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Accessing, running and sharing applications and data presents researchers with many challenges. Cloud computing and social networking technologies have the potential to simplify or eliminate many of these challenges. Cloud computing technologies can provide scientists with transparent and on-demand access to applications served over the Internet in a dynamic and scalable manner. Social networking...
Automating the execution of applications in grid computing environments is a complicated task due to the heterogeneity of computing resources, resource usage policies, and application requirements. Applications differ in memory usage, performance, scalability and storage usage. Having knowledge of this information can aid in matching jobs to resources and in selecting appropriate configuration parameters...
ldquoWeb 2.0rdquo and ldquocloud computingrdquo are revolutionizing the way IT infrastructure is accessed and managed. Web 2.0 technologies such as blogs, wikis and social networking platforms provide Internet users with easier mechanisms to produce Web content and to interact with each other. Cloud computing technologies are aimed at running applications as services over the Internet on a scalable...
Grid computing technologies have made it possible for researchers to get access to more computational resources than ever before. However, heterogeneity of resources, scheduling policy and applications makes it difficult to manage the execution of jobs in such environments. The manner in which a job is configured to run on one resource may not be appropriate for another resource. Long running jobs...
Computational jobs that take days, weeks or months to run usually cannot be executed as a single job due to system failures and scheduling constraints. Instead the job must be split into a series of shorter jobs. Solutions for managing the execution of such jobs in grid environments must address many issues. Participating systems and their properties can change over time and therefore it is important...
Grid computing environments are heterogeneous in terms of the types of computer systems, the applications being run, and the policies governing the use of systems. To support automation in such environments common models to describe systems, applications and scheduling policies are needed. Common models enable the interoperation and reuse of tools transparent to the underlying differences in the environment...
Understanding how resources are allocated in a cluster or grid environment can be a daunting task. The number of schedulable processors has increased dramatically, resulting in a greater need for resource allocation analysis tools. This increase comes from hardware developments as well as inter-domain resource sharing. This paper describes a toolset for monitoring the allocation of resources in a...
Many job schedulers for clusters allow system administrators to specify a variety of policy constraints that can be used to implement site objectives. An example of a site objective could be to achieve high system utilization while at the same time giving users fair access to the resources. Policy can include such things as constraints on walltime, number of processors and memory that can be requested,...
Many grid computing projects involve sharing and transferring large volumes of data. It is often important to maximise data throughput. Grid computing environments can be highly heterogeneous in nature and could involve large round trip times between collaborating sites. The actual throughput achieved could be much lower than the available bandwidth due to TCP dynamics. One technique that helps improve...
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