IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
The ANL/IBM SP Scheduling System
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Packing Schemes for Gang Scheduling
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Theory and Practice in Parallel Job Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Selective Reservation Strategies for Backfill Job Scheduling
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
Resource Co-Allocation in Computational Grids
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
GriPhyN and LIGO, Building a Virtual Data Grid for Gravitational Wave Scientists
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Scheduling with Advanced Reservations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
QoS guided min-min heuristic for grid task scheduling
Journal of Computer Science and Technology - Grid computing
Dynamic Scheduling of Parallel Jobs with QoS Demands in Multiclusters and Grids
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
A Comparison of Two Methods for Building Astronomical Image Mosaics on a Grid
ICPPW '05 Proceedings of the 2005 International Conference on Parallel Processing Workshops
Grid capacity planning with negotiation-based advance reservation for optimized QoS
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Heuristic Scheduling of Grid Workflows Supporting Co-Allocation and Advance Reservation
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
The portable batch scheduler and the maui scheduler on linux clusters
ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Multisite co-allocation algorithms for computational grid
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Parallel job scheduling — a status report
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
JSSPP'10 Proceedings of the 15th international conference on Job scheduling strategies for parallel processing
MORPHOSYS: Efficient Colocation of QoS-Constrained Workloads in the Cloud
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Cranduler: a dynamic and reusable scheduler for cloud infrastructure service
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Hi-index | 0.00 |
Advances in the development of large scale distributed computing systems such as Grids and Computing Clouds have intensified the need for developing scheduling algorithms capable of allocating multiple resources simultaneously. In principle, the required resources may be allocated by sequentially scheduling each resource individually. However, such a solution can be computationally expensive, hence inappropriate for time-sensitive applications, and may lead to deadlocks. In this work we present an efficient online algorithm for co-allocating resources that also provides support for advance reservations. The algorithm utilizes data structures specifically designed to organize the temporal availability of resources, and implements co-allocation through efficient range searches that identify all available resources simultaneously. We use simulations driven by real workloads to show that the co-allocation algorithm scales to systems with large numbers of users and resources, and we perform an in-depth comparative analysis against existing batch scheduling mechanisms. Our findings indicate that the online scheduling algorithms may achieve higher utilization while providing smaller delays and better QoS guarantees without adding much complexity.