Dynamic Scheduling and Pricing in Wireless Cloud Computing

2014 ◽  
Vol 13 (10) ◽  
pp. 2283-2292 ◽  
Author(s):  
Shaolei Ren ◽  
Mihaela van der Schaar
Keyword(s):  
Cloud Computing ◽  
Dynamic Scheduling ◽  
Wireless Cloud

scholarly journals REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT

2020 ◽  
Vol 5 (1) ◽  
pp. 85-89
Author(s):  
Dinkan Patel ◽  
Anjuman Ranavadiya
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Dynamic Scheduling ◽  
Scheduling Algorithms ◽  
Minimum Time ◽  
Cloud Environment ◽  
Static Scheduling ◽  
On Demand

Cloud Computing is a type of Internet model that enables convenient, on-demand resources that can be used rapidly and with minimum effort. Cloud Computing can be IaaS, PaaS or SaaS. Scheduling of these tasks is important so that resources can be utilized efficiently with minimum time which in turn gives better performance. Real time tasks require dynamic scheduling as tasks cannot be known in advance as in static scheduling approach. There are different task scheduling algorithms that can be utilized to increase the performance in real time and performing these on virtual machines can prove to be useful. Here a review of various task scheduling algorithms is done which can be used to perform the task and allocate resources so that performance can be increased.

Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing

2019 ◽  
Vol 75 (10) ◽  
pp. 6386-6450 ◽  
Author(s):  
Farinaz Hemasian-Etefagh ◽  
Faramarz Safi-Esfahani
Keyword(s):  
Cloud Computing ◽  
Dynamic Scheduling

scholarly journals Job Scheduling Based on Dependent and Independent Task using Particle Swarm Optimization

2020 ◽  
Vol 9 (4) ◽  
pp. 1600-1606
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Execution Time ◽  
Mobile Cloud Computing ◽  
Job Scheduling ◽  
Dynamic Scheduling ◽  
Particle Swarm ◽  
Mobile Cloud ◽  
Swarm Optimization ◽  
Dependent Tasks

Mobile Cloud Computing is an accumulation of both Cloud Computing and Mobile Computing. In cloud computing resources are deployed to a client on-demand basis. Mobile cloud computing is similar to cloud computing except that some devices involved in mobile cloud computing should be mobile. The demand for MCC has been increasing due to its scalability, reliability, high QOS (Quality Of Services), longer battery life, large storage capacity. Mobile cloud computing aims to take benefit of limited resources provided by a cloud provider. Task scheduling is a major concept involved in executing a task. In cloud computing job scheduling is required to execute each job without any deadlock. But the scheduling of dependent tasks is a problem in cloud systems. This problem is an NP-complete problem and can be solved using various heuristic and metaheuristic approaches. These approaches give high-quality solutions with reasonable execution time. Particle Swarm Optimization (PSO) is one of these meta-heuristic approaches that solve the problem of grid scheduling. In this paper, we address the problem encounter in dynamic scheduling. In dynamic scheduling, each task has its own deadline completion time. The task that arrived earlier in the system occupied the resources first and later arrived tasks are rejected because their execution time exceeds the deadline. In this paper, we proposed PSO with a variable job identifier that identifies independent and dependent tasks from the population. The particles are arranged with a grid dynamically and influence swarm to minimize execution time and waiting time simultaneously. The experimental studies show that the proposed approach is more efficient than other PSO based approaches as described in the literature

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