A Load Balancing in Task Allocation of a Multiagent System

2014 ◽  
Author(s):  
Chattrakul Sombattheera
Keyword(s):  
Load Balancing ◽  
Task Allocation ◽  
Multiagent System

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.

scholarly journals A new load balancing strategy by task allocation in edge computing based on intermediary nodes

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Guangshun Li ◽  
Yonghui Yao ◽  
Junhua Wu ◽  
Xiaoxiao Liu ◽  
Xiaofei Sheng ◽  
...  
Keyword(s):  
Load Balancing ◽  
Real Time ◽  
Task Allocation ◽  
Normal Load ◽  
Task Assignment ◽  
Edge Computing ◽  
Heavy Load ◽  
Light Load ◽  
Assignment Model ◽  
Classification Evaluation

AbstractThe latency of cloud computing is high for the reason that it is far from terminal users. Edge computing can transfer computing from the center to the network edge. However, the problem of load balancing among different edge nodes still needs to be solved. In this paper, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The intermediary node is used to monitor the global information to obtain the real-time attributes of the edge nodes and complete the classification evaluation. First, edge nodes can be classified to three categories (light-load, normal-load, and heavy-load), according to their inherent attributes and real-time attributes. Then, we propose a task assignment model and allocate new tasks to the relatively lightest load node. Experiments show that our method can balance load among edge nodes and reduce the completion time of tasks.

Load Balancing Task Allocation (LBTA)

2008 ◽  
pp. 1-13
Author(s):  
Deo Prakash Vidyarthi ◽  
Biplab Kumer Sarker ◽  
Anil Kumar Tripathi ◽  
Laurence Tianruo Yang
Keyword(s):  
Load Balancing ◽  
Task Allocation

scholarly journals Task Scheduling Allocation based on Task Completion Time in Cloud Environment

2019 ◽  
Vol 8 (4) ◽  
pp. 5207-5213
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Task Allocation ◽  
Completion Time ◽  
Resource Provisioning ◽  
Task Completion ◽  
Task Completion Time ◽  
Survey Paper

Cloud computing is a prominent computing model wherein shared resources can be given as per the customer request at a time. The available resources in the cloud are gathered to execute several tasks that are submitted by the customer. While implementing the tasks, there is a need to optimize performance in terms of execution time, response time and resource utilization of the cloud. The optimization of the mentioned factors in the Cloud Computing can be achieved by one of the major areas known as Load balancing which refers to dealing with client requests from diverse application servers that are functioning in the cloud. An efficient Load Balancing algorithm enables the cloud to be more proficient and enhances customer contentment. So, this survey paper highlights the latest studies regarding the application of Load Balancing techniques for task allocation such as resource allocation (RA) strategies, cloud task scheduling centered on Load Balancing, dynamic Resource Allocation schemes, and cloud resource provisioning scheduling heuristics. Finally, Load Balancing performance for task allocation methods is compared based on task completion time.

Division of Labor and Task Allocation

1999 ◽  
Author(s):  
Eric Bonabeau ◽  
Marco Dorigo ◽  
Guy Theraulaz
Keyword(s):  
Task Performance ◽  
Division Of Labor ◽  
Social Insects ◽  
Task Allocation ◽  
Multiagent System ◽  
Threshold Model ◽  
Response Threshold ◽  
Response Threshold Model ◽  
Response Thresholds ◽  
Colony Level

Many species of social insects have a division of labor. The resilience of task allocation exhibited at the colony level is connected to the elasticity of individual workers. The behavioral repertoire of workers can be stretched back and forth in response to perturbations. A model based on response thresholds connects individual-level plasticity with colony-level resiliency and can account for some important experimental results. Response thresholds refer to likelihood of reacting to task-associated stimuli. Low-threshold individuals perform tasks at a lower level of stimulus than high-threshold individuals. An extension of this model includes a simple form of learning. Within individual workers, performing a given task induces a decrease of the corresponding threshold, and not performing the task induces an increase of the threshold. This double reinforcement process leads to the emergence of specialized workers, that is, workers that are more responsive to stimuli associated with particular task requirements, from a group of initially identical individuals. The fixed response threshold model can be used to allocate tasks in a multiagent system, in a way that is similar to market-based models, where agents bid to get resources or perform tasks. The response threshold model with learning can be used to generate differentiation in task performance in a multiagent system composed of initially identical entities. Task allocation in this case is emergent and more robust with respect to perturbations of the system than when response thresholds are fixed. An example application to distributed mail retrieval is presented. In social insects, different activities are often performed simultaneously by specialized individuals. This phenomenon is called division of labor [253, 272]. Simultaneous task performance by specialized workers is believed to be more efficient than sequential task performance by unspecialized workers [188, 253]. Parallelism avoids task switching, which costs energy and time. Specialization allows greater efficiency of individuals in task performance because they “know” the task or are better equipped for it. All social insects exhibit reproductive division of labor: only a small fraction of the colony, often limited to a single individual, reproduces.

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