Abstract
In the past decade, the cloud environment has emerged as a resource-enriched technology for computational modeling. Most cloud data centers are deployed far from the device layer, which may cause higher service delivery time. Therefore, Fog servers are implemented to retain the commitment of minimum service delivery time. The computational resources (processing unit, memory, storage) and network bandwidth are to be appropriately scheduled to make an efficient system. For delay and resource-constrained devices, the essential issues include minimum service latency and improved system performance by offloading non-sensitive devices to the cloud layer. In this study, an adaptive bandwidth adjustment technique is proposed considering the sensitivity of the applications and load on the multiple queues at the access point based on which, a compelling dynamic bandwidth ratio is calculated. Further, blocking probability of task and task dropping probability is used as task offloading probability to overcome the dropping ratio of type-II applications. The random early detection (RED) algorithm is implemented in queueing management to maximize the system performance. Extensive simulation results highlighted that the proposed model significantly improves the system performance in terms of network delay, queue delay, and overall response time over traditional algorithms.










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Chauhan, N., Banka, H. & Agrawal, R. Adaptive bandwidth adjustment for resource constrained services in fog queueing system. Cluster Comput 24, 3837–3850 (2021). https://doi.org/10.1007/s10586-021-03378-1
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DOI: https://doi.org/10.1007/s10586-021-03378-1