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Expensive dataflow queries which may involve large-scale computations operating on significant volumes of data are typically executed on distributed platforms to improve application performance. Among these, cloud computing has emerged as an attractive option for users to execute dataflows allowing them to select proper configurations (e.g., number of machines) to achieve desired trade-offs between execution time and monetary cost. Discovering dataflow schedules that exhibit the best trade-offs within a plethora of potential solutions can be challenging, especially in a heterogeneous environment where resource characteristics like performance and price can be varied. To increase resource utilization, users may also submit multiple dataflows for execution concurrently. Traditionally, building fair schedules (schedules where the slowdown of all dataflows due to resource sharing is similar) while achieving good performance is a major concern. However, considering fairness in the cloud computing setting where monetary cost is part of the optimization objectives significantly increases the difficulty of the scheduling problem. This paper proposes an algorithm for the scheduling of multiple dataflows on heterogeneous clouds that identifies Pareto-optimal solutions (schedules) in the three-dimensional space formed from the different trade-offs between overall execution time, monetary cost and fairness. The results show that in most cases the proposed approach can provide solutions with fairer schedules without significantly impacting the quality of the execution time to monetary cost skyline compared to the state of the art where the fairness of a solution is not taken into account. • Fairness for the scheduling of multiple dataflows on the Cloud where cost is crucial. • Heuristic for Pareto-efficient solutions with respect to makespan, cost and fairness. • Impact of the prioritization scheme and the pruning method used on the skyline. [ABSTRACT FROM AUTHOR]

Power and energy efficiency are now critical concerns in extreme-scale high-performance scientific computing. Many extreme-scale computing systems today (for example: Top500) have tight integration of multicore CPU processors and accelerators (mix of Graphical Processing Units, Intel Xeon Phis, or Field Programmable Gate Arrays) empowering them to provide not just unprecedented computational power but also to address these concerns. However, such integration renders these systems highly heterogeneous and hierarchical, thereby necessitating design of novel performance, power, and energy models to accurately capture these inherent characteristics. There are now several extensive research efforts focusing exclusively on power and energy efficiency models and techniques for the processors composing these extreme-scale computing systems. This article synthesizes these research efforts with absolute concentration on predictive power and energy models and prime emphasis on node architecture. Through this survey, we also intend to highlight the shortcomings of these models to correctly and comprehensively predict the power and energy consumptions by taking into account the hierarchical and heterogeneous nature of these tightly integrated high-performance computing systems. [ABSTRACT FROM AUTHOR]

Power and energy efficiency are now critical concerns in extreme-scale high-performance scientific computing. Many extreme-scale computing systems today (for example: Top500) have tight integration of multicore CPU processors and accelerators (mix of Graphical Processing Units, Intel Xeon Phis, or Field Programmable Gate Arrays) empowering them to provide not just unprecedented computational power but also to address these concerns. However, such integration renders these systems highly heterogeneous and hierarchical, thereby necessitating design of novel performance, power, and energy models to accurately capture these inherent characteristics. There are now several extensive research efforts focusing exclusively on power and energy efficiency models and techniques for the processors composing these extreme-scale computing systems. This article synthesizes these research efforts with absolute concentration on predictive power and energy models and prime emphasis on node architecture. Through this survey, we also intend to highlight the shortcomings of these models to correctly and comprehensively predict the power and energy consumptions by taking into account the hierarchical and heterogeneous nature of these tightly integrated high-performance computing systems. [ABSTRACT FROM AUTHOR]

Cloud computing enables users to provision resources on demand and execute applications in a way that meets their requirements by choosing virtual resources that fit their application resource needs. Then, it becomes the task of cloud resource providers to accommodate these virtual resources onto physical resources. This problem is a fundamental challenge in cloud computing as resource providers need to map virtual resources onto physical resources in a way that takes into account the providers’ optimization objectives. This article surveys the relevant body of literature that deals with this mapping problem and how it can be addressed in different scenarios and through different objectives and optimization techniques. The evaluation aspects of different solutions are also considered. The article aims at both identifying and classifying research done in the area adopting a categorization that can enhance understanding of the problem. [ABSTRACT FROM AUTHOR]