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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2211.15739 (cs)
[Submitted on 28 Nov 2022]

Title:CWD: A Machine Learning based Approach to Detect Unknown Cloud Workloads

Authors:Mohammad Hossain, Derssie Mebratu, Niranjan Hasabnis, Jun Jin, Gaurav Chaudhary, Noah Shen
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Abstract:Workloads in modern cloud data centers are becoming increasingly complex. The number of workloads running in cloud data centers has been growing exponentially for the last few years, and cloud service providers (CSP) have been supporting on-demand services in real-time. Realizing the growing complexity of cloud environment and cloud workloads, hardware vendors such as Intel and AMD are increasingly introducing cloud-specific workload acceleration features in their CPU platforms. These features are typically targeted towards popular and commonly-used cloud workloads. Nonetheless, uncommon, customer-specific workloads (unknown workloads), if their characteristics are different from common workloads (known workloads), may not realize the potential of the underlying platform. To address this problem of realizing the full potential of the underlying platform, we develop a machine learning based technique to characterize, profile and predict workloads running in the cloud environment. Experimental evaluation of our technique demonstrates good prediction performance. We also develop techniques to analyze the performance of the model in a standalone manner.
Comments: 7 pages, 4 figures, Appeared at The MLSys'22 Workshop on Cloud Intelligence(AIOps), In conjunction with the 5th Conference on Machine Learning and Systems
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
Cite as: arXiv:2211.15739 [cs.DC]
  (or arXiv:2211.15739v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.2211.15739

Submission history

From: Niranjan Hasabnis [view email]
[v1] Mon, 28 Nov 2022 19:41:56 UTC (1,938 KB)
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