Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network
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Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network Seyedeh Maedeh Mirmohseni1 · Chunming Tang1 · Amir Javadpour1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The integration of the Internet of Things (IoT) and cloud environment has led to the creation of Cloud of Things, which has given rise to new challenges in IoT area. In this paper, using the Markov model learning method and calculating the need probability of each object to resources shortly to reduce latency and maximize network utilization, allocating resources in the fog layer has been possible and processed. By using simulations in the CloudSim platform, it is examined the processor productivity for the number of tasks, the workflow overhead for the number of tasks, physical machine’s energy consumption for the number of tasks, the data locality for the number of tasks, resource utilization for the number of tasks, and completion of task for the number of tasks and compared with the SMDP (SemiMarkov decision processes) and MDP methods, results show that the proposed research is effective and promising. Keywords Network utilization · Markov model · Fog architecture · Cloud of Things
1 Introduction The Internet of Things is a new technology where communication goes beyond the communication of human to human, human to machine and human to computer. In IoT environments, billions or trillions of objects can communicate concurrently in the environment and exchange information simultaneously [1, 2]. IoT has become increasingly important in the areas of industrial applications, transportation, and different personal applications such as e-health care, smart cities, etc., and today, the influence of IoT is well-known around the world. IoT is a communication revolution between intelligent objects being able to see, hear and think, and communicate with each other without human intervention [2–6]. IoT provides a platform for the data generated * Chunming Tang [email protected] Seyedeh Maedeh Mirmohseni [email protected] Amir Javadpour [email protected] 1
School of Mathematics and Computer Science, Guangzhou University, Guangzhou 510006, China
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by sensors and various hardware devices to be processed by data analysis systems such as machine learning and so-called it makes the devices intelligent. This intelligence means making the more appropriate and optimal decisions, which all of the steps would be far from human intervention [2]. In a network of this scalable scope and completely heterogeneous, it is essential to search, locate and discover the resources and services they provide. Due to the enormous volume of data in the IoT, the limited systems are not able to interact with this volume of data. In this case, cloud computing takes action. IoT and cloud computing working in integration make a new paradigm, known as Cloud of Things (CoT), which can help reach future goals of the Internet [2, 7]. For preventing unnecessary
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