| Abstrakt: |
The increasing heterogeneity of traffic in the Internet of Things (IoT) service demands presents a challenge for computing nodes to meet the computing resources and link bandwidth required. To address this, IoT requests have been virtualized and organized in service function chaining (SFC), which requires a host among hybrid cloud‐fog computing nodes. To find a suitable host for each service function (SF) request, a typical migration algorithm has been used. However, this approach delayed the response due to propagation delays in searching for a valid nearby node. Mission‐critical service demands may not even be served at all. This article addresses this issue by proposing two novel approaches: nearest candidate node selection (NCNS) and fastest candidate node selection (FCNS). These approaches employ software‐defined network (SDN) controllers and the Markovian arrival process, Markovian service process, and single host (M/M/1) queuing model to monitor the maximum possible latency required to meet the SF service demand by each computing node and finally assigning into one with least latency. With the use of these methods, heuristic monitoring of computing resources is made possible, allowing the selection of the most suitable host computing nodes based on proximity or minimal round‐trip time. Moreover, priority‐based fastest candidate node selection (PB‐FCNS), an adaptation of FCNS, accounts for concurrent service requests using the general arrival process, general service process, and single host node (G/G/1) queuing model. Compared to traditional migration algorithms, NCNS and FCNS provide significant improvements in reducing round‐trip time by 5% and decreasing the probability of unsuccessful service function chains by 55%. Despite the cost of installation, employing these methods in conjunction with SDN controllers can reduce latency, maximize service success rates, and guarantee the delivery of heterogeneous service functions. [ABSTRACT FROM AUTHOR] |
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