitoring of server or service up-time. Server or service up-time is but one of the aspects of service availability that need to be addressed as cloud services are dependent on much more than just the physical or virtual server on which they reside. Increasingly, the data communication infrastructure supporting the cloud service from the provider to the consumer also needs to be assured even though this might be outside the direct control of the service provider.  

To ensure mainstream adoption of cloud services, consumers must be confident that the services that are required or the data that is hosted by cloud services is available quickly when and where they need it. Otherwise, why not continue with current approaches? Mainstream consumers are noted for being more conservative and pragmatic in their choice of solutions, so addressing this concern must be a top priority for continued expansion of cloud service adoption.

Therefore, building the infrastructure to test and monitor cloud services is essential.

Testing and monitoring cloud services

From a testing and monitoring perspective, there are a number of layers one can address:

•    The Wide Area Network (WAN) providing data communication services between the enterprise customer and the cloud service – fundamental to service assurance and testing of end-to-end service availability

•    The data center infrastructure comprising servers and data communication between servers (LAN), where service availability and uptime of this equipment is key as well as efficient use of resources to ensure service efficiency

•    The monitoring infrastructure in the data center that is the basis for service assurance which itself needs to efficient

•    The individual servers and monitoring appliances that are based on servers that must also follow efficiency and availability principles to assure overall service efficiency and service availability
 
Testing end-to-end

The first test that can be performed is testing end-to-end availability. At a basic level, this involves testing connectivity, but can also involve some specific testing relevant for cloud services, such as latency measurement. Several commercial systems exist for testing latency in a WAN environment. These are most often used by financial institutions to determine the time it takes to execute financial transactions with remote stock exchanges, but can also be used by cloud service providers to test the latency of the connection to enterprise customers.

This solution requires the installation at the enterprise of a network appliance for monitoring latency, which could also be used to test connectivity. Such an appliance could also be used for troubleshooting and SLA monitoring.

Typically the cloud service provider does not own the WAN data communication infrastructure. However, using network monitoring and analysis appliances at both the data center and the enterprise, it is possible to measure the performance of the WAN in providing the data communication service required. The choice of WAN data communication provider should also be driven by the ability of this provider to provide performance data in support of agreed SLAs. In other words, this provider should have the monitoring and analysis infrastructure in place to assure services.

From reaction to service assurance

Network monitoring and analysis of the data center infrastructure is also crucial as cloud service providers need to rely less on troubleshooting and more on service assurance strategies. In typical IT network deployments, a reactive strategy is preferred whereby issues are dealt with in a troubleshooting manner as they arise. For enterprise LAN environments, this can be acceptable in many cases, as some downtime can be tolerated. However, for cloud service providers, downtime is a disaster! If customers are not confident in the cloud service provider’s ability to assure service availability, they will be quick to find alternatives or even revert to a local installation.

A service assurance strategy involves constant monitoring of the performance of the network and services so that issues can be identified before they arise. Network and application performance monitoring tools are available from a number of vendors for precisely this purpose.

The power of virtualization

One of the technology innovations of particular use to cloud service providers is virtualization. The ability to consolidate multiple cloud services onto as few physical servers as possible provides tremendous efficiency benefits by lower cost, space and power consumption. In addition, the ability to move virtual machines supporting cloud services from one physical server to another allows efficient use of resources in matching time-of-day demand, as well as allowing fast reaction to detected performance issues.

One of the consequences of this consolidation is the need for higher speed interfaces as more data needs to be delivered to each server. This, in turn requires that the data communication infrastructure is dimensioned to provide this data, which in turn demands that the network monitoring infrastructure can keep up with the data rates without losing data. This is far from a given, so cloud service providers need to pay particular attention to the throughput performance of network monitoring and analysis appliances to ensure that they can keep up also in the future.

Within the virtualized servers themselves, there are also emerging solutions to assist in monitoring performance. Just as network and application performance monitoring appliances are available to monitor the physical infrastructure, there are now available virtualized versions of these applications for monitoring virtual applications and communication between virtual machines.

There are also virtual test applications that allow a number of virtual ports to be defined that can be used for load-testing in a cloud environment. This is extremely useful for testing whether a large number of users can access a service without having to deploy a large test network. An ideal tool for cloud service providers.

Bringing virtualization to network monitoring and analysis

While virtualization has been used to improve service efficiency, the network monitoring and analysis infrastructure is still dominated by single server implementations. In many cases, this is because the network monitoring and analysis appliance requires all the processing power it can get. However, there are opportunities to consolidate appliances, especially as servers and server CPUs increase performance on a yearly basis.

Solutions are now available to allow multiple network monitoring and analysis applications to be hosted on the same physical server. If all the applications are based on the same operating system, intelligent network adapters have the ability to ensure that data is shared between these applications, which often need to analyze the same data at the same time, but for different purposes.

However, for situations where the applications are based on different operating systems, virtualization can be used to consolidate them onto a single physical server. Demonstrations have shown that up to 32 applications can thus be consolidated using virtualization.

By pursuing opportunities for consolidation of network monitoring and analysis appliances, cloud service providers can further improve service efficiency.

Preparing for mainstream adoption

Mainstream adoption of cloud services is just around the corner and to take full advantage of this demand, cloud service providers can use the existing tools and concepts described above to assure service availability in a cost effective and efficient manner.

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!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The U.S. Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

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 Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The U.S. Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

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" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

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 advanced supercomputers. Read more…

By Tiffany Trader

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 phase of neural networks (NN). Read more…

By Tiffany Trader

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. 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 campaign. Read more…

By John Russell

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 assets. Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

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 new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Leading Solution Providers

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 will be Japan’s “fastest AI supercomputer,” 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 was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

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 the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. 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 network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

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