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Flint: batch-interactive data-intensive processing on transient servers

Authors:

Prateek Sharma,

Prashant ShenoyAuthors Info & Claims

EuroSys '16: Proceedings of the Eleventh European Conference on Computer Systems

Article No.: 6, Pages 1 - 15

https://doi.org/10.1145/2901318.2901319

Published: 18 April 2016 Publication History

Abstract

Cloud providers now offer transient servers, which they may revoke at anytime, for significantly lower prices than on-demand servers, which they cannot revoke. The low price of transient servers is particularly attractive for executing an emerging class of workload, which we call Batch-Interactive Data-Intensive (BIDI), that is becoming increasingly important for data analytics. BIDI workloads require large sets of servers to cache massive datasets in memory to enable low latency operation. In this paper, we illustrate the challenges of executing BIDI workloads on transient servers, where revocations (akin to failures) are the common case. To address these challenges, we design Flint, which is based on Spark and includes automated checkpointing and server selection policies that i) support batch and interactive applications and ii) dynamically adapt to application characteristics. We evaluate a prototype of Flint using EC2 spot instances, and show that it yields cost savings of up to 90% compared to using on-demand servers, while increasing running time by < 2%.

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Kim KLee KChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Making Cloud Spot Instance Interruption Events VisibleProceedings of the ACM on Web Conference 202410.1145/3589334.3645548(2998-3009)Online publication date: 13-May-2024
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cover image ACM Other conferences

EuroSys '16: Proceedings of the Eleventh European Conference on Computer Systems

April 2016

605 pages

ISBN:9781450342407

DOI:10.1145/2901318

General Chairs:
Cristian Cadar
Imperial College London, UK
,
Peter Pietzuch
Imperial College London, UK
,
Program Chairs:
Kimberly Keeton
HP Labs
,
Rodrigo Rodrigues
Instituto Superior Técnico (Univ. Lisbon) and INESC-ID, Lisbon, Portugal

Copyright © 2016 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 the author(s) 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].

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Published: 18 April 2016

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National Science Foundation

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EuroSys '16

EuroSys '16: Eleventh EuroSys Conference 2016

April 18 - 21, 2016

London, United Kingdom

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EuroSys '16 Paper Acceptance Rate 38 of 180 submissions, 21%;

Overall Acceptance Rate 241 of 1,308 submissions, 18%

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Cited By

Bostandoost RLechowicz AHanafy WBashir NShenoy PHajiesmaili M(2024)LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain DemandProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661942(27-45)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3661942
Kim KLee KChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Making Cloud Spot Instance Interruption Events VisibleProceedings of the ACM on Web Conference 202410.1145/3589334.3645548(2998-3009)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645548
Jiang HZhang XJoe-Wong C(2023)DOLL: Distributed OnLine Learning Using Preemptible Cloud Instances2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)10.23919/WiOpt58741.2023.10349831(175-182)Online publication date: 24-Aug-2023
https://doi.org/10.23919/WiOpt58741.2023.10349831
Wang YYu JYu Z(2023)Resource scheduling techniques in cloud from a view of coordination: a holistic survey从协同视角论云资源调度技术：综述Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.210029824:1(1-40)Online publication date: 23-Jan-2023
https://doi.org/10.1631/FITEE.2100298
Hanafy WBostandoost RBashir NIrwin DHajiesmaili MShenoy PChien AEilam TPorter GAnderson TJosephson CPark J(2023)The War of the Efficiencies: Understanding the Tension between Carbon and Energy OptimizationProceedings of the 2nd Workshop on Sustainable Computer Systems10.1145/3604930.3605709(1-7)Online publication date: 9-Jul-2023
https://dl.acm.org/doi/10.1145/3604930.3605709
Chhetri MForkan AVo QNepal SKowalczyk R(2023)Towards Proactive Risk-Aware Cloud Cost Optimization Leveraging Transient ResourcesIEEE Transactions on Services Computing10.1109/TSC.2023.325347316:4(3014-3026)Online publication date: 1-Jul-2023
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Xu HLiu PAhmed SDa Silva DHu L(2023)Adaptive Fragment-Based Parallel State Recovery for Stream Processing SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.325199734:8(2464-2478)Online publication date: Aug-2023
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Kadupitiya JJadhao VSharma P(2022)SciSpot: Scientific Computing On Temporally Constrained Cloud Preemptible VMsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.315727233:12(3575-3588)Online publication date: 1-Dec-2022
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Wu YMa KYan XLiu ZCai ZHuang YCheng JYuan HYu F(2022)Elastic Deep Learning in Multi-Tenant GPU ClustersIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.306496633:1(144-158)Online publication date: 1-Jan-2022
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Lee SHwang JLee K(2022)SpotLake: Diverse Spot Instance Dataset Archive Service2022 IEEE International Symposium on Workload Characterization (IISWC)10.1109/IISWC55918.2022.00029(242-255)Online publication date: Dec-2022
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