Q-MAC: QoS and mobility aware optimal resource allocation for dynamic application offloading in mobile cloud computing

2017 ◽  
Author(s):  
Mahinur Akter ◽  
Fatema Tuz Zohra ◽  
Amit Kumar Das
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Optimal Resource Allocation ◽  
Optimal Resource ◽  
Application Offloading

A Mobility-Aware Optimal Resource Allocation Architecture for Big Data Task Execution on Mobile Cloud in Smart Cities

2018 ◽  
Vol 56 (2) ◽  
pp. 110-117 ◽  
Author(s):  
Asma Enayet ◽  
Md. Abdur Razzaque ◽  
Mohammad Mehedi Hassan ◽  
Atif Alamri ◽  
Giancarlo Fortino
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Smart Cities ◽  
Mobile Cloud ◽  
Task Execution ◽  
Optimal Resource Allocation ◽  
Optimal Resource

A Novel Application Offloading Algorithm and an Optimized Application Servers Placement for Mobile Cloud Computing

2015 ◽  
Vol 6 (4) ◽  
pp. 19-38
Author(s):  
Amal Ellouze ◽  
Maurice Gagnaire
Keyword(s):  
Cloud Computing ◽  
Quality Of Experience ◽  
Mobile Cloud Computing ◽  
Energy Savings ◽  
Mobile Cloud ◽  
Mobile Terminals ◽  
Application Servers ◽  
Application Offloading ◽  
Computing Capacity

An application's offloading algorithm to over go the limitations of mobile terminals, namely their lack of computing capacity and limited battery autonomy is introduced. The proposed Mobile Application's Offloading algorithm enables to shift applicative jobs from mobile handsets to remote servers. The novelty of MAO consists in considering the Quality of Experience as an additional decision test before proceeding to application offloading. Based on various traffic scenarios, researchers study the efficiency of the MAO algorithm and show its performance in terms of rejected jobs and energy savings. First, the researchers consider the case where the application servers were placed systematically at the antenna's site. For a more realistic context of Mobile Cloud Computing, they extend the analysis by considering the case where the remote servers can be placed at different splitting points of the infrastructure. They assess by means of closed-forms fitting functions the performance of the MAO algorithm. Authors end this article with proposing an optimized applications servers placement.

Fault aware hybrid harmony search technique for optimal resource allocation in cloud

2021 ◽  
pp. 1-13
Author(s):  
Punit Gupta ◽  
Sanjeet Bhagat ◽  
Pradeep Rawat
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Harmony Search ◽  
Optimal Solution ◽  
Performance Criteria ◽  
Cloud System ◽  
Optimal Resource Allocation ◽  
Time Duration ◽  
Task Failure ◽  
Optimal Resource

The evolution of cloud computing is increasing exponentially which provides everything as a service. Clouds made it possible to move a huge amount of data over the networks on-demand. It removed the physical necessity of resources as resources are available virtually over the networks. Emerge of new technologies improvising the cloud system and trying to overcome cloud computing challenges like resource optimization, securities etc. Proper utilization of resources is still a primary target for the cloud system as it will increase the cost and time efficiency. Cloud is a pay-per-uses basis model which needs to perform in a flexible manner with the increase and decrease in demand on every level. In general, cloud is assumed to be non-faulty but faulty is a part of any system. This article focuses on the hybridization of Neural networks with the harmony Search Algorithm (HSA). The hybrid approach achieves a better optimal solution in a feasible time duration in the faulty environment to improve the task failure and improve reliability. The harmony Search approach is inspired from the music improvisation technique, where notes are adjusted until perfect harmony is matched. HS (Harmony search) is chosen, as it is capable to provide an optimal solution in a feasible time, even for complex optimization problems. An ANN-HS model is introduced to achieve optimal resource allocation. The presented model is inspired by Harmony Search and ANN. The proposed model considers multi-objective criteria. The performance criteria include execution time, task failure count and power consumption(Kwh).

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