The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless Environment
Serverless computing has gained significant attention in recent years. However, the cold start problem remains a major challenge, not only because of the substantial latency it introduces to function execution time, but also because frequent cold starts lead to poor resource utilization, especially...
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| Format: | Article |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7632 |
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| author | Louai Shiekhani Hui Wang Wen Shi Jiahao Liu Yuan Qiu Chunhua Gu Weichao Ding |
| author_facet | Louai Shiekhani Hui Wang Wen Shi Jiahao Liu Yuan Qiu Chunhua Gu Weichao Ding |
| author_sort | Louai Shiekhani |
| collection | DOAJ |
| description | Serverless computing has gained significant attention in recent years. However, the cold start problem remains a major challenge, not only because of the substantial latency it introduces to function execution time, but also because frequent cold starts lead to poor resource utilization, especially during workload fluctuations. To address these issues, we propose a multi-level scheduling solution: the Hybrid Model. This model is designed to reduce the frequency of cold starts while maximizing container utilization. At the global level (across invokers), the Hybrid Model employs a skewness-aware scheduling strategy to select the most appropriate invoker for each request. Within each invoker, we introduce a greedy buffer-aware scheduling method that leverages the available slack (remaining buffer) of warm containers to aggressively encourage their reuse. Both the global and the local schedule are tightly integrated with a prediction component- The Hybrid Predictor- that combines Auto-Regressive Integrated Moving Average ARIMA (linear trends) and Random Forest (non-linear residuals + environment-aware features) for 5-min workload forecasts. The Hybrid Model is implemented on Apache OpenWhisk and evaluated using Azure-like traces and real FaaS applications. The evaluations show that the Hybrid Model achieves up to 34% SLA violation reductions compared to three state-of-the-art approaches and maintains the container utilization to be more than 80%. |
| format | Article |
| id | doaj-art-772a38ce5b59450897bcc0d875e45d66 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-772a38ce5b59450897bcc0d875e45d662025-08-20T03:32:31ZengMDPI AGApplied Sciences2076-34172025-07-011514763210.3390/app15147632The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless EnvironmentLouai Shiekhani0Hui Wang1Wen Shi2Jiahao Liu3Yuan Qiu4Chunhua Gu5Weichao Ding6School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaShanghai Aerospace Electronic Technology Institute, Shanghai 201109, ChinaSchool of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaShanghai Aerospace Electronic Technology Institute, Shanghai 201109, ChinaSchool of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaServerless computing has gained significant attention in recent years. However, the cold start problem remains a major challenge, not only because of the substantial latency it introduces to function execution time, but also because frequent cold starts lead to poor resource utilization, especially during workload fluctuations. To address these issues, we propose a multi-level scheduling solution: the Hybrid Model. This model is designed to reduce the frequency of cold starts while maximizing container utilization. At the global level (across invokers), the Hybrid Model employs a skewness-aware scheduling strategy to select the most appropriate invoker for each request. Within each invoker, we introduce a greedy buffer-aware scheduling method that leverages the available slack (remaining buffer) of warm containers to aggressively encourage their reuse. Both the global and the local schedule are tightly integrated with a prediction component- The Hybrid Predictor- that combines Auto-Regressive Integrated Moving Average ARIMA (linear trends) and Random Forest (non-linear residuals + environment-aware features) for 5-min workload forecasts. The Hybrid Model is implemented on Apache OpenWhisk and evaluated using Azure-like traces and real FaaS applications. The evaluations show that the Hybrid Model achieves up to 34% SLA violation reductions compared to three state-of-the-art approaches and maintains the container utilization to be more than 80%.https://www.mdpi.com/2076-3417/15/14/7632cold startpredictionresource managementschedulingserverless computing |
| spellingShingle | Louai Shiekhani Hui Wang Wen Shi Jiahao Liu Yuan Qiu Chunhua Gu Weichao Ding The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless Environment Applied Sciences cold start prediction resource management scheduling serverless computing |
| title | The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless Environment |
| title_full | The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless Environment |
| title_fullStr | The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless Environment |
| title_full_unstemmed | The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless Environment |
| title_short | The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless Environment |
| title_sort | hybrid model prediction based scheduling and efficient resource management in a serverless environment |
| topic | cold start prediction resource management scheduling serverless computing |
| url | https://www.mdpi.com/2076-3417/15/14/7632 |
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