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Enabled by high-speed networking in commercial, scientific, and government settings, the realm of high performance is burgeoning with greater amounts of computational and storage resources. Large-scale systems such as computational grids consume a significant amount of energy due to their massive sizes. The energy and cooling costs of such systems are often comparable to the procurement costs over a year period. In this survey, we will discuss allocation and scheduling algorithms, systems, and software for reducing power and energy dissipation of workflows on the target platforms of single processors, multicore processors, and distributed systems. Furthermore, recent research achievements will be investigated that deal with power and energy efficiency via different power management techniques and application scheduling algorithms. The article provides a comprehensive presentation of the architectural, software, and algorithmic issues for energy-aware scheduling of workflows on single, multicore, and parallel architectures. It also includes a systematic taxonomy of the algorithms developed in the literature based on the overall optimization goals and characteristics of applications.

Three-way joint optimization of performance ( P ), energy ( E ), and temperature ( T ) in scheduling parallel tasks to multiple cores poses a challenge that is staggering in its computational complexity. The goal of the PET optimized scheduling ( PETOS ) problem is to minimize three quantities: the completion time of a task graph, the total energy consumption, and the peak temperature of the system. Algorithms based on conventional multi-objective optimization techniques can be designed for solving the PETOS problem. But their execution times are exceedingly high and hence their applicability is restricted merely to problems of modest size. Exacerbating the problem is the solution space that is typically a Pareto front since no single solution can be strictly best along all three objectives. Thus, not only is the absolute quality of the solutions important but “the spread of the solutions” along each objective and the distribution of solutions within the generated tradeoff front are also desired. A natural alternative is to design efficient heuristic algorithms that can generate good solutions as well as good spreads -- note that most of the prior work in energy-efficient task allocation is predominantly single- or dual-objective oriented. Given a directed acyclic graph (DAG) representing a parallel program, a heuristic encompasses policies as to what tasks should go to what cores and at what frequency should that core operate. Various policies, such as greedy, iterative, and probabilistic, can be employed. However, the choice and usage of these policies can influence a heuristic towards a particular objective and can also profoundly impact its performance. This article proposes 16 heuristics that utilize various methods for task-to-core allocation and frequency selection. This article also presents a methodical classification scheme which not only categorizes the proposed heuristics but can also accommodate additional heuristics. Extensive simulation experiments compare these algorithms while shedding light on their strengths and tradeoffs.

This paper addresses the joint optimization of performance, energy, and temperature, termed as PEToptimization. This multi-objective PET-optimization is achieved in scheduling DAGs on multi-core systems. Our technique is based on multi-objective evolutionary algorithm (MOEA) for finding Pareto optimal solutions using scheduling and voltage selection. These solutions are not necessarily scalar values but can be in a vector form. We developed a Strength Pareto Evolutionary Algorithm [2] (SPEA) based solution which is inherently superior to several other MOEA methods. The proposed algorithm obtains the Pareto vectors (or fronts) efficiently. The work is novel and original in the sense that no previous such optimization work has been reported to our knowledge for the PET-optimization scheduling problem. The strength of the proposed algorithm is that it achieves diverse range of energy and thermal improvements while staying close to the performance-optimal point to ensure efficient trade-off solutions. The proposed approach consists of two-steps. In the first step, Pareto fronts are generated. In the second step, one most optimal solution is selected. Simulation results on several benchmark task graph applications demonstrate that efficient solutions can be selected using the proposed selection method in polynomial time.

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