Scalable Grid Resource Allocation for Scientific Workflows Using Hybrid Metaheuristics

Abstract

Abstract. Grid infrastructure is a valuable tool for scientific users, but it is characterized by a high level of complexity which makes it difficult for them to quantify their requirements and allocate resources. In this paper, we show that resource trading is a viable and scalable approach for scientific users to consume resources. We propose the use of Grid resource bundles to specify supply and demand combined with a hybrid metaheuristic method to determine the allocation of resources in a market-based approach. We evaluate this through the application domain of scientific workflow execution on the Grid.

Trading of cloud of things resources

“…This should be a simple foundation of a CoT trade system which would be more complicated in reality. To optimally match resource providers and consumers, there is a well-known resource matching optimisation problem [34]. This is done using intermediary brokers who maintain a list of resource requests and offers, matching them if possible.…”

“…The problem is non-trivial, involving multi-attribute consumer and provider bundles. These algorithms have been used to support combinatorial exchange problems in Cluster Computing [35], Grid Computing [34] and Cloud Computing [32] applications.…”

System Architecture Directions for Tangible Cloud Computing

“…To optimally match resource providers and consumers is a well-known resource matching optimization problem [22]. This is done using intermediary brokers who maintain a list of resource requests and offers, matching them if possible.…”