Title: MLIM-Cloud: a flexible information monitoring middleware in large-scale cloud environments
Authors: Tienan Zhang
Addresses: School of Computer and Communication, Hunan Institute of Engineering, Hunan Province, China
Abstract: In large-scale cloud platforms, information monitoring service is essential for capturing the performance of underlying resources and understanding the behaviours of various applications in different circumstances. In this paper, we present a flexible information monitoring middleware, namely multi-level information monitoring for cloud (MLIM-Cloud), and our motivation is to enable users to perform their monitoring operations in a non-intrusive and transparent manner in any virtualised infrastructure. In the MLIM-Cloud framework, three kinds of monitoring entities are designed for collecting, processing and achieving various kinds of runtime information at different infrastructure levels, including physical machines, VM instances, and up-level applications. In addition, the MLIM-Cloud middleware is both platform-independent and platform-interoperable, which means it can be easily deployed on different kinds of cloud platforms. To investigate the performance of MLIM-Cloud, an extensive set of experiments are conducted in a real-world cloud platform. The experimental results show that comparing with many existing monitoring services, the MLIM-Cloud middleware exhibits better adaptiveness and robustness when the cloud system is in presence of dynamic and unpredictable workloads.
Keywords: cloud computing; information monitoring service; virtual machine; information filter; resource allocation.
International Journal of Computational Science and Engineering, 2020 Vol.22 No.2/3, pp.233 - 242
Received: 05 Nov 2018
Accepted: 10 Jul 2019
Published online: 18 May 2020 *