By Topic

Adaptive resource management in PaaS platform using Feedback Control LRU algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Rui Hu ; Res. Inst. of China Mobile, Beijing, China ; Yong Li ; Yan Zhang

Cloud computing gets more and more popular because of its abilities on offering flexible dynamic IT infrastructure, QoS (Quality of Service) guaranteed computing environments and configurable software services. Cloud computing supports three service models: SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service). PaaS provides users with an application development and hosting platform with great reliability, scalability and convenience and it has many advantages in helping customers create applications compared with other service models. However, as a typical distributed system with limited computing resources, PaaS platform has to address the problems of resource management in order to achieve satisfactory QoS as well as efficient resource utilization. This paper presents an adaptive resource management algorithm called Feedback Control LRU (FC-LRU) which integrates the feedback control technique with LRU (Least Recently Used) algorithm. Simulation is conducted to evaluate the performance of FC-LRU and the results demonstrate that FC-LRU achieves satisfactory performance: it enables PaaS platform to maintain a low missed deadline ratio and high CPU utilization under the conditions of multitasks with different workloads.

Published in:

Cloud and Service Computing (CSC), 2011 International Conference on

Date of Conference:

12-14 Dec. 2011