With the rapid development of data and IT technology, cloud computing is gaining more and more attention, and many users are attracted to this paradigm because of the reduction in cost and the dynamic allocation of resources. Load balancing is one of the main challenges in cloud computing system. It redistributes workloads across computing nodes within cloud to minimize computation time, and to improve the use of resources. This paper proposes an enhanced ‘Active VM load balancing algorithm’ based on fuzzy logic and k-means clustering to reduce the data center transfer cost, the total virtual machine cost, the data center processing time and the response time. The proposed method is realized using Java and CloudAnalyst Simulator. Besides, we have compared the proposed algorithm with other task scheduling approaches such as Round Robin algorithm, Throttled algorithm, Equally Spread Current Execution Load algorithm, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). As a result, the proposed algorithm performs better in terms of service rate and response time.
With the advancement of technology, healthcare and monitoring systems face significant issues. Internet of Things (IoT) in healthcare enables real-time health monitoring at a low cost. This paper aims to provide a medical telemonitoring system based on IoT for healthcare applications for doctors and paramedical staff. The system is made up of many sensors that can capture electrocardiograms (ECGs) in real-time and measure the temperature of the human body. The designed circuit is implemented and the obtained results are analyzed and discussed.
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