An algorithm for network and data-aware placement of multi-tier applications in cloud data centers. (15th November 2017)

• Record Type: Journal Article
• Authors: Ferdaus, Md Hasanul; Murshed, Manzur; Calheiros, Rodrigo N.; Buyya, Rajkumar
• Is Part Of: Journal of network and computer applications. Volume 98(2017)
• Page Start: 65
• Page End: 83
• Publication Date: 2017-11-15
• Subjects: Virtual machine -- Network-aware -- Storage -- Data center -- Placement -- Optimization -- Cloud application -- Cloud computing; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2017.09.009
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.