1、 NANCHANG UNIVERSITY课程名称: 学术英语 题 目: A Study of Energy Efficient _Cloud Computing Powered by_Wireless Energy Transfer _英语班级: 理工 1615 班 专业/年级: 物联网工程 161 班 姓名/学号: (47) 二零一八年六月A Study of Energy Efficient Cloud Computing Powered by Wireless Energy Transfer1A Study of Energy Efcient Mobile Cloud Computing
2、 Powered by Wireless Energy TransferAbstractAchieving long battery lives or even self-sustainability has been a long standing challenge for designing mobile devices. This study presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer
3、(MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute
4、 given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CP
5、U-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability, under the energy harvesting and deadline constraints. Furthermore, this study reveals that the two simple solutions t
6、o achieve the object to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, the two kinds of modes suggest that the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control. And the futur
7、e aspect to study is simply to be answer. Key words: wireless power transfer; energy harvesting communications; mobile cloud computing; energy efficient computingA Study of Energy Efficient Cloud Computing Powered by Wireless Energy Transfer1A Study of Energy Efficient Cloud Computing Powered by Wir
8、eless Energy Transfer2Introduction Mobile cloud computing (MCC) as an emerging computing paradigm integrates cloud computing and mobile computing to enhance the computation performance of mobile devices. The objective of MCC is to extend powerful computing capability of the resource-rich clouds to t
9、he resource-constrained mobile devices (e.g., laptop, tablet and smartphone) so as to reduce computation time, conserve local resources, especially battery, and extend storage capacity. To achieve this objective, MCC needs to transfer resource-intensive computations from mobile devices to clouds, re
10、ferred to as computation offloading. The core of computation offloading is to decide on which computation tasks should be executed on the mobile device or on the cloud, and how to schedule local and cloud resource to implement task offloading. The explosive growth of Internet of Things (IOT) and mob
11、ile communication is leading to the deployment of tens of billions of cloud-based mobile sensors and wearable computing devices in near future (Huang & Chae, 2010). Prolonging their battery lives and enhancing their computing capabilities are two key design challenges. They can be tackled by two pro
12、mising technologies: microwave power transfer (MPT) for powering the mobiles computation-intensive tasks from the mobiles to the cloud and mobile computation offloading (MCO). Two technologies are seamlessly integrated in the current work to develop a novel design framework for realizing wirelessly
13、powered mobile cloud computing under the criterion of maximizing the probability of successfully computing given data, called computing probability. The framework is feasible since MPT has been proven in various experiments for powering small devices such as sensors or even small-scale airplanes and
14、 helicopters. Furthermore, sensors and wearable computing devices targeted in the framework are expected to be connected by the cloud- based IOT in the future, providing a suitable platform for realizing MCO.Materials MCO has been an active research area in computer science where research has focuse
15、d on designing mobile-cloud systems and software architectures, virtual machine migration design in the cloud and code partitioning techniques in the mobiles for reducing the energy consumption and improving the computing performance of mobiles. Nevertheless, implementation of MCO requires data tran
16、smission and message passing over wireless channels, incurring transmission power consumption. The existence of such a tradeoff has motivated cross-disciplinary research on jointly designing MCO and adaptive transmission algorithms to maximize the mobile energy savings. A stochastic control algorith
17、m was A Study of Energy Efficient Cloud Computing Powered by Wireless Energy Transfer2proposed for adapting the offloaded components of an application to a time-varying wireless channel. Furthermore, multiuser computation offloading in a multi-cell system was explored by Shinohara (2014), where the
18、radio and computational resources were jointly allocated for maximizing the energy savings under the latency constraints.According to Swan (2012), the threshold-based offloading policy was derived for the system with intermittent connectivity between the mobile and cloud. Lastly, the CPU-cycle frequ
19、encies are jointly controlled with MCO given a more skilled and increasingly appropriateA Study of Energy Efficient Cloud Computing Powered by Wireless Energy Transfer3wireless channel. The framework is further developed in the current work to include the new feature of MPT (Kosta et al., 2012). Thi
20、s introduces several new design challenges. Among others, the algorithmic design of local computing and offloading becomes more complex under the energy harvesting constraint due to MPT, which prevents energy consumption from exceeding the amount of harvested energy at every time instant. Another ch
21、allenge is that MPT and offloading time share the mobile antenna and the time division has to be optimized.Now the technology is being further developed to power wireless communications. This has resulted in the emergence of an active field called simultaneous wireless information and power transfer
22、 (SWIPT). The MPT technology has been developed for point-to-point high power transmission in the past decades (Brown, 1984). Furthermore, existing wireless networks such as cognitive radio and cellular networks have been redesigned to feature MPT. Most prior work on SWIPT aims at optimizing communi
23、cation techniques to maximize the MPT efficiency and system throughput. In contrast, the current work focuses on optimizing the local computing and offloading under a different design criterion of maximum computing probability (Huang & Lau, 2014).Methods and ResultsConsider a single-user system comp
24、rising one multi-antenna base station (BS) using transmit/receive beamforming for transferring power to a single-antenna mobile or relaying offloaded data from the mobile to the cloud. To compute a fixed amount of data, the mobile operates in one of the two available modes:Local computing and offloa
25、ding: in the mode of local computing, MPT occurs simultaneously as computing based on the controllable CPU-cycle frequencies. Nevertheless, in the mode of offloading, the given computation duration is adaptively partitioned for separate MPT and offloading since they share the mobile antenna (Shinoha
26、ra, 2014). Assume that the mobile has the knowledge of statistics information of CPU cycles and channel state information (CSI). The individual modes as well as mode selection are optimized for maximizing the computing probability under the energy harvesting and deadline constraints. For tractabilit
27、y, the metric is transformed into equivalent ones, namely average mobile energy consumption and mobile energy savings, for the modes of local computing and offloading, respectively. Compared with the prior work, the current work integrates MPT with the mobile cloud computing, which introduces new th
28、eoretical challenges. In particular, the energy A Study of Energy Efficient Cloud Computing Powered by Wireless Energy Transfer3harvesting constraint arising from MPT makes the optimization problem for local computing non-convex. To tackle the challenge, the convex relaxation technique is applied wi
29、thout compromising the optimality of the solution. It is shown in the sequel that the local computing policy is a special case of the current work where the transferred power is sufficiently high by Swan (2012). Furthermore, the case of dynamic channel for mobile cloud computing is explored. Approxi
30、mation methods are used for deriving the simple and close-to-optimal policies.Mobile mode selection: The above results are combined to select the mobile mode for maximizing the computing probability. Given feasible computing in both modes, the only oneA Study of Energy Efficient Cloud Computing Powe
31、red by Wireless Energy Transfer4yielding the larger energy savings is preferred and the selection criterion is derived in terms of thresholds on the BS transmission power as well as the deadline for computing (Huang et al., 2012).Optimal data allocation for a dynamic channel: Last, the above results
32、 are extended to the case of a dynamic channel, modeled as independent and identically distributed. block fading, and non-causal CSI at the mobile (acquired from e.g., channel prediction). The problem of optimizing an individual mobile mode (local computing or offloading) is formulated based on the
33、master-and-slave model using the same metric as the fixed-channel counterpart (Kumar & Liu, 2013). ConclusionWireless and mobile computing technologies provide more possibilities for accessing services conveniently. Mobile devices will be improved in terms of power, CPU, and storage. Mobile cloud co
34、mputing has emerged as a new paradigm and extension of cloud computing.By two kinds of available modes, we can purely know of the Energy Efcient Mobile Cloud Computing. Through my study for the Mobile Cloud Computing, we are here exposing two simple solutions to solve this problem. Although my resea
35、rch is pretty basic, it still benefit the process of the development for mobile cloud computing and how to make it energy efficient. We believe that exploring other alternatives, such as introducing a middleware based architecture using an optimizing offloading algorithm, could help better the avail
36、able frameworks and provide more efficient and more flexible solutions to the MCC users. We know that the kind of technology will play an increasing important role in our daily life in the future. By this study, we better know of the newest development in our science area. This work can be extended
37、to several interesting directions: First, full-duplex transmission can be implemented in the pro- posed system to support simultaneous MPT and computation offloading to improve the power transfer efficiency. Second, the current work focusing on a single-computing task can be generalized to the scena
38、rio of computing a multi-task program, which involves program partitioning and simultaneous local computing and offloading. Last, it is interesting to extend the current design for single-user mobile cloud computing system to the multiuser system that requires joint design of radio and computational
39、 resource allocation for mobile cloud computing.A Study of Energy Efficient Cloud Computing Powered by Wireless Energy Transfer4ReferencesBrown, W. (1984). The history of power transmission by radio waves. Technological Sanitation Engineering, 4. Huang, C. & Chae, H. (2010). “Energy efficient mobile
40、 cloud computing powered by wireless energy transfer (extended version)”, 2010 IEEE International Conference on Systems, Mobile Cloud Computing, 1. Huang, D., Wang, P. & Niyato, H. (2012). “A dynamic offloading algorithm for mobile computing”, 2014 IEEE Trans, Wireless Commun, 19911995.A Study of En
41、ergy Efficient Cloud Computing Powered by Wireless Energy Transfer5Huang, L. & Lau, Z. (2014). “Enabling wireless power transfer in cellular networks for science: Architecture, modeling and deployment”, 2010 IEEE International Conference on Systems, Mobile Cloud Computing, 2. Kosta, S., Aucinas, A.
42、& Hui, H. (2012). Think Air: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. Journal of Science, 4.Kumar, K. & Liu, L. (2013). A survey of computation offloading for mobile systems. Journal of Science, 5.Shinohara, N. (2014). Wireless Power Transfer Via Radio waves. Technological Sanitation Engineering, 4.Swan, M. (2012). Sensor mania! The Internet of Things, wearable computing, objective metrics, and the quantified self 2.0. Intelligence Science, 5.