Effective distributed scheduling of parallel workloads
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Stochastic analysis of gang scheduling in parallel and distributed systems
Performance Evaluation
Scheduling with implicit information in distributed systems
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
A closer look at coscheduling approaches for a network of workstations
Proceedings of the eleventh annual ACM symposium on Parallel algorithms and architectures
A simulation-based study of scheduling mechanisms for a dynamic cluster environment
Proceedings of the 14th international conference on Supercomputing
Job Characteristics of a Production Parallel Scientivic Workload on the NASA Ames iPSC/860
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Workload Evolution on the Cornell Theory Center IBM SP2
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Dynamic Partitioning in Different Distributed-Memory Environments
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
A Comparative Evaluation of Implicit Coscheduling Strategies for Networks of Workstations
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Demand-based coscheduling of parallel jobs on multiprogrammed multiprocessors
Demand-based coscheduling of parallel jobs on multiprogrammed multiprocessors
Task scheduling performance in distributed systems with time varying workload
Neural, Parallel & Scientific Computations
Coscheduling in Clusters: Is It a Viable Alternative?
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
International Journal of High Performance Computing Applications
A comprehensive performance and energy consumption analysis of scheduling alternatives in clusters
The Journal of Supercomputing
Stochastic analysis of multiserver systems
ACM SIGMETRICS Performance Evaluation Review
Xen and co.: communication-aware CPU scheduling for consolidated xen-based hosting platforms
Proceedings of the 3rd international conference on Virtual execution environments
Coscheduled distributed-Web servers on system area network
Journal of Parallel and Distributed Computing
Performance implications of virtualizing multicore cluster machines
Proceedings of the 2nd workshop on System-level virtualization for high performance computing
Performance implications of failures in large-scale cluster scheduling
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
Pitfalls in parallel job scheduling evaluation
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
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Scheduling in large-scale parallel systems has been and continues to be an important and challenging research problem. Several key factors, including the increasing use of off-the-shelf clusters of workstations to build such parallel systems, have resulted in the emergence of a new class of scheduling strategies, broadly referred to as dynamic coscheduling. Unfortunately, the size of both the design and performance spaces of these emerging scheduling strategies is quite large, due in part to the numerous dynamic interactions among the different components of the parallel computing environment as well as the wide range of applications and systems that can comprise the parallel environment. This in turn makes it difficult to fully explore the benefits and limitations of the various proposed dynamic coscheduling approaches for large-scale systems solely with the use of simulation and/or experimentation.To gain a better understanding of the fundamental properties of different dynamic coscheduling methods, we formulate a general mathematical model of this class of scheduling strategies within a unified framework that allows us to investigate a wide range of parallel environments. We derive a matrix-analytic analysis based on a stochastic decomposition and a fixed-point iteration. A large number of numerical experiments are performed in part to examine the accuracy of our approach. These numerical results are in excellent agreement with detailed simulation results. Our mathematical model and analysis is then used to explore several fundamental design and performance tradeoffs associated with the class of dynamic coscheduling policies across a broad spectrum of parallel computing environments.