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Ming Li

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Proceedings Article | 28 July 2022 Paper

Research on resource allocation technology based on cloud computing

Shang Wu, Ming Li, XiaoYu Yin, DongBo Yu, Yong Zhang, Bing Jiang, Yong Li

Proceedings Volume 12303, 123030R (2022) https://doi.org/10.1117/12.2642918

KEYWORDS: Scanning laser ophthalmoscopy, Clouds, Computing systems, Statistical analysis, Computer simulations, Computer architecture, Prototyping, Lithium, Computer programming, Web 2.0 technologies

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With the rapid growth of Internet applications, cloud computing systems are hosting an increasing number of long services, such as web search, e-commerce, social networking, and online video. These long services often require predictable response times to provide a high quality user experience. In this paper, we first maintain an adaptive sliding window whose size will be calculated online based on the currently monitored request arrival rate changes, and then plan future short-term resource reservations for long services based on this sliding window that can satisfy the tail-latency SLOs. The third challenge is to eliminate tail-delay SLO violations due to bursty workloads at runtime. Tail-delay SLO violations can still occur because work negatives are all bursty and it is difficult to achieve zero error in predicting workloads. This paper addresses this challenge by designing a runtime monitoring mechanism that dynamically adjusts resource allocation based on the statistical characteristics of the response latency of requests. The main contributions of this paper are as follows: (1) A fine-grained resource scaling engine for containerized long services is proposed to minimize the resource waste due to overcapacity reservation while satisfying the tail-latency SLOs.(2) A hybrid resource reservation scheme is proposed that can handle both periodic and acyclic workloads of long services.(3) An online resource redeployment mechanism is designed to eliminate potential tail latency SLO violations due to workload bursts.(4) Implemented a prototype system for HRSE.

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