New Hybrid Task Scheduling Algorithm with Fuzzy Logic Controller in Grid Computing

Distributed heterogeneous architecture is extensively applied to a diversity of large scale research projects conducive to solve complex computational problems. Mentioned distributed systems consist of multiple heterogenous linked processing units used to handle the continuous arrival jobs. The tasks scheduling problem is concerned with resource allocation strategies to assign jobs to available computing resources. The load balancing of linked resources becomes a main issue to select in each task schedule the adequate computing resource. Our proposal consists of combining Q-learning with ACO (Ant Colony Optimization) to solve the tasks allocation dilemma. In our proposed Fuzzy Hybrid Framework, Fuzzy ants are used to calculate at each scheduling operation, the novel reward values whereas Q-learning is used to select the suitable Worker Machine. The simulation findings confirmed the efficiency of the proposed framework due to the significant decrease of the makespan.