Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006). (June 2015)

• Record Type: Journal Article
• Authors: Pacini, Elina; Mateos, Cristian; García Garino, Carlos
• Is Part Of: Advances in engineering software. Volume 84(2015)
• Page Start: 31
• Page End: 47
• Publication Date: 2015-06
• Subjects: Cloud Computing -- Scientific problems -- Job scheduling -- Swarm intelligence -- Ant Colony Optimization -- Genetic Algorithms; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2015.01.005
• Languages: English
• ISSNs: 0965-9978

Abstract

Abstract: The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.