@article{(Open Science Index):https://publications.waset.org/pdf/11599,
	  title     = {Heuristics Analysis for Distributed Scheduling using MONARC Simulation Tool},
	  author    = {Florin Pop},
	  country	= {},
	  institution	= {},
	  abstract     = {Simulation is a very powerful method used for highperformance
and high-quality design in distributed system, and now
maybe the only one, considering the heterogeneity, complexity and
cost of distributed systems. In Grid environments, foe example, it is
hard and even impossible to perform scheduler performance
evaluation in a repeatable and controllable manner as resources and
users are distributed across multiple organizations with their own
policies. In addition, Grid test-beds are limited and creating an
adequately-sized test-bed is expensive and time consuming.
Scalability, reliability and fault-tolerance become important
requirements for distributed systems in order to support distributed
computation. A distributed system with such characteristics is called
dependable. Large environments, like Cloud, offer unique
advantages, such as low cost, dependability and satisfy QoS for all
users. Resource management in large environments address
performant scheduling algorithm guided by QoS constrains. This
paper presents the performance evaluation of scheduling heuristics
guided by different optimization criteria. The algorithms for
distributed scheduling are analyzed in order to satisfy users
constrains considering in the same time independent capabilities of
resources. This analysis acts like a profiling step for algorithm
calibration. The performance evaluation is based on simulation. The
simulator is MONARC, a powerful tool for large scale distributed
systems simulation. The novelty of this paper consists in synthetic
analysis results that offer guidelines for scheduler service
configuration and sustain the empirical-based decision. The results
could be used in decisions regarding optimizations to existing Grid
DAG Scheduling and for selecting the proper algorithm for DAG
scheduling in various actual situations.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {6},
	  number    = {1},
	  year      = {2012},
	  pages     = {27 - 33},
	  ee        = {https://publications.waset.org/pdf/11599},
	  url   	= {https://publications.waset.org/vol/61},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 61, 2012},
	}