Abstract:
Grid Computing due to its effective sharing of heterogeneous resources,
economy and portability has become the center of attention for execution of
resource hungry divisible processes. But along with the promising characteristics
of grid computing it also poses great challenges in its implementation due to the
geographical distant resources owned by individuals with differing access and
cost policies.
The vast applications of Grid Computing in the field of e-sciences gave
rise to the need for bulk scheduling (i.e. scheduling of a bulk of jobs as a unique
entity). Splitting the bulk may result in a very large number of jobs, making it a
hideous job for schedulers and also very time consuming in case of centralized
schedulers.
In order to exploit the true potential of grid computing workloads need to
be scheduled efficiently amongst the participating machines. Centralized
schedulers have been implemented to perform the job of load balancing but the
grid as a whole lacks the essence of autonomy and self organization. By
introducing decentralized schedulers, the limitations such as scalability, posed by
centralized schedulers can be tackled. And dependency on a central scheduler is
overcome by applying lower level autonomous schedulers. A decentralized
approach for bulk scheduling at site level and subgrid level by deploying
autonomous site level and low level schedulers is proposed.
