Article

A Survey and Taxonomy of Cyber Foraging of Mobile Devices

IEEE Communications Surveys &amp Tutorials (Impact Factor: 6.49). 01/2012; 14(4):1232-1243. DOI: 10.1109/SURV.2011.111411.00016

ABSTRACT With the ever-increasing advancement of mobile device technology and their pervasive usage, users expect to run their applications on mobile devices and get the same performance as if they used to run their applications on powerful non-mobile computers.
There is a challenge though in that mobile devices deliver lower performance than
traditional less-constrained and non-mobile computers because they are constrained by
weight, size, and mobility in spite of all their advancements in recent years. One of the ...

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    ABSTRACT: This paper presents a quantitative study on the energy-traffic tradeoff problem from the perspective of entire Wireless Local Area Network (WLAN). We propose a novel Energy-Efficient Cooperative Offloading Model (E2COM) for energy-traffic tradeoff, which can ensure the fairness of energy consumption of mobile devices and reduce the computation repetition and eliminate the Internet data traffic redundancy through cooperative execution and sharing computation results. We design an Online Task Scheduling Algorithm (OTS) based on a pricing mechanism and Lyapunov optimization to address the problem without predicting future information on task arrivals, transmission rates and so on. OTS can achieve a desirable trade-off between the energy consumption and Internet data traffic by appropriately setting the tradeoff coefficient. Simulation results demonstrate that E2COM is more efficient than no offloading and cloud offloading for a variety of typical mobile devices, applications and link qualities in WLAN. I. INTRODUCTION Mobile devices (e.g. smart phones) have become increasingly popular in our daily lives, whereas the capacity of mobile devices is severely constrained by the restricted battery power. An efficient way to reduce the computation overhead is to offload computing tasks to powerful machines or to the cloud. Mobile devices can save energy and reduce execution delay of applications through offloading tasks to the cloud. Several solutions have been proposed for computation offloading, such as MAUI [1], Clonecloud [2], SmartDiet [3]. However, these research efforts of offloading technology mainly focus on optimizing energy consumption of a single device. On the other hand, though the coverage ratio of 3G networks is much higher than WiFi networks [4], there exist some challenges for offloading tasks to remote cloud through 3G [5]. For example, 3G provides Internet services with lower bandwidth, higher communication latency and higher energy consumption compared with WLAN [6]. Moreover, the growth rate of 3G network capacity cannot catch up with the demand of mobile Internet data traffic [7]. WLAN is considered as a solution to ease the traffic pressure on 3G. However, many mobile communications access the Internet through the same Access Controller (AC) or Access Points (AP) simultaneously , which causes serious congestion and lower available
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    ABSTRACT: Current estimates of mobile data traffic in the years to come foresee a 1,000 increase of mobile data traffic in 2020 with respect to 2010, or, equivalently, a doubling of mobile data traffic every year. This unprecedented growth demands a significant increase of wireless network capacity. Even if the current evolution of fourth-generation (4G) systems and, in particular, the advancements of the long-term evolution (LTE) standardization process foresees a significant capacity improvement with respect to third-generation (3G) systems, the European Telecommunications Standards Institute (ETSI) has established a roadmap toward the fifth-generation (5G) system, with the aim of deploying a commercial system by the year 2020 [1]. The European Project named ?Mobile and Wireless Communications Enablers for the 2020 Information Society? (METIS), launched in 2012, represents one of the first international and large-scale research projects on fifth generation (5G) [2]. In parallel with this unparalleled growth of data traffic, our everyday life experience shows an increasing habit to run a plethora of applications specifically devised for mobile devices, (smartphones, tablets, laptops)for entertainment, health care, business, social networking, traveling, news, etc. However, the spectacular growth in wireless traffic generated by this lifestyle is not matched with a parallel improvement on mobile handsets? batteries, whose lifetime is not improving at the same pace [3]. This determines a widening gap between the energy required to run sophisticated applications and the energy available on the mobile handset. A possible way to overcome this obstacle is to enable the mobile devices, whenever possible and convenient, to offload their most energy-consuming tasks to nearby fixed servers. This strategy has been studied for a long time and is reported in the literature under different names, such as cyberforaging [4] or computation offloading [5], [6]. In recent years, a strong impu- se to computation offloading has come through cloud computing (CC), which enables the users to utilize resources on demand. The resources made available by a cloud service provider are: 1) infrastructures, such as network devices, storage, servers, etc., 2) platforms, such as operating systems, offering an integrated environment for developing and testing custom applications, and 3) software, in the form of application programs. These three kinds of services are labeled, respectively, as infrastructure as a service, platform as a service, and software as a service. In particular, one of the key features of CC is virtualization, which makes it possible to run multiple operating systems and multiple applications over the same machine (or set of machines), while guaranteeing isolation and protection of the programs and their data. Through virtualization, the number of virtual machines (VMs) can scale on ?demand, thus improving the overall system computational efficiency. Mobile CC (MCC) is a specific case of CC where the user accesses the cloud services through a mobile handset [5]. The major limitations of today?s MCC are the energy consumption associated to the radio access and the latency experienced in reaching the cloud provider through a wide area network (WAN). Mobile users located at the edge of macrocellular networks are particularly disadvantaged in terms of power consumption and, furthermore, it is very difficult to control latency over a WAN. As pointed out in [7]?[9], humans are acutely sensitive to delay and jitter: as latency increases, interactive response suffers. Since the interaction times foreseen in 5G systems, in particular in the so-called tactile Internet [10], are quite small (in the order of milliseconds), a strict latency control must be somehow incorporated in near future MCC. Meeting this constraint requires a deep ?rethinking of the overall service chain, from the physical layer up to virtualization.
    IEEE Signal Processing Magazine 01/2014; 31(6):45-55. DOI:10.1109/MSP.2014.2334709 · 4.48 Impact Factor
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    IEEE Mobile Cloud 2015, San Francisco, USA; 01/2015

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