Mobile Edge Computing”, IEEE [5] C

Mobile Edge Computing: A Survey on Architecture and Computation Offloading Pavel Mach, IEEE Member, Zdenek Becvar, IEEE Member Abstract—Technological evolution of mobile user equipments a mobile operator and the Internet. The MCC brings several (UEs), such as smartphones or laptops, goes hand-in-hand with advantages [2]; 1) extending battery lifetime by offloading evolution of new mobile applications. However, running compu- energy consuming computations of the applications to the tationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. cloud, 2) enabling sophisticated applications to the mobile Suitable solution extending the battery life-time of the UEs users, and 3) providing higher data storage capabilities to the is to offload the applications demanding huge processing to a users. Nevertheless, the MCC also imposes huge additional conventional centralized cloud (CC). Nevertheless, this option load both on radio and backhaul of mobile networks and introduces significant execution delay consisting in delivery of introduces high latency since data is sent to powerful farm the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and of servers that are, in terms of network topology, far away make the offloading unsuitable for real-time applications. To from the users. cope with the delay problem, a new emerging concept, known as To address the problem of a long latency, the cloud services mobile edge computing (MEC), has been introduced. The MEC should be moved to a proximity of the UEs, i.e., to the brings computation and storage resources to the edge of mobile edge of mobile network as considered in newly emerged network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC edge computing paradigm. The edge computing can be un- computing resources can be exploited also by operators and third derstood as a specific case of the MCC. Nevertheless, in parties for specific purposes. In this paper, we first describe the conventional MCC, the cloud services are accessed via major use cases and reference scenarios where the MEC is the Internet connection [3] while in the case of the edge applicable. After that we survey existing concepts integrating computing, the computing/storage resources are supposed to MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this be in proximity of the UEs (in sense of network topology). survey is, then, focused on user-oriented use case in the MEC, Hence, the MEC can offer significantly lower latencies and i.e., computation offloading. In this regard, we divide the research jitter when compared to the MCC. Moreover, while the MCC on computation offloading to three key areas: i) decision on is fully centralized approach with farms of computers usually computation offloading, ii) allocation of computing resource placed at one or few locations, the edge computing is supposed within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss to be deployed in fully distributed manner. On the other hand, open research challenges yet to be addressed in order to fully the edge computing provides only limited computational and enjoy potentials offered by the MEC. storage resources with respect to the MCC. A high level comparison of key technical aspects of the MCC and the edge computing is outlined in Table I. I. INTRODUCTION The first edge computing concept bringing the computa- The users’ requirements on data rates and quality of service tion/storage closer to the UEs, proposed in 2009, is cloudlet (QoS) are exponentially increasing. Moreover, technologi- [4]. The idea behind the cloudlet is to place computers with cal evolution of smartphones, laptops and tablets enables high computation power at strategic locations in order to to emerge new high demanding services and applications. provide both computation resources and storage for the UEs Although new mobile devices are more and more powerful in vicinity. The cloudlet concept of the computing ”hotspots” in terms of central processing unit (CPU), even these may not is similar to WiFi hotspots scenario, but instead of Internet be able to handle the applications requiring huge processing arXiv:1702.05309v2 [cs.IT] 13 Mar 2017 connectivity the cloudlet enables cloud services to the mobile in a short time. Moreover, high battery consumption still users. The fact that cloudlets are supposed to be mostly poses a significant obstacle restricting the users to fully enjoy accessed by the mobile UEs through WiFi connection is seen highly demanding applications on their own devices. This motivates development of mobile cloud computing (MCC) concept allowing cloud computing for mobile users [1]. In TABLE I: High level comparison of MCC and Edge comput- the MCC, a user equipment (UE) may exploit computing ing concepts and storage resources of powerful distant centralized clouds Technical aspect MCC Edge computing (CC), which are accessible through a core network (CN) of Deployment Centralized Distributed This work has been supported by the grant of Czech Technical University Distance to the UE High Low in Prague No. SGS17/184/OHK3/3T/13. Latency High Low The authors are with the Department of Telecommunication Engi- Jitter High Low neering, Faculty of Electrical Engineering, Czech Technical University Computational power Ample Limited in Prague, Prague, 166 27 Czech Republic (email:[email protected]; [email protected]). Storage capacity Ample Limited as a disadvantage since the UEs have to switch between Telecommunications Standards Institute (ETSI) [24]. The so- the mobile network and WiFi whenever the cloudlet services lution outlined by ETSI is known as Mobile Edge Com- are exploited [2]. Moreover, QoS (Quality of Service) of the puting (MEC). The standardization efforts relating the MEC mobile UEs is hard to fulfill similarly as in case of the MCC, are driven by prominent mobile operators (e.g., DOCOMO, since the cloudlets are not an inherent part of the mobile Vodafone, TELECOM Italia) and manufactures (e.g., IBM, network and coverage of WiFi is only local with limited Nokia, Huawei, Intel). The main purpose of ISG MEC group support of mobility. is to enable an efficient and seamless integration of the cloud The other option enabling cloud computing at the edge computing functionalities into the mobile network, and to help is to perform computing directly at the UEs through ad- developing favorable conditions for all stakeholders (mobile hoc cloud allowing several UEs in proximity to combine operators, service providers, vendors, and users). their computation power and, thus, process high demanding Several surveys on cloud computing have been published applications locally [5]-[14]. To facilitate the ad-hoc cloud, so far. In [3], the authors survey MCC application models several critical challenges need to be addressed; 1) finding and highlight their advantages and shortcomings. In [25], proper computing UEs in proximity while guaranteeing that a problem of a heterogeneity in the MCC is tackled. The processed data will be delivered back to the source UE, 2) heterogeneity is understood as a variability of mobile de- coordination among the computing UEs has to be enabled vices, different cloud vendors providing different services, despite the fact that there are no control channels to facil- infrastructures, platforms, and various communication medium itate reliable computing, 3) the computing UEs has to be and technologies. The paper identifies how this heterogeneity motivated to provide their computing power to other devices impacts the MCC and discusses related challenges. The au- given the battery consumption and additional data transmission thors in [26] survey existing efforts on Cloud Mobile Media, constraints, 4) security and privacy issues. which provides rich multimedia services over the Internet and A more general concept of the edge computing, when mobile wireless networks. All above-mentioned papers focus, compared to cloudlets and ad-hoc clouds, is known as a fog in general, on the MCC where the cloud is not allocated computing. The fog computing paradigm (shortly often ab- specifically at the edge of mobile network, but it is accessed breviated as Fog in literature) has been introduced in 2012 by through the Internet. Due to a wide potential of the MEC, Cisco to enable a processing of the applications on billions of there is a lot of effort both in industry and academia focusing connected devices at the edge of network [15]. Consequently, on the MEC in particular. Despite this fact, there is just one the fog computing may be considered as one of key enablers survey focusing primarily on the MEC [27] that, however, of Internet of Things (IoT) and big data applications [16] as it only briefly surveys several research works dealing with the offers: 1) low latency and location awareness due to proximity MEC and presents taxonomy of the MEC by describing of the computing devices to the edge of the network, 2) wide- key attributes. Furthermore, the authors in [28] extensively spread geographical distribution when compared to the CC; 3) surveys security issues for various edge computing concepts. interconnection of very large number of nodes (e.g., wireless On top of that, the authors in [29] dedicate one chapter to the sensors), and 4) support of streaming and real time applica- edge computing, where applications of economic and pricing tions [15]. Moreover, the characteristics of the fog computing models are considered for resource management in the edge can be exploited in many other applications and scenarios such computing. as smart grids, connected vehicles for Intelligent Transport In contrast to the above-mentioned surveys, we describe Systems (ITS) or wireless sensor networks [17]-[20]. key use cases and scenarios for the MEC (Section II).

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