Multiprocessor Job Scheduling Research Articles

Energy efficient non-clairvoyant scheduling for unbounded-speed multi-core machines

With the increasing consumption of energy in data centers, the demand for energy efficient multiprocessor job scheduling is growing dramatically. Besides flow time, energy conservation has become a significant issue and drawn enormous interest. In this paper, an online non-clairvoyant scheduling algorithm Significance-based Multiprocessor Round Robin (SbMRR) is proposed. SbMRR utilizes the unbounded speed model, where the range of the speed of any processor is from zero to infinity. To validate the effectiveness of the algorithm, mathematical and simulation-based analysis are conducted which demonstrates that SbMRR provides the minimum sum of significance-based flow time and energy consumed. SbMRR is O(α)-competitive, more precisely ((α + 1)⁄((1–1⁄αβ)))-competitive, where β ≥ 4 a constant. The competitive ratio of SbMRR is least to date. SbMRR provides the minimum sum of energy consumed and significance-based flow-time.

Self-learning genetic algorithm

In this paper, a self-learning genetic algorithm for solving combinatorial optimization problems is considered. The self-learning consists in changing the values of the probabilities of crossing and mutation in accordance with changing the value of the fitness function after operations in the next iteration of the algorithm. The results of comparing the proposed algorithm with the Holland algorithm by the problems of multiprocessor job scheduling and subset sum problem are presented.

Hybrid intelligent algorithm [improved particle swarm optimization (PSO) with ant colony optimization (ACO)] for multiprocessor job scheduling

Efficient multiprocessor scheduling is essentially the problem of allocating a set of computational jobs to a set of processors to minimize the overall execution time. The main issue is how jobs are partitioned in which total finishing time and waiting time is minimized. Minimization of these two criteria simultaneously, is a multi objective optimization problem. There are many variations of this problem, most of which are NP-hard problem, so we must rely on heuristics to solve the problem instances. Many heuristic-based approaches have been applied to finding schedules that minimize the execution time of computing tasks on parallel processors. Particle swarm optimization (PSO) is currently employed in several optimization and search problems due to its ease and ability to find solutions successfully. A variant of PSO, called as improved particle swarm optimization (ImPSO) has been developed in this paper and is hybridized with the ant colony optimization (ACO) to achieve better solutions. The proposed hybrid algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. In addition hybrid algorithm using improved particle swarm optimization (ImPSO) with artificial immune system (AIS) is also implemented for the same set of problems to compare with the proposed hybrid algorithm (ImPSO with ACO). It was observed that the proposed hybrid approach (Improved PSO with ACO) gives better results in experiments and reduces finishing and waiting time simultaneously. Key words: Particle swarm optimization (PSO), improved particle swarm optimization (ImPSO), ant colony optimization (ACO), job scheduling, finishing time, waiting time.

Resource Selection Algorithms for Economic Scheduling in Distributed Systems

In this paper, we present slot selection algorithms in economic models for independent job batch scheduling in distributed computing with non-dedicated resources. Existing approaches towards resource co-allocation and multiprocessor job scheduling in economic models of distributed computing are based on search of time-slots in resource occupancy schedules. The sought time-slots must match requirements of necessary span, computational resource properties, and cost. Usually such scheduling methods consider only one suited variant of time-slot set. This paper discloses a scheduling scheme that features multi-variant search. Two algorithms of linear complexity for search of alternative variants are compared. Having several optional resource configurations for each job makes an opportunity to perform an optimization of execution of the whole batch of jobs and to increase overall efficiency of scheduling.

Embedded simulation on a multiprocessor job scheduling system with inspection

This paper first develops architecture for a multiprocessor job scheduling system with an embedded simulation technique. The architecture provides a shell for applications that are characterized by two scheduling policies, a heuristic algorithm policy and a First-In-First-Out (FIFO) policy. These policies are implemented in the simulation model by using the embedded technique. The paper evaluates these two policies using the queue length, waiting time and flow time as the criteria to compare the performance of these two scheduling policies. Next we designed two simulation situations using two different real world applications. The purpose is to examine the performances of multiprocessor systems with and without inspection operations and two different scheduling policies. The two applications, berth allocation for the container terminal operations and production scheduling arrangement in an Original Equipment Manufacturer (OEM) power supply factory, are studied. The final results show that a proper scheduling policy will perform better than the traditional FIFO approach for a multiprocessor system. Our study also provides guidelines on balancing a system with the addition of a final inspection activity.

A simple linear time approximation algorithm for multi-processor job scheduling on four processors

Multiprocessor job scheduling problem has become increasingly interesting, for both theoretical study and practical applications. Theoretical study of the problem has made significant progress recently, which, however, seems not to imply practical algorithms for the problem, yet. Practical algorithms have been developed only for systems with three processors and the techniques seem difficult to extend to systems with more than three processors. This paper offers new observations and introduces new techniques for the multiprocessor job scheduling problem on systems with four processors. A very simple and practical linear time approximation algorithm of ratio bounded by 1.5 is developed for the multi-processor job scheduling problem P 4|fix|C max, which significantly improves previous results. Our techniques are also useful for multiprocessor job scheduling problems on systems with more than four processors.

A Note on System-on-Chip Test Scheduling Formulation

While many different formulations of the embedded core test scheduling problem (ECTSP) have been proposed in test literature recently, a single unified presentation of ECTSP in terms of conventional scheduling patterns has been lacking. There exists a large body of literature on multi-processor scheduling which can be directly applied to ECTSP; in this paper the author presents an introduction to scheduling notation and demonstrates the mapping between many important test scheduling problems like power-constrained, precedence constrained, and defect-oriented scheduling to conventional multi-processor job scheduling problems. Two examples are presented to illustrate this mapping. This unified presentation should make the existing body of knowledge in Operations Research scheduling research easily accessible to test engineers and test automation tool developers.

Non-clair voy ant multiprocessor scheduling of jobs with changing execution characteristics

This work theoretically proves that Equi-partition efficiently schedules multiprocessor batch jobs with different execution characteristics. Motwani, Phillips, and Torng (Proc. 4th Annu. ACMISIAM Symp. on Discrete Algorithms, pp. 422-431, Austin, 1993) show that the mean response time of jobs is within two of optimal for fully parallelizable jobs. We extend this result by considering jobs with multiple phases of arbitrary nondecreasing and sublinear speedup functions. Having no knowledge of the jobs being scheduled (non-clairvoyant) one would not expect it to perform well. However, our main result shows that the mean response time obtained with Equi-partition is no more than 2 + √ ≈ 3.73 times the optimal. The paper also considers schedulers with different numbers of preemptions and jobs with more general classes of speedup functions. Matching lower bounds are also proved.

A Polynomial Time Approximation Scheme for General Multiprocessor Job Scheduling

Recently, there have been considerable interests in the multiprocessor job scheduling problem, in which a job can be processed in parallel on one of several alternative subsets of processors. In this paper, a polynomial time approximation scheme is presented for the problem in which the number of processors in the system is a fixed constant. This result is the best possible because of the strong NP-hardness of the problem and is a significant improvement over the past results: the best previous result was an approximation algorithm of ratio $7/6 + \epsilon$ for 3-processor systems based on Goemans's algorithm for a restricted version of the problem.

Scheduling in the dark

We considered non-clairvoyant multiprocessor scheduling of jobs with arbitrary arrival times and changing execution characteristics. The problem has been studied extensively when either the jobs all arrive at time zero, or when all the jobs are fully parallelizable, or when the scheduler has considerable knowledge about the jobs. This paper considers for the first time this problem without any of these three restrictions although our algorithm is given more resources than the adversary. We provide new upper and lower bound techniques applicable in this more difficult scenario. The results are of both theoretical and practical interest. In our model, a job can arrive at any arbitrary time and its execution characteristics can change through the life of the job from being anywhere from fully parallelizable to completely sequential. We assume that the scheduler has no knowledge about the jobs except for knowing when a job arrives and when it completes. (This is why we say that the scheduler is completely in the dark.) Given all this, we prove that the scheduler algorithm Equi-partition, though simple, performs within a constant factor as well as the optimal scheduler as long as it is given at least twice as many processors. Moreover, we prove that if none of the jobs are “strictly” fully parallelizable, then Equi-partition performs competitively with no extra processors. We also consider other variations: faster processors; fewer preemptions; and a wider range of execution characteristics.

Scheduling memory constrained jobs on distributed memory parallel computers

We consider the problem of multiprocessor scheduling of jobs whose memory requirements place lower bounds on the fraction of the machine required in order to execute. We address three primary questions in this work:1. How can a parallel machine be multiprogrammed with minimal overhead when jobs have minimum memory requirements?2. To what extent does the inability of an application to repartition its workload during runtime affect the choice of processor allocation policy?3. How rigid should the system be in attempting to provide equal resource allocation to each runnable job in order to minimize average response time?This work is applicable both to parallel machines and to networks of workstations supporting parallel applications.

An Analysis of Preemptive Multiprocessor Job Scheduling

The preemptive scheduling of a partially ordered set of tasks is studied. A class of scheduling heuristics is introduced, and the performance of schedules in this class is analyzed with respect to the least finishing time optimality criterion. If there are m processors, then the finishing time of any schedule in the class is at most [Formula: see text] times worse than optimal, independent of the speeds of the processors. Examples are given which indicate that there are schedules which may be as bad as [Formula: see text] times worse than optimal even for machines with one fast processor.