Prediction-driven resource provisioning for serverless container runtimes
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917815931371520 |
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| author | Tomaras, Dimitrios Tsenos, Michail Kalogeraki, Vana |
| author_facet | Tomaras, Dimitrios Tsenos, Michail Kalogeraki, Vana |
| contents | In recent years Serverless Computing has emerged as a compelling cloud based model for the development of a wide range of data-intensive applications. However, rapid container provisioning introduces non-trivial challenges for FaaS cloud providers, as (i) real-world FaaS workloads may exhibit highly dynamic request patterns, (ii) applications have service-level objectives (SLOs) that must be met, and (iii) container provisioning can be a costly process. In this paper, we present SLOPE, a prediction framework for serverless FaaS platforms to address the aforementioned challenges. Specifically, it trains a neural network model that utilizes knowledge from past runs in order to estimate the number of instances required to satisfy the invocation rate requirements of the serverless applications. In cases that a priori knowledge is not available, SLOPE makes predictions using a graph edit distance approach to capture the similarities among serverless applications. Our experimental results illustrate the efficiency and benefits of our approach, which can reduce the operating costs by 66.25% on average. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prediction-driven resource provisioning for serverless container runtimes Tomaras, Dimitrios Tsenos, Michail Kalogeraki, Vana Distributed, Parallel, and Cluster Computing In recent years Serverless Computing has emerged as a compelling cloud based model for the development of a wide range of data-intensive applications. However, rapid container provisioning introduces non-trivial challenges for FaaS cloud providers, as (i) real-world FaaS workloads may exhibit highly dynamic request patterns, (ii) applications have service-level objectives (SLOs) that must be met, and (iii) container provisioning can be a costly process. In this paper, we present SLOPE, a prediction framework for serverless FaaS platforms to address the aforementioned challenges. Specifically, it trains a neural network model that utilizes knowledge from past runs in order to estimate the number of instances required to satisfy the invocation rate requirements of the serverless applications. In cases that a priori knowledge is not available, SLOPE makes predictions using a graph edit distance approach to capture the similarities among serverless applications. Our experimental results illustrate the efficiency and benefits of our approach, which can reduce the operating costs by 66.25% on average. |
| title | Prediction-driven resource provisioning for serverless container runtimes |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.19215 |