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Heterogeneity-Aware Proactive Elastic Resource Allocation for Serverless Applications
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AI
DOI：10.1109/TSC.2024.3350711.png)
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摘要
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Serverless computing is a popular cloud computing model that offers on-demand resource allocation and pay-as-you-go application execution. However, there are still challenges in allocating resources for workflow applications: inaccurate and inefficient resource estimation, high-latency inter-function communication, and long server readiness time. Therefore, we propose the heterogeneity-aware Proactive serverLess wOrkflow Elastic Allocation method (PLOEA) to address these issues and optimize infrastructure costs for cloud service providers (CSPs) while meeting the diverse needs of developers. Specifically, we propose a resource configuration estimation method for heterogeneous workflow applications that builds an ensemble multi-task expert classifier to analyze individual and common resource usage patterns, ensuring estimation accuracy and efficiency. Further, we propose a group allocation strategy for multiple applications that optimizes the spatiotemporal distribution of instances by considering the allocation urgency, communication affinity between functions, and the multi-core architecture of servers. Furthermore, we present a proactive server elastic scaling method that senses workload features, including workload level, trend, and magnitude changes, and combines them with CSP's attention differences to guide the server scaling size. Finally, experiments based on public datasets prove that PLOEA provides better service quality and cost efficiency than existing methods.
Keyword:
Servers
Costs
Predictive models
Estimation
Runtime
Quality of service
Media
Instance allocation
NUMA
resource estimation
server scaling
serverless
workflow
workload prediction
期刊
IF:
5.8
论文数:
2.2K
被引数:
6.5K
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