Edge computing in the Internet of Things brings applications and content closer to the users by introducing an additional computational layer at the network infrastructure, between cloud and the resource-constrained data producing devices and user equipment. This way, the opportunistic nature of the operational environment is addressed by introducing computational power in location with low latency and high bandwidth. However, location-aware deployment of edge computing infrastructure requires careful placement scheme for edge servers. To provide the best possible Quality of Service for the user applications, their proximity needs to be optimized. Moreover, the deployment faces practical limitations in budget limitations, hardware requirements of servers and in online load balancing between servers. To address these challenges, we formulate the edge server placement as a capacitated location-allocation problem, while minimizing the distance between servers and access points of a real city-wide Wi-Fi network deployment. In our algorithm, we utilize both upper and lower server capacity constraints for load balancing. Furthermore, we enable sharing of workload between servers to facilitate deployments with low capacity servers. The performance of the algorithm is demonstrated in placement scenarios, exemplified by high capacity servers for edge computing and low capacity servers for Fog computing, with different parameters in a real-world data set. The data set consists of both dense deployment of access points in central areas, but also sparse deployment in suburban areas within the same network infrastructure. In comparison, we show that previous approaches do not sufficiently address such deployment. The presented algorithm is able to provide optimal placements that minimize the distances and provide balanced workload with sharing by following the capacity constraints.
To maintain the sufficient QoS within the budget limitations, two main approaches were used for determining the required number of servers. First, a tolerated distance from server was decided and the number of servers was minimized given that the distance constraint is met for each access point @cite_15 @cite_30 @cite_2 . Second, the number of servers was based on budget and servers were placed so that the best proximity is obtained @cite_24 @cite_49 @cite_20 @cite_14 @cite_11 . A third approach was to minimize the distance while penalizing for the number of servers @cite_16 .
@cite_30 @cite_14 @cite_24 @cite_49 @cite_2 @cite_15 @cite_16 @cite_20 @cite_11
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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. A device for selectively moistening the flap of envelopes has a moistening member and a pivoting moistening deflector selectively operable to moisten the flap or not. It further comprises a pivoting slide bar to the front of the moistening member and actuated between a protection position and a retracted position in front of the moistening member. The moistening deflector is adapted to cooperate with the slide bar and the moistening member and is coupled to the bar so that they are actuated virtually simultaneously. The device finds applications in automatic mail handling. Edge server placement problem is a hot topic in mobile edge computing. In this paper, we study the problem of energy-aware edge server placement and try to find a more effective placement scheme with low energy consumption. Then, we formulate the problem as a multi-objective optimization problem and devise a particle swarm optimization based energy-aware edge server placement algorithm to find the optimal solution. We evaluate the algorithm based on the real dataset from Shanghai Telecom and the results show our algorithm can reduce more than 10 energy consumption with over 15 improvement in computing resource utilization, compared to other algorithms. Remote clouds are gradually unable to achieve ultra-low latency to meet the requirements of mobile users because of the intolerable long distance between remote clouds and mobile users and the network congestion caused by the tremendous number of users. Mobile edge computing, a new paradigm, has been proposed to mitigate aforementioned effects. Existing studies mostly assume the edge servers have been deployed properly and they just pay attention to how to minimize the delay between edge servers and mobile users. In this paper, considering the practical environment, we investigate how to deploy edge servers effectively and economically in wireless metropolitan area networks. Thus, we address the problem of minimizing the number of edge servers while ensuring some QoS requirements. Aiming at more consistence with a generalized condition, we extend the definition of the dominating set, and transform the addressed problem into the minimum dominating set problem in graph theory. In addition, two conditions are considered for the capacities of edge servers: one is that the capacities of edge servers can be configured on demand, and the other is that all the edge servers have the same capacities. For the on-demand condition, a greedy based algorithm is proposed to find the solution, and the key idea is to iteratively choose nodes that can connect as many other nodes as possible under the delay, degree and cluster size constraints. Furthermore, a simulated annealing based approach is given for global optimization. For the second condition, a greedy based algorithm is also proposed to satisfy the capacity constraint of edge servers and minimize the number of edge servers simultaneously. The simulation results show that the proposed algorithms are feasible. We present @math , a decision support framework to provision edge servers for online services providers (OSPs). @math takes advantage of the increasingly flexible edge server placement, which is enabled by new technologies such as edge computing platforms, cloudlets and network function virtualization, to optimize the overall performance and cost of edge infrastructures. The key difference between @math and traditional server placement approaches lies on that @math can discover proper unforeseen edge locations which significantly improve the efficiency and reduce the cost of edge provisioning. We show how @math effectively identifies promising edge locations which are close to a collection of users merely with inaccurate network distance estimation methods, e.g., geographic coordinate (GC) and network coordinate systems (NC). We also show how @math comprehensively considers various pragmatic concerns in edge provisioning, such as traffic limits by law or ISP policy, edge site deployment and resource usage cost, over-provisioning for fault tolerance, etc., with a simple optimization model. We simulate @math using real network data at global and county-wide scales. Measurement-driven simulations show that with a given cost budget @math can improve user performance by around 10-45 percent at global scale networks and 15-35 percent at a country-wide scale network. 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.