Energy Efficient End Device Aware Solution Through SDN in Edge-Cloud Platform

Abstract

Recently, the networking industries have gone through tremendous changes. It demands high-speed operations and complex problem-solving abilities. To manage these evolutions Internet-of-Things (IoT) is a proposed solution from several technical corners. Numerous researchers and government organizations showing their interest to provide solutions with IoT implementation. Handling a huge amount of network data, its privacy and security, Quality of Service (QoS) requirements and heterogeneity of underlying networking components are the various challenges in IoT implementations. To provide the solution, Software Defined Networking (SDN) is becoming a bliss in managing such complex networking problems. The allocation of the Virtual Machines (VMs) into the end device is an NP-Hard combinatorial optimization problem. We formulate the problem by using simple Additive Weighing (SAW) or Weighted Sum Method (WSM) to allocate the VMs asymmetrically based on CPU Utilization and Memory usage to optimize the energy. The proposed algorithm ServerCons minimizes the number of live migrations and the number of nodes used as well as the energy usage is at par with the state of art algorithms such as First-Fit-Decreasing(FFD), Best-Fit-Decreasing (BFD), and Modified-Best-Fit-Decreasing (MBFD).

Keywords

• Cloud computing
• end device aware
• energy efficiency
• IoT
• SDN