Interference Aware Workload Scheduling for Latency Sensitive Tasks in Cloud Environment
Autor: | Chinmaya Kumar Swain, Aryabartta Sahu |
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Rok vydání: | 2021 |
Předmět: |
Numerical Analysis
business.industry Computer science Distributed computing Quality of service Hypervisor Cloud computing computer.software_genre Virtualization Computer Science Applications Theoretical Computer Science Computational Mathematics Service-level agreement Resource (project management) Computational Theory and Mathematics Virtual machine Scalability business computer Software |
Zdroj: | Computing. 104:925-950 |
ISSN: | 1436-5057 0010-485X |
Popis: | The virtualization technology enhances resource utilization and scalability in the cloud environment. Multiple virtual machines with divergent specifications in terms of hardware and software can be run on a single physical machine. Performance of the applications degrades due to interference when multiple applications are executed simultaneously. This performance degradation affects the quality of service and service level agreement in a cloud environment. In this work, we design an interference-aware workload scheduling approach to execute latency-sensitive tasks in the cloud system. Here we built an interference prediction model to manage the interference efficiently and validated that model in a virtualized environment using Xen hypervisor. We further design a resource prediction model to predict the future resource requirement for the set of tasks using modified double exponential smoothing. This prediction model helps to deploy the required number of physical machines for each time duration. Using these two prediction models, we develop an interference-aware workload scheduling approach that minimizes the effect of interference to achieve a better quality of service in the cloud environment. The extensive simulations with Google cluster data show that our proposed approach improves task guarantee ratio and priority guarantee ratio by 3.32% and 3.63% respectively on average, while improving the resource utilization around 17.26% as compared to other state-of-the-art approaches. |
Databáze: | OpenAIRE |
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