Fuzzy Logic-Based Algorithm Resource Scheduling for Improving the Reliability of Cloud Computing

Zavvar, Mohammad; Rezaei, Meysam; Garavand, Shole; Ramezani, Farhad

doi:10.17576/apjitm-2016-0501-04

Cited by 7 publications

(4 citation statements)

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“…It is necessary to note that for highly variable weather conditions, thermal dynamic rating. Three different models have been widely used for overhead line calculation namely CIGRE, IEEE, and IEC (International Electrochemical Commission) [28], [35]- [42]. In [29], an explanation of all three heating type models has been carried out that have been expressed in Eqn.…”

Section: Existing Ieee and Cigri Standardmentioning

confidence: 99%

“…The corona effect or in another way electric discharge phenomenon can occur in the high voltage TL because of the ionization of the fluid-like air surrounding the conductor that is electrically charged. This corona occurrence depends on the rate of the collision of the molecules in TL [30] and the molecule collisions in a conductor line can be increased with the increase of conductor temperature [35], [41]. Meanwhile, high temperatures can increase the molecule collision in the conductor that can increase the corona loss in the TL.…”

Section: Conductor Temperaturementioning

confidence: 99%

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An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

et al. 2021

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Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi (π) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved π model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.INDEX TERMS Dynamic thermal current ratings, data acquisition system, information technology, phasor measurement unit, reliability, sensors, sensor cloud system, wide area measurement. NOMENCLATURE ABBREVIATIONS ACSR Aluminum Conductor Steel Rectifier CTE Conductor Temperature EvaluationThe associate editor coordinating the review of this manuscript and approving it for publication was Eyuphan Bulut .

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Section: Existing Ieee and Cigri Standardmentioning

confidence: 99%

Section: Conductor Temperaturementioning

confidence: 99%

An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

et al. 2021

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“…Fuzzy logic (FL) is the logical fundamental method of thinking, which is rough instead of being precise. Several studies have been conducted regarding fuzzy logic methods including their usage in resource scheduling to improve the reliability of cloud computing [17] or in the medical area for diagnosing coronary heart disease [18], in addition to their implementation in hardware-based maximum power point tracking controller for PV systems [19]. e significance of FL stems from the fact that most methods of human thinking and particularly good judgment thinking are rough [16].…”

Section: Introductionmentioning

confidence: 99%

Protect Mobile Travelers Information in Sensitive Region Based on Fuzzy Logic in IoT Technology

Memon

Shaikh

Hasan

et al. 2020

Security and Communication Networks

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The Internet of Things (IoT) is susceptible to several identities, primarily based on attacks. However, these attacks are controlling for IoT due to extraordinary growth in consumers’ density and slight analysis with low power access nodes. In this work, we explore the possible flaws associated with security for IoT environment insensitively meant for transfer conditions. We proposed a novel design aimed at detecting a spoofing attack that inspects the probability distributions of received power founded for the regions designed for mobile (moving) users. Additionally, we examine the influence on the Confidentiality Scope of targeted consumers in the absence and presence of observer. Our approaches were done through simulation results used for three diverse regions. Grounded on outcomes, we suggest an algorithm called MTFLA, which will guarantee detection and protection techniques intended to protect vastly sensitive areas, i.e., wherever the chance of an attack is maximized. We provide a comparison among various security algorithms prepared for the energy consumption of different patterns. Simulation results revealed that the proposed algorithm for protection (MTFL) is verified to be energy-proficient (secure garnering). It decreases the energy prerequisite for encrypting the data. We evaluated our techniques over simulation results for sensitive region information built on fuzzy logic.

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“…[37] have presented and evaluated a new scheduling algorithm that is an efficient technique for scheduling virtual machines between data centers using fuzzy logic. A similar work [38] focuses on the allocation and scheduling of resources as well as improving the reliability of cloud computing. The typical fuzzy model as shown in Figure . 2.5 is customized to be based on the governing rules which consider factors of cost, trust, length of processes, and priority.…”

Section: Significance and Related Workmentioning

confidence: 99%

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

Pandey¹

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Data-intensive science applications in fields such as e.g., bioinformatics, health sciences, and material discovery are becoming increasingly dynamic and demanding with resource requirements. Researchers using these applications which are based on advanced scientific workflows frequently require a diverse set of resources that are often not available within private servers or a single Cloud Service Provider (CSP). For example, a user working with Precision Medicine applications would prefer only those CSPs who follow guidelines from HIPAA (Health Insurance Portability and Accountability Act) for implementing their data services and might want services from other CSPs for economic viability. With the generation of more and more data these workflows often require deployment and dynamic scaling of multi-cloud resources in an efficient and high-performance manner (e.g., quick setup, reduced computation time, and increased application throughput). At the same time, users seek to minimize the costs of configuring the related multi-cloud resources. While performance and cost are among the key factors to decide upon CSP resource selection, the scientific workflows often process proprietary/confidential data that introduces additional constraints of security postures. Thus, users have to make an informed decision on the selection of resources that are most suited for their applications while trading off between the key factors of resource selection which are performance, agility, cost, and security (PACS). Furthermore, even with the most efficient resource allocation across multi-cloud, the cost to solution might not be economical for all users which have led to the development of new paradigms of computing such as volunteer computing where users utilize volunteered cyber resources to meet their computing requirements. For economical and readily available resources, it is essential that such volunteered resources can integrate well with cloud resources for providing the most efficient computing infrastructure for users. In this dissertation, individual stages such as user requirement collection, user's resource preferences, resource brokering and task scheduling, in lifecycle of resource brokering for users are tackled. For collection of user requirements, a novel approach through an iterative design interface is proposed. In addition, fuzzy interference-based approach is proposed to capture users' biases and expertise for guiding their resource selection for their applications. The results showed improvement in performance i.e. time to execute in 98 percent of the studied applications. The data collected on user's requirements and preferences is later used by optimizer engine and machine learning algorithms for resource brokering. For resource brokering, a new integer linear programming based solution (OnTimeURB) is proposed which creates multi-cloud template solutions for resource allocation while also optimizing performance, agility, cost, and security. The solution was further improved by the addition of a machine learning model based on naive bayes classifier which captures the true QoS of cloud resources for guiding template solution creation. The proposed solution was able to improve the time to execute for as much as 96 percent of the largest applications. As discussed above, to fulfill necessity of economical computing resources, a new paradigm of computing viz-a-viz Volunteer Edge Computing (VEC) is proposed which reduces cost and improves performance and security by creating edge clusters comprising of volunteered computing resources close to users. The initial results have shown improved time of execution for application workflows against state-of-the-art solutions while utilizing only the most secure VEC resources. Consequently, we have utilized reinforcement learning based solutions to characterize volunteered resources for their availability and flexibility towards implementation of security policies. The characterization of volunteered resources facilitates efficient allocation of resources and scheduling of workflows tasks which improves performance and throughput of workflow executions. VEC architecture is further validated with state-of-the-art bioinformatics workflows and manufacturing workflows.

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Fuzzy Logic-Based Algorithm Resource Scheduling for Improving the Reliability of Cloud Computing

Cited by 7 publications

References 0 publications

An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

Protect Mobile Travelers Information in Sensitive Region Based on Fuzzy Logic in IoT Technology

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

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