The objective of congestion control is to maximize network bandwidth utilization and minimize the average flow completion time in data center networks. Key performance indicators required to achieve this objective are high throughput and low packet latency. Existing approaches have proposed various methods to control packet delivery based on various network events or parameters such as packet loss, bottleneck bandwidth, RTT, and queue length. However, these methods often result in suboptimal packet transmission states that compromise throughput or latency. We provide a method to overcome this drawback. To this end, we introduce a congestion control method called PRTT (Predicted RTT) that leverages predicted RTT for congestion control. By using accurately predicted RTT values, PRTT dynamically adjusts the packet delivery rate to control the number of in-flight packets. This enables the transmission to approach states where the buffer holds only a few packets while fully utilizing the link bandwidth. Experimental results show that PRTT achieves higher throughput, lower latency, and shorter flow completion times than other state-of-the-art methods, particularly under bursty traffic. These results demonstrate that PRTT offers a promising solution for congestion control.

The paper addresses the challenge of scheduling multiple output priority queues of a switch in a real-world setting characterized by bursty traffic and diverse traffic priorities. Existing queue scheduling methods primarily employ two types of strategies: priority-based scheduling and weight-based scheduling. However, there is a lack of scheduling methods that can simultaneously handle bursty traffic in a timely manner and maintain priority-based scheduling. This paper addresses this issue by formulating the problem as a restless multi-armed bandit problem, and a queue scheduling method is proposed to balance priority service provisioning and bursty traffic processing. The proposed queue scheduling method operates efficiently in a timely manner based on instant queue length and utilizes the Whittle index method to achieve an asymptotically optimal solution. This design facilitates packet forwarding by considering the instant states of queues, offering improvements over existing methods. Experimental results demonstrate that the proposed method achieves a more efficient balance between priority service provisioning and bursty traffic processing compared to other state-of-the-art methods. Additionally, the proposed method results in better balanced network performance metrics, such as queue length, packet delay, and packet loss, thus efficiently supporting various applications that generate bursty traffic randomly.

Federated Learning (FL) is a distributed machine learning method that ensures the privacy and security of participants’ data by avoiding direct data upload to a central node for training. However, the traditional FL typically applies a star structure with cloud servers as the central aggregator for the model parameters from different terminals, leading to problems such as central failure, malicious tampering and malicious participants, resulting in training errors or system crashes. To address these issues, a permissioned blockchain is used to build a secure and reliable data-sharing platform among participating terminals, replacing the central aggregator in the traditional FL called blockchain-based federated learning. However, the block generation method of the blockchain system may introduce significant latency in the federated learning where distributed model parameters upload randomly, resulting in low efficiency of the federated learning. To overcome this, we propose a block generation strategy that groups terminals and generates a block for each group, which minimizes the latency of a single round of federated learning, and an optimal block generation algorithm that considers data distribution, terminal resources, and network resources is provided. The analysis shows that the proposed algorithm can effectively obtain the optimal solution of block generation to minimize the authentication time, and we conduct extensive experiments that demonstrate the time efficiency of the proposed algorithm.

Network function virtualization (NFV) achieves cost-effective network service provisioning through exploitation of virtualization and automation by decoupling network functions (software) from dedicated hardware. The software of the various devices can then be hosted by low-cost general computation devices rather than by more expensive dedicated devices. To obtain a specific network service, the traffic flow is steered to go through a specific order of network functions that are hosted by cloud computing, and this network function sequence is known as a service function chaining (SFC). To allocate computation resources for network functions and bandwidth resources between network functions in a physical network is the SFC embedding problem. In this article, we consider the SFC embedding problem in multi-domain networks, where no domain information, like domain topology and network resource, is disclosed among domains. We propose a new optimization algorithm based on column generation method to solve this problem, which is distributedly computed in each domain. To further improve the scalability, we also provide two heuristic algorithms. We selected two networks one large (158 nodes) and one small (18 nodes) to numerically validate the proposed algorithms and demonstrate that the acceptance ratio obtained by the heuristic algorithms is close (within 5.6 percent) to that of the optimal algorithm.

Network function virtualization (NFV) is a new networking paradigm based on decoupling network functions from dedicated hardware, so these network functions can be run as pieces of software on general-purpose computation servers, which are called virtual network functions. In addition to guarantee the service qualities provided by NFV networks comparable to those provided by traditional telecommunication networks, energy consumption becomes one of the challenges faced by NFV. This is due to a large number of general computation servers that consume a significant amount of energy. We address here the problem of how to provide an energy-aware service function chaining (SFC) embedding in NFV networks with a hierarchical resource allocation, where an SFC has a set of virtual network functions to be executed in a specific sequential order providing a specific network service. Assuming a dynamic traffic scenario, we introduce for this new problem an integer linear programming (ILP) and three polynomial heuristic algorithms for resource allocation. All three heuristic algorithms achieve energy savings by shutting down idle devices and balance the tradeoff between energy cost and SFC request acceptance ratio. Numerical results demonstrate the quality of the proposed heuristic algorithms in terms of acceptance ratio by comparing them with the ILP method and a method extended from an exiting algorithm despite the fact that they save energy.

Edge computing is an indispensable technology that overcomes delay limitations of cloud computing. In edge computing, computational resources are deployed at the network edge, and computational tasks and data of end terminals can be efficiently processed by edge nodes. Considering the computational resource limitations of edge nodes, collaborative edge computing integrates computational resources of edge nodes and provides more efficient computing services for end terminals. This article considers a computation offloading problem in collaborative edge computing networks, where computation offloading and resource allocation are optimized by means of a collaborative load shedding approach: a terminal can offload a computing task to an edge node, which either can process the task with its computing resource or further offload the task to other edge nodes. Long-term objectives and long-term constraints are considered, and Lyapunov optimization is applied to convert the original nonconvex computation offloading problem into a second problem that approximate the original problem and it is still nonconvex but has a special structure, which gives rise to a new distributed algorithm that optimally solves the second problem. Finally, the performance and provable bound of the distributed algorithm is theoretically analyzed. Numerical results demonstrate that the distributed algorithm can achieve a guaranteed long-term performance, and also demonstrate the improvement in performance achieved over the case of computation offloading without collaborating edge nodes.

Edge computing is a promising technology that offers data analysis and computing for Internet of Things (IoT) services at the network edge. It has the potential to significantly reduce the latency and improve the reliability of IoT services by allowing computation workloads and local data generated by IoT devices to be offloaded to edge nodes. This paper aims to develop algorithms for efficient provision of both job assignment and resource allocation for edge computing networks. The main objective is to minimize the long-term average of the response time delay subject to constraints on computation resources and power consumption. We apply a drift-plus-penalty based Lyapunov optimization approach to convert the original problem into an upper bound optimization problem. We then relax the latter to a convex optimization problem. Finally, a distributed algorithm based on branch-and-bound approach is provided and the gap between the distributed algorithm solution and the optimal solution of the original problem is theoretically analyzed. Numerical results based on extensive experiments have demonstrated that our distributed algorithm can achieve the required performance of edge computing that supports IoT systems, under static traffic conditions as well as under dynamic environments with time-varying traffic.

Dual-frequency radar is a preferred solution for moving target localization because of its low complexity and cost. Although several studies for dual-frequency radar are addressed in the literature, all of those methods are designed for the synchronous mode. In this letter, a new localization method based on phase compensation is proposed for dual-frequency radar in the asynchronous mode. Compared with the synchronous mode, the proposed method is suitable for most commercial short-range radars with the single local oscillator architecture and can provide a cost-effective solution. Moreover, both the theoretical variance and Cramer-Rao lower bound are derived for the performance analysis in the asynchronous mode. Computer simulations were conducted to verify the validity of the proposed method.