Abstract
With the rapid resource requirements of Internet of Things applications, cloud computing technology is regarded as a promising paradigm for resource provision. To improve the efficiency and effectiveness of cloud services, it is essential to improve the resource fairness and achieve energy savings. However, it is still a challenge to schedule the virtual machines in an energy-efficient manner while taking into consideration the resource fairness. In view of this challenge, a fair energy-efficient virtual machine scheduling method for Internet of Things applications is designed in this article. Specifically, energy and fairness are analyzed in a formal way. Then, a virtual machine scheduling method is proposed to achieve the energy efficiency and further improve the resource fairness during the executions of Internet of Things applications. Finally, experimental evaluation demonstrates the validity of our proposed method.
Highlights
Internet of Things (IoT), proposed by Prof
To the best of our knowledge, few of the works for the virtual machine (VM) scheduling have been done for IoT applications in the cloud environment, while taking both energy consumption and resource fairness into consideration
As IoT applications consume a large number of physical resources, including computation, storage, and so on, we mainly focus on the energy consumption due to the execution of IoT applications
Summary
Internet of Things (IoT), proposed by Prof. Ashton in 1999, has become one of the most popular technologies recently.[1,2] In the past few years, the connotation of IoT has been deployed rapidly, and the applications of IoT continue expanding enormously as well. To the best of our knowledge, few of the works for the VM scheduling have been done for IoT applications in the cloud environment, while taking both energy consumption and resource fairness into consideration. Different types of physical machines (PMs) are deployed to provide resources for IoT applications. To facilitate VM scheduling for IoT applications, the task requests of IoT applications are quantified by each type of resources in section ‘‘Resource fairness analysis.’’ To schedule VMs in an energy-efficient and fair manner, the unused resources should be identified. Amazon EC2 provides various VM instance types to accommodate the demand performance of cloud renters, including CPU-optimized instances, high I/O instances, and so on.[14] Generally, if the dominant resource of a PM is CPU, this PM is employed to respond to the CPU-optimized VMs. Algorithm 3 PM identification and classification.
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