Joint resource allocation and offloading strategies in cloud enabled cellular networks

Abstract

The numerous features installed in recent mobile phones opened the door to a wide range of applications involving localization, storage, photo and video taking and communication. A significant number of applications involve user generated content and require intensive processing which limits dramatically the battery lifetime of featured mobile terminals. Mobile cloud computing has been recently proposed as a promising solution allowing the mobile users to run computing-intensive and energy parsimonious applications. This new feature requires new functionalities inside the cellular network architecture and needs appropriate resource allocation strategies which account for computation and communication in the same time. In this paper we present promising options to upgrade 4G architecture to support these new features. We also present two resource allocation strategies accounting for both computation and radio resources. These strategies are devised so that to minimize the energy consumption of the mobile terminals while satisfying predefined delay constraints. We compare online learning based solutions where the network adapts dynamically to the application that is run on mobile terminals, and pre-calculated offline solutions which are employed when a certain level of knowledge about the application and the channel conditions is available at the network side. We show, that even with imperfect knowledge about the application, pre-calculated offline strategies offer better performance in terms of energy consumption of mobile terminals.IEEE ICC 2015 -Next Generation Networking Symposium 978-1-4673-6432-4/15/$31.00 ©2015 IEEE

5G-destekli Mobil Uç Hesaplama Çerçevesi

“…MEC sisteminde offloading veri iletimi yukarda bahsedildiği gibi iki şekilde yapılmaktadır. Çalışma [3]'te ise UE (User Equipment) için uygun bir yürütme gecikmesinde, enerji tüketimini minimize etmek amacıyla bir optimizasyon problemi formüle edilmiştir. Bu optimizasyon problemi Markov chain process min-max aralığında formüle edilmiştir.…”

Joint Uplink/Downlink Optimization for Backhaul-Limited Mobile Cloud Computing With User Scheduling

“…Lastly, to obtain an inner convexification for the energy constraint C.2 that satisfies the conditions B1-B6 in Sec. III-B, we utilize the concave-convex structure of the rate function r ul in Q ul as in (18), to rewrite constraint C.2 as…”

“…where r ul in + Q ul and r ul in − Q ul −in are given in (18). Using the linearization (19), we then obtain the desired upper bound on C.2 as…”

Energy-optimal resource scheduling and computation offloading in small cell networks

“…The online approach is based on learning from past to find a policy that can cope with unknown environment [9]. In [12], we have studied the online learning approach for our problem and have shown that it is suboptimal in terms of energy consumption due to its imperfect knowledge on the data arrival and the channel state distributions. In this paper, we investigate the offline approach that requires a priori knowledge of the channel statistics and the application properties.…”