Agent-Based Decentralized Grid Model

Kang, Hui; Li, Jiahui; Zhang, Lin; Feng, Tie; Yin, Qiuwen; Liu, Yujun

doi:10.1109/access.2020.2999490

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…Calculate the fitness values for all emperor penguin using eqn (40) Update the current iteration r by 1…”

Section: End Formentioning

confidence: 99%

“…Grid quality of service trustworthiness is evaluated using Bayesian network [39].Trustworthiness of service providers is calculated based on QoS which is not effective, since when the risk value of service providers is low, it does not handle large amount of requests. Agent based decentralized grid structure is proposed to overwhelm the drawbacks of the centralized grid structure [40].This research used traditional decentralized grid model that takes much time to complete the process of scheduling and resource allocation that increase high latency. All the requests are migrated to the nearest node which leads to frequent migration that reduces the performance of migration.…”

Section: Major Problem Statementmentioning

confidence: 99%

“…The proposed SparkGrid is evaluated by comparing with the previous approaches such as RL-Grid [37], Bayesian-Grid [39], Agent-Grid [40] in terms of makespan, failure rate, grid utilization, resource prediction accuracy, request start time, risk of worker, memory usage, response time and resource availability.…”

Section: Comparative Analysismentioning

confidence: 99%

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SparkGrid: Blockchain Assisted Secure Query Scheduling and Dynamic Risk Assessment for Live Migration of Services in Apache Spark based Grid Environment

G.M.

Nalini

2022

Preprint

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Grid computing is an emerging technology that enabled the heterogeneous collection of data and provisioning of services to the users. Due to the high amount of incoming heterogeneous request, grid computing needs an efficient scheduling to reduce execution time and satisfy Service Level Agreement (SLA) and Quality of Service (QoS) requirements. For that purpose, we proposed SprakGrid method to reduce execution time and satisfying SLA, QoS requirements. The proposed work includes four consecutive phases which are explained as follows, in first we perform user authentication in order to ensure the legitimacy of the users using Elliptic Curve based Chaos Theory (ECCT) algorithm which generate secret key and stored it into the blockchain. In second we perform query scheduling for resource discovery using Soft Actor Critic (SAC) algorithm by considering 3P’s parameters which is performed by spark environment that schedules optimal resources based on the service request. In third, we perform risk assessment and request dropping, in which the risk nodes of workers are evaluated by master node. To address the resource wastage by attacker, this research evaluates the risk value in a dynamic manner using Shannon entropy. Based on the risk assessment the requestsare classified into two classes such as normal and malicious. In fourth we perform service live migration, in which the malicious requests are dropped and normal request are migrated from source node to target node using Multi-Constraints based Emperor Penguin Optimization (MC-EPO). Finally, simulation is performed by GridSim and the simulation results demonstrate that the proposed SparkGrid method achieves superior performance compared to other state-of-the art methods.

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“…Calculate the fitness values for all emperor penguin using eqn (40) Update the current iteration r by 1…”

Section: End Formentioning

confidence: 99%

Section: Major Problem Statementmentioning

confidence: 99%

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SparkGrid: Blockchain Assisted Secure Query Scheduling and Dynamic Risk Assessment for Live Migration of Services in Apache Spark based Grid Environment

G.M.

Nalini

2022

Preprint

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Human machine interface (HMI) based on a multi-agent system in a water purification plant

Mendoza

Andramuño

Álvarez

et al. 2021

J. Phys.: Conf. Ser.

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The applications of multi-agent systems (MAS) are growing increasingly in the industrial field due to the advantages inherent to their characteristics and properties, the use of distributed automation architectures, which have satisfactorily solved control problems that its complexity and dynamic behavior have not been properly resolved with other approaches under these conditions, intelligent agents must meet the requirements of current automation systems, such as autonomy, flexibility, reconfiguration, in concurrent and collaborative systems, which traditionally do not have been designed to satisfy these characteristics. In the present work, a distributed architecture is proposed for the design of an intelligent agent in a Human-Machine Interface (HMI) for the supervision of the filtering stage of a water purification plant, characterized by the ability to collaborate with the other agents that make up the entire plant. For the projection and design of the system, the Unified Modeling Language (UML) and Petri nets (PN) are used for the simulation and validation of the system, and the implementation of the agent from macros in C language, starting from a methodology of multi-agent design that is applied in this document. The implementation of the intelligent agent in an HMI associated with multi-agent architecture, which allowed to evaluate its behavior through the analysis of the properties of the PN and experimental tests, demonstrating the correct operation of the device, response times and its dynamic behavior based on of the functional requirements of the water purification plant and comparisons with similar works.

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Agent-Based Decentralized Grid Model

Cited by 2 publications

References 29 publications

SparkGrid: Blockchain Assisted Secure Query Scheduling and Dynamic Risk Assessment for Live Migration of Services in Apache Spark based Grid Environment

SparkGrid: Blockchain Assisted Secure Query Scheduling and Dynamic Risk Assessment for Live Migration of Services in Apache Spark based Grid Environment

Human machine interface (HMI) based on a multi-agent system in a water purification plant

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