Energy-Aware Task Scheduling on Heterogeneous Computing Systems With Time Constraint

Abstract

As a technique to help achieve high performance in parallel and distributed heterogeneous computing systems, task scheduling has attracted considerable interest. In this paper, we propose an effective Cuckoo Search algorithm based on Gaussian random walk and Adaptive discovery probability which combined with a cost-to-time ratio Modification strategy (GACSM), to address task scheduling on heterogeneous multiprocessor systems using Dynamic Voltage and Frequency Scaling (DVFS). First, to overcome the shortcomings of poor performance in exploitation of the cuckoo search algorithm, we use chaos variables to initialize populations to maintain the population diversity, a Gaussian random walk strategy to balance the exploration and exploitation capabilities of the algorithm, and an adaptive discovery probability strategy to improve population diversity. Then, we apply the improved Cuckoo Search (CS) algorithm to assign tasks to resources, and a widely used downward rank heuristic strategy to find the corresponding scheduling sequence. Finally, we apply a cost-to-time ratio improvement strategy to further improve the performance of the improved CS algorithm. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show its superiority in comparison with the state-of-the-art methods. INDEX TERMS Task scheduling, DVFS, cuckoo search algorithm, heterogeneous multiprocessor system.

Dynamic and Static Energy Efficient Scheduling of Task Graphs on Multiprocessors: A Heuristic

“…Table 5 briefly compare these algorithms with their properties. Table 6 provides details of the parameters used to generate widely used real task graphs: LU Decomposition (LUD) [17], Fast Fourier Transform (FFT) and Random task graphs [4]. Approximately ten thousand graphs have been generated by varying graph parameters as listed in table 6 using the tool, Task Graph Generator [38].…”

“…T HE static scheduling of task graphs or Directed Acyclic Graphs (DAGs) on heterogeneous multiprocessors is an NP-Hard problem [1], [2]. This problem gained more attention in the research community with the increase in energy consumption of multiprocessors [3], [4]. However, most of the published literature deals with optimizing performance (schedule length or makespan) along with only dynamic power consumption [5], [6].…”

“…This problem gained more attention in the research community with the increase in energy consumption of multiprocessors [3], [4]. However, most of the published literature deals with optimizing performance (schedule length or makespan) along with only dynamic power consumption [5], [6]. Dynamic voltage and frequency scaling (DVFS) is one widely employed technique to reduce the dynamic power consumption by running certain tasks on low voltage-frequency pairs.…”

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

“…If reasonable solutions are not taken to reduce the huge power supply required by the huge number of embedded devices, it will bring severe energy shortage and environmental pollution to the world. In real‐time embedded systems, the tradeoff optimization between energy consumption and performance has become a key issue 4‐6 …”

“…Our problem of TSDA on HDSMS is to find a solution for heterogeneous task assignment and placement strategy of task‐related data; thus, it is more complex than just considering the heterogeneous assignment problem. Task scheduling problems are usually solved with heuristics or meta‐heuristics 6,20‐22 . Heuristics are often based on greedy selection strategies, and their performance is not very agile in complex situations 6,17,18,23‐27 .…”