Handover Minimized Service Region Partition for Mobile Edge Computing in Wireless Metropolitan Area Networks

Guan, Xiaodong; Wan, Xili; Ye, Feng; Choi, Baek‐Young

doi:10.1109/isc2.2018.8656951

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…For the IoT technology, a proactive network association can be a sufficient solution for reducing latency in MEC systems [ 53 , 54 ]. As a solution for reducing the handover in MEC-based networks, in [ 55 ], a region partitioning approach is presented, where this method is evaluated for both random and real traces. In another work [ 56 ], the dynamic service placement is employed, where offline algorithms are used for determining the optimal service for minimizing the average cost.…”

Section: Literature Review and Motivation Of This Workmentioning

confidence: 99%

Mobility-Aware Offloading Decision for Multi-Access Edge Computing in 5G Networks

Jahandar

Kouhalvandi

Shayea

et al. 2022

Sensors

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Multi-access edge computing (MEC) is a key technology in the fifth generation (5G) of mobile networks. MEC optimizes communication and computation resources by hosting the application process close to the user equipment (UE) in network edges. The key characteristics of MEC are its ultra-low latency response and real-time applications in emerging 5G networks. However, one of the main challenges in MEC-enabled 5G networks is that MEC servers are distributed within the ultra-dense network. Hence, it is an issue to manage user mobility within ultra-dense MEC coverage, which causes frequent handover. In this study, our purposed algorithms include the handover cost while having optimum offloading decisions. The contribution of this research is to choose optimum parameters in optimization function while considering handover, delay, and energy costs. In this study, it assumed that the upcoming future tasks are unknown and online task offloading (TO) decisions are considered. Generally, two scenarios are considered. In the first one, called the online UE-BS algorithm, the users have both user-side and base station-side (BS) information. Because the BS information is available, it is possible to calculate the optimum BS for offloading and there would be no handover. However, in the second one, called the BS-learning algorithm, the users only have user-side information. This means the users need to learn time and energy costs throughout the observation and select optimum BS based on it. In the results section, we compare our proposed algorithm with recently published literature. Additionally, to evaluate the performance it is compared with the optimum offline solution and two baseline scenarios. The simulation results indicate that the proposed methods outperform the overall system performance.

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Section: Literature Review and Motivation Of This Workmentioning

confidence: 99%

Mobility-Aware Offloading Decision for Multi-Access Edge Computing in 5G Networks

Jahandar

Kouhalvandi

Shayea

et al. 2022

Sensors

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“…Although the cloud computing with MEC could provide a well-established distribution model and application platform [13,14], the MEC network still has to deal with many problems on its own. The migration and handover of services are relatively frequent in MEC scenario considering users' movement, which may induce service interruption and additional cost [15]. Especially for highly mobile users, they accelerate the frequency of services' migration and handover, impacting on the stability of the total MEC network.…”

Section: Introductionmentioning

confidence: 99%

Dynamic service migration in ultra-dense multi-access edge computing network for high-mobility scenarios

Lin

Zhao

et al. 2020

J Wireless Com Network

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The multi-access edge computing (MEC) has higher computing power and lower latency than user equipment and remote cloud computing, enabling the continuing emergence of new types of services and mobile application. However, the movement of users could induce service migration or interruption in the MEC network. Especially for highly mobile users, they accelerate the frequency of services’ migration and handover, impacting on the stability of the total MEC network. In this paper, we propose a hierarchical multi-access edge computing architecture, setting up the infrastructure for dynamic service migration in the ultra-dense MEC networks. Moreover, we propose a new mechanism for users with high mobility in the ultra-dense MEC network, efficiently arranging service migrations for users with high-mobility and ordinary users together. Then, we propose an algorithm for evaluating migrated services to contribute to choose the suitable MEC servers for migrated services. The results show that the proposed mechanism can efficiently arrange service migrations and more quickly restore the services even in the blockage. On the other hand, the proposed algorithm is able to make a supplement to the existing algorithms for selecting MEC servers because it can better reflect the capability of migrated services.

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“…In our previous work [11], we noticed the impact of the cloudlet planning and started with an initial step of carefully partitioning the entire WMAN into disjoint service regions. However, in [11] only the minimization of service handover costs is discussed, while the cloudlet construction costs and operation costs are ignored. Therefore, in the proposed solution, we only suggested the way to determine the service regions without the planning of cloudlets' physical placement and capacities.…”

Section: Introductionmentioning

confidence: 99%

“…Real mobility traces: contain the traces of 320 taxi cabs in Rome, Italy from February 1st, 2014 to March 2nd, 2014 [31]. During this period, each taxi reported its GPS coordinates every 7 s. Figure 4 [11] draws the taxi traces. The x and y axes in Figure 4 represent the latitude and longitude respectively.…”

mentioning

confidence: 99%

A Long-Term Cost-Oriented Cloudlet Planning Method in Wireless Metropolitan Area Networks

et al. 2019

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As an extension of remote cloud data centers, cloudlets process the workloads from mobile users at the network edge, thereby satisfying the requirements of resource-intensive and latency-sensitive applications. One of the fundamental yet important issues for cloudlet infrastructure providers (ISP) is how to plan the placement and capacities of cloudlets so that minimize their long-term cost with a guarantee on service delay. However, existing work mostly focuses on resource provision or resource management for mobile services on existing cloudlets, while very little attention has been paid to the cloudlet placement and capacity planning problem. In contrast to those studies, we aim to optimize the long-term total cost of cloudlets' ISPs through intelligently planning the location and capacities of cloudlets under constraints on the service delay experienced by mobile users. This problem is then decomposed into two sub-problems and algorithms are devised to solve it. Evaluations on randomly generated traces and real traces exhibit the superior performance of the proposed solution on saving ISP's long-term cost.Despite the benefits of sinking the data and computation to the network edge, new concerns on the placement and capacity planning of cloudlets have risen. It is not a trivial problem to balance the service performance and the infrastructure providers' (ISP) costs while additionally considering user mobility, cloudlets' coverage, and the large scale of Wireless Metropolitan Area Network (WMAN). Specifically, deploying more cloudlets closer to mobile users could reduce transmission delay and communication costs. However, this will definitely add more costs of purchasing and operating physical servers. Furthermore, to cope with user mobility, service migration has to be performed when a mobile user moves from the service region of one cloudlet to another. Service migration ensures that users could seamlessly access network services, yet it poses additional migration costs and may suffer from handover failure. Therefore, to deploy latency-sensitive and resource-intensive mobile services at network edge, one of the vital yet essential issues is to plan the cloudlets, including where to permanently place the cloudlets, what is the service region of each cloudlet, and how many physical resources should be assigned to each cloudlet considering the ISPs' cost and performance requirements by mobile users. Figure 1 demonstrates an example of a cloudlet planning in a WMAN with multiple access point (AP) cells. As shown in Figure 1, each AP is covered by exactly one cloudlet and each cloudlet is co-existed with one AP in its service region. Cloudlet Cloudlet CloudletPlacement of a cloudlet Service region of a cloudlet

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Handover Minimized Service Region Partition for Mobile Edge Computing in Wireless Metropolitan Area Networks

Cited by 5 publications

References 12 publications

Mobility-Aware Offloading Decision for Multi-Access Edge Computing in 5G Networks

Mobility-Aware Offloading Decision for Multi-Access Edge Computing in 5G Networks

Dynamic service migration in ultra-dense multi-access edge computing network for high-mobility scenarios

A Long-Term Cost-Oriented Cloudlet Planning Method in Wireless Metropolitan Area Networks

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