Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

Bangui, Hind; Ge, Mouzhi; Bühnová, Barbora; Rakrak, Said; Raghay, Said; Pitner, Tomáš

doi:10.3390/jsan6040025

Cited by 21 publications

(20 citation statements)

References 106 publications

(101 reference statements)

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“…The MCDA concept [11,12] is a collection of various algorithms that support the selection of the best solution among several alternatives to ensure specific application requirements. Thus, in this subsection, we conduct a review of literature that specifically targets the advantages of applying MCDA algorithms and concepts to edge computing.…”

Section: Mcda In Edge Computingmentioning

confidence: 99%

“…Based on Table 1 and our previous study [11] in which we described how the most certain and uncertain MCDA methods enhance the offloading process in mobile cloud computing as well as the selection of cloud services in general, we found that the TOPSIS is the most proposed ranking approach in the literature, since it is one of the famous fundamental multi-criteria decision-making ranking approaches in MCDA. The main features of TOPSIS [56] are chosen as the alternatives that simultaneously have the shortest distance from the ideal solution and the farthest distance from the In this paper, we identify and analyze the criteria that have a direct influence on the selection phase and express clearly the most important characteristics of fog and cloud platforms.…”

Section: Ranking Fog/cloud Servicesmentioning

confidence: 99%

“…In this paper, we address this challenge with the help of multi-criteria decision analysis (MCDA) [11,12] applied to the context of the edge/cloud paradigm. To this end, we first provide a summary of the most common technologies related to edge computing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

et al. 2018

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Smart cities aim at integrating various IoT (Internet of Things) technologies by providing many opportunities for the development, governance, and management of user services. One of the ways to support this idea is to use cloud and edge computing techniques to reduce costs, manage resource consumption, enhance performance, and connect the IoT devices more effectively. However, the selection of services remains a significant research question since there are currently different strategies towards cloud computing, including services for central remote computing (traditional cloud model) as well as distributed local computing (edge computing). In this paper, we offer an integrated view of these two directions and the selection among the edge technologies based on MCDA (Multiple Criteria Decision Analysis) algorithms. To this end, we propose a foglet as a middleware that aims at achieving satisfactory levels of customer services by using fuzzy similarity and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to facilitate the rating and selection of services in the fog-to-cloud environment. Then, we describe the selection process with a numerical example, and conclude our work with an outline of future perspectives.

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Section: Mcda In Edge Computingmentioning

confidence: 99%

Section: Ranking Fog/cloud Servicesmentioning

confidence: 99%

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Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

et al. 2018

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“…Bangui et. al [6] and Wu [7] published surveys on multiobjective decision-making to offload computation from mobile devices to the cloud. Although these approaches relate to our work, mobile devices are subject to constraints that do not necessarily apply to IoT gateways.…”

Section: State Of the Artmentioning

confidence: 99%

Offloading Execution from Edge to Cloud: A Dynamic Node-RED Based Approach

Sosa¹,

Király²,

Rodriguez³

2018

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

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Fog computing enables use cases where data produced in end devices are stored, processed, and acted on directly at the edges of the network, yet computation can be offloaded to more powerful instances through the edge to cloud continuum. Such offloading mechanism is especially needed in case of modern multi-purpose IoT gateways, where both demand and operation conditions can vary largely between deployments. To facilitate the development and operations of gateways, we implement offloading directly as part of the IoT rapid prototyping process embedded in the software stack, based on Node-RED. We evaluate the implemented method using an image processing example, and compare various offloading strategies based on resource consumption and other system metrics, highlighting the differences in handling demand and service levels reached.

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“…Resource orchestration in cloud system is dissimilar from the customary network systems and the characteristics of the operators, cloud's services, and architecture yields some necessities in resource management which should be sensibly addressed [29,30]. It includes discovery, allocation, and monitoring of physical resources, such as RAM, CPU cores, network bandwidth, and disk space.…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework for Resource Orchestration in OpenStack Cloud Platform

2018

TIIS

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This work is mainly focused on two major topics in cloud platforms by using OpenStack as a case study: management and provisioning of resources to meet the requirements of a service demanded by remote end-user and relocation of virtual machines (VMs) requests to offload the encumbered compute nodes. The general framework architecture contains two subsystems: 1) An orchestrator that allows to systematize provisioning and resource management in OpenStack, and 2) A resource utilization based subsystem for vibrant VM relocation in OpenStack. The suggested orchestrator provisions and manages resources by: 1) manipulating application program interfaces (APIs) delivered by the cloud supplier in order to allocate/control/manage storage and compute resources; 2) interrelating with software-defined networking (SDN) controller to acquire the details of the accessible resources, and training the variations/rules to manage the network based on the requirements of cloud service. For resource provisioning, an algorithm is suggested, which provisions resources on the basis of unused resources in a pool of VMs. A sub-system is suggested for VM relocation in a cloud computing platform. The framework decides the proposed overload recognition, VM allocation algorithms for VM relocation in clouds and VM selection.In the framework of cloud computing, resource provisioning is the method of assigning networking, compute, and (ultimately) energy resources to a cluster of applications, in a way that propose to jointly fulfill the performance necessities of the users of the cloud resources, the applications, and the infrastructure suppliers. The requirements of the suppliers revolve around operational and effective resource deployment within the limitations of SLAs with the cloud users. Operational resource deployment is usually achieved through virtualization tools, which enable numerical pooling of resources through customers and applications. The goals of the cloud users are subjective to the scaling of existing resources as a concequence of changing application demands, their availability and application performance [1].In this study, an instrumentation framework for resource provisioning and organization is suggested, which allows to automate resource organization and provisioning for users across network and clouds by: 1) manipulating APIs delivered by the cloud supplier in order to compute and allocate/control/manage storage resources; 2) networking with SDN controller to obtain the details of the existing resources, and inculcating the rules/changes to organize the network dependent on the cloud service necessities. For resource distribution, a proposed algorithm distributes resources on the basis of unused resources in a pool of VMs. Moreover, a service perception model is used, which is supplied to the orchestrator for provisioning the resources dynamically and analytically based on the necessities of the demands made by remote end-user.To that end, in this paper, we present an integrated orchestration of cloud and network resour...

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Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

Cited by 21 publications

References 106 publications

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

Offloading Execution from Edge to Cloud: A Dynamic Node-RED Based Approach

A Novel Framework for Resource Orchestration in OpenStack Cloud Platform

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