Solving Job Scheduling Problem in Computational Grid Systems Using a Hybrid Algorithm

Grid computing is a high performance distributed computing system that consists of different types of resources such as computing, storage, and communication. The main function of the job scheduling problem is to schedule the resource-intensive user jobs to available grid resources efficiently to achieve high system throughput and to satisfy user requirements. The job scheduling problem has become more challenging with the ever-increasing size of grid systems. The optimal job scheduling is an NP-complete problem which can easily be solved by using meta-heuristic techniques. This chapter presents a hybrid algorithm for job scheduling using genetic algorithm (GA) and cuckoo search algorithm (CSA) for efficiently allocating jobs to resources in a grid system so that makespan, flowtime, and job failure rate are minimized. This proposed algorithm combines the advantages of both GA and CSA. The results have been compared with standard GA, CSA, and ant colony optimization (ACO) to show the importance of the proposed algorithm.

Solution Merging in Matheuristics for Resource Constrained Job Scheduling

Matheuristics have been gaining in popularity for solving combinatorial optimisation problems in recent years. This new class of hybrid method combines elements of both mathematical programming for intensification and metaheuristic searches for diversification. A recent approach in this direction has been to build a neighbourhood for integer programs by merging information from several heuristic solutions, namely construct, solve, merge and adapt (CMSA). In this study, we investigate this method alongside a closely related novel approach—merge search (MS). Both methods rely on a population of solutions, and for the purposes of this study, we examine two options: (a) a constructive heuristic and (b) ant colony optimisation (ACO); that is, a method based on learning. These methods are also implemented in a parallel framework using multi-core shared memory, which leads to improving the overall efficiency. Using a resource constrained job scheduling problem as a test case, different aspects of the algorithms are investigated. We find that both methods, using ACO, are competitive with current state-of-the-art methods, outperforming them for a range of problems. Regarding MS and CMSA, the former seems more effective on medium-sized problems, whereas the latter performs better on large problems.

JOB ORDER INPUT FOR EFFICIENT EXACT MINIMIZATION OF TOTAL TARDINESS IN TIGHT-TARDY PROGRESSIVE SINGLE MACHINE SCHEDULING WITH IDLING-FREE PREEMPTIONS

A schedule ensuring the exactly minimal total tardiness can be found with the respective integer linear programming problem. An open question is whether the exact schedule computation time changes if the job release dates are input into the model in reverse order. The goal is to ascertain whether the job order in tight-tardy progressive single machine scheduling with idling-free preemptions influences the speed of computing the exact solution. The Boolean linear programming model provided for finding schedules with the minimal total tardiness is used. To achieve the said goal, a computational study is carried out with the purpose of estimating the averaged computation time for both ascending and descending orders of job release dates. Instances of the job scheduling problem are generated so that schedules which can be obtained trivially, without the exact model, are excluded. As in the case of equal-length jobs, it has been ascertained that the job order really influences the speed of computing schedules whose total tardiness is minimal. Scheduling two to five jobs is executed on average faster by the descending job order input, where 1 to 3 % speed-up is expected. Further increment of the number of jobs to be scheduled cannot guarantee any speed-up even on average. This result is similar to that in the case of equal-length jobs, but there is no regularity in such an efficient job order input. Without any assurance for a single job scheduling problem, the efficient exact minimization of total tardiness by the descending job order input must be treated as on average only.

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Over two decades, Heterogeneous Computing Systems (HCS) are offering large amount of federated computing resources, spanning across different administrative domains, to compute-intensive user applications. Efficient job schedulers are required to allocate HCS resources to user applications to satisfy system provider and user requirements. Offline scheduling is most popular kind of job scheduling in heterogeneous system, in which jobs are collected in batch and scheduled together. Job scheduling in HCS has become NP-hard problem due to system scale, federated structure and high resource as well as job heterogeneity. Simple queuing and deterministic heuristics have failed to provide optimal solution to NP-hard job scheduling problem. Due to NP-hard nature of job scheduling problem, there is always a scope to propose new scheduling solutions using meta-heuristics. Offline scheduling in HCS has been focused more on scheduling independent sequential tasks viz. Bag-of-tasks or Many-tasks. Offline scheduling of parallel jobs (composed of collaborating tasks with no precedence) in HCS has not gained much attention. In this paper, a novel hybrid multi-objective meta-heuristic known as HCSPSO, which combines the qualities of Cuckoo search (CS) and Particle Swarm Optimization (PSO), has been proposed to schedule batch of parallel jobs in multi-cluster HCS platform. Proposed HCSPSO policy is extensively compared with different heuristics and metaheuristics using different resource configurations and real supercomputing workload logs. Comparative results have showed the dominance of the proposed hybrid scheduling algorithm over other algorithms.