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Distributed QoS-aware scheduling in storm

Authors:

Valeria Cardellini,

Vincenzo Grassi,

Francesco Lo Presti,

Matteo NardelliAuthors Info & Claims

DEBS '15: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems

Pages 344 - 347

https://doi.org/10.1145/2675743.2776766

Published: 24 June 2015 Publication History

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Abstract

Storm is a distributed stream processing system that has recently gained increasing interest. We extend Storm to make it suitable to operate in a geographically distributed and highly variable environment such as that envisioned by the convergence of Fog computing, Cloud computing, and Internet of Things.

References

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V. Cardellini, V. Grassi, F. Lo Presti, and M. Nardelli. Distributed QoS-aware scheduling in Storm. Technical Report DICII RR-15.7, Univ. Roma Tor Vergata, 2015. www.ce.uniroma2.it/publications/RR-15.7.pdf.

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Cited By

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Ecker RKaragiannis VSober MSchulte S(2025)Latency-Aware Placement of Stream Processing Operators in Modern-Day Stream Processing FrameworksJournal of Parallel and Distributed Computing10.1016/j.jpdc.2025.105041(105041)Online publication date: Jan-2025
https://doi.org/10.1016/j.jpdc.2025.105041
Pan XJin YXi MLi Y(2024)Deployment perspective of service pattern: Solve dynamic services with heterogeneous carrier description2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00045(234-243)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00045
Ecker RKaragiannis VSober MEbrahimi ESchulte S(2024)Latency-Aware Placement of Stream Processing OperatorsEuro-Par 2023: Parallel Processing Workshops10.1007/978-3-031-50684-0_3(30-41)Online publication date: 16-Apr-2024
https://doi.org/10.1007/978-3-031-50684-0_3
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Index Terms

Distributed QoS-aware scheduling in storm
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

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Published In

DEBS '15: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems

June 2015

385 pages

ISBN:9781450332866

DOI:10.1145/2675743

General Chairs:
Frank Eliassen
University of Oslo, Norway
,
Roman Vitenberg
University of Oslo, Norway

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]

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Association for Computing Machinery

New York, NY, United States

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Published: 24 June 2015

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DEBS '15: The 9th ACM International Conference on Distributed Event-Based Systems

June 29 - July 3, 2015

Oslo, Norway

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Overall Acceptance Rate 145 of 583 submissions, 25%

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Ecker RKaragiannis VSober MSchulte S(2025)Latency-Aware Placement of Stream Processing Operators in Modern-Day Stream Processing FrameworksJournal of Parallel and Distributed Computing10.1016/j.jpdc.2025.105041(105041)Online publication date: Jan-2025
https://doi.org/10.1016/j.jpdc.2025.105041
Pan XJin YXi MLi Y(2024)Deployment perspective of service pattern: Solve dynamic services with heterogeneous carrier description2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00045(234-243)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00045
Ecker RKaragiannis VSober MEbrahimi ESchulte S(2024)Latency-Aware Placement of Stream Processing OperatorsEuro-Par 2023: Parallel Processing Workshops10.1007/978-3-031-50684-0_3(30-41)Online publication date: 16-Apr-2024
https://doi.org/10.1007/978-3-031-50684-0_3
Nguyen HChien A(2023)Storm-RTS: Stream Processing with Stable Performance for Multi-Cloud and Cloud-edge2023 IEEE 16th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD60044.2023.00015(45-57)Online publication date: Jul-2023
https://doi.org/10.1109/CLOUD60044.2023.00015
Sun DWu MYang ZSajjanhar ABuyya R(2023)A two-tier coordinated load balancing strategy over skewed data streamsThe Journal of Supercomputing10.1007/s11227-023-05473-z79:18(21028-21056)Online publication date: 18-Jun-2023
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Huang XShao ZYang Y(2022)POTUS: Predictive Online Tuple Scheduling for Data Stream Processing SystemsIEEE Transactions on Cloud Computing10.1109/TCC.2020.303257710:4(2863-2875)Online publication date: 1-Oct-2022
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Smolka SMann Z(2022)Evaluation of fog application placement algorithms: a surveyComputing10.1007/s00607-021-01031-8104:6(1397-1423)Online publication date: 11-Feb-2022
https://doi.org/10.1007/s00607-021-01031-8
Qi TRodriguez M(2021)A Traffic and Resource Aware Online Storm SchedulerProceedings of the 2021 Australasian Computer Science Week Multiconference10.1145/3437378.3444365(1-10)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1145/3437378.3444365
Li HDai HLiu ZFu HZou Y(2021)Dynamic energy-efficient scheduling for streaming applications in stormComputing10.1007/s00607-021-00961-7Online publication date: 8-Jun-2021
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Liu FJin ZMu WZhu WZhang YWang W(2021)DROAllocator: A Dynamic Resource-Aware Operator Allocation Framework in Distributed Streaming ProcessingNetwork and Parallel Computing10.1007/978-3-030-79478-1_30(349-360)Online publication date: 23-Jun-2021
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Adaptive online scheduling in storm

R-Storm: Resource-Aware Scheduling in Storm

Meteorological data layout and task scheduling in a multi-cloud environment

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