Testing a New Algorithm for Cloud Computing

By Ian Armas Foster

June 27, 2013

Computing in the cloud brings about certain challenges as a result of having to deal with probability of network delays. As such, optimized job scheduling and related job completion estimation times take on a new importance. Researchers from the University of York took on a couple of algorithms designed to schedule cloud tasks and compared and contrasted them.

“It is almost inevitable for grids and clouds to experience significant variations in demand, which can lead to transient periods of overload where some jobs have to wait,” the researchers noted in an introduction to the problem. “Industrial users indicated their desire for response times of jobs to be proportionate to the jobs’ execution times.”

As such, this research was meant to investigate scheduling protocols as they relate to HPC applications and tasks running in a grid or cloud environment. The researchers evaluated three scheduling algorithms: Projected Schedule Length Ratio (P-SLR), which they developed in a previous article, along with Shortest Remaining Time First (SRTF), and Longest Remaining Time First (LRTF), a baseline just to show ineffectiveness considering the desire of users for wait time to approximate runtime.

The team was interested in ‘worst-case scenarios,’ or jobs whose runtimes were severely misestimated. As was the case with all the tests they ran, the LRTF model did not perform particularly well in this regard, as not only did it subject certain tasks to starvation, but also gave little allowance to initially small jobs that possessed runtime variation.

“The LRTF orderer, as expected, shows poorer worst-case responsiveness than any of the policies that do not consider execution time,” the researchers noted. “This is because it makes the smallest tasks starve, and these tasks are the ones whose SLR is most sensitive to waiting time.”

What that essentially means is users place a somewhat higher value on smaller tasks with regard to computations done in an environment that could be subject to network delays, such as the virtualized environments found in clouds or grids. That makes sense, as the cloud is used for experimental applications that expect to run multiple exploratory simulations in a relatively short amount of time.

 “In a realistic system, it is assumed that an estimate of execution time, albeit inaccurate, will be available from the user or from an automated job profiler. In simulation, however, the exact execution times are known in advance, so inaccuracies need to be introduced into the model.”

In this case, the job schedulers rely on an estimated job completion time. Those estimates are inaccurate enough that they any algorithm would need to take into account at least a baseline amount of intolerance. The hypothesis was that one of the algorithms between P-SLR and STRF would carry the day until a certain variation threshold was reached.

As it turned out, that threshold took hold at ten times the original estimated execution time. “The responsiveness performance of P-SLR was found to be robust below a certain threshold of execution time inaccuracy,” the researchers found. “This threshold was 10 times the original execution time of the task. Above this threshold, SRTF was able to provide better responsiveness.”

After jobs show a 1000% increase from estimated to actual response time, the SRTF ‘starved out’ some jobs. While this sounds less than ideal, those jobs were least reliant on wait time and low priority, meaning it was fine for them to be set aside. Perhaps such a starvation would have indicated a flaw in the job itself. “The divergence after 1000% is due to this guarantee because SRTF is letting the largest tasks starve. The largest tasks have SLRs which are least sensitive to waiting time, keeping the worst-case SLR fairly low.”

With that said, the SRTF starved out not just high variance jobs. P-SLR, according to the research, adds a guarantee that no job will be left behind, giving it the edge before that threshold of 1000% variance. “The difference between P-SLR and SRTF in this range is not statistically significant, which shows the strength of the P-SLR policy as it adds the guarantee of non-starvation.”

As it turns out, the important distinction between P-SLR and SRTF is that the structure of P-SLR does not allow for what the researchers called job starvation whereas SRTF will essentially forget jobs occasionally.

 “Users desire fair treatment of their jobs. An example of a particularly unfair situation is if some jobs experience starvation (unbounded waiting time) under overload.” In this respect, even though SRTF starved out some jobs, its levity was more pronounced. “P-SLR was not able to give the best fairness compared to SRTF once any significant estimation inaccuracies were present, because SRTF is better at keeping SLRs low for small tasks whose SLRs are more sensitive to longer waiting times.”

The P-SLR algorithm created by the researchers at the University of York is such that jobs won’t be put on hold indefinitely when subject to variables like network delays such as are apparent in cloud environments. However, if a proper method to estimate response time within 1000% is not found or utilized, P-SLR’s usefulness decreases.

Subscribe to HPCwire's Weekly Update!

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

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire