Energy Efficient Resource Allocation in Federated Fog Computing Networks

Abstract

There is a continuous growth in demand for time sensitive applications which has shifted the cloud paradigm from a centralized computing architecture towards distributed heterogeneous computing platforms where resources located at the edge of the network are used to provide cloud-like services. This paradigm is widely known as fog computing. Virtual machines (VMs) have been widely utilized in both paradigms to enhance the network scalability, improve resource utilization, and energy efficiency. Moreover, Passive Optical Networks (PONs) are a technology suited to handling the enormous volumes of data generated in the access network due to their energy efficiency and large bandwidth. In this paper, we utilize a PON to provide the connectivity between multiple distributed fog units to achieve federated (i.e., cooperative) computing units in the access network to serve intensive demands. We propose a mixed integer linear program (MILP) to optimize the VM placement in the federated fog computing units with the objective of minimizing the total power consumption while considering inter-VM traffic. The results show a significant power saving as a result of the proposed optimization model by up to 52%, in the VM-allocation compared to a baseline approach that allocates the VM requests while neglecting the power consumption and inter-VMs traffic in the optimization framework.

A Fog Computing-Based Efficient Data Management Smart Home Architecture

“…Furthermore, Fog based solution helps reduce energy consumption, latency and network bandwidth usage with the CCTV smart optical camera data being process at the network's edge. [8].…”

“…Furthermore, these Fog computing resources have limited capacity and are heterogeneous regarding transient storage, processing, and computational capabilities. Consequently, the management of these Fog computing resources becomes a priority to ensure Quality of Service (QoS) through effective resource allocation [7], [8]. [9] indicates the challenges associated with the use of Cloud based computing which include higher cost of communication, increased latency, higher energy consumption and severe bottlenecks in the network bandwidth usage.…”

“…Although, the Cloud possesses robust computing, processing, and storage capabilities, with most applications hosted in the Cloud, the share distance between the smart devices and the Cloud contributes to the some of the challenges associated with its use. Consequently, there has been a shift with researchers proposing the use of Fog computing as an extension to the Cloud [8]. CCTV monitoring and surveillance solutions especially within a smart home are not only relevant but critical to safety and security as modern cameras possess optical sensors for object and facial recognition technology [10], [11].…”

A Loading-Aware TDMA Sleep Scheme to Improve Power Consumption Performance on Medium to High Traffic for NG-EPON Networks

“…They developed a method for setting both the gratuitous grant interval and the size at levels that decreased the total upstream grant time and improved power efficiency in time-division multiplexing (TDM) PONs. Alqahtani et al [15] utilized the connectivity of a PON to connect multiple distributed fog units for the purpose of cooperative computing. In their work, they adopted mixed-integer linear programming (MILP) to optimize virtual machine placement in order to meet intensive demand and reduce power consumption.…”