关键词： Mobile Edge Computing   Task assignment   User mobility   Offloading   COMPUTATION  

摘要： Mobile Edge Computing (MEC) has emerged as a prospective computing paradigm to provide pervasive computing and storage services for mobile and big data applications. In MEC, many small cell base stations (sBSs) are deployed to establish a mobile edge network (MEN). These sBSs can be usually accessed directly by mobile users. The computational tasks are first offloaded from mobile users to the MEN and then executed in one or several specific sBSs in the MEN. While the offloading decision has been well studied, the task execution delay on the MEN side is overlooked. This paper aims at reducing the task execution delay by task scheduling in MENs. Specifically, we jointly consider the task properties, the user mobility and network constraints. The problem is formalized as a constraint satisfaction problem and a lightweight heuristic solution is proposed for fast scheduling. We conduct simulation experiments to study the performance of the proposed work. The results show that our work is able to significantly reduce the task execution delay in MENs and thus reduces the end-to-end delay for MEC tasks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： HTTP live streaming   mobile edge computing   network function virtualization   quality of experience   video caching   QUALITY   ADAPTATION   DELIVERY  

摘要： HTTP-based live streaming has become increasingly popular in recent years, and more users have started generating 4K live streams from their devices (e.g., mobile phones) through social-media service providers like Facebook or YouTube. If the audience is located far from a live stream source across the global Internet, TCP throughput becomes substantially suboptimal due to slow start and congestion control mechanisms. This is especially the case when the end-to-end content delivery path involves radio access network at the last mile. As a result, the data rate perceived by a mobile receiver may not meet the high requirement of 4K video streams, which causes deteriorated quality-of-experience (QoE). In this paper, we propose a scheme named edge-based transient holding of live segment (ETHLE), which addresses the abovementioned issue by performing context-aware transient holding of video segments at the mobile edge with virtualized content caching capability. Through holding the minimum number of live video segments at the mobile edge cache in a context-aware manner, the ETHLE scheme is able to achieve seamless 4K live streaming experiences across the global Internet by eliminating buffering and substantially reducing initial startup delay and live stream latency. It has been deployed as a virtual network function at an LTE-A network, and its performance has been evaluated using real live stream sources that are distributed around the world. The significance of this paper is that by leveraging virtualized caching resources at the mobile edge, we address the conventional transport-layer bottleneck and enable QoE-assured Internet-wide live streaming services with high data rate requirements.

关键词： Mobile edge computing   RAN slicing   two-level problem   information prediction  

摘要： In 5G networks, mobile edge computing (MEC) and radio access network (RAN) slicing can support the evolving services and satisfy the diverse service requirements efficiently, thereby, being promising technologies. In this letter, the work is focused on the RAN slicing between MEC services and traditional services. An information prediction and dynamic programming based RAN slicing algorithm is proposed. It guarantees the inter-slice isolation and realizes the intra-slice customization. Furthermore, it can optimize the network utility with high fairness in polynomial time complexity.

关键词： Mobile edge computing   computation offloading   radio access networks   parallel computing   multiple access   stochastic geometry   queueing   STOCHASTIC GEOMETRY   SPECTRAL EFFICIENCY   CELLULAR NETWORKS   RESOURCE   SYSTEMS   COMMUNICATION   DESIGN   DELAY   TIER  

摘要： Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), an MEC network can be decomposed as a radio access network cascaded with a CS network. Based on the architecture, we investigate network-constrained latency performance, namely communication latency and computation latency, under the constraints of radio-access connectivity and CS stability. To this end, a spatial random network is modeled featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Given the model and the said network constraints, we derive the scaling laws of communication latency and computation latency with respect to network-load parameters (density of mobiles and their task-generation rates) and network-resource parameters (bandwidth, density of APs/CSs, and CS computation rate). Essentially, the analysis involves the interplay of the theories of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latencies, network connectivity, and network stability. The results provide useful guidelines for MEC-network provisioning and planning by avoiding either of the cascaded radio access network or CS network being a performance bottleneck.

关键词： Auction   economic model   mobile edge computing   resource allocation   HETEROGENEOUS CELLULAR NETWORKS   STOCHASTIC GEOMETRY   RESOURCE-ALLOCATION   WIRELESS NETWORKS   SCHEME   INTERFERENCE   CHALLENGES   FRAMEWORK   STATION  

摘要： Energy harvesting techniques extend the lifetime of Internet of Things (IoT), whereas yield an unprecedented paradigm shift in network design. Base stations (BSs), being powered by self-contained energy harvesting modules, may keep OFF during recharging, leading to super-frequent handovers for nodes and high network dynamics. Under such dynamics, multiple cooperating BSs for the service of a "smart thing," referred to as coordinated multipoint (CoMP), become a feasible solution by effectively improving communication reliability. To the best of knowledge, this is the first work to consider CoMP uplink transmission to alleviate outage caused by energy harvesting of BSs in IoT. A simple CoMP uplink transmission scheme is proposed for each node of IoT to transmit to two cooperating BSs in a K-tier heterogeneous network. The performance of the proposed scheme is analyzed using tools of stochastic geometry, in terms of availability and average rate, considering the effect of energy capacity, energy charging rate, in a K-tier heterogeneous network. Performance evaluation through extensive simulations are conducted and it is shown that the simulation results fit well with the analytical ones. The performance of the proposed scheme is then compared with that of the non-CoMP scheme, and it is found that the proposed scheme can significantly improve availability and average rate.

关键词： 5G   caching   mobile edge computing   transfer learning   WIRELESS   DELIVERY   SCHEME  

摘要： To address the vast multimedia traffic volume and requirements of user quality of experience in the next-generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named learning-based cooperative caching (LECC) strategy based on mobile edge computing architecture to reduce transmission cost while improving user quality of experience for future mobile networks. In LECC, we exploit a transfer learning-based approach for estimating content popularity and then formulate the proactive caching optimization model. As the optimization problem is NP-hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate and decrease content delivery latency and transmission cost in comparison with known existing caching strategies.

关键词： Mobile edge computing   virtual machine replicacopy   multiple applications   average response time   normalized total cost   WIRELESS CELLULAR NETWORKS   RESOURCE-ALLOCATION   MANAGEMENT  

摘要： Although mobile edge computing (MEC), as an extension of the cloud computing paradigm to edge networks, overcomes some obstacles of traditional mobile cloud computing, i.e., the reduced response time particularly, it is a nontrivial task to efficiently deploy virtual machine replica copies (VRCs) supporting multiple applications among numerous MEC servers in edge networks. To combat this issue, we are motivated to investigate in detail the optimal placement of VRCs to minimize the average response time (MART) in the MEC architecture with various requests demand among multiple applications and capacity constraints of MEC servers in edge networks. Besides optimal enumeration placement algorithm (OEPA) as benchmark, we design latency aware heuristic placement algorithm (LAHPA) with much lower computation complexity than that of OEPA. To enhance the performance of LAHPA on MART, clustering enhanced heuristic placement algorithm (CEHPA) is proposed, focusing on the optimal VRC placement in each cluster. We also develop substitution enhanced heuristic placement algorithm (SEHPA) to avoid falling into local optimal solutions. As corroborated by extensive simulation results, the performance of SEHPA on MART is very close to that of OEPA compared with LAHPA and CEHPA. Note that. CEHPA also outperforms LAHPA, and both are better than a general greedy placement algorithm. Furthermore, we evaluate the normalized total cost for services provision in edge networks, where SEHPA can also get more outstanding results than other algorithms.

关键词： mobile edge computing   multi-access edge computing   mobile edge computing architecture   hierarchical mobile cloud computing architecture   NETWORKS  

摘要： Due to the recent developments in mobile network technology and the supply of mobile devices, services that require high computing power and fast access speed, such as machine learning and multimedia streaming, are attracting attention. Mobile Edge Computing (MEC) has emerged. MEC allows servers to be located close to users to efficiently handle these services and provides users with ultra-low latency content delivery and powerful computing services. However, there has been a lack of research into the architecture required to efficiently use the computing power and resources of MEC. So, this paper proposes hierarchical MEC architecture in which MEC servers (MECS) are arranged in a hierarchical scheme to provide users with rapid content delivery, high computing performance, and efficient use of server resources.

关键词： Internet of Things (IoT)   Mobile edge computing   Cloudlets   Fog computing  

摘要： With the rapid development of mobile internet and Internet of Things applications, the conventional centralized cloud computing is encountering severe challenges, such as high latency, low Spectral Efficiency (SE), and non-adaptive machine type of communication. Motivated to solve these challenges, a new technology is driving a trend that shifts the function of centralized cloud computing to edge devices of networks. Several edge computing technologies originating from different backgrounds to decrease latency, improve SE, and support the massive machine type of communication have been emerging. This paper comprehensively presents a tutorial on three typical edge computing technologies, namely mobile edge computing, cloudlets, and fog computing. In particular, the standardization efforts, principles, architectures, and applications of these three technologies are summarized and compared. From the viewpoint of radio access network, the differences between mobile edge computing and fog computing are highlighted, and the characteristics of fog computing-based radio access network are discussed. Finally, open issues and future research directions are identified as well.

关键词： Mobile edge computing   multi-access edge computing   fog computing   network virtualization   dimensioning   clustering  

摘要： Mobile edge computing (MEC) is an emerging technology that aims at pushing applications and content close to the users (e.g., at base stations, access points, and aggregation networks) to reduce latency, improve quality of experience, and ensure highly efficient network operation and service delivery. It principally relies on virtualization-enabled MEC servers with limited capacity at the edge of the network. One key issue is to dimension such systems in terms of server size, server number, and server operation area to meet MEC goals. In this paper, we formulate this problem as a mixed integer linear program. We then propose a graph-based algorithm that, taking into account a maximum MEC server capacity, provides a partition of MEC clusters, which consolidates as many communications as possible at the edge. We use a dataset of mobile communications to extensively evaluate them with real world spatio-temporal human dynamics. In addition to quantifying macroscopic MEC benefits, the evaluation shows that our algorithm provides MEC area partitions that largely offload the core, thus pushing the load at the edge (e.g., with 10 small MEC servers between 55% and 64% of the traffic stay at the edge), and that are well balanced through time.