A comparative analysis of prominently used MCDM methods in cloud environment

Neeraj,; Goraya, Major Singh; Singh, Damanpreet

doi:10.1007/s11227-020-03393-w

Cited by 19 publications

(11 citation statements)

References 42 publications

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“…They are also commonly used to select cloud services; for example, in the article [ 26 ], the authors apply MCDM methods to the problem of choosing geographic regions for the Amazon Web Service cloud. In addition, a comparative analysis of the obtained ranking is carried out and verified both the time complexity of the different MCDM methods applied and the robustness of the classification methods.…”

Section: Related Workmentioning

confidence: 99%

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

Ferreira

Azevedo

Estrella

et al. 2023

Sensors

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With the development of mobile communications and the Internet of Things (IoT), IoT devices have increased, allowing their application in numerous areas of Industry 4.0. Applications on IoT devices are time sensitive and require a low response time, making reducing latency in IoT networks an essential task. However, it needs to be emphasized that data production and consumption are interdependent, so when designing the implementation of a fog network, it is crucial to consider criteria other than latency. Defining the strategy to deploy these nodes based on different criteria and sub-criteria is a challenging optimization problem, as the amount of possibilities is immense. This work aims to simulate a hybrid network of sensors related to public transport in the city of São Carlos - SP using Contiki-NG to select the most suitable place to deploy an IoT sensor network. Performance tests were carried out on five analyzed scenarios, and we collected the transmitted data based on criteria corresponding to devices, applications, and network communication on which we applied Multiple Attribute Decision Making (MADM) algorithms to generate a multicriteria decision ranking. The results show that based on the TOPSIS and VIKOR decision-making algorithms, scenario four is the most viable among those analyzed. This approach makes it feasible to optimally select the best option among different possibilities.

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Section: Related Workmentioning

confidence: 99%

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

Ferreira

Azevedo

Estrella

et al. 2023

Sensors

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“…MCDM methods, Analytic Hierarchy Process (AHP) [49,50], Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [51], ÉLimination et Choix Traduisant la REalité (ELECTRE) [52,53], preference ranking organization method for enrichment evaluation (PROMETHEE) [54], and VIKOR [55][56][57][58] are prominently used in the cloud environment [19]. Kumar et al [59] proposed a hybrid method using fuzzy logic with the integration of AHP and TOPSIS methods to select the cloud service.…”

Section: Related Workmentioning

confidence: 99%

“…If VM has newly arrived, then VM is rejected for scheduling (see lines [12][13][14][15]. If the size of the eligibleServers list is one, then the server is added to assignedServer list (see lines [17][18][19]. The list of assignedServer is used to store the optimal server on which VM can be scheduled.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…If A ′ does not have acceptable stability, then A ′ and A ′′ are two compromised solutions. The server which has the highest resource utilization is stored in the assignedServer list (see lines [16][17][18][19]. If the best alternative A ′ does not have an acceptable advantage, then there are several compromised solutions available.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method is used for ranking the servers. VIKOR is a lightweight method that has a low execution time, and it is useful where the VM scheduler does not know each criterion at the start [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method

2021

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This paper presents a Power and Resource Utilization-Aware Virtual Machine Scheduling (PRUVMS) algorithm for strengthening resource utilization and diminishing the energy consumption of servers in the cloud environment. The PRUVMS algorithm enhances the resource utilization by migrating the VMs from the underloaded/overloaded servers to a normal server, and it reduces the energy consumption by shutting down the underloaded servers after migrating the VMs. For selecting the suitable server for the VM placement, the ranking of the available servers is evaluated. An illustrative example is presented to validate the PRUVMS algorithm. Further, the PRUVMS algorithm is tested on the PlanetLab workload using the Cloud-Sim simulator. The proposed PRUVMS algorithm improves resource utilization by 68.22% and 37.53% and decreases the energy consumption by 35.53% and 31.34% in comparison with PABFD and CAVMP algorithms, respectively. The improvement in computational results shows the acceptability of the proposed scheduling algorithm in the cloud environment.

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Custom Cloud: An Efficient Model for Cloud Service Selection Based on Neural Network

Bothera

Mohnot

Garg

et al. 2022

Communications in Computer and Information Science

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A comparative analysis of prominently used MCDM methods in cloud environment

Cited by 19 publications

References 42 publications

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method

Custom Cloud: An Efficient Model for Cloud Service Selection Based on Neural Network

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