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Infrastructure-as-a-service (IaaS) cloud computing is revolutionizing how we approach computing. Compute resource consumers can eliminate the expense inherent in acquiring, managing, and operating IT infrastructure and instead lease resources on a pay-as-you-go basis. IT infrastructure providers can exploit economies of scale to mitigate the cost of buying(More)
Virtual machines provide flexible, powerful execution environments for Grid computing, offering isolation and security mechanisms complementary to operating systems, customization and encapsulation of entire application environments, and support for legacy applications. This paper describes a Grid service ¿ VMPlant ¿ that provides for automated(More)
Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible(More)
—This paper proposes and evaluates an approach to the parallelization, deployment and management of bioinformatics applications that integrates several emerging technologies for distributed computing. The proposed approach uses the MapReduce paradigm to parallelize tools and manage their execution, machine virtualization to encapsulate their execution(More)
One of the most important goals of data-center management is to reduce cost through efficient use of resources. Virtualization techniques provide the opportunity of carving individual physical servers into multiple virtual containers that can be run and managed separately. A key challenge that comes with virtualization is the simultaneous on-demand(More)
This paper describes the performance and interoperability issues that arise in the process of integrating cluster management systems into a wide-area network-computing environment, and provides solutions in the context of the Purdue University Network Computing Hubs (PUNCH). The described solution provides users with a single point of access to resources(More)
This paper describes and evaluates the application of three l o cal learning algorithms | nearest-neighbor, weighted-average, and locally-weighted p olynomial regression | for the prediction of run-speciic resource-usage on the basis of run-time input parameters supplied t o t o ols. A two-level knowledge base allows the learning algorithms to track(More)