Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

Abstract

Nowadays, fog computing as a complementary facility of cloud computing has attracted great attentions in research communities because it has extraordinary potential to provide resources and processing services requested for applications at the edge network near to users. Recent researchers focus on how efficiently engage edge networks capabilities for execution and supporting of IoT applications and associated requirement. However, inefficient deployment of applications’ components on fog computing infrastructure results bandwidth and resource wastage, maximum power consumption, and unpleasant quality of service (QoS) level. This paper considers reduction of bandwidth wastage in regards to application components dependency in their distributed deployment. On the other hand, the service reliability is declined if an application’s components are deployed on a single node for the sake of power consumption management viewpoint. Therefore, a mechanism for tackling single point of failure and application reliability enhancement against failure are presented. Then, the components deployment is formulated to a multi-objective optimization problem with minimization perspective of both power consumption and total latency between each pair of components associated to applications. To solve this combinatorial optimization problem, a multi-objective cuckoo search algorithm (MOCSA) is presented. To validate the work, this algorithm is assessed in different conditions against some state-of the arts. The simulation results prove the amount 42%, 29%, 46%, 13%, and 5% improvement of proposed MOCSA in terms of average overall latency respectively against MOGWO, MOGWO-I, MOPSO, MOBA, and NSGA-II algorithms. Also, in term of average total power consumption the improvement is about 43%, 28%, 41%, 30%, and 32% respectively.

Evaluation Model of Football Players’ Training and Teaching Actions Based on Artificial Intelligence

“…e image-based representation method means that the original data come from the image, and the relevant model is established to extract the information we want to obtain from the image. e global information of the data in the image is extracted, and a global feature extraction model [25,26]isestablished. In action recognition, the position changes in key parts of the human body in the action sequence are generally used for estimation.…”

Automatic generation of threat paths in internet of things‐based systems

“…It also demonstrates significant capability to measure quality parameters with reduced computing cost, time [43] and delay through enhanced diagnostic accuracy [44,45], reliability, resiliency, and improved privacy and security [46,47]. Low latency, energy saving [58] and minimal human interaction are among other advantages of the proposed method.…”

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

“…However, inefficient deployment of components on fog infrastructure results bandwidth, resource and energy wastage, reduce system performance. To solve this problem, Yaser Ramzanpoor et al [ 51 ] proposed a multi‑objective optimization algorithm for fog-IoHT applications. Authors used a multi-objective cuckoo search algorithm (MOCSA) to solve the multi‑objective optimization problem of both energy and latency.…”