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A large class of applications need to execute the same workflow on different datasets of identical size. Efficient execution of such applications necessitates intelligent distribution of the application components and tasks on a parallel machine, and the execution can be orchestrated by utilizing task, data, pipelined, and/or replicated parallelism. The scheduling problem that encompasses all of these techniques is called pipelined workflow scheduling, and it has been widely studied in the last decade. Multiple models and algorithms have flourished to tackle various programming paradigms, constraints, machine behaviors, or optimization goals. This article surveys the field by summing up and structuring known results and approaches. [ABSTRACT FROM AUTHOR]

Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper, we consider the problem of scheduling applications to ensure fair and efficient execution on a distributed network of processors. We limit our study to the case where communication is restricted to a tree embedded in the network, and the applications consist of a large number of independent tasks (Bags of Tasks) that originate at the tree's root. The tasks of a given application all have the same computation and communication requirements, but these requirements can vary for different applications. The goal of scheduling is to maximize the throughput of each application while ensuring a fair sharing of resources between applications. We can find the optimal asymptotic rates by solving a linear programming problem that expresses all necessary problem constraints, and we show how to construct a periodic schedule from any linear program solution. For single-level trees, the solution is characterized by processing tasks with larger communication-to-computation ratios at children with larger bandwidths. For multilevel trees, this approach requires global knowledge of all application and platform parameters. For large-scale platforms, such global coordination by a centralized scheduler may be unrealistic. Thus, we also investigate decentralized schedulers that use only local information at each participating resource. We assess their performance via simulation and compare to an optimal centralized solution obtained via linear programming. The best of our decentralized heuristics achieves the same performance on about 2/3 of our test cases but is far worse in a few cases. Although our results are based on simple assumptions and do not explore all parameters (such as the maximum number of tasks that can be held on a node), they provide insight into the important question of fairly and optimally scheduling heterogeneous applications on heterogeneous grids. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we assess the impact of heterogeneity on scheduling independent tasks on master-slave platforms. We assume a realistic one-port model where the master can communicate with a single slave at any time. We target both on-line and off-line scheduling problems, and we focus on simpler instances where all tasks have the same size. While such on-line problems can be solved in polynomial time on homogeneous platforms, we show that there does not exist any optimal deterministic algorithm for heterogeneous platforms. Whether the source of heterogeneity comes from computation speeds, or from communication bandwidths, or from both, we establish lower bounds on the competitive ratio of any deterministic algorithm. We provide such bounds for the most important objective functions: the minimization of the makespan (or total execution time), the minimization of the maximum response time (difference between completion time and release time), and the minimization of the sum of all response times. Altogether, we obtain nine theorems which nicely assess the impact of heterogeneity on on-line scheduling. For off-line scheduling, we prove several result for problems with release dates, either optimality or NP-hardness. These theoretical contributions are complemented on the practical side by the implementation of several heuristics on a small but fully heterogeneous MPI platform. Our results show the superiority of those heuristics which fully take into account the relative capacity of the communication links. [Copyright &y& Elsevier]

This paper focuses on energy minimization for the mapping and scheduling of real-time workflows under reliability constraints. Workflow instances are input periodically to the system. Each instance is composed of several tasks and must complete execution before the arrival of the next instance, and with a prescribed reliability threshold. While the shape of the dependence graph is identical for each instance, task execution times are stochastic and vary from one instance to the next. The reliability threshold is met by executing several replicas for each task. The target platform consists of identical processors equipped with Dynamic Voltage and Frequency Scaling (DVFS) capabilities. A different frequency can be assigned to each task replica to save energy, but it may have negative effect on the deadline and reliability target. This difficult tri-criteria mapping and scheduling problem (energy, deadline, reliability) has been studied only recently for workflows with arbitrary dependence constraints. We investigate new mapping and scheduling strategies based upon layers in the task graph. These strategies better balance replicas across processors, thereby decreasing the time overlap between different replicas of a given task, and saving energy. We compare these strategies with two state-of-the-art approaches and a reference baseline on a variety of benchmark workflows. Our best heuristics achieve an average energy gain of 60% over the competitors and of 82% over the baseline. • Tri-criteria mapping and scheduling problem for real-time workflows of arbitrary shape. • First general solution without any limitation on the number of replicas used. • Innovative solution based upon layers of the workflow outperforms competitors. [ABSTRACT FROM AUTHOR]

In this paper, we discuss several algorithms for scheduling divisible workloads on heterogeneous systems. Our main contributions are (i) new optimality results for single-round algorithms and (ii) the design of an asymptotically optimal multi-round algorithm. This multi-round algorithm automatically performs resource selection, a difficult task that was previously left to the user. Because it is periodic, it is simpler to implement, and more robust to changes in the speeds of the processors and/or communication links. On the theoretical side, to the best of our knowledge, this is the first published result assessing the absolute performance of a multi-round algorithm. On the practical side, extensive simulations reveal that our multi-round algorithm outperforms existing solutions on a large variety of platforms, especially when the communication-to-computation ratio is not very high (the difficult case). [Copyright &y& Elsevier]