# Multi-Agent Dynamic Fog Service Placement Approach
###### Abstract  
Fog computing as a paradigm was offered more than a decade ago to solve Cloud Computing issues. Long transmission distances, higher data flow, data loss, latency, and energy consumption lead to providing services at the edge of the network. But, fog devices are known for being mobile and heterogenous. Their resources can be limited, and their availability can be constantly changing. A service placement optimization is needed to meet the QoS requirements. We propose a service placement orchestration, which functions as a multi-agent system. Fog computing services are represented by agents that can both work independently and cooperate. Service placement is being completed by a two-stage optimization method. Our service placement orchestrator is distributed, services are discovered dynamically, resources can be monitored, and communication messages among fog nodes can be signed and encrypted as a solution to the weakness of multi-agent systems due to the lack of monitoring tools and security.  
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(2) network, (3) cloud services and resources, (4) software-defined resource management, and (5) IoT applications and solutions. Please see Figure 1 for more details.  
Even though the number of fog computing layers may sometimes slightly differ, the end-device layer will always consist of IoT devices such as smartphones, tables, computers, sensors [10], remote machines, and smart vehicles [11], which communicate in a wired or wireless manner [12] with the fog layer. These end-devices may have limited resources [13] such as storage and computational power, as well as limited bandwidth.  
Multi-agent systems (MASs) in turn, as the review article [14] claims, gained excessive attention from scientists of different areas such as a computer science and civil engineering. They can solve complex tasks by breaking them down into smaller ones. Each task can be assigned to agents that function independently from each other. Agents can make decisions on taking actions based on their action history, interactions, and its goal. Multi-agent systems recently have been a popular research topic, and they are applied in such areas as unmanned aerial vehicles, industrial internet of things, and wireless sensor networks as per the publication [15]. However, as it added in the same publication, irrespective of all the benefits, multi-agent systems are very vulnerable to network attacks due to an open communication environment and the complexity of the system. It lacks an integrated system to monitor and manage the activities of all the network nodes. Information exchange is usually very high, but the information flow cannot be verified, and therefore the system is at a security risk.  
This paper includes the following sections: Introduction, Related Work Review, Service Placement Orchestrator Implementation, Materials and Methods, Results, Discussion, and Conclusions. Apart from general information in the Introduction, the Related Work Review section gives more focused details of the problems and potential solutions in other research works. Orchestrator design is defined in the Service Placement Orchestrator Implementation section based on relevant characteristics. The chapter of Materials and Methods discusses the hardware and software as well as test methods. The Results, Discussion, and Conclusions sections give an insight into the experimental results, followed by assumptions and result conclusions.