Benefits of cache-affinity scheduling in shared-memory multiprocessors: a summary
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Multiprocessor Organization—a Survey
ACM Computing Surveys (CSUR)
Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
Performance characteristics of gang scheduling in multiprogrammed environments
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Affinity scheduling of unbalanced workloads
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Improved Utilization and Responsiveness with Gang Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Improvements in Parallel Job Scheduling Using Gang Service
ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
Understanding The Linux Kernel
Understanding The Linux Kernel
Operating system multilevel load balancing
Proceedings of the 2006 ACM symposium on Applied computing
Grouped distributed queues: distributed queue, proportional share multiprocessor scheduling
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Balancing power consumption in multiprocessor systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
| Hi-index | 12.05 |
The aim of this paper is to describe a fuzzy expert system for load balancing in a symmetric multiprocessor environment. Load balancing algorithms are used to share the load of the system fairly among the processors. The developed load balancing algorithm use on demand based approach instead of the periodic load balancing in order to get fast and fair load balancing with minimal computational overhead. It uses the number of threads per processor and total load of the system as inputs. The method is compared to the periodic and another developed on demand based algorithms. The results show that during the load balancing the periodic algorithm causes temporary idle periods in the processors whereas the developed on demand-based algorithms respond faster to the fluctuating load level, stabilize the load more equally among the processors and increase the performance of the system. The results also proof that the fuzzy load balancer achieves the best load balance among the processors as well as the fastest response time.