The convergence of computing and networking heralds a promising paradigm for future sixth-generation (6G) systems, enabling low-latency services for end users by deploying modern applications, which typically consist of multiple interdependent functions, at the network edge. However, unstable and short-range Device-to-Device (D2D) links often restrict offloading options, forcing users to either face limited access to nearby devices or rely on distant cloud resources, thereby underutilizing edge resources and diminishing user experience. To address this challenge, we propose utilizing base stations to relay traffic between unconnected edge devices. Additionally, we strategically reuse historical function placements to balance re-deployment costs against dynamic request adaptation. Then, aiming to minimize storage, computation, and transmission resource consumption costs along with function replacement costs, we formulate the Dependent Function Scheduling (DFS) problem as a Mixed Integer Non-Linear Programming (MINLP) problem, which is NP-hard even in single-slot scenarios. We introduce a random rounding-based approach to derive high-quality integer solutions for the single-slot DFS problem. Building on this, we further develop an efficient online algorithm for the general multi-slot DFS problem, which is proved to achieve near-optimal performance with high probability. Extensive real-trace simulations demonstrate that our proposed method significantly outperforms state-of-the-art baselines, achieving up to a 68.22% reduction in total cost.

In decentralized edge computing environments, user devices need to perceive the status of neighboring devices, including computational availability and communication delays, to optimize task offloading decisions. However, probing the real-time status of all devices introduces significant overhead, and probing only a few devices can lead to suboptimal decision-making, considering the massive connectivity and non-stationarity of edge networks. Aiming to balance the status probing cost and task offloading performance, we study the joint transmission and computation status probing problem, where the status and offloading delay on edge devices are characterized by general, bounded, and non-stationary distributions. The problem is proved to be NP-hard, even with known offloading delay distributions. To handle this case, we design an efficient offline method that guarantees a$(1-1/e)$approximation ratio via leveraging the submodularity of the expected offloading delay function. Furthermore, for scenarios with unknown and non-stationary offloading delay distributions, we reformulate the problem using the piecewise-stationary combinatorial multi-armed bandit framework and develop a change-point detection-based online status probing (CD-OSP) algorithm. CD-OSP can timely detect environmental changes and update probing strategies via using the proposed offline method and estimating offloading delay distributions. We prove that CD-OSP achieves a regret of$\mathcal {O}(NV\sqrt{T\ln T})$, with$N$,$V$, and$T$denoting the numbers of stationary periods, edge devices, and time slots, respectively. Extensive simulations and testbed experiments demonstrate that CD-OSP significantly outperforms state-of-the-art baselines, which can reduce the probing cost by up to 16.18X with a 2.14X increase in the offloading delay.

Measuring traffic metrics is indispensable in virtual networks as it is the basis for a wide range of applications, such as network diagnostics and performance evaluation of the network algorithms. However, existing measurement schemes fail to have all these excellent characteristics simultaneously: 1) fine-grained, i.e. to obtain per packet level information. 2) lightweight, namely low CPU and bandwidth overhead. 3) network-wide, which means obtaining metrics of the whole network, e.g. per packet path. 4) easy-to-deploy, which refers to deployment without additional modification of Maximum Transmission Units (MTUs). We design vNetRadar, a virtual network measurement system, which has these excellent characteristics simultaneously. Specifically, vNetRadar 1) identifies each packet without increasing the size of each packet, to obtain network-wide metrics without MTU modification, 2) allocates each packet an area in memory, called backpack, and carries metadata in it to largely reduce bandwidth overhead. vNetRadar is implemented based on the extended Berkeley Packet Filter (eBPF) and is mainly in kernel space, avoiding the CPU overhead of copying packets to user space when performing the fine-grained measurement. Evaluation results show that the easy-to-deploy vNetRadar can get fine-grained network-wide metrics with low CPU and bandwidth overhead.

The explosive growth of global distributed services has led to a massive increase in bulk multicast data transfers over the inter-datacenter Wide-Area Network. While many solutions have been proposed to improve the performance of inter-DC bulk data transfers, they are insufficient to optimize multicast transfers because they fail to explore the characteristics of multicast transfers and network topology. This paper presents FlexCast, a flexible and efficient solution to optimize the completion times for multicast transfers. FlexCast takes advantage of topological characteristics to divide network sites into groups, partition receivers into subsets, and construct load-adaptive Steiner trees for receiver partitions to reduce completion time. It also employs a flexible multicast model for parallel transmission. For better performance FlexCast uses multiple scheduling policies to handle offline request submission, and for greater efficiency it adopts a combination of small-scale optimization and fast heuristic to address online request submission quickly. Simulations on real-world topologies show that FlexCast improves the completion time for multicast receivers by up to 80% compared to prior solutions.

As the key infrastructure for emerging 5G and Internet-of-Things (IoT) applications, micro data centers would be widely deployed at network edges to provide high-bandwidth low-latency cloud service. In these systems, applications would deliver large-size data objects among servers for various purposes like service deployment, application scale-up, and data duplication on demand. Accordingly, reducing delivery time is crucial for the optimization of service delay and system utilization. To accelerate the delivery, this article proposes a multisource-aware adaptive data transmission solution, Parallel Push (PPUSH), by leveraging the fact that data objects in the cloud are generally replicated among servers by design. At the high level, PPUSH achieves efficient delivery of multisource data by launching multiple push flows in parallel; and at the low level, it decouples transfers from different sources by encoding data objects with rateless RaptorQ code, and further employing novel congestion controls to prioritize the bandwidth allocation of concurrent tasks respecting their remaining sizes. Fluid model analysis along with Mininet-based test and packet-level simulation shows that, unlike DCTCP and other proposals, push is robust to packet loss and achieves provable prioritized bandwidth allocation. Extensive simulation results imply that, with above advantages, PPUSH could achieve very efficient data delivery by making use of all available data sources: for instance, compared with the straightforward design of equal-size task split and fair bandwidth allocation, its adaptive task assignment and prioritized traffic scheduling reduce the average task completion time in a tested scenario by 1.495× and 1.329×, respectively, demonstrating a total improvement of 1.586×, when enabled at the same time.