Abstract
Edge computing brings powerful computing ability to the proximity of IoT devices to guarantee latency constraints, making it one essential technology for supporting intelligent applications in future Internet of Things (IoT). Collaboration among edge computing servers (ECSs) with limited resources is an efficient solution to enhance the capability of edge network, and placement of ECSs and service functions (SFs) impose significant influences on system performance. This paper explores the collaboration among ECSs by considering the simultaneous and heterogeneous consumption of different computing resources. The service deployment and application assignment in regional edge computing enabled IoT (EdgeIoT) are investigated. A collaborative service deployment and application assignment (ColSDA) algorithm is proposed to render the final edge service deployment strategy, including the placement of ECSs and SFs as well as the assignment of applications to ECSs. In ColSDA, the minimum number of ECSs to be placed is obtained by the proposed minimum resource ration increase (MinRI) algorithm. Computing loads are then balanced by the load-balancing reassignment (LBRA) algorithm. After placing ECSs, a search and swap (SeSw) algorithm is proposed to further increase the number of tasks processed by locally deployed ECSs. Simulation results demonstrate that the number of required ECSs under the premise of guaranteeing the quality of service (QoS) can be significantly reduced by establishing collaboration among ECSs. Besides, the proposed ColSDA algorithm can provide the service deployment and application assignment strategy for a given region EdgeIoT as expected.
Highlights
Edge computing moves the capability of processing sophisticated tasks generated by emerging intelligent applications to the proximity of applications, making it promising architecture for future Internet of Things (IoT) to remedy the deficiency of cloud computing about latency that is more difficult to address in next-generation mobile networks [1], [2]
Three feasible collaboration methods were introduced by considering the resources-limitation of edge computing servers (ECSs), the heterogeneous resources’ consumption of applications, and base resources’ consumption of service functions (SFs)
We formulated the primary objective of minimizing the number of ECSs to be placed as a vector bin packing problem, and the minimum resource ratio increase (MinRI) algorithm was proposed to obtain the minimum ECSs
Summary
The resource efficiency and system performance can be improved by establishing collaboration among ECSs and assigning application tasks according to their resources-consumption properties, which provide the potential of reducing the cost of network deployment. The SF placement (SFP) refers to placing SFs on pre-deployed ECSs and scheduling of the transmission or assignment of tasks from applications to corresponding SFs [19], [20], which can provide flexible edge service management to obtain optimal system performance like service latency, the number of applications processed in edge network, and data volume further offloaded to cloud server [29]–[32]. Given the above motivation, this paper investigates the edge service deployment in regional IoT like IIoT where facilities running sophisticated applications are pre-deployed and working continuously.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
