Title: CPPM: a lightweight performance prediction middleware for cloud platforms

Authors: Xiao Peng

Addresses: College of Computer Science and Technology, Hunan Institute of Engineering, Xiang-Tan 411100, Hunan Province, China

Abstract: As more and more commercial clouds have been applied in various areas, how to evaluate the performance of a cloud platform has become an important issue that needs to be addressed. Furthermore, an effective performance prediction mechanism is of significant value for improving the current cloud services, such as resource allocation and task scheduling. In the paper, we present the design and prototype implementation of a performance prediction system, namely cloud performance prediction middleware (CPPM), which is aiming at providing a set of lightweight and flexible services on existing cloud infrastructure so as to allow cloud providers monitoring, estimating and predicting the runtime performance from various aspects. The CPPM enables cloud providers to make more efficient and fine-grained resource management and scheduling policies based on their short-term workload prediction mechanism; also it provides an application-level performance prediction service which uses skeleton approach to capture execution characteristics of the running applications so as to predict their actual runtime performance and efficiency. Extensive experiments are conducted to examine the effectiveness and efficiency of the CPPM.

Keywords: cloud computing; performance evaluation; workload; quality of service.

DOI: 10.1504/IJITM.2019.103055

International Journal of Information Technology and Management, 2019 Vol.18 No.4, pp.419 - 434

Received: 13 May 2016
Accepted: 12 Jun 2017
Published online: 15 Oct 2019 *

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