Wide acceptance of mobile phones and their resource hungry applications have highlighted resource limitations of mobile devices. In this regard, cloud computing has provided mobile phones with unlimited resources in order to help them overcome their constraints and enable them to support wider range of applications; so, mobile devices can outsource their tasks to public or local clouds. To accommodate to exponential growth of requests, user requests should be distributed to different cloudlets and then transparently and dynamically redirected to the servers according to the latest network and server status. Therefore, finding the best place to off-load is vital and crucial to both functionality and performance of the system. However, accurate and timely parameters of network and servers’ status are improbable to achieve, so the traditional algorithms cannot perform effectively and fully efficient. As a solution in this paper, an adaptive neuro-fuzzy inference system is proposed and trained to assign tasks to the servers efficiently. The trained system is robust to imprecise context information and is tolerable measurement noise and errors. We have considered improving both system performance and user quality of service parameters in this paper. Simulation results demonstrate that, compared with other server selection schemes, the proposed scheme can achieve higher resource utilization (utilization is a percentage of time that a server is busy doing something), provide better user-perceived quality of service, and efficiently deal with network dynamics. Simulation results show that our proposed algorithm excels over the compared works in terms of performance, at the best case about 30% and at the worst case about 8.93%.
The Journal of Supercomputing – Springer Journals
Published: Feb 20, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera