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Interference Aware Workload Scheduling for Latency Sensitive Tasks in Cloud Environment

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM University, Guntur, Andhra Pradesh, India

    Chinmaya Kumar Swain

  2. Department of Computer Science and Engineering, IIT Guwahati, Assam, India

    Aryabartta Sahu

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  1. Chinmaya Kumar Swain
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  2. Aryabartta Sahu
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Correspondence to Chinmaya Kumar Swain.

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Swain, C.K., Sahu, A. Interference Aware Workload Scheduling for Latency Sensitive Tasks in Cloud Environment. Computing 104, 925–950 (2022). https://doi.org/10.1007/s00607-021-01014-9

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