Visible to the public Optimizing Task Assignment with Minimum Cost on Heterogeneous Embedded Multicore Systems Considering Time Constraint

TitleOptimizing Task Assignment with Minimum Cost on Heterogeneous Embedded Multicore Systems Considering Time Constraint
Publication TypeConference Paper
Year of Publication2017
AuthorsZheng, H., Zhang, X.
Conference Name2017 ieee 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids)
Date Publishedmay
KeywordsAlgorithm design and analysis, battery-based embedded systems, computer systems, DAG, data-dependent aperiodic tasks, directed acyclic graph, directed graphs, dynamic programming, dynamic programming algorithm, Embedded systems, energy consumption, Energy efficiency, execution time, Guaranteed Probability, Heterogeneous Embedded Multicore System, heterogeneous embedded multicore systems, heterogeneous multicore architectures, Metrics, minimum cost, Multicore Computing, multicore computing security, multiprocessing systems, optimal energy efficiency, optimizing task heterogeneous assignment with probability algorithm, OTHAP, probability, processor and voltage assignment with probability problem, Processor scheduling, processor scheduling algorithm, Program processors, pubcrawl, PVAP problem, real-time embedded systems, Real-time Systems, reliability, resilience, Resiliency, Scalability, Signal processing algorithms, system performance model, Task Assignment, task assignment optimization, task completion probability, time constraint
AbstractTime and cost are the most critical performance metrics for computer systems including embedded system, especially for the battery-based embedded systems, such as PC, mainframe computer, and smart phone. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments. Through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures this paper proposes an optimal energy efficiency and system performance model and the OTHAP (Optimizing Task Heterogeneous Assignment with Probability) algorithm to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with areal-time embedded systems guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task and The experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).
Citation Keyzheng_optimizing_2017