Design and Analysis of Multi - Heuristic Based Solution for Task Graph Scheduling Problem

Heuristic based strategies have always been of interest for researchers to achieve sub-optimal solutions for various NP-Complete problems. Human evolution based methods have been an inspiration for research since ages. One of the many evolutionary strategies based on the principle of genetic algorithm have been able to provide much sought after sub-optimal solutions for various NP-Complete problems. One of the most sought after NP-Complete problem is Task graph scheduling i.e. optimally execute the schedule of tasks on available parallel and distributed environment so as to achieve efficient utilization of available resources. Task scheduling is a multi-objective combinatorial optimization problem, with key parameters being reduced completion time and effective load balance on the available resources. Various algorithms have been proposed by various authors to achieve the above mentioned goal with the help of various heuristics like list scheduling, task duplication and critical path based. The algorithms proposed by various authors like Highest Level First Execution Time (HLFET), Modified Critical Path (MCP), Duplication Scheduling Heuristic (DSH), Linear Clustering (LC) and Dynamic Critical Path (DCP), belonging to each heuristic mentioned before will be taken under study. Previously these algorithms have been individually reported to be efficient in some certain restricted environment parameters with certain limitations; offering very preliminary improvement on the state of art of one single type of environment. Designers face difficulty in choosing the optimal algorithm for the generalized environment. This paper will identify the gaps in existing literature that forms the base of every research focusing in the direction of improvement of task graph scheduling algorithms. Further, this paper will propose a hybrid meta-heuristic i.e. Genetic Algorithm based task graph scheduling solution and perform a comparative study of aforementioned algorithms.

Learning Based Genetic Algorithm for Task Graph Scheduling

Nowadays, parallel and distributed based environments are used extensively; hence, for using these environments effectively, scheduling techniques are employed. The scheduling algorithm aims to minimize the makespan (i.e., completion time) of a parallel program. Due to the NP-hardness of the scheduling problem, in the literature, several genetic algorithms have been proposed to solve this problem, which are effective but are not efficient enough. An effective scheduling algorithm attempts to minimize the makespan and an efficient algorithm, in addition to that, tries to reduce the complexity of the optimization process. The majority of the existing scheduling algorithms utilize the effective scheduling algorithm, to search the solution space without considering how to reduce the complexity of the optimization process. This paper presents a learner genetic algorithm (denoted by LAGA) to address static scheduling for processors in homogenous computing systems. For this purpose, we proposed two learning criteria named Steepest Ascent Learning Criterion and Next Ascent Learning Criterion where we use the concepts of penalty and reward for learning. Hence, we can reach an efficient search method for solving scheduling problem, so that the speed of finding a scheduling improves sensibly and is prevented from trapping in local optimal. It also takes into consideration the reuse idle time criterion during the scheduling process to reduce the makespan. The results on some benchmarks demonstrate that the LAGA provides always better scheduling against existing well-known scheduling approaches.