Dynamic resource allocation exploiting mobility prediction in mobile edge computing

Plachy, Jan; Bečvář, Zdeněk; Strinati, Emilio Calvanese

doi:10.1109/pimrc.2016.7794955

Cited by 108 publications

(88 citation statements)

References 12 publications

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“…In the surveyed articles, Wang et al [49] consider CPU cycles, Singh et al [44] consider Millions of Instructions per Second (MIPS), and Rodrigues et al [70] consider the number of processors per cloudlet. At a conceptual level, Zamani et al [73] consider different computing resources based on the average number of tasks completed per unit of time, and Plachy et al [68] allocate computational resources in the form of VMs.…”

Section: Computation and Communicationmentioning

confidence: 99%

“…For example, Tärneberg et al [67] study applicationdriven placement and present a system model for mobile cloud network with a dynamic placement algorithm that guarantees application performance, minimizes cost, and tackles resource asymmetry problems. Plachy et al [68] propose a cooperative and dynamic VM placement algorithm associated with another cooperative algorithm for selecting a suitable communication path. They use VM migration to solve user mobility problems.…”

Section: Migrationmentioning

confidence: 99%

“…Aazam et al [43] consider resources located at only one physical location, a fog node, but considering resources within the same architectural level does most often not mean that the resources are located at the same physical location. For example, Oueis [63] considers resources on different cells, Gu et al [66] and Plachy et al [68] on different base stations. Fricker et al [69] and Rodrigues et al [70] consider task placement and migration on different types of edge devices (datacenters for the former and cloudlets for the latter).…”

Section: Single-levelmentioning

confidence: 99%

“…Note that this does not mean that the works do not consider mobility at all, only that the mobility is not on the supply side. Works including mobility on the demand side only are, for example, Plachy et al [68] who consider that computational resources needed by a user are allocated in a stationary base station in a VM, which can be transferred to another base station if the user is moving. Similar solutions are presented by Tärneberg et al [67], Gomes et al [13], Oueis [63], Fan et al [62], and Wang et al [77].…”

Section: Summary Of Edge Resource Locationmentioning

confidence: 99%

“…When looking at the different applications used in the surveyed articles presented in the earlier sections, the first finding is that the majority of them (66%) do not consider a specific application in their study. Instead, they refer to generic applications such as IoT services [59], real-time applications [68], latency-sensitive applications [58], or name some applications but only as an illustration. Table 5 presents the remaining papers according to which type of application they consider.…”

Section: Functional Propertiesmentioning

confidence: 99%

See 4 more Smart Citations

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

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Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

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Section: Computation and Communicationmentioning

confidence: 99%

Section: Migrationmentioning

confidence: 99%

Section: Single-levelmentioning

confidence: 99%

Section: Summary Of Edge Resource Locationmentioning

confidence: 99%

Section: Functional Propertiesmentioning

confidence: 99%

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A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

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One‐on‐one contract game–based dynamic virtual machine migration scheme for Mobile Edge Computing

Kim

2017

Trans Emerging Tel Tech

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In the evolution toward fifth-generation networks, Mobile Edge Computing (MEC) is an emerging paradigm, conceived to meet the ever-increasing computational demands of mobile applications. Within the access range of mobile devices, the MEC technique promises the enablement of efficient Mobile Cloud Computing services.In an MEC system, virtual machine (VM) migration is a key issue; VM migration is the process of moving a VM from an edge node to another edge node. To improve service quality and system performance, the VM migration method has a dual focus on the MEC system's computation and communication resources. In this study, we formulate the VM migration problem as a one-on-one contract game model and develop a learning-based price control mechanism to effectively handle the MEC's resource.By using the game methodology and learning process, our approach is able to capture the dynamics of MEC systems, and it interacts continually with an unknown system environment. Finally, extensive simulation results are provided to demonstrate the capability of the proposed approach in achieving, with respect to existing MEC schemes, both higher resource utilization and system throughput, as well as reduced service drop ratio and reduced service delay.

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Resource allocation in PD‐NOMA–based mobile edge computing system: Multiuser and multitask priority

Paymard

Mokari

2019

Trans Emerging Tel Tech

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Mobile edge computing (MEC) is a new network architecture concept that enables cloud computing capabilities at the edge of the cellular network, and hence, MEC has the ability to process the computation tasks in close proximity to the network edge. In this paper, we consider a multiuser MEC system with multiple computation tasks such that each computation task is independent with different priorities. For this purpose, we devise a novel multiuser, multitask, and nonpreemptive priority M/G/1 queuing model in the MEC server environment. In doing so, we propose a priority-based task scheduling policy that aims to maximize the profit of mobile network operator by jointly optimizing service rate, transmit power, and subcarrier allocation with satisfying power and delay constraints. This is necessary to handle the physical channel opportunities to obtain higher spectral efficiency in the proposed task offloading scheme.In this line, we also consider power-domain nonorthogonal multiple access (PD-NOMA) technique. Numerical results show that, by using the Priority queuing in PD-NOMA-SCA and Priority queuing in PD-NOMA-matching schemes, the performance can be improved considerably compared to No-priority queuing in OMA-SCA and No-priority queuing in OMA-matching, eg, the Priority queuing in PD-NOMA-SCA scheme improves nearly 54% compared to the No-priority queuing in OMA-SCA scheme. It is noteworthy that the performance gap between exhaustive search and the Priority queuing in PD-NOMA-SCA scheme is nearly 7%.

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Dynamic resource allocation exploiting mobility prediction in mobile edge computing

Cited by 108 publications

References 12 publications

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

One‐on‐one contract game–based dynamic virtual machine migration scheme for Mobile Edge Computing

Resource allocation in PD‐NOMA–based mobile edge computing system: Multiuser and multitask priority

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