Resource brokering using a multi‐site resource allocation strategy for computational grids

Abstract

SUMMARYGrid computing employs heterogeneous resources which may be installed on different platforms, hardware/software, computer architectures, and perhaps using different computer languages to solve large-scale computational problems. As many more Grids are being developed worldwide, the number of multi-institutional collaborations is growing rapidly. However, to realize Grid computing's full potential, it is expected that Grid participants must be able to share one another's resources. This paper presents a resource broker that employs the multi-site resource allocation (MSRA) strategy and the dynamic domain-based network information model that we propose to allocate Grid resources to submitted jobs, where the Grid resources may be dispersed at different sites, and owned and governed by different organizations or institutes. The jobs and resources may also belong to different clusters/sites. Resource statuses collected by the Ganglia, and network bandwidths gathered by the Network Weather Service, are both considered in the proposed scheduling approach. A dynamic domain-based model for network information measurement is also proposed to choose the most appropriate resources that meet the jobs' execution requirements. Experimental results show that MSRA outperformed the other tested strategies.

Co-scheduling Deadline-Sensitive Applications in Large-scale Grid Systems

“…With the development of grid middleware, many effective co-allocation frameworks have be developed and deployed in practical grid systems. However, studies on co-allocation policy are relative left behind, especially in the area of QoS-based co-allocation policy [2,13].…”

“…In the QoS requirements, deadline-guarantee is one of the requirements that frequently be mentioned [9,13]. The difficulties of providing deadline-guaranteed when co-allocating resources are as following: (1) Lacking of a general measurement to evaluate the capability of heterogeneous resources, which makes it difficult to find the optimal co-allocation scheme for guaranteeing user's deadline requirement [2,14].…”