Locality-sensitive task allocation and load balancing in networked multiagent systems: Talent versus centrality

2011 ◽  
Vol 71 (6) ◽  
pp. 822-836 ◽  
Author(s):  
Yichuan Jiang ◽  
Zhaofeng Li
Keyword(s):  
Load Balancing ◽  
Multiagent Systems ◽  
Task Allocation ◽  
Networked Multiagent Systems

Prescribed-Time Consensus and Containment Control of Networked Multiagent Systems

2019 ◽  
Vol 49 (4) ◽  
pp. 1138-1147 ◽  
Author(s):  
Yujuan Wang ◽  
Yongduan Song ◽  
David J. Hill ◽  
Miroslav Krstic
Keyword(s):  
Multiagent Systems ◽  
Containment Control ◽  
Networked Multiagent Systems

Multiobjective Evolutionary Algorithms MOEA to Solve Task Allocation Problems in Multiagent Systems for DC MicroGrid

2014 ◽  
Vol 700 ◽  
pp. 24-27 ◽  
Author(s):  
Mauricio Mauledoux ◽  
Edilberto Mejía-Ruda ◽  
Oscar I. Caldas
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Multiagent Systems ◽  
Task Allocation ◽  
The Other ◽  
Optimal Distribution ◽  
Multiobjective Evolutionary Algorithms ◽  
Distributed Energy ◽  
Dc Microgrid ◽  
Multi Agents

The work is devoted to solve allocation task problem in the distributed energy way in multi agents systems with multi-objective genetic algorithms. The paper shows the main advantages of genetic algorithms and the way to apply a new genetic operator using the solution information of the other agents for saving energy in the search of expand the solution of the optimal distribution.

Efficient task scheduling on virtual machine in cloud computing environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahfooz Alam ◽  
Mahak ◽  
Raza Abbas Haidri ◽  
Dileep Kumar Yadav
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machine ◽  
Task Allocation ◽  
Completion Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Content Type ◽  
Minimum Completion Time

Purpose Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization. Design/methodology/approach Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing. Findings To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time. Originality/value The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

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