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Prediction and Dynamic Adjustment of Resources for Latency-Sensitive Virtual Network Functions

In this paper, we propose a scheme to employ predictive multiple regression models for dynamic resource adjustment of latency-sensitive virtual network functions (VNFs). The objective is to optimize resource allocation with agile control so that both latency and resource utilization meet the target performance requirements, despite the fluctuations in the workload. As a use case of latency-sensitive VNF, we select the Internet-of-things directory service (IoT-DS). IoT-DS is capable of storing up to a billion IoT devices records and provide fast database lookup with few milliseconds of latency. Our experimental results with an operational VNF serving as the IoT-DS show that dynamic adjustment of CPU by the proposed scheme reduces CPU resource requirements by 21.9% and avoids lookup latency requirement violations by 58.2 % compared to a threshold rule-based conventional algorithm. Besides, our scheme can provide agile vertical CPU scaling within a one-second window, which is five times faster than a related machine learning-based prior work.