Hybrid meta-heuristic algorithms for independent job scheduling in grid computing

Younis, Muhanad Tahrir; Yang, Shengxiang

doi:10.1016/j.asoc.2018.05.032

Cited by 33 publications

(22 citation statements)

References 38 publications

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“…Se observa que los métodos de optimización cuentan con unas fortalezas y debilidades, al igual que los enfoques metaheurísticos; entonces porque no unir las fortalezas de estos con el objeto de lograr el diseño de un enfoque más adecuado para la solución del problema de planificación grid. Por ejemplo, se puede combinar las búsquedas evolutivas con búsquedas locales; de hecho, la hibridación entre diferentes metaheurísticos ha demostrado eficacia para muchos problemas difíciles de superar con un métodoúnico [59,60,61].…”

Section: Enfoques Heurísticos Híbridosunclassified

Estrategias de planificación para datos y procesos en computación Grid: estado del arte

Bermúdez

Pérez

2019

Ing.USBMed

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Resumen. La distribución y la naturaleza compartida y heterogénea de la computación grid, hace que su objetivo de ofrecer aplicaciones con poder computacional colectivo, sea un gran reto. El objetivo es presentar el resultado de una investigación sistemática sobre aspectos teóricos de las temáticas que convergen en el diseño y desarrollo de planificadores de datos y procesos en computación grid. Se tomó como referente la metodología planteada para desarrollo de estados de arte, que cuenta con dos fases principales: heurística y hermenéutica. En el proceso se analizaron los fundamentos teóricos, tales como: la arquitectura, las etapas que conforman el proceso realizado por parte de un planificador grid y los tipos de planificación existentes, todo ello con el objeto de abordar los diferentes modelos y enfoques computacionales, heurísticos y metaheurísticos que permiten un funcionamiento eficiente y eficaz de maneraóptima de los planificadores grid, permitiendo mitigar en cierto grado los problemas presentados en la planificación grid. La optimización de la planificación de recursos grid, es unárea que se encuentra en desarrollo, dado que las problemáticas principales como: demora en la planificación y el proceso de optimización, están aún en etapa de desarrollo. Palabras Clave. Computación grid; heurística; metaheurística; planificación grid; procesos; trabajos.Abstract. The distribution, the shared and heterogeneous nature of grid computing, makes its objective of offering applications with a collective computational power, be a great challenge. The objetive is to present the result of the systematic research about theoretical aspects about converge topics in the design and development of data and process planners in GRID computation. The methodology proposed for the development of art states was taken as a reference, which has two main phases: heuristics and hermeneutics. In this process, the theoretical foundations are analyzed like the architecture, the stages that conforms the process performed by a GRID planner and the existing planner types; all this with the aim of addressing the different computational, heuristic and metaheuristic models and approaches that allows the efficient and effective operation of GRID planners, allowing to mitigate in certain degree the problems presented in GRID planning. The optimization of grid resource planning is an area that is under development, given that the main problems such as: delay in planning and the optimization process, are still in the development stage.

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Section: Enfoques Heurísticos Híbridosunclassified

Estrategias de planificación para datos y procesos en computación Grid: estado del arte

Bermúdez

Pérez

2019

Ing.USBMed

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“…Examples of such parallel jobs are high performance computing (HPC) application such as electromagnetic computing [10], plasma simulations [11], and combinatorial and mathematical studies [12,13]. Secondly the JSP issue in HCS platforms is a popular NP-hard problem due to federated resources, problem size, high heterogeneity of computational resources as well as applications and dynamic nature of resources [14]. Therefore there is always an opportunity to develop new optimal solutions for scheduling jobs using metaheuristics.…”

Section: A Motivationmentioning

confidence: 99%

“…Therefore there is always an opportunity to develop new optimal solutions for scheduling jobs using metaheuristics. Thirdly hybridization of metaheuristics [14][26] [30] is often used by researchers to solve diverse problem areas including offline sequential task scheduling in HCS platforms. Hybrid meta-heuristic combine the best qualities of more than one metaheuristic to provide best optimal solution of the undertaken problem and found to produce better results than stand-alone metaheuristics.…”

Section: A Motivationmentioning

confidence: 99%

“…Such heuristics produced good results for small problem and system sizes. However, to effectively deal with the NP-hard JSP problem in complex HCS platforms, several meta-heuristic based offline solutions are developed over the years [14][15][16][17] [30]. The major focus of the research was on scheduling independent jobs i.e.…”

Section: Related Workmentioning

confidence: 99%

“…The performance of CSGA was found to be better when compared with GA, CS and ACO with ETC model benchmarks. Authors in [14] proposed two strongly coupled hybrid metaheuristics ACO-VNS and GA-VNS for offline sequential task scheduling to minimize makespan. The performance of hybrid algorithms was compared with many heuristics and metaheuristics.…”

Section: B Scheduling On Independent Sequential Jobsmentioning

confidence: 99%

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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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Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Beigrezaei

Haghighat

Mirtaheri

2020

Int J Communication

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Summary In Data Grid systems, quick data access is a challenging issue due to the high latency. The failure of requests is one of the most common matters in these systems that has an impact on performance and access delay. Job scheduling and data replication are two main techniques in reducing access latency. In this paper, we propose two new neighborhood‐based job scheduling strategies and a novel neighborhood‐based dynamic data replication algorithm (NDDR). The proposed algorithms reduce the access latency by considering a variety of practical parameters for decision making and the access delay by considering the failure probability of a node in job scheduling, replica selection, and replica placement. The proposed neighborhood concept in job scheduling includes all the nodes with low data transmission costs. Therefore, we can select the best computational node and reduce the search time by running a hierarchical and parallel search. NDDR reduces the access latency through selecting the best replica by performing a hierarchical search established based on the access time, storage queue workload, storage speed, and failure probability. NDDR improves the load balancing and data locality by selecting the best replication place considering the workload, temporal locality, geographical locality, and spatial locality. We evaluate our proposed algorithms by using Optorsim Simulator in two scenarios. The simulations confirm that the proposed algorithms improve the results compared with similar existing algorithms by 11%, 15%, 12%, and 10% in terms of mean job time, replication frequency, mean data access latency, and effective network usage, respectively.

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Hybrid meta-heuristic algorithms for independent job scheduling in grid computing

Cited by 33 publications

References 38 publications

Estrategias de planificación para datos y procesos en computación Grid: estado del arte

Estrategias de planificación para datos y procesos en computación Grid: estado del arte

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

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