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WITCAT: A Workload Spike Targeted Cloud Management Solution

Published: 11 October 2018 Publication History

Abstract

The cloud computing technology offers consistent access to large-scale computing capabilities, thereby bringing convenience to life. However, the virtualized cloud systems are still too vulnerable to maintain performance scalability and service agility once a task burst surges in without any warning. A mounting account of research has been conducted on proper strategies for accurate workload prediction as well as effective resource reservation and arrangement, but commonly cloud providers seek help to strategies that deploy excessive resources, adding overhead cost and sacrificing the cloud's advantage of scalability, or otherwise fail to reconfigure timely and properly, causing dissatisfaction and even financial loss, which are not expected by both cloud providers and clients.
In this paper, we present a holistic solution called Workload Spike Targeted Cloud Management Solution (WITCAT) for virtualized cloud systems with three fundamental modules as a whole, which was seldom proposed before. By learning historical taskflow patterns, WITCAT can effectively classify the arriving tasks into clusters that feature respective workload traits. Then two different prediction means are employed to continually forecast the arrival rate and attributes of workloads for respective clusters, under two different characteristic scenarios: normal scenarios and bursty scenarios. Last, we employ a reservation strategy, makes full use of the available resources, strengthening the effectiveness of cloud service provisioning under workload spike.
As far as our knowledge reaches, the contributions are three-fold.
• We improve the clustering method for task characterization, where a Mahalanobis-distance-bused k-means clustering is adopted to eliminate the relevance among tasks' attributes.
• We employ a traffic-oriented two-scenario integrated prediction method, with a control knob that monitors the increment of tasks and triggers prediction means alternation for different workload scenarios.
• We develop a prediction-based heuristic algorithm for resource reservation and provisioning, reserving enough space in CPU and memory ahead of time for bursts without disabling the cloud's scalibility.
We conduct extensive experiments using Google cloud traces and the results outperform other scheduling algorithms in guarantee ratio (25.8% improved), total energy consumption (17.3% saved) and resource utilization (18.2% improved), which further indicates the advantages of our proposed solution towards task traffic bursts.

Cited By

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  • (2019)Reasoning Based Workload Performance Prediction in Cloud Data Centers2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom.2019.00073(431-438)Online publication date: Dec-2019

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  1. WITCAT: A Workload Spike Targeted Cloud Management Solution

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    cover image ACM Conferences
    SoCC '18: Proceedings of the ACM Symposium on Cloud Computing
    October 2018
    546 pages
    ISBN:9781450360111
    DOI:10.1145/3267809
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 11 October 2018

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    Author Tags

    1. Workload spike
    2. resource reservation
    3. task characterization
    4. trend prediction

    Qualifiers

    • Poster
    • Research
    • Refereed limited

    Funding Sources

    • Science Fund for Distinguished Young Scholars in Hunan Province
    • the National Natural Science Foundation of Chin

    Conference

    SoCC '18
    Sponsor:
    SoCC '18: ACM Symposium on Cloud Computing
    October 11 - 13, 2018
    CA, Carlsbad, USA

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    Overall Acceptance Rate 169 of 722 submissions, 23%

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    Cited By

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    • (2019)Reasoning Based Workload Performance Prediction in Cloud Data Centers2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom.2019.00073(431-438)Online publication date: Dec-2019

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