research-article

alsched: algebraic scheduling of mixed workloads in heterogeneous clouds

Authors:

Alexey Tumanov,

Gregory R. Ganger,

Michael A. KozuchAuthors Info & Claims

SoCC '12: Proceedings of the Third ACM Symposium on Cloud Computing

Article No.: 25, Pages 1 - 7

https://doi.org/10.1145/2391229.2391254

Published: 14 October 2012 Publication History

Abstract

As cloud resources and applications grow more heterogeneous, allocating the right resources to different tenants' activities increasingly depends upon understanding tradeoffs regarding their individual behaviors. One may require a specific amount of RAM, another may benefit from a GPU, and a third may benefit from executing on the same rack as a fourth. This paper promotes the need for and an approach for accommodating diverse tenant needs, based on having resource requests indicate any soft (i.e., when certain resource types would be better, but are not mandatory) and hard constraints in the form of composable utility functions. A scheduler that accepts such requests can then maximize overall utility, perhaps weighted by priorities, taking into account application specifics. Experiments with a prototype scheduler, called alsched, demonstrate that support for soft constraints is important for efficiency in multi-purpose clouds and that composable utility functions can provide it.

References

[1]

Hadoop, 2012. http://hadoop.apache.org.

[2]

G. Ananthanarayanan, A. Ghodsi, S. Shenker, and I. Stoica. Disk-locality in datacenter computing considered irrelevant. In Proc. of the 13th USENIX Conference on Hot Topics in Operating Systems, HotOS'13, pages 12--12. USENIX Association, 2011.

Digital Library

[3]

A. D. Ferguson, P. Bodik, S. Kandula, E. Boutin, and R. Fonseca. Jockey: guaranteed job latency in data parallel clusters. In Proc. of the 7th ACM european conference on Computer Systems, EuroSys '12, pages 99--112, 2012.

Digital Library

[4]

B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. Joseph, R. Katz, S. Shenker, and I. Stoica. Mesos: A platform for fine-grained resource sharing in the data center. In Proc. of the 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI'11), 2011.

Digital Library

[5]

T. Kelly. Utility-directed allocation. Technical Report HPL-2003-115, Internet Systems and Storage Laboratory, HP Labs, June 2003.

[6]

T. Kelly. Combinatorial auctions and knapsack problems. In Proc. of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3, AAMAS '04, pages 1280--1281, 2004.

Digital Library

[7]

M. Kozuch, M. Ryan, R. Gass, S. Schlosser, D. O'Hallaron, J. Cipar, E. Krevat, J. López, M. Stroucken, and G. Ganger. Tashi: location-aware cluster management. In Proc. of the 1st Workshop on Automated Control for Datacenters and Clouds, 2009.

Digital Library

[8]

K. Lai. Markets are dead, long live markets. SIGecom Exch., 5(4): 1--10, July 2005.

Digital Library

[9]

K. Lai, L. Rasmusson, E. Adar, L. Zhang, and B. A. Huberman. Tycoon: An implementation of a distributed, market-based resource allocation system. Multiagent Grid Syst., 1(3): 169--182, Aug. 2005.

Digital Library

[10]

C. B. Lee and A. E. Snavely. Precise and realistic utility functions for user-centric performance analysis of schedulers. In Proc. of the 16th international symposium on High performance distributed computing, HPDC '07, pages 107--116. ACM, 2007.

Digital Library

[11]

C. Reiss, A. Tumanov, G. R. Ganger, R. H. Katz, and M. A. Kozuch. Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In Proc. of the 3nd ACM Symposium on Cloud Computing, SOCC '12, 2012.

Digital Library

[12]

C. Reiss, A. Tumanov, G. R. Ganger, R. H. Katz, and M. A. Kozuch. Towards understanding heterogeneous clouds at scale: Google trace analysis. Technical Report ISTC-CC-TR-12-101, Intel Science and Technology Center for Cloud Computing, Apr 2012.

[13]

B. Sharma, V. Chudnovsky, J. L. Hellerstein, R. Rifaat, and C. R. Das. Modeling and synthesizing task placement constraints in Google compute clusters. In Proc. of the 2nd ACM Symposium on Cloud Computing, SOCC '11, pages 3: 1--3: 14. ACM, 2011.

Digital Library

[14]

I. Stoica, H. Abdel-wahab, and A. Pothen. A microeconomic scheduler for parallel computers. In Proc. of the Workshop on Job Scheduling Strategies for Parallel Processing, pages 122--135. Springer-Verlag, 1994.

Digital Library

[15]

J. Wilkes. Utility functions, prices, and negotiation. Technical Report HPL-2008-81, HP Labs, July 2008.

Cited By

Kumar NRuiz PMenon VKabiljo IPundir MNewell ALee DWang LTang CGavrilovska ATerry D(2024)Optimizing resource allocation in hyperscale datacentersProceedings of the 18th USENIX Conference on Operating Systems Design and Implementation10.5555/3691938.3691965(507-528)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691938.3691965
Manterola Lasa ATalluri SDe Matteis TIosup ABalsamo SKnottenbelt WAbad CShang W(2024)The Cost of Simplicity: Understanding Datacenter Scheduler Programming AbstractionsProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645038(166-177)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645038
Chen ZJiang FChen BLi YZhang YHuang CYang RJiang FChen JXiang WCheng GShi RMa NZhang WZhang T(2024)Resource Allocation with Service Affinity in Large-Scale Cloud Environments2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00397(5280-5293)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00397
Show More Cited By

Index Terms

alsched: algebraic scheduling of mixed workloads in heterogeneous clouds
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

Recommendations

Stratus: cost-aware container scheduling in the public cloud
SoCC '18: Proceedings of the ACM Symposium on Cloud Computing

Stratus is a new cluster scheduler specialized for orchestrating batch job execution on virtual clusters, dynamically allocated collections of virtual machine instances on public IaaS platforms. Unlike schedulers for conventional clusters, Stratus ...
PPS: Fair and efficient black-box scheduling for multi-tenant GPU clusters
Abstract
Multi-tenant GPU clusters are common, where users purchase GPU quota to run their neural network training jobs. However, strict quota-based scheduling often leads to cluster under-utilization, while allowing quota groups to use excess GPUs ...
Highlights
- Existing GPU cluster scheduling strategies trade-off between fairness and efficiency.
- Aggregate job statistics are predictable for large-scale production GPU clusters.
- Our scheduler PPS achieves both fairness and efficiency by ...
A Conceptual Platform of SLA in Cloud Computing
DASC '11: Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing

Cloud computing is a promising technology, where the infrastructure, developing platform, software and storage are delivered as a service. With the development of cloud computing, more and more cloud service providers emerge. However, there are no ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SoCC '12: Proceedings of the Third ACM Symposium on Cloud Computing

October 2012

325 pages

ISBN:9781450317610

DOI:10.1145/2391229

Program Chairs:
Michael Carey
UC Irvine
,
Steven Hand
University of Cambridge

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SOCC '12

Sponsor:

SOCC '12: ACM Symposium on Cloud Computing

October 14 - 17, 2012

California, San Jose

Acceptance Rates

Overall Acceptance Rate 169 of 722 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

43
Total Citations
View Citations
621
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kumar NRuiz PMenon VKabiljo IPundir MNewell ALee DWang LTang CGavrilovska ATerry D(2024)Optimizing resource allocation in hyperscale datacentersProceedings of the 18th USENIX Conference on Operating Systems Design and Implementation10.5555/3691938.3691965(507-528)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691938.3691965
Manterola Lasa ATalluri SDe Matteis TIosup ABalsamo SKnottenbelt WAbad CShang W(2024)The Cost of Simplicity: Understanding Datacenter Scheduler Programming AbstractionsProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645038(166-177)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645038
Chen ZJiang FChen BLi YZhang YHuang CYang RJiang FChen JXiang WCheng GShi RMa NZhang WZhang T(2024)Resource Allocation with Service Affinity in Large-Scale Cloud Environments2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00397(5280-5293)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00397
Rong KBudiu MSkiadopoulos ASuresh LTai A(2023)Scaling a Declarative Cluster Manager Architecture with Query Optimization TechniquesProceedings of the VLDB Endowment10.14778/3603581.360359916:10(2618-2631)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.14778/3603581.3603599
Fingler HZhu ZYoon EJia ZWitchel ERossbach C(2023)Disaggregated GPU Acceleration for Serverless ApplicationsACM SIGOPS Operating Systems Review10.1145/3606557.360656057:1(10-20)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1145/3606557.3606560
Manterola Lasa ATalluri SIosup AVieira MCardellini VDi Marco ATuma P(2023)A Reference Architecture for Datacenter Scheduler Programming Abstractions: Design and Experiments (Work In Progress Paper)Companion of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578245.3585035(57-63)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1145/3578245.3585035
Nissim RSchwartz O(2023)Stragglers in Distributed Matrix MultiplicationJob Scheduling Strategies for Parallel Processing10.1007/978-3-031-43943-8_4(74-96)Online publication date: 15-Sep-2023
https://doi.org/10.1007/978-3-031-43943-8_4
Fingler HZhu ZYoon EJia ZWitchel ERossbach C(2022)DGSF: Disaggregated GPUs for Serverless Functions2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS53621.2022.00077(739-750)Online publication date: May-2022
https://doi.org/10.1109/IPDPS53621.2022.00077
Chowdhury FDi Natale FMoody AMohror KYu W(2022)DFMan: A Graph-based Optimization of Dataflow Scheduling on High-Performance Computing Systems2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS53621.2022.00043(368-378)Online publication date: May-2022
https://doi.org/10.1109/IPDPS53621.2022.00043
Mishra SMohapatra SMishra BSahoo S(2021)Analysis of Mobile Cloud ComputingResearch Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing10.4018/978-1-7998-5339-8.ch001(1-24)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5339-8.ch001
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten