Energy-efficient virtual machine (VM) selection and placement in fog computing deal with optimizing the allocation of VMs to fog nodes in a fog computing network to minimize energy consumption and satisfy the requirement of Qos. Factors such as the processing capabilities and energy consumption of the fog nodes, the requirements of the VMs, and the network traffic and communication patterns between the VMs are considered. VM placement should be optimized to accommodate fluctuations in VM demand, cloud/fog resource workloads, or network status.
To optimize virtual machine (VM) placement and minimize meshing and processing in a three-cell fog system, and also aims to reduce the overall power consumption during the placement of the VMs. The analysis also looks at the impact of various factors such as traffic between VMs and their users, VM workload, number of workloads and user profiles, and distance between fog nodes and users.
VMs can be migrated or cloned to other servers inside the same data center or geographically allocated data centers. The VM placement algorithm balances server processing and storage resources. Energy-efficient VM placement on end-to-end Cloud Fog architecture with end-to-end architecture in attention. VM power consumption is committed by the hosting server, signifying that CPU utilization and power consumption of a server are relatively correlated.
The goal is to balance the trade-off between energy consumption and performance to provide a more sustainable and cost-effective solution. From a CPU aspect, primarily the workload of the VM and the number of users it serves to follow one of two profiles:
• Constant
• Linear
• Interference between VMs: Interference between VMs can occur when multiple VMs share the same fog node, resulting in reduced performance and increased energy consumption.
• Limited Fog Node Resources: Fog nodes have limited resources such as processing power, memory, and storage, which can limit the number of VMs that can be allocated to a single node.
• Complex Decision Making: Selecting the most appropriate fog node for each VM and optimizing the allocation of VMs to fog nodes can be a complex decision-making process that involves considering multiple factors such as processing capabilities, energy consumption, network traffic patterns, and QoS requirements.
• Real-time Monitoring and Feedback: Developing real-time monitoring and feedback mechanisms that continuously monitor the energy consumption and performance of the VMs and fog nodes are crucial for ensuring energy efficiency in fog computing systems.
• Interference Management: Managing the interference between VMs that share the same fog node is a crucial challenge for ensuring energy efficiency in fog computing. Future research must address this challenge by developing algorithms that minimize interference between VMs.
• Energy Modeling and Prediction: Accurately modeling and predicting the energy consumption of the fog nodes and VMs is crucial for effective energy-efficient VM selection and placement. Future research must address this challenge by developing more accurate and detailed energy models that consider the dynamic nature of fog computing networks.
• An improved multi-objective eagle algorithm for virtual machine placement in a cloud environment.
• Seagull optimization algorithm-based multi-objective VM placement in edge-cloud data centers.
• Synergies between resource sustainability and energy performance of cloud servers: The role of virtual machine repacking approach.
