Task Scheduling and Load Balancing for Minimization of Response Time in IoT Assisted Cloud Environments

The Internet of Things (IoT) necessitates a new processing paradigm that incorporates cloud scalability while reducing network latency by utilising resources closer to the network edge. On the one hand, it’s difficult to achieve such flexibility within the edge-to-cloud continuum, which consists of a distributed networked ecosystem of heterogeneous computing resources. IoT traffic dynamics, on the other hand, and the growing need for low-latency services necessitate decreasing reaction time and balancing service location. For cost-effective system administration and operations, fog computing load-balancing will become a cornerstone. Though virtualization attempts to instantaneously balance the load of the overall network, there’s still the possibility of capacity excessive usage or under development. Heavily loaded systems degrade efficiency, while undercharged systems use bandwidth inefficiently. Because of inadequate load distribution, overburdened systems emit additional energy, driving up the cost of coolers as well as adding significantly to the warming of the planet. Throughout most situations, cooling towers consume higher electricity than core IT technology. Despite the benefits of cloud computing as a distributed pool of resources and services, certain new IoT applications are not cloud-ready. Wind farms and smart traffic light systems, for example, have unique characteristics and requirements “(e.g., large-scale, geo-distribution) (e.g., very low and predictable latency)”. This research paper has considered secondary method of data collection to gather relevant and statistical data related to research topic.