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8th IEEE International Conference on Cloud Computing, Data Science and Engineering (Confluence) / Global Technology, Innovation and Enterpreneurship Summit

作者： Shahid, Mohammad Raza, Zahid Alam, Mahfooz Aligarh Muslim Univ Dept Commerce Aligarh Uttar Pradesh India Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India Al Barkaat Coll Grad Studies Dept Comp Sci Aligarh Uttar Pradesh India

ISBN: (纸本)9781538617199

computational grid is computation platform of geographically distributed heterogeneous computational machines which can be accessed by needy users via a single interface. Efficient scheduling is the core concern in computational grid for achieving the user's need with optimizing Quality of Service (QoS) parameters. The mapping problem of batch of jobs onto computational nodes with heterogeneities optimizing QoS with precedence constraints in grid had been manifested as NP-Complete. Present study presented the Precedence based Batch Scheduling Strategy (PBSS) in which independent jobs in the batch are considered of having non-communicating modules with precedence constraints represented by Direct Acyclic Graph (DAG). A comparative performance analysis is done to evaluate the significance of PBSS with its peers for the middleware.

关键词： computational grid DAG Scheduling Precedence Constraints Levelized Batch Scheduling Turnaround Time

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Dynamic and Adaptive Fault Tolerant Scheduling With QoS Consideration in computational grid

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IEEE ACCESS 2017年 5卷 7853-7873页

作者： Haider, Sajjad Nazir, Babar Shaheed Zulfiqar Ali Bhutto Inst Sci & Technol Dept Comp Islamabad 44000 Pakistan Natl Univ Modern Languages Dept Comp Sci Islamabad 44000 Pakistan COMSATS Inst Informat Technol Dept Comp Sci Abbottabad 22010 Pakistan

Provisioning fault-tolerant scheduling in computational grid is a challenging task. Most of the existing fault tolerant scheduling schemes are either geared toward proactive or reactive. Proactive schemes emphasize on the reasons responsible for generating faults, whereas reactive mechanisms come into effect after failure detection. Unlike most existing mechanisms, we present a novel, dynamic, adaptive, and hybrid fault-tolerant scheduling scheme based on proactive and reactive approaches. In the proactive approach, the resource filtration algorithm picks resources based on resource location, availability, and reliability. Unlike most existing schemes, which rely on remotely connected resources, the proposed algorithm prefers to employ locally available resources as they might have less failure tendency. To cope with the frequent turnover problem, the proposed scheme calculates resource availability time based on various newly identified parameters (e.g., mean time between availability) and picks highly available nodes for task execution. Resource reliability is an indispensable consideration in the proposed scheme and is calculated based on parameters such as jobs success or failure ratio and the types of failures encountered. We employ an optimal resource identification algorithm to determine and select optimal resources for job execution. The performance of the proposed scheme is validated through the gridSim toolkit. Compared with contemporary approaches, experimental results demonstrate the effectiveness and efficiency of the proposed scheme in terms of various performance metrics, such as wall clock time, throughput, waiting and turnaround time, number of checkpoints, and energy consumption.

关键词： computational grid fault tolerant scheduling availability reliability genetic algorithm

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Reliable and efficient hierarchical organization model for computational grid

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2017年 104卷 191-205页

作者： Abdullah, Aref M. Ali, Hesham A. Haikal, Amira Y. Taiz Univ Comp & Informat Syst Ctr Taizi Yemen Mansoura Univ Fac Engn Mansoura Egypt

Although the hierarchical model appears to be an effective solution to organize the resource managements in grid systems which have more stringent demand for both scalability and efficiency, it has some limitations which need to be addressed. For example, the master/manager resources in different levels represent single points of failure and they may be sources of bottleneck and communication overhead especially if they are not efficiently selected. Moreover, the dynamic and fault-prone nature of grids cannot be treated by static structures while the manual construction and repairing are also prohibitive due to the highly caused overhead which often represents a significant obstruction to an efficient resource utilization (especially for those with intermittent availabilities). The main objective of this paper is to first introduce a self-repairing n-try dynamic hierarchical grid model for scheduling and load balancing in which each master resource will be replicated on one of its children resources. Second, an efficient methodology to elect masters-replicas resources is proposed. In this methodology, the masters-replicas are selected based on both resource reliability (in terms of MTBF) and resource proximity from the other nodes in specified groups (in terms of communication latency). Validation of the proposed methodology based on the proposed model is done via simulation. Experimental results show that the proposed model has a great impact on the overall performance. Compared to other approaches, the simulations show that our approach decrements the average completion time (ACT) by 18.9%-25%, increases the tree stability ratio (TSR) up to 26.2%-27.1%, and minimizes the total communication overhead (TCO) by 4.4%-18.7% in the range of system parameter values examined. (C) 2017 Elsevier Inc. All rights reserved.

关键词： computational grid Fault tolerance grid scheduling Load balancing Distributed system

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A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in computational grid

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INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2018年第2期9卷 1-15页

作者： Ghosh, Tarun Kumar Das, Sanjoy Haldia Inst Technol Dept Comp Sci & Engn Haldia India Kalyani Univ Dept Engn & Technol Studies Kalyani W Bengal India

Scheduling jobs in computational grids is considered as NP-complete problem owing to the heterogeneity of shared resources. The resources belong to many distributed administrative domains that enforce various management policies. Therefore, the use of meta-heuristics are more appropriate option in obtaining optimal results. In this article, a novel hybrid population-based global optimization algorithm, called the Hybrid Firefly Algorithm and the Differential Evolution (HFA-DE), is proposed by combining the merits of both the Firefly Algorithm and Differential Evolution. The Firefly Algorithm and the Differential Evolution are executed in parallel to support information sharing amongst the population and thus enhance searching efficiency. The proposed HFA-DE algorithm reduces the schedule makespan, processing cost, and improves resource utilization. The HFA-DE is compared with the standard Firefly Algorithm, the Differential Evolution and the Particle Swarm Optimization algorithms on all these parameters. The comparison results exhibit that the proposed algorithm outperforms the other three algorithms.

关键词： computational grid Differential Evolution Firefly Algorithm Job Scheduling Makespan Particle Swarm Optimization Resource Utilization

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Optimization of Workload Scheduling in computational grid 5th

Optimization of Workload Scheduling in Computational Grid

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5th International Conference on Frontiers in Intelligent Computing - Theory and Applications (FICTA)

作者： Goswami, Sukalyan Das, Ajanta Inst Engn & Management Kolkata India Birla Inst Technol Kolkata Campus Ranchi Bihar India

ISBN: (纸本)9789811031533;9789811031526

computational grid houses powerful resources to execute computation-intensive jobs, which are submitted by the clients. Resources voluntarily become available in the grid, as a result of which, this collaborative computing becomes more cost effective than traditional HPC. In the grid, since, the participating resources are of varying capabilities, load balancing becomes an essential requirement. This workload distribution mechanism among available resources aims to minimize makespan, optimize resource usage, and prevent overloading of any resource. Eventually, the resources need to be prioritized based on their capability and demand in the current scenario. Thus, prioritization of resources balances workload in grid. In the proposed workload scheduling algorithm, nearest deadline first-scheduled (NDFS), resource ranking, and subsequent job scheduling maintains balanced load across the grid. The ranking of resources in computational grid is achieved using analytic hierarchy process (AHP) model. The primary objective of this paper is to optimize the workload of grid environment while executing multiple jobs ensuring maximum resource utilization within minimum execution time. Service quality agreement (SQA) is met through proper scheduling of jobs among ranked resources. The grid test bed environment is set up with the help of Globus toolkit 5.2. This paper presents the simultaneous execution results of the benchmark codes of fast Fourier transform (FFT) and matrix multiplication in order to balance the workload in grid test bed.

关键词： computational grid Load balancing Resource ranking Analytic hierarchy process (AHP) Workload scheduling algorithm

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A green energy model for resource allocation in computational grid using dynamic threshold and GA

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2016年第Mar.期9卷 42-56页

作者： Kaushik, Achal Vidyarthi, Deo Prakash Jawaharlal Nehru Univ Sch Comp & Syst Sci Delhi 110067 India

computational grid helps compute intensive jobs in faster execution. By virtue of resource provisioning, very high demand of computational power can be facilitated though at the cost of high energy consumption. Many characteristic parameters are intended to be optimized, depending upon the requirements, while making resource allocation for the job execution in computational grid. Most often the green energy aspect, wherein one tries for better energy utilization, is ignored while allocating the grid resources to the jobs. Most of the grid scheduler aims to optimize the makespan, ignoring the energy aspect. Proposed work tries to optimize the energy making it a green energy model while making resource allocation in computational grid. In this, energy saving mechanism is implemented using a dynamic threshold method followed by the use of genetic algorithm which further consolidates the energy saving. It explores how effectively the jobs submitted to the grid can be executed with optimal energy uses and at the same time makes no compromise on other expected characteristic parameters. The proposed green energy model has been experimentally evaluated by its simulation. The result reveals the benefits and gives better insight for effective energy aware resource allocation. (C) 2016 Elsevier Inc. All rights reserved.

关键词： computational grid Green energy Resource allocation Genetic algorithm Makespan

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Swarm Intelligence Algorithm for Job Scheduling in computational grid 7

Swarm Intelligence Algorithm for Job Scheduling in Computati...

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7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS)

作者： Effatparvar, Mehdi Aghayi, Somayeh Asadzadeh, Vahid Dashti, Yosef Islamic Azad Univ Ardabil Branch Dept Comp Ardebil Iran Islamic Azad Univ Ardabil Branch Sama Tech & Vocat Training Coll Ardebil Iran Islamic Azad Univ Germi Branch Dept Comp Germi Iran

ISBN: (纸本)9781509006656

grid computing is actually the next generation of distributed systems. Its objective is to create a powerful, large and autonomous virtual computer. This computer is created through assembling countless heterogeneous resources with the aim of sharing them. Scheduling is one of the most important and challenging issue of such systems. An accurate and efficient schedule is required to increase the grid efficiency. grid resources belong to different management domains;each one applies different management policies. This paper proposed a new heuristic approach based on particle swarm optimization algorithm in order to scheduling the jobs in gird environment. The proposed algorithm would create an optimal scheduler to complete the jobs in the minimum flowtime and makespan.

关键词： Job Scheduling computational grid Particle Swarm Optimization Algorithm Flowtime Makespan

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A Green Energy Model for Resource Allocation in computational grid

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COMPUTER JOURNAL 2015年第7期58卷 1530-1547页

作者： Kaushik, Achal Vidyarthi, Deo Prakash Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

A computational grid helps in faster execution for compute-intensive jobs. Many characteristic parameters are intended to be optimized while making resource allocation for job execution in a computational grid. Most often, the green energy aspect, where one tries for better energy utilization, is ignored while allocating grid resources to jobs. The present work tries to optimize energy to make it a green energy model for resource allocation in a computational grid. It explores how effectively the jobs submitted to the grid can be executed, making no compromise on other expected characteristic parameters, and at the same time makes optimal use of energy. The proposed green energy model has been experimentally verified by its simulation. The result reveals the benefits and gives a better insight for effective resource allocation.

关键词： computational grid green energy resource allocation DVFS DPM quality of service

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Energy efficient and robust allocation of interdependent tasks on mobile ad hoc computational grid

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2015年第5期27卷 1226-1254页

作者： Shah, Sayed Chhattan Hankuk Univ Foreign Studies Dept Informat & Commun Engn Seoul South Korea

A mobile ad hoc computational grid is a distributed computing infrastructure that allows mobile nodes to share computing resources in a mobile ad hoc environment. Compared to traditional distributed systems such as grids and clouds, resource allocation in mobile ad hoc computational grids is not straightforward because of node mobility, limited battery power and an infrastructure-less network environment. The existing schemes are either based on a decentralized architecture that results in poor allocation decisions or assume independent tasks. This paper presents a scheme that allocates interdependent tasks and aims to reduce task completion time and the amount of energy consumed in transmission of data. This scheme comprises two key algorithms: resource selection and resource allocation. The resource selection algorithm is designed to select nodes that remain connected for a longer period, whereas the resource assignment or allocation algorithm is developed to allocate interdependent tasks to the nodes that are accessible at the minimum transmission power. The scheme is based on a hybrid architecture that results in effective allocation decisions, reduces the communication cost associated with the exchange of control information, and distributes the processing burden among the nodes. The paper also investigates the relationship between the data transfer time and transmission energy consumption and presents a power-based routing protocol to reduce data transfer costs and transmission energy consumption. Copyright (C) 2014 John Wiley & Sons, Ltd.

关键词： computational grid ad hoc networks resource allocation job scheduling mobile grids mobile clouds

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Level based batch scheduling strategy with idle slot reduction under DAG constraints for computational grid

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JOURNAL OF SYSTEMS AND SOFTWARE 2015年 108卷 110-133页

作者： Shahid, Mohammad Raza, Zahid Sajid, Mohammad Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

Scheduling in a grid environment optimizing the given objective parameters has been proven to be NP-complete. This work proposes a Level based Batch scheduling Strategy with Idle slot Reduction (LBSIR) while considering the inter module communication within the modules of the jobs represented using Direct Acyclic Graph (DAG) with the objective of optimizing the turnaround time and response time for a computational grid. The model works in two phases, allocation phase and idle slot reduction phase. Allocation phase begins by dividing the batch into a number of partitions as per the precedence level/depth level followed by the assignment of sub-jobs/modules from the partition to the best fit node in terms of the execution time offered for all the partitions. The idle slots generated during the allocation phase in each depth level are then reduced by inserting the best fit modules into these slots in the idle slot reduction phase after allocation of modules from higher depth level. Levelized allocation ensures minimizing the average response time being very useful for user interactive applications. An experimental study of the proposed strategy has been performed by comparing it with other similar methods having common objectives for evaluating its place in the middleware. (C) 2015 Elsevier Inc. All rights reserved.

关键词： computational grid Level based batch scheduler DAG constraints

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