A performance comparison of the contiguous allocation strategies in 3D mesh connected multicomputers

The performance of contiguous allocation strategies can be significantly affected by the distribution of job execution times. In this paper, the performance of the existing contiguous allocation strategies for 3D mesh multicomputers is re-visited in the context of heavy-tailed distributions (e.g., a Bounded Pareto distribution). The strategies are evaluated and compared using simulation experiments for both First-Come-First-Served (FCFS) and Shortest-Service-Demand (SSD) scheduling strategies under a variety of system loads and system sizes. The results show that the performance of the allocation strategies degrades considerably when job execution times follow a heavy-tailed distribution. Moreover, SSD copes much better than FCFS scheduling strategy in the presence of heavy-tailed job execution times. The results also show that the strategies that depend on a list of allocated sub-meshes for both allocation and deallocation have lower allocation overhead and deliver good system performance in terms of average turnaround time and mean system utilization

Gang FTP scheduling of periodic and parallel rigid real-time tasks

In this paper we consider the scheduling of periodic and parallel rigid tasks. We provide (and prove correct) an exact schedulability test for Fixed Task Priority (FTP) Gang scheduler sub-classes: Parallelism Monotonic, Idling, Limited Gang, and Limited Slack Reclaiming. Additionally, we study the predictability of our schedulers: we show that Gang FJP schedulers are not predictable and we identify several sub-classes which are actually predictable. Moreover, we extend the definition of rigid, moldable and malleable jobs to recurrent tasks

List scheduling revisited

We consider the problem of scheduling n jobs on m identical parallel machines to minimize a regular cost function. The standard list scheduling algorithm converts a list into a feasible schedule by focusing on the job start times. We prove that list schedules are dominant for this type of problem. Furthermore, we prove that an alternative list scheduling algorithm, focusing on the completion times rather than the start times, yields also dominant list schedules for problems with sequence dependent setup times

Competitive-Ratio Approximation Schemes for Minimizing the Makespan in the Online-List Model

We consider online scheduling on multiple machines for jobs arriving one-by-one with the objective of minimizing the makespan. For any number of identical parallel or uniformly related machines, we provide a competitive-ratio approximation scheme that computes an online algorithm whose competitive ratio is arbitrarily close to the best possible competitive ratio. We also determine this value up to any desired accuracy. This is the first application of competitive-ratio approximation schemes in the online-list model. The result proves the applicability of the concept in different online models. We expect that it fosters further research on other online problems

Greed Works -- Online Algorithms For Unrelated Machine Stochastic Scheduling

This paper establishes performance guarantees for online algorithms that schedule stochastic, nonpreemptive jobs on unrelated machines to minimize the expected total weighted completion time. Prior work on unrelated machine scheduling with stochastic jobs was restricted to the offline case, and required linear or convex programming relaxations for the assignment of jobs to machines. The algorithms introduced in this paper are purely combinatorial. The performance bounds are of the same order of magnitude as those of earlier work, and depend linearly on an upper bound on the squared coefficient of variation of the jobs' processing times. Specifically for deterministic processing times, without and with release times, the competitive ratios are 4 and 7.216, respectively. As to the technical contribution, the paper shows how dual fitting techniques can be used for stochastic and nonpreemptive scheduling problems.Comment: Preliminary version appeared in IPCO 201

Online scheduling of splittable tasks

We consider online scheduling of splittable tasks on parallel machines. In our model, each task can be split into a limited number of parts, that can then be scheduled independently. We consider both the case where the machines are identical and the case where some subset of the machines have a (fixed) higher speed than the others. We design a class of algorithms which allows us to give tight bounds for a large class of cases where tasks may be split into relatively many parts. For identical machines we also improve upon the natural greedy algorithm in other classes of cases