Online Scheduling Of Parallel Jobs Research Articles

Online scheduling of parallel jobs with preemption on two identical machines

In this paper we consider the problem of online scheduling of parallel jobs with preemption on identical machines, where jobs arrive over time. The objective is to minimize the makespan. For the problem with two machines, we present a 1-competitive online algorithm.

Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids

We address non-preemptive non-clairvoyant online scheduling of parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. We analyze allocation strategies depending on the type and amount of information they require. We conduct a comprehensive performance evaluation study using simulation and demonstrate that our strategies perform well with respect to several metrics that reflect both user- and system-centric goals. Unfortunately, user run time estimates and information on local schedules does not help to significantly improve the outcome of the allocation strategies. When examining the overall Grid performance based on real data, we determined that an appropriate distribution of job processor requirements over the Grid has a higher performance than an allocation of jobs based on user run time estimates and information on local schedules. In general, our experiments showed that rather simple schedulers with minimal information requirements can provide a good performance.

Online scheduling of two type parallel jobs on identical machines

In this paper we consider an online scheduling of parallel jobs with preemption on identical machines, where jobs arrive over time. The objective is to minimize the makespan. For the problem that jobs have only two possible widths m j = 1 or m, we present an optimal online algorithm by using “temporary schedule”.

On-line hierarchical job scheduling on grids with admissible allocation

In this paper, we address non-preemptive online scheduling of parallel jobs on a Grid. Our Grid consists of a large number of identical processors that are divided into several machines. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable machine, while at the second stage, local scheduling is independently applied to each machine. We discuss strategies based on various combinations of allocation strategies and local scheduling algorithms. Finally, we propose and analyze a scheme named adaptive admissible allocation. This includes a competitive analysis for different parameters and constraints. We show that the algorithm is beneficial under certain conditions and allows for an efficient implementation in real systems. Furthermore, a dynamic and adaptive approach is presented which can cope with different workloads and Grid properties.

A reinforcement learning framework for utility-based scheduling in resource-constrained systems

This paper presents a general methodology for online scheduling of parallel jobs onto multi-processor servers in a soft real-time environment, where the final utility of each job decreases with the job completion time. A solution approach is presented where each server uses Reinforcement Learning for tuning its own value function, which predicts the average future utility per time step obtained from completed jobs based on the dynamically observed state information. The server then selects jobs from its job queue, possibly preempting some currently running jobs and “squeezing” some jobs into fewer CPUs than they ideally require to maximize the value of the resulting server state. The experimental results demonstrate the feasibility and benefits of the proposed approach.

On-line scheduling of parallel jobs in a list

We study an on-line parallel job scheduling problem, where jobs arrive one by one. A parallel job may require a number of machines for its processing at the same time. Upon arrival of a job, its processing time and the number of requested machines become known, and it must be scheduled immediately without any knowledge of future jobs. We present a 7-competitive on-line algorithm, which improves the previous upper bound of 12 by Johannes (J. Sched. 9:433---452, 2006). Furthermore, we investigate a special case in which the largest processing time is known beforehand.

Online scheduling of parallel jobs on two machines is 2-competitive

We consider online scheduling of parallel jobs on parallel machines. For the problem with two machines and the objective of minimizing the makespan, we show that 2 is a tight lower bound on the competitive ratio. For the problem with m machines, we derive lower bounds using an ILP formulation.

On-line scheduling of parallel jobs on two machines

We study the problem of on-line scheduling of parallel jobs on two machines. The jobs are parallel in the sense that each of them specifies the number of processors, in this case 1 or 2, required for simultaneous processing. The jobs are presented one by one. Upon receiving a job, we must assign the job to a time slot in the schedule before the next job is presented. No re-assignment is allowed. The goal is to minimize the makespan of the final schedule. There is a straightforward algorithm which achieves a competitive ratio of 2. In this paper we show that no on-line algorithm can have a competitive ratio less than 1 + 2 / 3 ( ≈ 1.816 ) . We also study two special cases of the problem: (i) Jobs arrive in a non-decreasing order of processing times where we give an optimal algorithm with competitive ratio 3/2; (ii) Jobs arrive in a non-increasing order of processing times where we show that no on-line algorithm has a competitive ratio less than 9/7 and give a greedy algorithm with a competitive ratio 4/3.

On an on-line scheduling problem for parallel jobs

This paper addresses the non-preemptive on-line scheduling of parallel jobs. In particular we assume that the release dates and the processing times of the jobs are unknown. It is already known that for this problem Garey and Graham's list scheduling algorithm achieves the competitive factor 2− 1 m for the makespan if m identical machines are available and if each job requires only a single machine for processing. Here, we show that the same factor also holds in the case of parallel jobs.

On-line scheduling of parallel jobs with runtime restrictions

Consider the execution of a parallel application that dynamically generates parallel jobs with specified resource requirements during its execution. We assume that there is not sufficient knowledge about the running times and the number of jobs generated in order to precompute a schedule for such applications. Rather, the scheduling decisions have to be made on-line during runtime based on incomplete information. We present several on-line scheduling algorithms for various interconnection topologies that use some a priori information about the job running times or guarantee a good competitive ratio that depends on the runtime ratio of all generated jobs. All algorithms presented have optimal competitive ratio up to small additive constants, and are easy to implement.

Fairness in parallel job scheduling

This paper introduces a new preemptive algorithm that is well suited for fair on-line scheduling of parallel jobs. Fairness is achieved by selecting job weights to be equal to the resource consumption of the job and by limiting the time span a job can be delayed by other jobs submitted after it. Further, the processing time of a job is not known when the job is released. It is proven that the algorithm achieves a constant competitive ratio for both the makespan and the weighted completion time for the given weight selection. Finally, the algorithm is also experimentally evaluated with the help of workload traces. Copyright © 2000 John Wiley & Sons, Ltd.

Optimal On-Line Scheduling of Parallel Jobs with Dependencies

We study the following general on-line scheduling problem. Paralleljobs arrive on a parallel machine dynamically according to thedependencies between them. Each job requests a certain number ofprocessors in a specific communication configuration, but its runningtime is not known until it is completed. We present optimal on-linealgorithms for PRAMs and one-dimensional meshes, and efficientalgorithms for hypercubes and general meshes. For PRAMs we obtainoptimal tradeoffs between the competitive ratio and the largestnumber of processors requested by any job. Our results demonstrate that on-line scheduling with dependenciesdiffers from scheduling without dependencies in several crucialaspects. First, it is essential to use virtualization, i.e., toschedule parallel jobs on fewer processors than requested. Second,the maximal number of processors requested by a job has significantinfluence on the performance. Third, the geometric structure of thenetwork topology is an even more important factor than in the absenceof dependencies.

Randomized On-line Scheduling of Parallel Jobs

We study randomized on-line scheduling on mesh machines. We show that for scheduling independent jobs randomized algorithms can achieve a significantly better performance than deterministic ones; on the other hand with dependencies randomization does not help.