NDLI: Energy efficient scheduling with probability and task migration considerations for soft real-time systems

Content Provider	IEEE Xplore Digital Library
Author	Ying Li Jianwei Niu Xiang Long Meikang Qiu
Copyright Year	2014
Description	Author affiliation: School of Computer Science and Engineering, Beihang University, Beijing 100191, China (Ying Li; Jianwei Niu; Xiang Long) \|\| Seidenberg School of Computer Science and Information Systems, Pace University, White Plains, NY 10606, USA (Meikang Qiu)
Abstract	The main challenges for embedded real-time systems which use battery as their power supply are both to satisfy the requirements of real-time systems and minimize the energy consumption. This paper studies the energy saving problem for DAG (Directed Acyclic Graph) tasks in soft real-time systems with heterogeneous multicore processors. Since soft real-time systems can tolerate occasional time violations and tasks are completed before deadlines with a given probability, this paper proposes a novel processor and voltage assignment scheme - Adaptive Processor and Voltage Assignment with Probability (APVAP) to realize the minimum energy consumption which can satisfy the requirements of time constraints under the given probability. Most of previous work focuses on multicore processor task assignment for predecessor and successor (P-S) tasks. However, this paper introduced affinity to indicate successor tasks can be re-allocated to more appropriate cores according to task features and workload. Besides, this paper introduces the concept of data migration energy (DME) to compute the transmission energy when a task is migrated to a different core and adopts Ratio between Time and Energy (RTE) to determine the most suitable tasks for migration to reduce energy consumption at the cost of execution time. The experimental results demonstrate that our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 30.7%).
Starting Page	287
Ending Page	293
File Size	117813
Page Count	7
File Format	PDF
ISBN	9781479948130
e-ISBN	9781479948116
DOI	10.1109/ComComAp.2014.7017212
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy consumption Program processors Multicore processing Migration energy Heterogeneous multicore processor Tin Affinity Real-time systems Silicon Resource management Task assignment
Content Type	Text
Resource Type	Article

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