Inadequate resource coordination and control can result in poor quality of experience (QoE) for user devices in heterogeneous edge-enabled cyber-physical systems. Unfortunately, in a cooperative edge network, existing studies have rarely jointly optimized communication, computing resources, and batch size for QoE guarantee when controlling task offloading. To this end, we investigate the problem of harnessing bandwidth, computation, and batch size for fair quality of experience (HARBOR) in a practical collaborative edge-AI environment, where UEs have different accuracy requirements of inference services and edge devices possess different batch processing capabilities. Specifically, we introduce the task completion efficiency as the task-completion-time-to-deadline ratio to quantify individual QoE. Then, we formulate the problem HARBOR as a mixed integer nonlinear programming with constraints of accuracy, bandwidth, computation, task hard deadlines and so on. The objective is to minimize the maximum task completion efficiency among all tasks to achieve task-level fairness. After providing the NP-hardness proof for HARBOR, we then devise an efficient scheme named e-HARBOR with a competitive ratio guarantee, to solve the decoupled sub-problems of HARBOR with calibrated long short-term memory network for resource prediction. Both testbed and simulation experiments evidently demonstrate that the proposed scheme works efficiently and scales well compared to baselines.

Graph neural networks (GNNs) are commonly used and have shown promising performance in recommendation systems. A major branch, heterogeneous GNNs, models heterogeneous information by leveraging side information for academic paper recommendations. These networks use message passing and high-order propagation to learn representations for users and items. However, existing recommendation methods perform high-order propagation, leading to sub-optimal representation learning. To address this issue, this article proposes a framework called MCAP, which uses relation-aware GNNs and executes low-pass propagation with matrix completion to enhance academic paper recommendations. The framework uses an attention mechanism to learn top- \(U\) relationships by constructing a user–user relation graph based on common authors and venues from interacted items. To efficiently and effectively capture semantic-aware similar items, MCAP builds an item–item relation graph by fusing side information of papers using text embedding models (e.g., Mistral) and large language models (e.g., GPT-3.5-Turbo, GLM-4). Finally, the relation-aware user–user and item–item graphs are incorporated into existing GNN-based models to generate representations of users and papers to enhance academic paper recommendations. The effectiveness of the MCAP is validated using four academic datasets, AMiner-PC, AMiner-WeChat, CiteULike, and DBLP, with user–item interactions and side information of papers. Comprehensive experiments show that the MCAP outperforms state-of-the-art models in terms of Recall@5, NDCG@5, and HR@5 with 69.2%, 70.5%, and 77.6% on the AMiner-WeChat dataset. The code for MCAP is available at https://github.com/THUDM/MCAP .

The advent of in-band network telemetry (INT) facilitates the dynamic and fine-grained monitoring of network conditions. Nevertheless, the current INT specification incurs a substantial measurement overhead, diminishing bandwidth utilization, and potential data fragmentation. Moreover, the rapid accumulation of telemetry reports may give rise to data lakes in the collector, resulting in computational burden and telemetry degradation. To address these two problems, we propose an integrated solution for high-efficiency in-band network telemetry, including an improved INT scheme, SF-INT, to reduce measurement overhead and the usage of SmartNIC to accelerate report processing. SF-INT employs network node registers to store the telemetry information collected at previous nodes by previous packets. When a packet arrives at one node, the node performs store-and-forward actions to determine the telemetry information stored in the current register and the telemetry information carried by the packet to the next hop. The packet carries only one node’s telemetry information during a telemetry process, thus keeping a constant packet size. In contrast, the standard INT increases the packet size due to the hop-by-hop insertion of metadata. SmartNIC offloads report processing from the CPU and obtains comprehensive telemetry data by analyzing a set of telemetry reports. The testbed experiment results have demonstrated that our solution reduces measurement overhead and efficiently processes reports at nearly the line rate.

Traditional network measurements mainly focus on end-to-end performance. In-band Network Telemetry (INT) makes a change by embedding telemetry instructions in the packet header and obtaining the status information of each network device. This information forms telemetry metadata forwarded with the packet hop by hop, such that we can observe network conditions more comprehensively. However, INT cannot control the route path or focus on the measurement of critical nodes, while the measurement of all nodes costs a lot. This paper proposes INToSR, a novel In-band Network Telemetry that fully leverages the flexible path programming capabilities of Segment Routing over IPv6 (SRv6). It first defines new SRv6 Endpoint Behaviors and puts INT instructions in the Arguments field. Thus, INToSR enables path control by setting an INT path using the SegmentList of SRv6 and monitors the metrics of critical nodes like queue length, link workload, and packet latency, achieving fine-grained network measurement. INToSR is also lightweight because it reduces the embedded telemetry metadata, the header overhead, and the processing delay. An experimental testbed has been built, including Tofino chips, which implement INToSR in the data plane, as well as the SDN controller and the INT report processing platform in the control plane. The experiment results have verified the feasibility of INToSR, which achieves the nanosecond-level network measurement and outperforms two existing methods. Finally, the accuracy of INToSR is evaluated in locating network congestion, which provides references for optimizing network performance.

Edge artificial intelligence (Edge-AI) is emerging with the proliferation of both multi-access edge computing (MEC) and AI. Cooperative Edge-AI can not only increase the computing resource utilization ratio with edge-edge collaboration, but also improve the big data processing efficiency of mobile end devices through computation offloading to a group of edge servers. Existing paradigms for cooperative edge-AI applications are not tailored for heterogeneous types of applications, thus harming the quality of experience (QoE) of different application users or operators in the network. This paper thus fills the gap by firstly defining the fairness index as service completion ratio, and then formulating the max-min fairness problem subject to edge server’s storage, computation, deadline constraints and so on. The problem is proven to be NP-hard through reduction from a well-known NP-complete problem, the multi-knapsack problem. To tackle the dynamics of both computing resources and channel fading conditions, a deep reinforcement learning algorithm is invented on the basis of buffer replay and evaluation-target networks, to derive the joint service deployment and computation offloading strategy. Extensive experimental results demonstrate that the proposed scheme named as Envision is at least 17× faster than the existing ORA algorithm.

Knowledge graph (KG) enhanced recommendation has demonstrated improved performance in the recommendation system (RecSys) and attracted considerable research interest. Recently the literature has adopted neural graph networks (GNNs) on the collaborative knowledge graph and built an end-to-end KG-enhanced RecSys. However, the majority of these approaches have three limitations: (1) treat the collaborative knowledge graph as a homogeneous graph and overlook the highly heterogeneous relationships among items, (2) lack of design to explicitly leverage the rich side information, and (3) overlook the rich knowledge in user preference.To fill this gap, in this paper, we explore the rich, heterogeneous relationship among items and propose a new KG-enhanced recommendation model called Collaborative Meta-Knowledge Enhanced Recommender System (MetaKRec). In particular, we focus on modeling the rich, heterogeneous semantic relationships among items and construct several collaborative Meta-KGs to explicitly depict the relatedness of the items under the guidance of meta-knowledge. In addition to the knowledge obtained from KG, we leverage user knowledge that extracts from user preference to construct the Meta-KGs. The constructed Meta-KGs can capture the knowledge from both the knowledge graph and user preference. Furthermore. we utilize a light convolution encoder to recursively integrate the item relationship in each collaborative Meta-KGs. This scheme allows us to explicitly gather the heterogeneous semantic relationships among items and encode them into the representations of items. In addition, we propose channel attention to fuse the item and user representations from different Meta-KGs. Extensive experiments are conducted on four real-world benchmark datasets, demonstrating significant gains over the state-of-the-art baselines on both regular and cold-start recommendation settings.