LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

Abstract

AbstractNowadays, fog computing has joined cloud computing as an emerging computing paradigm to provide resources at the network edge. Fog servers process data associated with Internet of Things (IoT) devices independently of cloud computing, thus saving bandwidth, resource reservations, and storage for real‐time applications with lower latency. Besides, cloud computing supports the integration of edge and cloud resources and facilitates the placement of IoT applications at the network edge. Recent researchers focus on how to deploy IoT services as components of IoT applications on fog computing units, where the loss of resources, energy, and bandwidth are minimized. This problem, known as the IoT service placement problem (SPP), is NP‐hard, and meta‐heuristic models are popular to address it. Each IoT service has its own requirements in terms of latency sensitivity, processor, memory, and storage. Meanwhile, fog computing units are heterogeneous and have limited resource capacities. Therefore, SPP should be addressed by considering the features of fog environment, tolerable delay, and network bandwidth. We formulate SPP as a multi‐objective optimization problem with the perspective of throughput, service cost, resource utilization, energy consumption, and service latency. To solve this problem, the learner performance‐based behavior (LPB) algorithm is presented as a meta‐heuristic model that originates from the MAPE‐K autonomous planning model. The proposed approach, LPB‐SPP, considers resource consumption distribution and service deployment prioritization, and also uses the concepts of elitism and balanced resource consumption to improve the placement process. The validation of LPB‐SPP has been done using different performance metrics and the results have been compared against state‐of‐the‐art algorithms. Simulations show that LPB‐SPP performs better in most comparisons.