Multi-objective redundancy hardening with optimal task mapping for independent tasks on multi-cores

Yuan，Bo1; Li，Bin2; Chen，Huanhuan3; Zeng，Zhigang4; Yao，Xin1,5

2019

10.1007/s00500-019-03937-0

SOFT COMPUTING   影响因子和分区

1432-7643

1433-7479

The rate of transient faults has increased significantly as the technology scales up. The tolerance of transient faults has become an important issue in the system design. Dual modular redundancy (DMR) and triple modular redundancy (TMR) are two commonly used techniques that can achieve fault detection and masking through executing redundant tasks. As DMR and TMR have different time and cost overheads, we must carefully determine which one should be used for each task (i.e., task hardening) to achieve the optimal system design. Furthermore, for multi-core systems, the system-level design includes the allocation of cores for the tasks (i.e., task mapping) as well. This paper aims at task hardening and mapping simultaneously for independent tasks on multi-cores with heterogeneous performances, in order to minimize the maximum completion time of all tasks (i.e., makespan). We demonstrate that once task hardening is given, task mapping of independent tasks can be achieved by employing min–max-weight perfect matching with a polynomial time complexity. Besides, as there is a trade-off between cost and time performance, we propose a multi-objective memetic algorithm (MOMA)-based task hardening method to obtain a set of solutions with different numbers of cores (i.e., costs), so the designer can choose different solutions according to different requirements. The key idea of the MOMA is to incorporate problem-specific knowledge into the global search of evolutionary algorithms. Our experimental studies have demonstrated the effectiveness of the proposed method and have shown that by combining the results of MOMA and MOEA we can provide a designer with a highly accurate set of solutions within a reasonable amount of time.

Fault tolerance Memetic algorithms Multi-cores Multi-objective optimization Task hardening Task mapping

SCI ; EI

英语

第一 ; 通讯

[2017YFC0804002] ; Science, Technology and Innovation Commission of Shenzhen Municipality[ZDSYS201703031748284] ; Royal Society[NA160545] ; [2017KSYS008] ; Shenzhen Peacock Plan[KQTD2016112514355531] ; National Natural Science Foundation of China[61503357]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications

WOS:000509686300019

Springer

20191606774707

Economic and social effects ; Fault detection ; Fault tolerance ; Fault tolerant computer systems ; Mapping ; Multiobjective optimization ; Polynomial approximation ; Redundancy ; Systems analysis

Surveying:405.3 ; Heat Treatment Processes:537.1 ; Digital Computers and Systems:722.4 ; Optimization Techniques:921.5 ; Numerical Methods:921.6 ; Systems Science:961 ; Social Sciences:971

COMPUTER SCIENCE

2-s2.0-85064160211

Scopus

1.Department of Computer Science and EngineeringSouthern University of Science and Technology,Shenzhen,518055,China
2.School of Information Science and TechnologyUniversity of Science and Technology of China,Hefei,230026,China
3.School of Computer Science and TechnologyUniversity of Science and Technology of China,Hefei,230026,China
4.School of Artificial Intelligence and AutomationHuazhong University of Science and Technology,Wuhan,430074,China
5.CERCIASchool of Computer ScienceUniversity of Birmingham,Birmingham,B15 2TT,United Kingdom

Yuan，Bo,Li，Bin,Chen，Huanhuan,et al. Multi-objective redundancy hardening with optimal task mapping for independent tasks on multi-cores[J]. SOFT COMPUTING,2019,24(2):981-995.

Yuan，Bo,Li，Bin,Chen，Huanhuan,Zeng，Zhigang,&Yao，Xin.(2019).Multi-objective redundancy hardening with optimal task mapping for independent tasks on multi-cores.SOFT COMPUTING,24(2),981-995.

Yuan，Bo,et al."Multi-objective redundancy hardening with optimal task mapping for independent tasks on multi-cores".SOFT COMPUTING 24.2(2019):981-995.