Virtually all of the rapidly increasing data traffic consumed by mobile users requires some kind of processing, normally performed at cloud servers. A recent thrust, mobile edge computing , moves such processing to servers within the cellular mobile network. The large temporal and spatial variations to which mobile data usage is subject could make the reduced latency that edge clouds offer come at an unacceptable cost in redundant and underutilized infrastructure. We present some first empirical results on this question, based on large scale sampled crowd-sourced traces from several major cities spanning multiple operators and identifying the applications in use. We find opportunities to obtain both high server utilization and low application latency, but the best approaches will depend on the individual network operator's deployment strategy and geographic specifics of the cities we study.
Mobile-edge computing has been recently introduced @cite_11 as a way to move the cloud'', i.e., the servers processing mobile traffic, closer to users, thus reducing the latency and load of networks. Network Function Virtualization is widely regarded to as an enabling technology for MEC (see, e.g., @cite_12 ). Recent works have studied the radio techniques needed to enable MEC @cite_0 , its relationship to the Internet-of-things @cite_16 and context-aware, next-generation networks @cite_5 . Closer to our scenario, the authors of @cite_7 study how caches and servers should be placed in the network as its load changes over time. With regard to MEC and caching, a prominent application is mobile video streaming. As an example, @cite_6 @cite_14 account for layered video coding techniques, and address the problem of placing the right layers at the right cache -- with @cite_6 also accounting for cooperation between operators. Other works @cite_13 @cite_10 aim at foreseeing the content demand, in order to proactively populate caches or serve users.
@cite_14 @cite_7 @cite_10 @cite_6 @cite_0 @cite_5 @cite_16 @cite_13 @cite_12 @cite_11
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In this paper, we present a Mobile Edge Computing (MEC) scheme for enabling network edge-assisted video adaptation based on MPEG-DASH (Dynamic Adaptive Streaming over HTTP). In contrast to the traditional over-the-top (OTT) adaptation performed by DASH clients, the MEC server at the mobile network edge can capture radio access network (RAN) conditions through its intrinsic Radio Network Information Service (RNIS) function, and use the knowledge to provide guidance to clients so that they can perform more intelligent video adaptation. In order to support such MEC-assisted DASH video adaptation, the MEC server needs to locally cache the most popular content segments at the qualities that can be supported by the current network throughput. Towards this end, we introduce a two-dimensional user Quality-of-Experience (QoE)-driven algorithm for making caching replacement decisions based on both content context (e.g., segment popularity) and network context (e.g., RAN downlink throughput). We conducted experiments by deploying a prototype MEC server at a real LTE-A based network testbed. The results show that our QoE-driven algorithm is able to achieve significant improvement on user QoE over 2 benchmark schemes. Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective of minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method ; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an @math tradeoff with @math as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters. Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters. 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. Designing mobile edge computing (MEC) systems by jointly optimizing communication and computation resources, which can help increase mobile batteries' lifetime and improve quality of experience for computation-intensive and latency-sensitive applications, has received significant interest. In this paper, we consider energy-efficient resource allocation schemes for a multi-user mobile edge computing system with inelastic computation tasks and non-negligible task execution durations. First, we establish a mathematical model to characterize the offloading of a computation task from a mobile to the base station (BS) equipped with MEC servers. This computation-offloading model consists of three stages, i.e., task uploading, task executing, and computation result downloading, and allows parallel transmissions and executions for different tasks. Then, we formulate the weighted sum energy consumption minimization problem to optimally allocate the task operation sequence, the uploading and downloading time durations as well as the starting times for uploading, executing and downloading, which is a challenging mixed discrete- continuous optimization problem and is NP-hard in general. We propose a method to obtain an optimal solution and develop a low-complexity algorithm to obtain a suboptimal solution, by connecting the optimization problem to a three-stage flow-shop scheduling problem and utilizing Johnson's algorithm as well as convex optimization techniques. Finally, numerical results show that the proposed sub-optimal solution outperforms existing comparison schemes. 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, 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 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 evaluate it 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 around 55 of the traffic stay at the edge), and that are well balanced through time. Mobile Edge Computing (MEC) plays a key role in the 5G roadmap, as a way to bring information technology (IT) services closer to the mobile users by empowering radio access points with additional functionalities, like caching or computation offloading capabilities. At the physical layer, some of the key technologies enabling very low latency mobile services are dense deployment of radio access points, massive MIMO and millimeter-wave (mmW) communications for radio access as well as for radio fronthaul backhaul. In this paper, we merge computation offloading techniques for mobile edge computing with mmW communications and show how the joint optimization of computation communication resources is crucial to design an energy efficient mobile edge computing system. In particular, we tackle the intermittency of mmW links by considering multi-link communications and show how to take advantage of preliminary estimation of blocking probabilities. Abstract With the rapid increase in the development of the Internet of Things and 5G networks in the smart city context, a large amount of data (i.e., big data) is expected to be generated, resulting in increased latency for the traditional cloud computing paradigm. To reduce the latency, mobile edge computing has been considered for offloading a part of the workload from mobile devices to nearby edge servers that have sufficient computation resources. Although there has been significant research in the field of mobile edge computing, little attention has been given to understanding the placement of edge servers in smart cities to optimize the mobile edge computing network performance. In this paper, we study the edge server placement problem in mobile edge computing environments for smart cities. First, we formulate the problem as a multi-objective constraint optimization problem that places edge servers in some strategic locations with the objective to make balance the workloads of edge servers and minimize the access delay between the mobile user and edge server. Then, we adopt mixed integer programming to find the optimal solution. Experimental results based on Shanghai Telecom’s base station dataset show that our approach outperforms several representative approaches in terms of access delay and workload balancing. Mobile edge computing (MEC) literally pushes cloud computing from remote datacenters to the life radius of end users. By leveraging the widely adopted ETSI network function virtualization (NFV) architecture, MEC provisions elastic and resilient mobile edge applications with proximity. Typical MEC virtualization infrastructure allows configurable placement policy to deploy mobile edge applications as virtual machines (VMs): affinity can be used to put VMs on the same host for inter-VM networking performance, while anti-affinity is to separate VMs for high availability. In this paper, we propose a novel model to track the availability and cost impact from placement policy changes of the mobile edge applications. We formulate our model as a stochastic programming problem. To minimize complexity challenge, we also propose a heuristic algorithm. With our model, the unit resource cost increases when there are less resources left on a host. Applying affinity would take up more resources of the host but saves network bandwidth cost because of co-location. When enforcing anti-affinity, experimental results show increases of both availability and inter-host network bandwidth cost. For applications with different resource requirements, our model is able to find their sweet points with the consideration of both resource cost and application availability, which is vital in a less robust MEC cloud environment. Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a challenging two-timescale stochastic optimization problem. Specifically, in the larger timescale, whether to execute a task locally at the mobile device or to offload a task to the MEC server for cloud computing should be decided, while in the smaller timescale, the transmission policy for the task input data should adapt to the channel side information. In this paper, we adopt a Markov decision process approach to handle this problem, where the computation tasks are scheduled based on the queueing state of the task buffer, the execution state of the local processing unit, as well as the state of the transmission unit. By analyzing the average delay of each task and the average power consumption at the mobile device, we formulate a power-constrained delay minimization problem, and propose an efficient one-dimensional search algorithm to find the optimal task scheduling policy. Simulation results are provided to demonstrate the capability of the proposed optimal stochastic task scheduling policy in achieving a shorter average execution delay compared to the baseline policies.