scholarly journals DYNAMIC JOB SCHEDULING IN GRID COMPUTING

2016 ◽  
pp. 186-189
Author(s):  
JANI KUNTESH KETAN ◽  
ARPITA SHAH
Keyword(s):  
Grid Computing ◽  
High Performance ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Ant Colony Algorithm ◽  
Job Scheduling ◽  
Heuristic Algorithms ◽  
Optimal Schedule ◽  
Heterogeneous Systems ◽  
Sequential Method

Grid computing is growing rapidly in the distributed heterogeneous systems for utilizing and sharing large-scale resources to solve complex scientific problems. Scheduling is the most recent topic used to achieve high performance in grid environments. It aims to find a suitable allocation of resources for each job. A typical problem which arises during this task is the decision of scheduling. It is about an effective utilization of processor to minimize tardiness time of a job, when it is being scheduled. Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources. The efficient scheduling of independent jobs in a heterogeneous computing environment is an important problem in domains such as grid computing. In general, finding optimal schedule for such an environment using the traditional sequential method is an NP-hard problem whereas heuristic approaches will provide near optimal solutions for complex problems. The Ant colony algorithm, which is one of the heuristic algorithms, suits well for the grid scheduling environment using stigmeric communication.

scholarly journals Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1221
Author(s):  
Tao Ren ◽  
Yan Zhang ◽  
Shuenn-Ren Cheng ◽  
Chin-Chia Wu ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Optimal Schedule ◽  
Differential Evolution Algorithm ◽  
Developed Countries ◽  
Productivity Level ◽  
Jobshop Scheduling

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

scholarly journals Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method

2019 ◽  
Vol 11 (7) ◽  
pp. 1826 ◽  
Author(s):  
Yuxia Cheng ◽  
Zhiwei Wu ◽  
Kui Liu ◽  
Qing Wu ◽  
Yu Wang
Keyword(s):  
Reinforcement Learning ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Scheduling Problem ◽  
Scheduling Policies ◽  
Monte Carlo Tree Search ◽  
Reward Function ◽  
Tasks Scheduling

Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.

Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization

2013 ◽  
Vol 662 ◽  
pp. 957-960 ◽  
Author(s):  
Jing Liu ◽  
Xing Guo Luo ◽  
Xing Ming Zhang ◽  
Fan Zhang
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Autonomous Systems ◽  
Particle Swarm ◽  
Cost Benefit ◽  
Swarm Optimization

Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed algorithm can obtain a better solution concerning the energy cost and profit.

scholarly journals METADATA RANKING AND PRUNING FOR FAILURE DETECTION IN GRIDS

2008 ◽  
Vol 18 (03) ◽  
pp. 371-390 ◽  
Author(s):  
DEMETRIOS ZEINALIPOUR-YAZTI ◽  
HARRIS PAPADAKIS ◽  
CHRYSSIS GEORGIOU ◽  
MARIOS D. DIKAIAKOS
Keyword(s):  
Grid Computing ◽  
Large Scale ◽  
Information Sources ◽  
Job Scheduling ◽  
Failure Detection ◽  
Evaluation Study ◽  
Full Potential ◽  
Processing Power ◽  
Available Information ◽  
Ranking Information

The objective of Grid computing is to make processing power as accessible and easy to use as electricity and water. The last decade has seen an unprecedented growth in Grid infrastructures which nowadays enables large-scale deployment of applications in the scientific computation domain. One of the main challenges in realizing the full potential of Grids is making these systems dependable. In this paper we present FailRank, a novel framework for integrating and ranking information sources that characterize failures in a grid system. After the failing sites have been ranked, these can be eliminated from the job scheduling resource pool yielding in that way a more predictable, dependable and adaptive infrastructure. We also present the tools we developed towards evaluating the FailRank framework. In particular, we present the FailBase Repository which is a 38GB corpus of state information that characterizes the EGEE Grid for one month in 2007. Such a corpus paves the way for the community to systematically uncover new, previously unknown patterns and rules between the multitudes of parameters that can contribute to failures in a Grid environment. Additionally, we present an experimental evaluation study of the FailRank system over 30 days which shows that our framework identifies failures in 93% of the cases and can achieve this by only fetching 65% of the available information sources. We believe that our work constitutes another important step towards realizing adaptive Grid computing systems.

Expected Cost Based Greedy Scheduling Algorithm for Out-Tree Task Graphs

2014 ◽  
Vol 556-562 ◽  
pp. 3431-3437 ◽  
Author(s):  
Jian Jun Zhang ◽  
Tian Hong Wang ◽  
Yu Zhuo Wang
Keyword(s):  
High Performance ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Heterogeneous Environments ◽  
Task Graphs ◽  
Schedule Length ◽  
Current Task ◽  
Greedy Scheduling Algorithm ◽  
Greedy Scheduling

Effective task scheduling is crucial for achieving high performance in heterogeneous computing environments. Whiling scheduling Out-Tree task graphs, many previous heterogeneity based heuristic algorithms usually require high scheduling costs and may not deliver good quality schedules with lower costs. Aiming at the characteristics of Out-Tree task graphs and the features of heterogeneous environments and adopting the strategy based on expected costs and task duplications, this paper proposes a greedy scheduling algorithm, which, at each scheduling step, tries to guarantee not to increase the schedule length, schedules the current task onto the used processor which minimizes its execution finish time; meanwhile, takes load balances into account to economize the use of processors. The comparative experimental results show that the proposed algorithm has higher scheduling efficiency and robust performance, which could produce better schedule which has shorter schedule length and less number of used processors.

scholarly journals Massively multi-user online platform for large-scale applications

2021 ◽  
Author(s):  
Allen Yen-Cheng Yu
Keyword(s):  
Grid Computing ◽  
High Performance ◽  
Large Scale ◽  
Distributed Applications ◽  
Service Architecture ◽  
Online Application ◽  
Online Platform ◽  
Massively Multiplayer Online Game ◽  
And Performance

Many large-scale online applications enable thousands of users to access their services simultaneously. However, the overall service quality of an online application usually degrades when the number of users increases because, traditionally, centralized server architecture does not scale well. In order to provide better Quality of Service (QoS), service architecture such as Grid computing can be used. This type of architecture offers service scalability by utilizing heterogeneous hardware resources. In this thesis, a novel design of Grid computing middleware, Massively Multi-user Online Platform (MMOP), which integrates the Peer-to-Peer (P2P) structured overlays, is proposed. The objectives of this proposed design are to offer scalability and system design flexibility, simplify development processes of distributed applications, and improve QoS by following specified policy rules. A Massively Multiplayer Online Game (MMOG) has been created to validate the functionality and performance of MMOP. The simulation results have demonstrated that MMOP is a high performance and scalable servicing and computing middleware.

scholarly journals ENHANCING JOB SCHEDULING IN CLOUD ENVIRONMENT: A REVIEW

2015 ◽  
Vol 14 (6) ◽  
pp. 5796-5802
Author(s):  
Mrs. Amita Rani ◽  
Dr. Mohita Garg
Keyword(s):  
Heterogeneous Computing ◽  
Job Scheduling ◽  
Heuristic Algorithms ◽  
Computing System ◽  
Technology Infrastructure ◽  
Computing Systems ◽  
Effective Utilization ◽  
Internet Users ◽  
Np Complete ◽  
Heterogeneous Computing Systems

Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous computing systems. The problem of mapping meta-tasks to a machine is shown to be NP-complete. The NP-complete problem can be solved only using heuristic approach. There are a number of heuristic algorithms that were tailored to deal with scheduling of independent tasks. Different criteria can be used for evaluating the efficiency of scheduling algorithms. The most important of them are makespan, flowtime and resource utilization. In this paper, a new heuristic algorithm for scheduling meta-tasks in heterogeneous computing system is presented. The proposed algorithm improves the performance in both makespan and effective utilization of resources by reducing the waiting time. 

scholarly journals Exploration based Genetic Algorithm for Job Scheduling on Grid Computing

2016 ◽  
Vol 5 (3) ◽  
pp. 91-100
Author(s):  
Hanaa Abdelrahman ◽  
Mohammed Bakri Bashir ◽  
Adil Yousif
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Large Scale ◽  
Job Scheduling ◽  
Optimization Problems ◽  
Scheduling Algorithm ◽  
Adaptive Method ◽  
Virtual Organizations ◽  
Good Effect ◽  
Exploration Process

Grid computing presents a new trend to distribute and Internet computing to coordinate large scale heterogeneous resources providing sharing and problem solving in dynamic, multi- institutional virtual organizations. Scheduling is one of the most important problems in computational grid to increase the performance. Genetic Algorithm is adaptive method that can be used to solve optimization problems, based on the genetic process of biological organisms. The objective of this research is to develop a job scheduling algorithm using genetic algorithm with high exploration processes. To evaluate the proposed scheduling algorithm this study conducted a simulation using GridSim Simulator and a number of different workload. The research found that genetic algorithm get best results when increasing the mutation and these result directly proportional with the increase in the number of job. The paper concluded that, the mutation and exploration process has a good effect on the final execution time when we have large number of jobs. However, in small number of job mutation has no effects.

scholarly journals Massively multi-user online platform for large-scale applications

2021 ◽  
Author(s):  
Allen Yen-Cheng Yu
Keyword(s):  
Grid Computing ◽  
High Performance ◽  
Large Scale ◽  
Distributed Applications ◽  
Service Architecture ◽  
Online Application ◽  
Online Platform ◽  
Massively Multiplayer Online Game ◽  
And Performance

Many large-scale online applications enable thousands of users to access their services simultaneously. However, the overall service quality of an online application usually degrades when the number of users increases because, traditionally, centralized server architecture does not scale well. In order to provide better Quality of Service (QoS), service architecture such as Grid computing can be used. This type of architecture offers service scalability by utilizing heterogeneous hardware resources. In this thesis, a novel design of Grid computing middleware, Massively Multi-user Online Platform (MMOP), which integrates the Peer-to-Peer (P2P) structured overlays, is proposed. The objectives of this proposed design are to offer scalability and system design flexibility, simplify development processes of distributed applications, and improve QoS by following specified policy rules. A Massively Multiplayer Online Game (MMOG) has been created to validate the functionality and performance of MMOP. The simulation results have demonstrated that MMOP is a high performance and scalable servicing and computing middleware.

QoS Oriented Enhancement based on the Analysis of Dynamic Job Scheduling in HPC

2014 ◽  
Vol 6 (4) ◽  
pp. 28-45 ◽  
Author(s):  
Reshmi Raveendran ◽  
D. Shanthi Saravanan
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Dynamic Scheduling ◽  
Future Research ◽  
Dynamic Allocation ◽  
The Impact

With the advent of High Performance Computing (HPC) in the large-scale parallel computational environment, better job scheduling and resource allocation techniques are required to deliver Quality of Service (QoS). Therefore, job scheduling on a large-scale parallel system has been studied to minimize the queue time, response time, and to maximize the overall system utilization. The objective of this paper is to touch upon the recent methods used for dynamic resource allocation across multiple computing nodes and the impact of scheduling algorithms. In addition, a quantitative approach which explains a trend line analysis on dynamic allocation for batch processors is depicted. Throughout the survey, the trends in research on dynamic allocation and parallel computing is identified, besides, highlights the potential areas for future research and development. This study proposes the design for an efficient dynamic scheduling algorithm based on the Quality-of-Service. The analysis provides a compelling research platform to optimize dynamic scheduling of jobs in HPC.

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