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Data center workloads are composed of multiresource jobs requiring a variety of computational resources including CPU cores, memory, disk space, and hardware accelerators. Mod- ern servers can run multiple jobs in parallel, but a set of jobs can only run in parallel if the server has sufficient resources to satisfy the demands of each job. It is generally hard to find sets of jobs that perfectly utilize all server resources, and choosing the wrong set of jobs can lead to low resource uti- lization. This raises the question of how to allocate resources across a stream of arriving multiresource jobs to minimize the mean response time across jobs — the mean time from when a job arrives to the system until it is complete. Current policies for scheduling multiresource jobs are com- plex to analyze and hard to implement. We propose a class of simple policies, called Markovian Service Rate (MSR) policies. We show that the class of MSR policies is throughput- optimal, in that if a policy exists that can stabilize the sys- tem, then an MSR policy exists that stabilizes the system. We derive bounds on the mean response time under an MSR policy, and show how our bounds can be used to choose an MSR policy that minimizes mean response time.
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On Studying CPU Performance of CloudLab Hardware
Empirical performance measurements of computer systems almost always exhibit variability and anomalies. Run-to-run and server-to-server variations are common for CPU, memory, disk, and network performance characteristics. In our previous work, we focused on taming performance variability for memory, disk, and network and established an interactive analysis service at: https://confirm.fyi/ to help users of the CloudLab testbed better plan and conduct their experiments. In this paper, we describe our analysis of CPU variability based on over 1.3M performance measurements from nearly 1,800 servers and present our initial findings. The focus of this work is on capturing hardware variability, which can make repeatable experiments more difficult and can impact conclusions; it it this important for systems researchers to understand. (We note that, though we do not study it in this work, in the cloud, multi-tenancy and resource sharing an exacerbate the problem.) Variability also inevitably impacts performance and operation of middleware and high-level applications, contributing to the straggler problems in many domains, including HPC, Big Data, and Machine Learning, and on many types of cyberinfrastructures. We analyze the data from the CloudLab servers allocated in an exclusive fashion, with no virtualization. While our analysis focuses on the testbed that aims to promote reproducible research, we believe our approach and the findings can be of value to people who manage, analyze, and utilize shared computing resources in supercomputers, clouds, and datacenters.
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- Award ID(s):
- 1743363
- PAR ID:
- 10197399
- Date Published:
- Journal Name:
- Proceedings of the Worksop on Midscale Education and Research Infrastructure and Tools (MERIT)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICNP.2019.8888128
