In fog computing, applications and services are placed closer to the edge devices, such as sensors and IoT devices, to reduce latency and improve response time. The placement also enables more efficient use of network resources and reduces the data transmitted to the cloud.
Services in fog computing can include data processing, storage, and communication services and applications, including industrial automation, surveillance, and smart energy management systems. Service placement of fog computing focuses on different application scenarios, network assumptions, and target capabilities. Service placement is deployed to solve specific use cases that severely limit fog infrastructure adoption into common resource management systems.
A service placement mechanism that leverages contextual information such as location, time and quality of service to optimize service distribution in the fog landscape. A service placement identifies applicable nodes within a network topology to boost the count of services deployed in the fog scape by meeting application response time requirements.
• Resource Awareness: Consider the available resources in the fog nodes, such as processing power, memory, and bandwidth, to determine where an application or service can be placed for optimal performance.
• Collaborative Placement: Multiple fog nodes collaborate and communicate to determine the most appropriate placement of an application or service, considering the available resources and performance requirements.
• The issues of service placement in fog infrastructure are analyzed with several solutions based on different application scenarios :
• Deploy a set of services individually abstracted as a single component.
• Deploy a set of services abstracted as a group of interdependent components.
• Each provides a set of services abstracted as a directed acyclic graph (DAG).
• Smart City.
• Smart Healthcare
• Smart agriculture.
• Performance evaluation: Developing methods for evaluating the performance of fog computing and the placement of applications and services, including metrics such as latency, throughput, and energy consumption.
• Dynamic placement: Developing methods for dynamically placing applications and services in fog computing based on changing network conditions and resource utilization.
• An autonomous IoT service placement methodology in fog computing
• An autonomous IoT service placement methodology in fog computing
• A survey of optimization algorithms for fog computing service placement
• A cost-efficient IoT service placement approach using whale optimization algorithm in a fog computing environment
