{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:46:31Z","timestamp":1747190791851,"version":"3.40.5"},"reference-count":25,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2022,8,19]]},"abstract":"<jats:p>Fog computing is becoming a dynamic and sought-after computing prototype for Internet of Things (IoT) application deployments. It works in conjunction with the cloud computing environment. Load balancing, which is employed by IoT applications when deciding, which fog or cloud computing nodes to use, is one of the most critical components for enhancing resource efficiency and avoiding problems like overloading or underloading. However, for IoT applications, ensuring that all CPU nodes are evenly distributed in terms of latency and energy utilization remains a challenge. To solve these issues, this work introduces Differential Grey Wolf (DGW) load balancing with stochastic Bellman deep reinforced resource optimization (DGW-SBDR) in fog situations. A Differential Evolution-based Grey Wolf Optimization algorithm based on load balancing has been developed for optimal resource management. The Grey Wolf Optimization algorithm, which employs differential evolution, assigns jobs to virtual machines based on user demands (VMs). In the event of an overutilized VM pool, a grey wolf optimization strategy based on differential evolution can detect both under and overutilized VMs, allowing for smooth transit between fogs. This step disables a number of virtual machines in order to reduce latency. In a Stochastic Gradient and Deep Reinforcement Learning-based Resource Allocation Model, a stochastic gradient bellman optimality function and Deep Reinforcement Learning are integrated for optimal resource allocation. According to the proposed method, QoS may be supplied to end-users by reducing energy consumption and better managing cache resources utilizing stochastic gradient bellman optimality.<\/jats:p>","DOI":"10.1155\/2022\/3183701","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T14:50:09Z","timestamp":1660920609000},"page":"1-13","source":"Crossref","is-referenced-by-count":1,"title":["Differential Grey Wolf Load-Balanced Stochastic Bellman Deep Reinforced Resource Allocation in Fog Environment"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6181-257X","authenticated-orcid":true,"given":"S. V.","family":"Nethaji","sequence":"first","affiliation":[{"name":"PG & Research Department of Computer Science, Rajah Serfoji Government College (Autonomous), (Affiliated to Bharathidasan University, Tiruchirappalli), Thanjavur, India"},{"name":"PG Department of Computer Science, M. R. Government Arts College, (Affiliated to Bharathidasan University, Tiruchirappalli), Mannargudi, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4744-6750","authenticated-orcid":true,"given":"M.","family":"Chidambaram","sequence":"additional","affiliation":[{"name":"PG & Research Department of Computer Science, Rajah Serfoji Government College (Autonomous), (Affiliated to Bharathidasan University, Tiruchirappalli), Thanjavur, India"}]}],"member":"311","reference":[{"volume-title":"Dynamic Energy Efficient Resource Allocation Strategy for Load Balancing inFog Environment","year":"2020","author":"Z. A. AneesUrRehman","key":"1"},{"issue":"6","key":"2","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1007\/s11276-018-1699-y","article-title":"Dynamic energy\u2014efficient resource allocation in wireless powered communication network","volume":"25","author":"J. 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Batista","year":"2021"},{"volume-title":"A Novel Strategy to Achieve Bandwidth CostReduction and Load Balancing in aCooperative Three-Layer Fog-Cloud Computing Environment","year":"2020","author":"M. MohdShahriarMaswood","key":"7"},{"volume-title":"DRALBA: Dynamic and Resource AwareLoad Balanced Scheduling Approachfor Cloud Computing","year":"2021","author":"S. Nabi","key":"8"},{"volume-title":"Modeling an Optimized Approach for LoadBalancing in Cloud","year":"2020","author":"M. Junaid","key":"9"},{"volume-title":"Talaat, Effective Prediction and Resource Allocationmethod (EPRAM) in Fog Computing Environmentfor Smart Healthcare System, Multimedia Tools and Applications","year":"2022","author":"M. Fatma","key":"10"},{"article-title":"Efficient resource allocation in fog computing using QTCS model","year":"2021","author":"M. Iyapparaja","key":"11"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.23919\/jcn.2021.000041"},{"volume-title":"Dynamic Resource Allocation in Fog-Cloud HybridSystems Using Multi-Criteria AHP Techniques","year":"2020","author":"S. Mishra","key":"13"},{"volume-title":"Joint Resource Allocation for Device-To-Device Communication Assisted Fog Computing","year":"2019","author":"C. Yi","key":"14"},{"volume-title":"A Resource Management Model forDistributed Multi-Task Applicationsin Fog Computing Networks","year":"2021","author":"F. Hosseinpour","key":"15"},{"key":"16","article-title":"Advanced optimization technique for scheduling IoT tasks in cloud-fogcomputing environments","volume-title":"Future Generation Computer Systems","author":"M. AbdElaziz","year":"2021"},{"volume-title":"Dynamic Resource Allocation and ComputationOffloading for IoT Fog Computing System","year":"2020","author":"Z. Chang","key":"17"},{"volume-title":"Multi-objective Optimization for Task Offloading Based on Network Calculusin Fog Environments, Digital Communications and Networks","year":"2021","author":"Q. Ren","key":"18"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2020.2993561"},{"key":"20","article-title":"To optimize the multi accesses download time using schedulingapproach in fog computing","volume-title":"Materials Today: Proceedings","author":"M. Keerthika","year":"2020"},{"volume-title":"Joint Resource Optimization for UAV-EnabledMultichannel Internet of Things Based onIntelligent Fog Computing","year":"2020","author":"X. Liu","key":"21"},{"volume-title":"Multi-Objective Task Scheduling Approachfor Fog Computing","year":"2021","author":"M. 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