Conventional cellular networks are insufficient for large-scale IoT communications, leading to the ongoing standardization of Cellular-IoT (C-IoT). This report focuses on C-IoT systems performing periodic data transmission and analyzes the performance of LTE and NB-IoT. By modeling and evaluating both control and data communication procedures, we found that NB-IoT can accommodate up to 8.7 times more devices than LTE, but suffers from higher latency. We also demonstrated that immediate release of wireless resources can increase capacity by up to 17.7 times.

Mobile cellular networks are now serving all kinds of Internet of Things (IoT) communications. Since current contention-based random access and radio resource allocation are optimized for traditional human communications, massive IoT communications cannot be efficiently accommodated. For this reason, standardization activities for connecting IoT devices, such as Cellular-IoT (C-IoT), have emerged. However, there have been few studies devoted to the evaluation of the performance of the C-IoT communications with periodic data transmissions, despite their being the common characteristics of many IoT communications.Herein, we evaluate the capacity of mobile cellular networks in accommodating periodic C-IoT communications, focusing on differences in performance between LTE and Narrowband-IoT (NB-IoT) networks. To achieve this, we conduct end-to-end performance analyses of both control and data planes, including the random access procedure, radio resource allocation, and bearer establishment in EPC network. Moreover, we determined the effect of immediate release of radio resources considered in 3GPP. Numerical evaluation results show that NB-IoT can accommodate more IoT devices than LTE, although this results in significant latency in data transmission. Furthermore, we find that the number of IoT devices that can be accommodated increases up to 20.7 times with immediate release of radio resources.

In response to the growing demand for cellular networks, it is essential to improve the capacity of mobile core networks. Especially, in terms of accommodating machine-to-machine/Internet-of-Things (M2M/IoT) terminals into cellular networks, the load on the control and the user planes of the mobile core network increases massively. To deal with this problem, it is possible to apply virtualization technologies, such as software-defined network and network function virtualization. However, few existing studies evaluate such solutions for mobile core networks numerically and in detail. In this paper, we first evaluate mobile core network architectures with virtualization technologies and control/user (C/U) plane separation using the mathematical analysis. We also propose a novel bearer aggregation method to reduce the control plane load to accommodate massive M2M/IoT terminals. The result of numerical evaluation shows that the capacity of the mobile core network can be increased by up to 32.8% with node virtualization and C/U plane separation, and further by 201.4% by using bearer aggregation. Moreover, to maintain the performance of the mobile core network, we should carefully determine where the bearer aggregation is applied and when the shared bearer for each terminal is determined based on application characteristics and the number of accommodated M2M/IoT terminals.

What is the best way to evaluate the capacity of a mobile core network with visualization technologies and C/U plane separation using SDN? How can we increase the capacity, especially for accommodating massive M2M/IoT terminals? With increasing demand for cellular networks, enhancing the capacity of the mobile core networks is an urgent issue. In particular, when it comes to accommodating M2M/IoT terminals for cellular networks, the increasing load on the control plane of the mobile core network, as well as user plane, becomes a serious problem. While applying virtualization technologies such as SDN and NFV is one possible solution, there are almost no existing works on numerical or concrete evaluation of such solutions. In this paper, on the basis of mobile core networks with virtualized nodes and C/U plane separation, we first propose a bearer aggregation method for decreasing the control plane load to accommodate massive M2M/IoT terminals. We then show our mathematical analysis of the performance of mobile core networks based on a simple queuing theory. Specifically, we focus on the effect of the node virtualization and C/U plane separation and on the design parameters of the bearer aggregation. The numerical evaluation results show that we can increase the capacity of the mobile core network by up to 32.8% with node virtualization and C/U plane separation, and by an additional 201.4% with bearer aggregation. We also explain that to maintain the performance of the mobile core network, we should carefully determine where the bearer aggregation is applied and when the shared bearer for each UE is determined on the basis of application characteristics and the number of M2M/IoT terminals to be accommodated.

In recent years, the Microservices Architecture (MSA) has garnered attention for enhancing flexibility, scalability, and extensibility. This is achieved by dividing applications traditionally offered as monolithic services into a group of microservices, in order to cope with the increase in IoT communication traffic, the growth in per-user communication volume, and the diversification of communication requirements. Meanwhile, the operational environments for microservices—Serverless and Container platforms—operate based on fundamentally different scaling algorithms. The ability of each environment to handle sudden load fluctuations against microservices is a crucial factor when selecting an operational platform.

This report models the behavior of network systems by incorporating the distinct scaling characteristics of both operational environments. Furthermore, a simulator based on this model is constructed, and various load conditions are simulated to quantitatively evaluate the performance of network systems utilizing Serverless and Container platforms.

The evaluation results reveal that, in the Container environment, the scaling delay based on past CPU utilization is the primary cause of increased waiting time during load fluctuations. In contrast, in the Serverless environment, the waiting time changes complexly due to the trade-off between request arrival frequency and instance idle timeout. We also show that in the Container environment, implementing the request queue in the load balancer instead of inside each container significantly suppresses the accumulation of long-waiting requests during sudden load spikes, leading to a substantial improvement in system responsiveness. The findings of this report provide essential guidelines for designing and setting parameters for optimal microservice operational environments in network systems that handle high-frequency and highly variable traffic.