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Every generation of wireless technologies needs to bring a set of new system capabilities to enable future applications and services, the sixth generation mobile system (6G) is no exception. This paper provides an overview of the technology transformation from the communication-centric the forth generation mobile system (4G) and the fifth generation mobile system (5G) to the compute-centric 6G with the cloud-native system framework as the foundation of the next generation technologies. We explain what 6G plans to achieve, the fundamental reasons for this technology transformation, the architecture framework and enabling technologies to achieve 6G cloud-native technology objectives. This paper intends to provide a technical deep dive on the 6G cloud-native system to trigger more discussions, innovations and bring the technology transformation from concept to reality.

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Abstract-Electricity consumption comprises a significant fraction of total operating cost in data centers. System operators are required to reduce electricity bill as much as possible. In this paper, we consider utilizing available energy storage capability in data centers to reduce electricity bill under real-time electricity market. Laypunov optimization technique is applied to design an algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. As far as we know, our work is the first to explore the problem of electricity cost saving using energy storage in multiple data centers by considering both timediversity and location-diversity of electricity price.

This paper considers the problem of joint congestion control and scheduling in wireless networks with quality of service (QoS) guarantees. Different from per-destination queueing in the existing works, which is not scalable, this work considers per-link queueing at each node, which significantly reduces the number of queues per node. Under per-link queueing, we formulate the joint congestion control and scheduling problem as a network utility maximization (NUM) problem and use a dual decomposition method to separate the NUM problem into two sub-problems, i.e., a congestion control problem and a scheduling problem. Then, we develop a sliding mode (SM) based distributed congestion control scheme, and prove its convergence and optimality property. Different from the existing schemes, our congestion control scheme is capable of providing multi-class QoS under the general scenario of multi-path and multi-hop; in addition, it is robust against network anomalies, e.g., link failures, because it can achieve multi-path load balancing. Index TermsSliding mode control, per-link queue, QoS, multi-path, stochastic optimization, interactive multimedia, robustness against network anomalies.