Heuristics Analysis for Distributed Scheduling using MONARC Simulation Tool

Simulation is a very powerful method used for high- performance 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 perfo rmance 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 a nd creating an adequately-sized test-bed is expensive and time con suming. Scalability, reliability and fault-tolerance become important requirements for distributed systems in order to su pport distributed computation. A distributed system with such charact eristics is called dependable. Large environments, like Cloud, offer u nique advantages, such as low cost, dependability and sat isfy QoS for all users. Resource management in large environments address performant scheduling algorithm guided by QoS constrains. This paper presents the performance evaluation of schedu ling heuristics guided by different optimization criteria. The algo rithms for distributed scheduling are analyzed in order to sat isfy 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 consi sts in synthetic analysis results that offer guidelines for schedule r service configuration and sustain the empirical-based decis ion. The results could be used in decisions regarding optimizations to existing Grid DAG Scheduling and for selecting the proper algorit hm for DAG scheduling in various actual situations.