Many enterprise applications require performing different tasks on different systems (or processing entities). Both the types of tasks and processing entities can be very heterogeneous. Such enterprise applications can be supported by workflow automation. In this paper, we discuss specification

a proper allocation of sensing tasks to ensure the timeliness of tasks and the quality of sensing data. In this paper, we investigate the heterogeneous sensing task allocation problem aiming at minimizing the total penalty caused by the tardiness of tasks. We prove this problem is NP

-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead it is based on principles of high dimensional dynamical systems in combination with statistical learning theory, and can

execution times. In this paper, we examine the e ects of synchronization and communication on the expected execution times of heterogeneous algorithmic structures. Speci cally, we consider structures containing two di erent types of tasks, where the execution times of the tasks follow one of two di erent

Abstract—Heterogeneous systems with CPUs and computational accelerators such as GPUs, FPGAs or the upcoming Intel MIC are becoming mainstream. In these systems, peak performance includes the performance of not just the CPUs but also all available accelerators. In spite of this fact, the majority

scheduling targets only homogeneous platforms. In this paper we develop a method for extending existing scheduling algorithms for task-parallel applications on heterogeneous platforms to the mixed-parallel case. After detailing the foundations of our method and our assumptions, we present a case study

. Task migration across heterogeneous platforms is one of the great challenges of distributed computing. While several approaches are known, very few actual implementations are available. The problem is even harder in the context of high performance computing, where it is imperative to not restrict

a program into more tasks than processors, and migrating tasks to implement adaptation. To be effective this requires efficient task migration. Furthermore, typically non-dedicated clusters contain different machines hence heterogeneous task migration is required. Gardens supports efficient task

is critical. Hadoop’s performance is closely tied to its task scheduler, which implicitly assumes that cluster nodes are homogeneous and tasks make progress linearly, and uses these assumptions to decide when to speculatively re-execute tasks that appear to be stragglers. In practice, the homogeneity

this paper is organized as follows. Section 2 defines the computational environment parameters that were varied in the simulations. Descriptions of the 11 mapping heuristics are found in Section 3. Section 4 examines selected results from the simulation study. A list of implementation parameters and procedures that could be varied for each heuristic is presented in Section 5