scholarly journals Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment

2019 ◽  
Vol 9 (9) ◽  
pp. 1730 ◽  
Author(s):  
Binh Minh Nguyen ◽  
Huynh Thi Thanh Binh ◽  
Tran The Anh ◽  
Do Bao Son
Keyword(s):  
Task Scheduling ◽  
Completion Time ◽  
Fog Computing ◽  
Operating Cost ◽  
Operating Costs ◽  
Scheduling Problem ◽  
Computing Environment ◽  
Swarm Optimization ◽  
New Approach ◽  
Modified Particle Swarm Optimization

In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.

Pso Optimization algorithm for Task Scheduling on The Cloud Computing Environment

2014 ◽  
Vol 13 (9) ◽  
pp. 4886-4897 ◽  
Author(s):  
Zahraa Tarek Abdelhamed Elmana ◽  
Magdy Zakria ◽  
Fatma A. Omara
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Computation Cost ◽  
Modified Pso ◽  
Available Resources

The Cloud computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand. The Scheduling problem for cloud computing environment has a lot of awareness as the applications tasks could be mapped to the available resources to achieve better results. One of the main existed algorithms of task scheduling on the available resources on the cloud environment is based on the Particle Swarm Optimization (PSO). According to this PSO algorithm, the applications tasks are allocated to the available resources to minimize the computation cost only. In this paper, a modified PSO algorithm has been introduced and implemented for solving task scheduling problem in the cloud. The main idea of the modified PSO is that the tasks are allocated on the available resources to minimize the execution time in addition to the computation cost. This modified PSO algorithm is called Modified Particle Swarm Optimization (MPOS).The MPOS evaluations have been illustrated using different time, and cost parameters and their effects in the performance measures such as utilization, speedup, and efficiency. According to the implementation results, it is found that the modified MPOS algorithm outperforms the existed PSO.

scholarly journals Task Scheduling Algorithms in Cloud Computing: A Review

2021 ◽  
Vol 12 (4) ◽  
pp. 1041-1053
Author(s):  
Ibrahim Mahmood Ibrahim, Et. al.
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Scheduling Algorithms ◽  
Task Completion ◽  
The Internet ◽  
Computing Environment ◽  
Cloud Computing Environment

Cloud computing is the requirement based on clients and provides many resources that aim to share it as a service through the internet. For optimal use, Cloud computing resources such as storage, application, and other services need managing and scheduling these services. The principal idea behind the scheduling is to minimize loss time, workload, and maximize throughput. So, the scheduling task is essential to achieve accuracy and correctness on task completion. This paper gives an idea about various task scheduling algorithms in the cloud computing environment used by researchers. Finally, many authors applied different parameters like completion time, throughput, and cost to evaluate the system.

scholarly journals Fixed Task Scheduling of Industrial Robot using Genetic Algorithm Based Travelling Salesman Problem

2020 ◽  
Vol 9 (5) ◽  
pp. 1771-1775
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Shortest Path ◽  
Completion Time ◽  
Travelling Salesman Problem ◽  
Industrial Robots ◽  
Scheduling Problem ◽  
Search Technique ◽  
Travelling Salesman ◽  
Algorithm Search

The task scheduling of any industrial robots is a prior requirement to effectively use the capability by obtaining shortest path with optimum completion time. In this article, we have presented Travelling Salesman Problem (TSP) with Genetic Algorithm (GA) search technique based task scheduling technique for obtaining optimum shortest path of the task.TSP finds an optimal solution to search for the shortest route by considering every location for completing the required tasks by setting up GA. This article embrace the adaption and implementation of the Genetic Algorithm search strategy for the task scheduling problem in the cooperative control of multiple resources for getting shortest path with minimize the completion time for two zone specific task allocation problem. It can be inferred from the simulation results that the Genetic Algorithm search technique can be considered as a viable solution for the task scheduling problem.

Sign in / Sign up

Export Citation Format

Share Document