Multi-criteria genetic algorithm applied to scheduling in multi-cluster environments

Abstract

Scheduling and resource allocation to optimize performance criteria in multi-cluster heterogeneous environments is known as an NP-hard problem, not only for the resource heterogeneity, but also for the possibility of applying co-allocation to take advantage of idle resources across clusters. A common practice is to use basic heuristics to attempt to optimize some performance criteria by treating the jobs in the waiting queue individually. More recent works proposed new optimization strategies based on Linear Programming techniques dealing with the scheduling of multiple jobs simultaneously. However, the time cost of these techniques makes them impractical for large-scale environments. Population-based meta-heuristics have proved their effectiveness for finding the optimal schedules in large-scale distributed environments with high resource diversification and large numbers of jobs in the batches. The algorithm proposed in the present work packages the jobs in the batch to obtain better optimization opportunities. It includes a multi-objective function to optimize not only the Makespan of the batches but also the Flowtime, thus ensuring a certain level of QoS from the users' point of view. The algorithm also incorporates heterogeneity and bandwidth awareness issues, and is useful for scheduling jobs in large-scale heterogeneous environments. The proposed meta-heuristic was evaluated with a real workload trace. The results show the effectiveness of the proposed method, providing solutions that improve the performance with respect to other well-known techniques in the literature.

A Review of Various Scheduling Techniques Considering Energy Efficiency in WSN

“…The prescribed tolerance hence plays critical part in terminating genetic algorithm. [15]- [17] The approach using ant colony and honey bee algorithm can be used in order to enhance job scheduling performance. The ant colony algorithm utilizes to select path which is optimal in nature.…”

Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments

“…Carretero and Xhafa presented in [17] an extensive study of GAs for designing efficient Grid schedulers where makespan and flowtime are minimized to include QoS in the solutions, but considering independent jobs without inter-cluster communications. Gabaldon et al [18] presented a GA-based scheduling meta-heuristic able to optimize the makespan together with the flowtime, thus providing a certain level of QoS from the users point of view.…”

“…In an earlier work [18], the authors presented GA-MF, a GA-based technique with the aim of increasing the system throughput for batch workloads, using the makespan and flowtime as the optimization criteria. Due to the increasing importance of developing energy-aware systems to reduce the environmental footprint, the authors propose a new multi-objective GA, named MOGA, focused on reducing both energy consumption and makespan.…”

“…The execution time of a job in an heterogeneous federated cluster is calculated using the model presented in [18], where resource heterogeneity and network saturation is considered. The energy consumption is modeled by Eq.…”

“…If we run out of computational nodes in the allocation process, MOGA predicts the first job to finish using the job execution model presented in [18] and then releases the allocated nodes for the subsequent jobs.…”