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PARCEL: Proxy Assisted BRowsing in Cellular networks for Energy and Latency reduction
"... Today’s web page download process is ill suited to cellu-lar networks resulting in high latencies and radio energy us-age. While there have been notable prior attempts at tackling the challenge with assistance from proxies (cloud), achiev-ing a responsive and energy efficient browsing experience rem ..."
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Today’s web page download process is ill suited to cellu-lar networks resulting in high latencies and radio energy us-age. While there have been notable prior attempts at tackling the challenge with assistance from proxies (cloud), achiev-ing a responsive and energy efficient browsing experience remains an elusive goal. In this paper, we make a fresh at-tempt at addressing the challenge by proposing PARCEL. PARCEL splits functionality between the mobile device and the proxy based on their strengths, and in a manner distinct from both traditional browsers and existing cloud-heavy ap-proaches. We conduct extensive evaluations over an oper-ational LTE network using a prototype implementation of PARCEL. Our results show that PARCEL reduces page load times by 49.6%, and radio energy consumption by 65% compared to traditional mobile web browsers. Further, our results showPARCEL continues to performwell under client interactions, owing to its judicious functionality split. 1
PARCEL: Proxy Assisted BRowsing in Cellular networks for Energy and Latency reduction
"... Today’s web page download process is ill suited to cellular networks resulting in high page load times and radio energy usage. While there have been notable prior attempts at tackling the challenge with assistance from proxies (cloud), achieving a responsive and energy efficient browsing expe-rience ..."
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Today’s web page download process is ill suited to cellular networks resulting in high page load times and radio energy usage. While there have been notable prior attempts at tackling the challenge with assistance from proxies (cloud), achieving a responsive and energy efficient browsing expe-rience remains an elusive goal. In this paper, we make a fresh attempt at addressing the challenge by proposing PAR-CEL. PARCEL splits functionality between the mobile de-vice and the proxy based on their respective strengths, and in a manner distinct from both traditional browsers and ex-isting cloud-heavy approaches. We conduct extensive eval-uations over an operational LTE network using a prototype implementation of PARCEL. Our results show that PAR-CEL reduces page load times by 49.6%, and radio energy consumption by 65 % compared to traditional mobile web browsers. Further, our results show that PARCEL contin-ues to perform well under client interactions, owing to its judicious functionality split.
Computation Offloading: Overview, Frameworks and Challenges
"... In spite of major advances in a mobile cloud computing, applications are still very limited due to poor computation processing power, memory and limited battery life of a mobile device. Recently, offloading computation intensive task to the cloud has become a great solution to improve both battery l ..."
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In spite of major advances in a mobile cloud computing, applications are still very limited due to poor computation processing power, memory and limited battery life of a mobile device. Recently, offloading computation intensive task to the cloud has become a great solution to improve both battery life and performance of mobile devices. Offloading makes use of quality of services concept to remove mobile application limitations. This paper covers overview, frameworks and challenges of computation offloading. General Terms Overview, process, frameworks and challenges.
Research Article Flexible Framework for Real-Time Embedded Systems Based on Mobile Cloud Computing Paradigm
, 2015
"... License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The development of applications as well as the services for mobile systems faces a varied range of devices with very heterogeneous capabilities whose response times ar ..."
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License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The development of applications as well as the services for mobile systems faces a varied range of devices with very heterogeneous capabilities whose response times are difficult to predict. The research described in this work aims to respond to this issue by developing a computational model that formalizes the problem and that defines adjusting computing methods. The described proposal combines imprecise computing strategies with cloud computing paradigms in order to provide flexible implementation frameworks for embedded or mobile devices. As a result, the imprecise computation scheduling method on the workload of the embedded system is the solution to move computing to the cloud according to the priority and response time of the tasks to be executed and hereby be able to meet productivity and quality of desired services. A technique to estimate network delays and to schedule more accurately tasks is illustrated in this paper. An application example in which this technique is experimented in running contexts with heterogeneous work loading for checking the validity of the proposed model is described. 1.
research.iiit.ac.in
, 2013
"... The availability of increasingly richer applications is provid-ing surprisingly wide range of functionalities and new use cases on mobile devices. Even tough mobile devices are be-coming increasingly more powerful, the resource utilization of richer application can overwhelm resources on these de-vi ..."
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The availability of increasingly richer applications is provid-ing surprisingly wide range of functionalities and new use cases on mobile devices. Even tough mobile devices are be-coming increasingly more powerful, the resource utilization of richer application can overwhelm resources on these de-vices. At the same time, ubiquitous connectivity of mobile devices also opens up the possibility of leveraging cloud re-sources. Seamless and flexible path to mobile cloud comput-ing requires recognizing opportunities where the application execution on cloud instead of mobile device. In this paper we propose a cloud aware scheduler for application offloading from mobile devices to clouds. We used learning based algo-rithm for predicting the gain attainable using performance monitoring and high level features. We evaluated prototype of our system on various workloads and under various con-ditions.
