Capacity planning and stochastic scheduling in grid computing environments

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Mapping Light Communication SLA-Based Workflows onto Grid Resources with Parallel Processing Technology

Service Level Agreements (SLAs) are currently one of the major research topics in Grid Computing. Amongmany system components for supporting of SLA-aware Grid-based workflows, the SLA mapping module receives an important position. Mapping light communication workflows is one main part of the mapping module. With the previously proposed mapping algorithm, the mapping module may become the bottleneck of the system when many requests come in a short period of time. This paper presents a parallel mapping algorithm for light communication SLA-based workflows, which can cope with the problem. Performance measurements deliver evaluation results on the quality of the method

Optimizing the Execution Time of the SLA-Based Workflow in the Grid with Parallel Processing Technology

Service Level Agreements (SLAs) is currently one of the major research topics in Grid Computing. Among many system components for the supporting of SLA-aware Gridbased workflow, the SLA mapping module receives important positions. Optimizing execution time is an important task of the mapping module as it helps in finding out a feasible solution when the Grid is busy or by eliminating the negative effects of the error. With the previously proposed algorithm for optimizing the execution time of the workflow, the mapping module may, when many requests come in a short period of time, become the bottleneck of the system. This paper presents a parallel mapping algorithm for optimizing the execution time of the workflow, which can cope with the problem. Performance measurements deliver evaluation results on the quality of the method

InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape

Mapping Heavy Communication SLA-based Workflows onto Grid Resources with Parallel Processing Technology

Service Level Agreements (SLAs) are currently one of the major research topics in Grid Computing. Amongmany system components for supporting of SLA-aware Grid jobs, the SLA mapping module holds an important position and the capability of the mapping module depends on the runtime of the mapping algorithm. With the previously proposed mapping algorithm, the mapping module may develop into the bottleneck of the system if many requests come in during a short period of time. This paper presents a parallel mapping algorithm to map heavy communication SLA-based workflow onto Grid resources which can cope with the problem. Performance measurements thereby deliver evaluation results showing the quality of the method