LBRO: Load Balancing for Resource Optimization in Edge Computing

Abstract

Mobile cloud computing and edge computing-based solutions provide means to offload tasks for resource-limited mobile devices. Mobile cloud computing provides remote cloud solutions while edge computing provides closer proximity-based solutions. Remote cloud solutions suffer from network latency and limited bandwidth challenges due to distance and dependency on the Internet. However, these challenges are addressed by edge-based solutions since the edge node is available in the same network. The use of Internet of Things-based solutions considering future Information Communication Technology infrastructure is on the rise resulting in the massive growth of digital equipment increasing the load at edge devices. Hence, some load balancing mechanism is required at the edge level to avoid resource congestion. The load balancing at the edge must consider the user’s preferences about edge resources such as personal computers or mobile devices. A user must declare which resources can be spared for other devices to avoid overprovisioning essential resources. We present Load Balancing for Resource Optimization (LBRO), a collaborative cloudlet platform to address load balancing challenges in edge computing considering users’ preferences. A comparative analysis of the proposed approach with the conventional edge-based approach yields that the proposed approach provides significantly improved results in terms of CPU, memory, and disk utilization.