Resource estimation and sharing in fog computing involves identifying, quantifying, and allocating computing resources, such as processing power, memory, and network bandwidth, among different nodes in a fog network.
In fog computing, resources can be shared among multiple devices in a decentralized manner, which can be beneficial for reducing the total computation cost and providing better response times to end-users. Consequently, effective resource estimation and sharing need sophisticated algorithms and protocols to accurately predict the resource requirements of different tasks and allocate resources accordingly.
Thus, effective resource estimation and sharing are critical for the success of fog computing and will likely play a key role in shaping the future of edge computing and the Internet of Things (IoT). The aim is to make the best use of available resources and distribute workloads effectively to ensure that the fog system can provide high levels of performance and reliability. Core components of resource sharing include
• User device
• User devices communicate with gateway devices to ascertain computing requests.
• Volunteer user device
• Volunteer user devices newscast resource data and accessible energy to nearby fog gateway nodes.
• Fog gateway
• Fog gateway is responsible for accepting resource requests, throb messages from users, and requests to perform arbitrary tasks to meet the quality of service.
The favors of resource estimation and sharing in fog computing
• Reduce service cost
• Increased performance of fog under Qos.
• Resource Competition: With limited resources available in fog computing, resource sharing can lead to competition among different tasks and applications, negatively impacting performance and efficiency.
• Interference: Sharing resources among multiple devices can also lead to interference and reduced performance, especially in cases where different tasks require conflicting resources.
• Inaccurate Resource Estimates: Resource estimation can be challenging, as it requires accurate predictions of the resource requirements of different tasks, which can be difficult to determine in practice.
• The overload amount is uncertain
• Decreased scalability in scheduling
• Fairness: Ensuring fair resource allocation among different tasks and users is a major challenge, as some tasks may require more resources than others, and some users may have higher priority needs.
• Dynamic Resource Availability: Fog networks are typically highly dynamic, with changing resource availability and unpredictable changes in demand, making it difficult to estimate and allocate resources accurately.
• Game Theory-Based Approaches: Using game theory to model resource allocation and competition in fog computing is a promising research direction, as it can provide a mathematical framework for understanding and solving resource allocation problems.
• Resource Management in Heterogeneous Fog Networks: Resource estimation and sharing in heterogeneous fog networks, where different devices have different capabilities and resource availability, is a challenging research problem that requires new approaches and techniques.
• A Stochastic Optimization Approach for Resource Allocation in Cloud-Fog Computing Environments"
• A Resource Allocation Framework for Energy-efficient Fog Computing
• A Distributed Resource Allocation Algorithm for Large-scale Fog Computing Networks"
• Enhancing Security and Privacy in Fog Computing through Blockchain-based Resource Allocation"
