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

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.