Dynamic resource provisioning in containerized edge systems with reconfigurable edge servers

(2025) 2025:25 Awoyemi et al. J Wireless Com Network https://doi.org/10.1186/s13638-025-02450-3 EURASIP Journal on Wireless Communications and Networking Open Access RESEARCH Dynamic resource provisioning in containerized edge systems with reconfigurable edge servers Babatunde S. Awoyemi1* , Mduduzi C. Hlophe1 and Bodhaswar T. Maharaj1 *Correspondence: 1 Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa Abstract Recent technological advancements have seen powerful computational resourceenriched virtual machines (VMs) being used for processing data in edge servers. However, the high energy demands and excessive overhead associated with launching VMs are major obstacles to achieving energy-efficient operations in multi-access edge computing environments. As a result, there has been a relentless acceleration toward container virtualization to provide containerized services at the edge. The lightweight nature of containers compared to VMs makes them a popular technology for edge computing platforms. However, two significant challenges have been identified. The first is the problem of providing real-time support for containerized edge systems (to combat issues of high latency, anomaly detection, and automated monitoring and control, among others). The other problem is that, although containers help reduce application deployment time, considerable network bandwidth is expended and longer download queues are experienced on each node in the network. We propose a dynamic resource provisioning scheme for containerized edge systems to address these challenges. The proposed scheme employs containerized reconfigurable edge servers, which enable computational task operations to be moved to the data source for easier and quicker completion. Then, a novel adaptive power management technique based on predictive control through finite system observations is used to effectively estimate and regulate the energy consumption in the edgebased network. The adaptive controller schedules computational resources on a time slot basis in an adaptive manner, while continuing to receive updates to plan future resource provisioning. The proposed technique is evaluated using welfare gain, server response rate, and energy consumption metrics and is shown to outperform recent comparative models significantly. Keywords: 6G, Adaptive controller, Containers, Edge computing, Massive IoT, MEC, Power management 1 Introduction Distributed computing, especially edge and mobile computing, has grown tremendously over the past few years and is gaining momentum in achieving efficiency—enough to achieve economic competitiveness in several applications. With 6G networks, the © The Author(s) 2025. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Awoyemi et al. J Wireless Com Network (2025) 2025:25 promise of quasi-infinite networking has arrived, with both arguments on coverage and the perspective of computational effectiveness and efficiency. As infrastructure vendors offer more computational resources with computational capabilities exceeding 2 GHz, and containerized edge servers progressively deployed, the shift to quasi-infinite networking is progressively taking place. However, deploying edge computing infrastructure requires careful placement of edge servers to improve application latencies and reduce data transfer load for opportunistic and mission-critical systems [1]. Control problem specifications from newer applications and application areas have opened new research directions, such as developing newer algorithms for faster online computations in distributed and stochastic situations. For instance, in most upcoming 6G applications, data transmission always has delay-sensitive connotations attached to them [2]. Delay-sensitive data require processing with minimal latency, which is why edge computing is being advocated over central cloud computing in progressive network design. Two main points prompted network designers to consider moving computation away from the core of the network to the edge. The first is the latency problem caused by high transmission delays. The second is the congestion buildup at the backbone of networks [3]. In response, multi-access edge computing (MEC) has become a much-welcome solution for migrating some of the network functions from the core to the edge. Bringing processing closer to the edge of the network, near the data sources, is a paradigm shift that opened the door to a host of opportunities toward improving network quality of service (QoS), thereby improving the welfare of end users [4]. One of the most important and striking applications of MEC in emerging communications is in large-scale or massive Internet-of-Things (mIoT) networks, where, without a doubt, it has been shown that several aspects of mIoT network can be improved by MEC. For instance, through edge computing, the joint distribution of both communication and computational resources can be used to optimize different aspects of 6G networks, such as reliability and efficiency. Also, proper optimization of edge computation processes would help in improving the decision-making processes of the network in terms of taking appropriate actions. Furthermore, delay-intensive applications, such as vehicular communication, whose QoS requirements cannot be guaranteed by the current 5G networks will be accommodated. In vehicular communication, high computational capacity is crucial, especially when highly computationally demanding high-level algorithms are employed [5]. Such algorithmic requirements are indispensable in MECenabled real-time autonomous missions and applications, such as autonomous driving and mobile robots. Due to the requirement of high transmission rates in emerging networks such as the 6G-based mIoT network being considered, the current status of the 5G networks poses a significant performance bottleneck. The main roadblock is the huge energy demand needed to support the n (...truncated)