Budget-Minimized Resource Allocation and Task Scheduling in Distributed Grid/Clouds

Pan Yi, Hui Ding, Byrav Ramamurthy
July 2013
Cite DOI URL

Abstract

The need for large-scale computing, storage and network capabilities by the scientific or business community has resulted in the development of cloud networks. Grid/Clouds users are provided with IT infrastructure (servers, storage, networks, etc.) as services called Infrastructure as a Service (IaaS). In this case, an efficient resource scheduling mechanism for allocating the infrastructure resources across the network will improve the resource efficiency in the cloud significantly. In this paper, we investigate the budget optimization of joint resources (storage, processor and network) allocation for IaaS model in distributed Grid/Clouds from the consumer’s perspective. We develop a Mixed Integer Linear Programming (MILP) formulation along with a new resource model and propose a Best-Fit heuristic algorithm with different job scheduling policies. Our goal is to minimize the expenditure for each user to obtain enough resources to execute their submitted jobs, while enabling the Grid/Cloud provider to accept as many job requests from the users as possible. Both MILP and heuristic are tested on a 10- node topology and the Google Datacenter topology. The results show that the heuristic method can achieve approximate optimal solutions to MILP; it can reduce the user expense by at least 30%. In addition, Best-Fit algorithm with SSF (simple job structure first) job scheduling policy has the lowest blocking rate, which is 5% 25% less than other job scheduling policies.

Type
Conference paper
Publication
2013 22nd International Conference on Computer Communication and Networks (ICCCN)
Resource Management Heuristic Algorithms Heuristic Programming Integer Programming Linear Programming Resource Allocation Processor Scheduling Grid Computing Scheduling Infrastructure as a Service Mixed Integer Linear Programming SSF Computational Modeling Cloud Networks Large-Scale Computing Best-Fit Hueristic Alorgithm Budget Optimization Budget-Minimized Resource Allocation Business Community Cloud Computing Distributed Grid-Cloud Computing Google Data Center Topology Infrastructure Resource Allocation IT Infrastructure LaaS Model MILP Formulation Node Topology Optimization Resource Scheduling Mechanism Scientific Community Simple Job Structure First Job Scheduling Policy Task Scheduling Transponders
Byrav Ramamurthy
Byrav Ramamurthy
Professor & PI

My research areas include optical and wireless networks, peer-to-peer networks for multimedia streaming, network security and telecommunications. My research work is supported by the U.S. National Science Foundation, U.S. Department of Energy, U.S. Department of Agriculture, NASA, AT&T Corporation, Agilent Tech., Ciena, HP and OPNET Inc.