Method for Dynamic Service Orchestration in Fog Computing

Abstract

Fog computing is meant to deal with the problems which cloud computing cannot solve alone. As the fog is closer to a user, it can improve some very important QoS characteristics, such as a latency and availability. One of the challenges in the fog architecture is heterogeneous constrained devices and the dynamic nature of the end devices, which requires a dynamic service orchestration to provide an efficient service placement inside the fog nodes. An optimization method is needed to ensure the required level of QoS while requiring minimal resources from fog and end devices, thus ensuring the longest lifecycle of the whole IoT system. A two-stage multi-objective optimization method to find the best placement of services among available fog nodes is presented in this paper. A Pareto set of non-dominated possible service distributions is found using the integer multi-objective particle swarm optimization method. Then, the analytical hierarchy process is used to choose the best service distribution according to the application-specific judgment matrix. An illustrative scenario with experimental results is presented to demonstrate characteristics of the proposed method.

Distributed Agent-Based Orchestrator Model for Fog Computing

“…To our knowledge, the most similar works include Casadei and Viroli [ 35 ], which presents a decentralized (federated) control approach, rather than fully distributed, and Jalali et al [ 36 ], which uses machine learning methods to predict optimal placement, while our approach includes the combinatorial placement algorithm—presented in our previous work [ 37 ]. The Fog Computing for Robotics and Industrial Automation (FORA) platform, proposed by Pop et al [ 38 ], also uses the collaboration and synchronization of fog nodes, therefore each node can make local decisions about which application tasks it will execute.…”

“…When a user end-device requests the nearest fog node to perform a task (makes a service request), the following steps are performed: The fog node (task receiver) identifies the type of task and the resources needed for such task. The fog node checks the free resources left in each neighboring fog node (including itself) that can serve the request and calculates the best placement for the requested service, using the combinatorial placement algorithm presented in our previous work [ 37 ]. The task is then placed on one of the fog nodes by activating the appropriate service needed for the user end-device.…”

“…The fog node checks the free resources left in each neighboring fog node (including itself) that can serve the request and calculates the best placement for the requested service, using the combinatorial placement algorithm presented in our previous work [ 37 ].…”

“…The service placement optimization method used in this research was proposed in [ 37 ]. This multi-objective optimization method is suitable for finding the best placement of n available services in k fog nodes according to the given constraints and conditions.…”

Edge Computing for Internet of Things

“…IoT applications that involve end-nodes, edge and fog computing are complex to manage. In [5], the authors propose a method for dynamically orchestrating services in a Fog architecture. The approach focuses on heterogeneous devices and the dynamic nature of the end devices.…”