{"title":"Fog Load Balancing Broker (FLBB)","authors":"Mandeep Kaur, Rajinder Sandhu, R. Mohana","doi":"10.1109/ICIIP53038.2021.9702669","DOIUrl":null,"url":null,"abstract":"For efficient and timely execution of an IoT job allocation of an appropriate set of resources throughout its life span is significant. Initial allocation of resources is done through job scheduling techniques and for managing the resources during the execution load balancing techniques are implemented. Load Balancing in distributed systems is as important as is Job Scheduling. Modern systems are technically capable to place job requests on the most appropriate set of resources at the beginning of the execution process. But in distributed environments resources behave dynamically and the status of busy, available, or free resources keeps on changing very frequently. In such conditions single time allocation of resources to a job request, till the end of execution cannot be sufficient. For efficient utilization of available resources, timely execution, and efficient delivery of response resource allocations must be revised during the life span of a job request. This paper proposes a load balancing solution that takes care of the changing states of resources in the fog environments and relocates the job request from one environment to another wherever is found beneficial. The proposed framework performs its task in two steps: first checks if the relocation is feasible or not and second to select a job for relocation and shift it to some other environment. This framework is specifically designed for fog environments where load balancing is a pivot point for effective and efficient resource utilization, bandwidth and to achieve the desired quality of service (QoS).","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Sixth International Conference on Image Information Processing (ICIIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIIP53038.2021.9702669","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
For efficient and timely execution of an IoT job allocation of an appropriate set of resources throughout its life span is significant. Initial allocation of resources is done through job scheduling techniques and for managing the resources during the execution load balancing techniques are implemented. Load Balancing in distributed systems is as important as is Job Scheduling. Modern systems are technically capable to place job requests on the most appropriate set of resources at the beginning of the execution process. But in distributed environments resources behave dynamically and the status of busy, available, or free resources keeps on changing very frequently. In such conditions single time allocation of resources to a job request, till the end of execution cannot be sufficient. For efficient utilization of available resources, timely execution, and efficient delivery of response resource allocations must be revised during the life span of a job request. This paper proposes a load balancing solution that takes care of the changing states of resources in the fog environments and relocates the job request from one environment to another wherever is found beneficial. The proposed framework performs its task in two steps: first checks if the relocation is feasible or not and second to select a job for relocation and shift it to some other environment. This framework is specifically designed for fog environments where load balancing is a pivot point for effective and efficient resource utilization, bandwidth and to achieve the desired quality of service (QoS).