Multidisciplinary approaches to artificial swarm intelligence for heterogeneous computing and cloud scheduling

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• 作者：Jinglian Wang ; Bin Gong ; Hong Liu ; Shaohui Li
• 关键词：Heterogeneous computing and cloud scheduling ; High performance clusters(HPC) ; Swarm intelligence ; Multidisciplinary approaches
• 刊名：Applied Intelligence
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：43
• 期：3
• 页码：662-675
• 全文大小：4,076 KB
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• 作者单位：Jinglian Wang (1) (2)
  Bin Gong (2)
  Hong Liu (3)
  Shaohui Li (3)
  
  1. School of Information and Electrical Engineering, Ludong University, Yantai, China
  2. School of Computer Science and Technology, Shandong University, Jinan, China
  3. School of Information Science and Technology, Shandong Normal University, Jinan, China
  
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497

文摘

Enabled to provide pervasive access to distributed resources in parallel ways, heterogeneous scheduling is extensively applied in large-scaled computing system for high performance. Conventional real-time scheduling algorithms, however, either disregard applications-security needs and thus expose the applications to security threats or run applications at inferior security levels without optimizing security performance. In recognition of high reliability, a security-aware model is firstly presented via quantization of security overheads of heterogeneous systems. Secondly, inspired by multi disciplines, the meta-heuristic is addressed based on the supercomputer hybrid architecture. On the other hand, some technological breakthroughs are achieved, including boundary conditions for different heterogeneous computing and cloud scheduling and descriptions of real-time variation of scheduling indexes (stringent timing and security constraints). Extensive simulator and simulation experiments highlight higher efficacy and better scalability for the proposed approaches compared with the other three meta-heuristics; the overall improvements achieve 8 %, 12 % and 14 % for high-dimension instances, respectively. Keywords Heterogeneous computing and cloud scheduling High performance clusters(HPC) Swarm intelligence Multidisciplinary approaches