Deciding which queue to join: Some counterexamples
Operations Research
The “largest variance first" policy in some stochastic scheduling problems
Operations Research
Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Scheduling jobs on heterogeneous processors
Annals of Operations Research
Semi-Distributed Load Balancing for Massively Parallel Multicomputer Systems
IEEE Transactions on Software Engineering
Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
Sequencing Tasks with Exponential Service Times to Minimize the Expected Flow Time or Makespan
Journal of the ACM (JACM)
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
How to solve it: modern heuristics
How to solve it: modern heuristics
Scheduling precedence-constrained jobs with stochastic processing times on parallel machines
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Task assignment with unknown duration
Journal of the ACM (JACM)
Performance of Scheduling Scientific Applications with Adaptive Weighted Factoring
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Analysis of LAS scheduling for job size distributions with high variance
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Stochastic Load Balancing and Related Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
On the Robustness Of Metaprogram Schedules
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Segmented Min-Min: A Static Mapping Algorithm for Meta-Tasks on Heterogeneous Computing Systems
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Task Matching and Scheduling in Heterogeneous Systems Using Simulated Evolution
IPDPS '01 Proceedings of the 10th Heterogeneous Computing Workshop â"" HCW 2001 (Workshop 1) - Volume 2
HOTOS '01 Proceedings of the Eighth Workshop on Hot Topics in Operating Systems
Guaranteeing fault tolerance through scheduling in real-time systems
Guaranteeing fault tolerance through scheduling in real-time systems
Measuring the Robustness of a Resource Allocation
IEEE Transactions on Parallel and Distributed Systems
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Parallel scheduling of complex dags under uncertainty
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Probabilistic QoS Guarantees for Supercomputing Systems
DSN '05 Proceedings of the 2005 International Conference on Dependable Systems and Networks
A Stochastic Optimization Algorithm Minimizing Expected Flow Times on Uniforn Processors
IEEE Transactions on Computers
Hi-index | 0.00 |
This paper diverges from the traditional load balancing, and introduces a new principle called the on-machine load balance rule. The on-machine load balance rule leads to resource allocations that are better in tolerating uncertainties in the processing times of the tasks allocated to the resources when compared to other resource allocations that are derived using the conventional ''across-the-machines'' load balancing rule. The on-machine load balance rule calls for the resource allocation algorithms to allocate similarly sized tasks on a machine (in addition to optimizing some primary performance measures such as estimated makespan and average response time). The on-machine load balance rule is very different from the usual across-the-machines load balance rule that strives to balance load across resources so that all resources have similar finishing times. We give a mathematical justification for the on-machine load balance rule requiring only liberal assumptions about task processing times. Then we validate with extensive simulations that the resource allocations derived using on-machine load balance rule are indeed more tolerant of uncertain task processing times.