scholarly journals A CRITICAL ANALYSIS OF SIMULATORS IN GRID

2015 ◽  
Vol 77 (4) ◽  
Author(s):  
Omar Dakkak ◽  
Suki Arif ◽  
Shahrudin Awang Nor
Keyword(s):  
Distributed Computing ◽  
Dynamic Scheduling ◽  
Scheduling Algorithm ◽  
Research Area ◽  
Scheduling Problems ◽  
Computing Environment ◽  
Parallel And Distributed Computing ◽  
Energy Concept ◽  
To Come ◽  
Load Balancing Algorithm

In parallel and distributed computing environment such as "The Grid", anticipating the behavior of the resources and tasks based on certain scheduling algorithm is a great challenging. Thus, studying and improving these types of environments becomes very difficult. Out of this, the developers have spent remarkable efforts to come up with simulators which facilitate the studies in this domain. In addition, these simulators have a significant role in enhancing and proposing many scheduling algorithms, and this in turn has reflected efficiently on the Grid. In this paper, we will present some of these tools, which are: GridSim for large scales distributed computing and parallel environment, Alea for tackling dynamic scheduling problems, Sim-G-Batch grid simulator for simulating the security and energy concept and Balls simulator for evaluation peer-to-peer with integrated load balancing algorithm. Furthermore, this paper aims to guide and assist the researcher to choose the proper tool that can fit the studied research area, by providing functionality analysis for the reviewed simulators..

scholarly journals A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6660
Author(s):  
Lihao Liu ◽  
Zhenghong Dong ◽  
Haoxiang Su ◽  
Dingzhan Yu
Keyword(s):  
Dynamic Scheduling ◽  
Environmental Changes ◽  
Search Algorithm ◽  
Integrated Management ◽  
Scheduling Algorithm ◽  
Earth Observation ◽  
Scheduling Problems ◽  
Satellite Systems ◽  
Earth Observation Satellites ◽  
Negotiation Mechanism

While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.

scholarly journals Task Resource Allocation in Grid using Swift Scheduler

2009 ◽  
Vol 4 (2) ◽  
pp. 158 ◽  
Author(s):  
K. Somasundaram ◽  
S. Radhakrishnan
Keyword(s):  
Waiting Time ◽  
Heuristic Search ◽  
Dynamic Scheduling ◽  
Weather Forecasting ◽  
Search Algorithm ◽  
Scheduling Algorithm ◽  
Computational Time ◽  
Parallel And Distributed Computing ◽  
Heuristic Search Algorithm ◽  
Computationally Intensive

In nature, Grid computing is the combination of parallel and distributed computing where running computationally intensive applications like sequence alignment, weather forecasting, etc are needed a proficient scheduler to solve the problems awfully fast. Most of the Grid tasks are scheduled based on the First come first served (FCFS) or FCFS with advanced reservation, Shortest Job First (SJF) and etc. But these traditional algorithms seize more computational time due to soar waiting time of jobs in job queue. In Grid scheduling algorithm, the resources selection is NPcomplete. To triumph over the above problem, we proposed a new dynamic scheduling algorithm which is the combination of heuristic search algorithm and traditional SJF algorithm called swift scheduler. The proposed algorithm takes care of Job’s memory and CPU requirements along with the priority of jobs and resources. Our experimental results shows that our scheduler reduces the average waiting time in the job queue and reduces the over all computational time.

scholarly journals A binary Bird Swarm Optimization based load balancing algorithm for cloud computing environment

2021 ◽  
Vol 11 (1) ◽  
pp. 146-160
Author(s):  
Kaushik Mishra ◽  
Santosh Kumar Majhi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Service Providers ◽  
Scheduling Algorithm ◽  
Simulation Environment ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Np Hard Problem ◽  
Load Balancing Algorithm

Abstract Task scheduling and load balancing are a concern for service providers in the cloud computing environment. The problem of scheduling tasks and balancing loads in a cloud is categorized under an NP-hard problem. Thus, it needs an efficient load scheduling algorithm that not only allocates the tasks onto appropriate VMs but also maintains the trade-off amidst VMs. It should keep an equilibrium among VMs in a way that reduces the makespan while maximizing the utilization of resources and throughput. In response to it, the authors propose a load balancing algorithm inspired by the mimicking behavior of a flock of birds, which is called the Bird Swarm Optimization Load Balancing (BSO-LB) algorithm that considers tasks as birds and VMs as destination food patches. In the considered cloud simulation environment, tasks are assumed to be independent and non-preemptive. To evaluate the efficacy of the proposed algorithm under real workloads, the authors consider a dataset (GoCJ) logged by Goggle in 2018 for the execution of cloudlets. The proposed algorithm aims to enhance the overall system performance by reducing response time and keeping the whole system balanced. The authors have integrated the binary variant of the BSO algorithm with the load balancing method. The proposed technique is analyzed and compared with other existing load balancing algorithms such as MAX-MIN, RASA, Improved PSO, and other scheduling algorithms as FCFS, SJF, and RR. The experimental results show that the proposed method outperforms when being compared with the different algorithms mentioned above. It is noteworthy that the proposed approach illustrates an improvement in resource utilization and reduces the makespan of tasks.

scholarly journals A hybrid Method to augment the efficiency of Distributed Computing System by DAG -Using Finest Task Allocation with Dual Mode Processors

2019 ◽  
Vol 8 (11) ◽  
pp. 4275-4284
Keyword(s):  
Distributed Computing ◽  
Task Allocation ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Service Rate ◽  
Additional Parameter ◽  
Computing Environment ◽  
Dual Mode ◽  
New Approach ◽  
Heterogeneous Distributed Computing

Distributed computing system creates or provides a platform having multiple computing nodes linked in a specified manner. On the basis of literature review of last few decades it has been noticed that most of distributed computing researchers have shown their effort to maintain load balancing between processors ,effective task scheduling and optimizing different parameters affecting execution cost and throughput .With these above scenario an additional parameter “Self reconfiguration of CPU” is also a countable parameter to augment the efficiency of distributed computing system .Through this research paper we want to present new approach of adaptive scheduling algorithm which is the mix output of effective task allocation to processor involved in computing and self-reconfiguration of those processors as per need of computing. By this proposed method we will optimize the execution cost, service rate and maximize the throughput as an outcome of organized processors consist in heterogeneous distributed computing system, resulting provide the considerable enhancement in the performance of Distributed computing environment.

Dynamic Scheduling in Inpatient Nursing

2009 ◽  
Vol 3 (2) ◽  
pp. 174-184 ◽  
Author(s):  
Mingang Cheng ◽  
◽  
Hiromi Itoh Ozaku ◽  
Noriaki Kuwahara ◽  
Kiyoshi Kogure ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Dynamic Scheduling ◽  
Scheduling Algorithm ◽  
Work Hours ◽  
Scheduling Problems ◽  
Improve Efficiency ◽  
Inpatient Nursing

To shorten the notoriously long waits for service in hospitals in Japan and to improve efficiency, we propose a scheduling algorithm with a 2-layer local search based on simulated annealing -- permutating (switching) (i) tasks among nurses and (ii) subtasks on each nurse. The scheduling algorithm generates a solution initializing our proposed dynamic scheduling to iteratively generate new, feasible schedules based on the scheduling algorithm to accommodate interruptions while preventing nurses' work hours from increasing. To verify the effectiveness of our proposed scheduling, we executed a set of nursing scheduling problems taken from those actually observed and focused on those that featuring frequent interruptions.

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