A multi-dimensional hierarchical performance evaluation model for edge cloud platform

Zhao, Yue; Feng, Xin; Chen, Na; Wang, Yaoguang; Yu, Yijun; Wang, Hongbo

doi:10.1016/j.procs.2018.03.013

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…There are different perspectives of approaching AHP for cloud computing performance measurements as mentioned in the literature review section (e.g. Golmohammadi et al, 2012;Garg et al, 2013;Taha et al, 2014;Ghazali et al, 2017;Zhao et al, 2018). However, unlike other studies of AHP implementation to cloud computing performance measurement, this study dissects the "best practice" (Kouatli, 2014(Kouatli, , 2016 performance criteria in IT environment, in general, and cloud computing environment, in specific, to achieve the PPP by benchmarking the divisions of a cloud service provider environment.…”

Section: Discussion and Implicationmentioning

confidence: 99%

“…Based on consumer perspective, Ghazali et al (2017) studied the selection of cloud service provider based on the security perspective where the AHP technique was proposed as mechanism of security measurement as a trust (SMaaT). Zhao et al (2018), on the contrary, also used the AHP model, but to measure the overall performance score for edge cloud computing platform where five categories were used for pairwise comparison. These were capacity, performance (speed), reliability, agility and equilibrium, and each category is classified into three levels.…”

Section: Literature Reviewmentioning

confidence: 99%

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People-process-performance benchmarking technique in cloud computing environment

Kouatli

2019

IJPPM

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Purpose Cloud computing is relatively a new type of technology demanding a new method of management techniques to attain security and privacy leading to customer satisfaction regarding “Business Protection” measure. As cloud computing businesses are usually composed of multiple colocation sites/departments, the purpose of this paper is to propose a benchmark operation to measure and compare the overall integrated people-process-performance (PPP) among different departments within cloud computing organization. The purpose of this paper is to motivate staff/units to improve the process performance and meet the standards in a competitive approach among business units. Design/methodology/approach The research method was conducted at Cirrus Ltd, which is a cloud computing service provider where a focus group consists of six IT professionals/managers. The objective of the focus group was to investigate the proposed technique by selecting the best practices relevant criteria, with the relevant sub-criteria as a benchmarking performance tool to measure PPP via an analytic hierarchy processing (AHP) approach. The standard pairwise comparative AHP scale was used to measure the performance of three different teams defined as production team, user acceptance testing team and the development team. Findings Based on best practice performance measurement (reviewed in this paper) of cloud computing, the proposed AHP model was implemented in a local medium-sized cloud service provider named “Cirrus” with their single site data center. The actual criteria relevant to Cirrus was an adaptation of the “Best practice” described in the literature. The main reason for the adaptation of criteria was that the principle of PPP assumes multiple departments/datacenters located in a different geographical area in large service providers. As Cirrus is a type of SMEs, the adaptation of performance measurement was based on teams within the same data center location. Irrelevant of this adaptation, the objective of measuring vendors KPI using the AHP technique as a specific output of PPP is also a valid situation. Practical implications This study provides guidance for achieving cloud computing performance measurement using the AHP technique. Hence, the proposed technique is an integrated model to measure the PPP under monitored cloud environment. Originality/value The proposed technique measures and manages the performance of cloud service providers that also implicitly act as a catalyst to attain trust in such high information-sensitive environment leading to organizational effectiveness of managing cloud organizations.

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Section: Discussion and Implicationmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

People-process-performance benchmarking technique in cloud computing environment

Kouatli

2019

IJPPM

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“…Secondly, the reward and punishment factors [16] are introduced to calculate the trust value of abnormal nodes according to the overall historical interaction, and the reward and punishment factors as follows:…”

Section: Reward and Punishment Mechanismmentioning

confidence: 99%

Trust evaluation model in new power system edge computing

Lu¹,

Wang²,

Li³

et al. 2023

International Conference on Optoelectronic Information and Functional Materials (OIFM 2023)

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“…The trust value of malicious entities remains stable at a low point and has a downward trend.To further verify that the trust evaluation model proposed in this paper can effectively distinguish between normal entities and malicious entities, k-means clustering with the number of clusters of 2 is carried out with the score given by the evaluation model as the characteristic data, to obtain the detection rate of the model. The trust evaluation model proposed in this paper based on the graph model and semantic time window is compared with the DTEM model presented in the literature[13] and the RFSN model mentioned in the literature[14] in terms of detection rate. The comparison results are shown in (b) of Figure3.…”

mentioning

confidence: 99%

Trust evaluation model for electric power mobile Internet environment based on graph and semantic time window

Yang

Han

Long

et al. 2023

Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023)

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At present, the time window setting of the multi-entity continuous trust evaluation model in the power mobile Internet environment lacks adaptability. The semantic retention of time series data is significant to time window setting. A trust evaluation model based on the graph model and semantic time window may provide a solution. Firstly, we analyze the entity interactions in the electric power mobile network environment and construct a global trust path diagram by combining the entropy scoring results of evidence data in this paper. Secondly, a semantic time window algorithm is proposed by combining the parameter-seeking process of the sequential data compression algorithm. Finally, update the trust value dynamically by combining the number of feature changes of entities in the global trust path graph and the semantic time window. Simulation experiments are conducted to analyze the trust value updating algorithm based on the graph model and semantic time window proposed in this paper. The experimental results show that the algorithm is feasible and accurate.

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A multi-dimensional hierarchical performance evaluation model for edge cloud platform

Cited by 4 publications

References 4 publications

People-process-performance benchmarking technique in cloud computing environment

People-process-performance benchmarking technique in cloud computing environment

Trust evaluation model in new power system edge computing

Trust evaluation model for electric power mobile Internet environment based on graph and semantic time window

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