Optimization of Performance and Scalability Measures across Cloud Based IoT Applications with Efficient Scheduling Approach

In recent decades, the technique of the Internet of Things (IoT) and cloud computing are widely integrated together. The resource-limited nature of IoT devices creates a requirement for middleware to manage a high volume of data in real-time. In such types of systems, the capability to add or remove services based on the application requirement with standard performance measures remains to be a major concern. Against this background, this article presents ant colony-based optimization techniques with MARKOV chains for efficient resource scheduling across cloud-based IoT systems with improved performance and Quality of Service (QoS) measures. It provides a proactive elasticity model for solving scalability issues across cloud-based IoT systems. The proposed work provides an efficient task scheduling algorithm for infinite time, Infrastructure as a Service (IaaS). It makes use of ant colony optimization techniques with continuous parameter MARKOV chains. Each successive path found by ants forms a MARKOV chain and the chain with the highest pheromone vector forms the optimal solution. The major contribution of the work is summarized as follows. The first is to find the optimal solution for task scheduling in IoT based cloud systems with continuous-time parameters. Next is to enhance the QoS with improved availability and reliability. Based on the proposed model, a prototype is developed and it is assessed with various amount of work patterns against two concurrent models. The results are promising in favour of the proposed system, with improved performance measures in terms of response time and request throughput.