Autonomic Resource Management for Fog Computing

Abstract

Fog computing is a distributed computing paradigm that extends cloud computing capabilities to the edge of the network and aims at reducing high latency and network congestion, which are characteristics of cloud computing. This recent paradigm enables portions of a transaction to be executed at a fog server and other portions at the cloud. Fog servers are generally not as robust as cloud servers; at peak loads, the data that cannot be processed by fog servers is processed by cloud servers. The data that need to be processed by the cloud is sent over a Wide Area Network (WAN). Therefore, only a fraction of the total data needs to travel through the WAN, as compared with a pure cloud computing paradigm. Additionally, the fog/cloud computing paradigm reduces the cloud processing load when compared with the pure cloud computing model. This article presents a multiclass closed-form analytic queuing network model that is used by an autonomic controller to dynamically change the fraction of processing between edge and cloud servers in order to maximize a utility function of response time and cost. The model was validated using both synthetic and real IoT traces. A detailed design of the autonomic controller is presented and a series of experiments compare the efficacy and efficiency of the controller versus a brute force optimal controller and versus an uncontrolled system using synthetic and real traces. The results show that the controller is able to maintain a high utility in the presence of wide variations of request arrival rates.