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research-article

HAS: : Hybrid auto-scaler for resource scaling in cloud environment

Published: 01 October 2018 Publication History

Abstract

Auto-scaling is a crucial mechanism that supports autonomic provisioning and de-provisioning of computing resources in accordance with fluctuating demands in a cloud environment. The success factor of autonomic provisioning depends on efficient resource utilization and response time performance metrics. Existing literature focuses on reactive or predictive auto-scaling mechanism where the computing system is unable to scale proportionally with the Slashdot effect or abrupt traffic bursts while these mechanisms are employed in a discrete fashion. Predictive methods strive to predict the future computational needs and subsequently obtain or release the resources in advance; however it could be directed to under-utilization. Hence, a Hybrid Auto-Scaler (HAS) is proposed to adjust the required resources automatically to the application in demand. HAS forecasts the future behaviour of the system using a time series method and deploys the anticipated resources by computing the required capacity through a queuing model. Further, it uses a reactive approach to scale out the resources in accordance as the provisioned resources are insufficient to deal with the current needs. HAS also balances the load efficiently by employing Continuous Time Markov Model (CTMM). The proposed HAS is validated with several benchmark workloads to achieve significant improvement in CPU utilization and response time.

Highlights

Developed a Hybrid Auto-Scaler (HAS) framework for automated resource scaling in cloud environment. It is a hybrid method that combines the Predictive and the Reactive method for effective auto-scaling process.
HAS employs Auto-Regression of order one for estimating the future arrival rate. A novel set of equations is proposed to compute the future resource requirement.
Reactive method is utilized only when the computed resources are insufficient to handle the workloads.
Continuous Time Markov Model is employed to allocate the resources and to balance the load.
HAS framework is validated in a real cloud environment for proving its efficiency in terms of resource utilization, response time and scalability.

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  • (2024)OptScaler: A Collaborative Framework for Robust Autoscaling in the CloudProceedings of the VLDB Endowment10.14778/3685800.368582917:12(4090-4103)Online publication date: 8-Nov-2024
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  • (2022)Why Is It Not Solved Yet?Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering10.1145/3489525.3511680(105-115)Online publication date: 9-Apr-2022

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Information

Published In

cover image Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing  Volume 120, Issue C
Oct 2018
406 pages

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 October 2018

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  1. Auto-scaling
  2. Virtualization
  3. Queuing model
  4. Continuous time Markov model

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View all
  • (2024)OptScaler: A Collaborative Framework for Robust Autoscaling in the CloudProceedings of the VLDB Endowment10.14778/3685800.368582917:12(4090-4103)Online publication date: 8-Nov-2024
  • (2022)A maturity model for AI-empowered cloud-native databases: from the perspective of resource managementJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00318-111:1Online publication date: 7-Sep-2022
  • (2022)Why Is It Not Solved Yet?Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering10.1145/3489525.3511680(105-115)Online publication date: 9-Apr-2022

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