A Grid Scheduling Based on Generalized Extremal Optimization for Parallel Job Model

Switalski, Piotr; Seredyński, Franciszek

doi:10.1007/978-3-642-31500-8_5

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Proposed solution took longer time and not feasible for scheduling bigger problems in large scale systems. Author developed the GEO based policy for batch of independent parallel jobs without any communication requirements for multiprocessor systems [21] and concluded that GEO-based schedulers can compete with GA-based schedulers any computing environment.…”

Section: A Scheduling On Parallel Jobsmentioning

confidence: 99%

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Chhabra*,

Singh,

Kahlon

2019

IJRTE

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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.

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Section: A Scheduling On Parallel Jobsmentioning

confidence: 99%

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Chhabra*,

Singh,

Kahlon

2019

IJRTE

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“…GEO is much simpler and requires the tuning of only one parameter, while GA requires the tuning of several parameters. Our earlier study (Switalski and Seredynski 2012) suggests that GEO-based schedulers can compete with GA-based schedulers working in a grid environment. In this paper we compare in detail both evolutionary approaches and show that a GEO-based scheduler is more effective in the sense of the quality of solutions and less computationally expensive while tuning parameters than a GA-based approach.…”

Section: Introductionmentioning

confidence: 99%

Scheduling parallel batch jobs in grids with evolutionary metaheuristics

Switalski

Seredyński

2014

J Sched

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In this paper we propose an efficient offline job scheduling algorithm working in a grid environment that is based on a relatively new evolutionary metaheuristic called generalized extremal optimization (GEO). We compare our experimental results with those obtained using a very popular evolutionary metaheuristic, the genetic algorithm (GA). The scheduling algorithm implies two-stage scheduling. In the first stage, the algorithm allocates jobs to suitable machines of a grid; GEO/GA is used for this purpose. In the second stage, jobs are independently scheduled on each machine; this is performed with a variant of a list scheduling algorithm. Both GEO and GA belong to the class of evolutionary algorithms, but GEO is much simpler and requires the tuning of only one parameter, whereas GA requires the tuning of several parameters. The results of the experimental study show that GEO, despite its simplicity, outperforms the GA in a whole range of scheduling instances and is much easier to use.

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Performance-aware energy-efficient parallel job scheduling in HPC grid using nature-inspired hybrid meta-heuristics

Chhabra

Singh

Kahlon

2020

J Ambient Intell Human Comput

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A Grid Scheduling Based on Generalized Extremal Optimization for Parallel Job Model

Cited by 3 publications

References 9 publications

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Scheduling parallel batch jobs in grids with evolutionary metaheuristics

Performance-aware energy-efficient parallel job scheduling in HPC grid using nature-inspired hybrid meta-heuristics

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