Stream-Based Admission Control and Scheduling for Video Transcoding in Cloud Computing

2013 ◽  
Author(s):  
A. Ashraf ◽  
F. Jokhio ◽  
T. Deneke ◽  
S. Lafond ◽  
I. Porres ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Admission Control ◽  
Video Transcoding

Admission Control for Aggregate Flow in Cloud Computing

2014 ◽  
Vol 7 (2) ◽  
pp. 69-78
Author(s):  
Yanbing Liu ◽  
Yunlong He ◽  
Jun Huang
Keyword(s):  
Cloud Computing ◽  
Admission Control

Admission control in cloud computing using game theory

2015 ◽  
Vol 72 (1) ◽  
pp. 317-346 ◽  
Author(s):  
Gaurav Baranwal ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Game Theory ◽  
Cloud Computing ◽  
Admission Control

scholarly journals A control-theoretic approach towards joint admission control and resource allocation of cloud computing services

2015 ◽  
Vol 25 (3) ◽  
pp. 159-180 ◽  
Author(s):  
Dimitrios Dechouniotis ◽  
Nikolaos Leontiou ◽  
Nikolaos Athanasopoulos ◽  
Athanasios Christakidis ◽  
Spyros Denazis
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Admission Control ◽  
Theoretic Approach ◽  
Computing Services ◽  
Cloud Computing Services

SaaS enabled admission control for MCMC simulation in cloud computing infrastructures

2017 ◽  
Vol 211 ◽  
pp. 88-97 ◽  
Author(s):  
J.L. Vázquez-Poletti ◽  
R. Moreno-Vozmediano ◽  
R. Han ◽  
W. Wang ◽  
I.M. Llorente
Keyword(s):  
Cloud Computing ◽  
Admission Control ◽  
Mcmc Simulation

scholarly journals Task admission control for application service operators in mobile cloud computing

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Xiaomin Jin ◽  
Wenqiang Hua ◽  
Zhongmin Wang
Keyword(s):  
Cloud Computing ◽  
Admission Control ◽  
Mobile Cloud Computing ◽  
Admission Policy ◽  
Mobile Cloud ◽  
Application Service ◽  
Markov Decision ◽  
Radio Resources ◽  
Further Development

Abstract The resource constraint has become an important factor hindering the further development of mobile devices (MDs). Mobile cloud computing (MCC) is a new approach proposed to extend MDs’ capacity and improve their performance by task offloading. In MCC, MDs send task requests to the application service operator (ASO), which provides application services to MDs and needs to determine whether to accept the task request according to the system condition. This paper studies the task admission control problem for ASOs with the consideration of three features (two-dimensional resources, uncertainty, and incomplete information). A task admission control model, which considers radio resource variations, computing, and radio resources, is established based on the semi-Markov decision process with the goal of maximizing the ASO’s profits while guaranteeing the quality of service (QoS). To develop the admission policy, a reinforcement learning-based policy algorithm, which develops the admission policy through system simulations without knowing the complete system information, is proposed. Experimental results show that the established model adaptively adjusts the admission policy to accept or reject different levels and classes of task requests based on the ASO load, available radio resources, and event type. The proposed policy algorithm outperforms the existing policy algorithms and maximizes the ASO’s profits while guaranteeing the QoS.

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