M2FBalancer: A mist-assisted fog computing-based load balancing strategy for smart cities

Abstract

Nowadays, cities are intended to change to a smart city. According to recent studies, the use of data from contributors and physical objects in many cities play a key element in the transformation towards a smart city. The ‘smart city’ standard is characterized by omnipresent computing resources for the observing and critical control of such city’s framework, healthcare management, environment, transportation, and utilities. Mist computing is considered a computing prototype that performs IoT applications at the edge of the network. To maintain the Quality of Service (QoS), it is impressive to employ context-aware computing as well as fog computing simultaneously. In this article, the author implements an optimization strategy applying a dynamic resource allocation method based upon genetic algorithm and reinforcement learning in combination with a load balancing procedure. The proposed model comprises four layers i.e. IoT layer, Mist layer, Fog layer, and Cloud layer. Authors have proposed a load balancing technique called M2F balancer which regulates the traffic in the network incessantly, accumulates the information about each server load, transfer the incoming query, and disseminate them among accessible servers equally using dynamic resources allocation method. To validate the efficacy of the proposed algorithm makespan, resource utilization, and the degree of imbalance (DOI) are considered as the scheduling parameter. The proposed method is being compared with the Least count, Round Robin, and Weighted Round Robin. In the end, the results demonstrate that the solutions enhance QoS in the mist assisted cloud environment concerning maximization resource utilization and minimizing the makespan. Therefore, M2FBalancer is an effective method to utilize the resources efficiently by ensuring uninterrupted service. Consequently, it improves performance even at peak times.

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

“…The makespan is the maximum completion time (CT) of a resource. It is denoted as follows, according to the equation in [ 34 ]: …”

An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum

“…When the number of tasks is less, the approach in [11] and the proposed approach produce approximate results due to the implementation of load balancing approaches. It is evident from Figure 3 that for a wide range of tasks and with heterogeneous Fog nodes, the proposed approach offers service time improvement of 51% to [24], 88% to [25], 87% to [15] and 67% to [11], thereby outperforming others because of the load balancing strategy.…”

“…The BLA [24] results in increasing the service time because of not considering the load balancing strategy. In [15], machines' heterogeneity has not been considered.…”

Multi Objective Hybrid Load Balancing Based Optimization Algorithm for Improving Fog Computing Performance

“…Cloud technology meets client requirements for programs, data, sharing facilities, and various equipment at the specified time, making services on request [1][2]. For web systems, most of the information requiring calculations, analysis, and storage has been transferred to internet services, which can affect latency, security, portability, and reliability [3]. With the emergence of place and latencies components, a platform could struggle to encounter such developers' poor delay requisites; however, a clouds element's closeness to its gadgets could significantly reduce delay [4].…”