This keynote paper: presents a 21 st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLAoriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3 rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21 st century vision.
Jobs submitted into a cluster have varying requirements depending on user-specific needs and expectations. Therefore, in utility-driven cluster computing, cluster Resource Management Systems (RMSs)
For applications in large-scale distributed systems, it is becoming increasingly important to provide reliable scheduling by evaluating the reliability of resources. However, most existing reputation models used for reliability evaluation ignore the critical influence of task runtime. In addition, most previous work uses list heuristics to optimize the makespan and reliability of workflow applications instead of Genetic Algorithms (GAs), which can give several satisfying solutions for choice. Hence, in this paper, we first propose the Reliability-Driven (RD) reputation, which is time-dependent and can be used to effectively evaluate the reliability of a resource in widely distributed systems. We then propose LookAhead Genetic Algorithm (LAGA) which utilizes the RD reputation to optimize both makespan and reliability of a workflow application. LAGA uses a novel evolution and evaluation mechanism: (i) the evolution operators evolve the task-resource mapping of a scheduling solution and (ii) the evaluation step determines the task order of solutions by using our proposed max-min strategy, which is the first two-phase strategy that can work with GAs. Our experiments show that the RD reputation improves the reliability of an application with more accurate reputations, while LAGA provides better solutions than existing list heuristics and evolves to better solutions more quickly than a traditional GA.
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