The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Scheduling to minimize average completion time: off-line and on-line algorithms
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Future Generation Computer Systems - Special issue on metacomputing
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Kerberized credential translation: a solution to web access control
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
Future Generation Computer Systems
A distributed bio-inspired method for multisite grid mapping
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
A meta-scheduling service for co-allocating arbitrary types of resources
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
MetaLoRaS: a predictable metascheduler for non-dedicated multiclusters
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
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Computational grids are increasingly being deployed in campus environments to provideunified access to distributed and heterogeneous resources such as clusters, storage arrays, networks, and scientific instruments. While the existing grid computing frameworks and protocols provide a robust set of mechanisms for user authentication, security, workflow and resource management; efficient scheduling of tasks on distributed and heterogeneous resources, termed as metascheduling, is an active area of research. In this paper, we describe MARS, an open-source metascheduling framework that can be integrated into existingcampus infrastructure to provide robust task scheduling and resource management capabilities. MARS uses a forecasting algorithm to predict resource-level scheduling parameters such as queue lengths, turn-around times, and resource utilization. These predicted values are then used to schedule tasks based on their priority levels. It allows preemption of lower-priority running tasks in favor of on-demand tasks. We have implemented heuristic and evolutionary scheduling algorithms in the present framework and evaluated it in a production environment consisting of several large Linux clusters. Our simulation results using actual workload traces from these clusters demonstrate the effectiveness of the current metascheduling framework.