As the marine economy continues to expand, the importance of efficient and reliable marine data collection has become increasingly evident. This paper investigates a multi-unmanned aerial vehicle (UAV)-assisted marine data collection network system, where multiple UAVs are deployed within a designated area to collect data from water buoy sensors (WBSs) and act as relays to offload the collected data to a central ship. The primary objective is to minimize the total system energy consumption, subject to constraints on access relationships, power scheduling, and movement trajectories. The formulated optimization problem is non-convex and highly complex due to the coupling of multiple variables. To address this challenge, we propose an alternating optimization algorithm that jointly optimizes the trajectories of multiple UAVs, the access selection of WBSs, the trajectory of the ship, the data offloading decisions, and the UAV transmit power scheduling in an iterative manner. The algorithm leverages techniques such as successive convex approximation (SCA) and greedy strategies to efficiently solve the decomposed sub-problems. Simulation results demonstrate that the proposed approach achieves significant performance improvements compared to several benchmark algorithms, highlighting its effectiveness in enhancing energy efficiency and system robustness in dynamic marine environments.

To achieve dynamic network measurement, trends build sketches on eBPF to avoid service interruptions. However, existing eBPF-based sketches suffer from high CPU consumption, leading to poor throughput and high latency and making them hard to measure high-speed traffic. Optimizing their performance requires users to refactor codes based on each sketch’s characteristics on eBPF, which is highly complex and time-consuming.In this paper, we argue that users should write sketches without concerning low-level eBPF performance optimizations, with the deployment automatically activating cross-sketch performance optimizations. We present Carrera, a library that offers domain-specific optimizations for eBPF-based sketches. Our contributions are (1) systematically analyzing the performance bottlenecks of eBPF-based sketches through microbenchmarks, (2) identifying practical optimizations, including hardware offloading, SIMD-accelerated hashing, traffic-aware flow index caching, prefetched randomization, and active data collection, to address the identified bottlenecks in eBPF-based sketches, (3) evaluating these optimizations with state-of-the-art sketches and demonstrating that Carrera improves throughput by up to 65% and reduces latency by up to 93% via testbed experiments.

Recent key-value (KV) caches are offloaded to programmable switches to offer high query processing performance. However, they suffer from both low accuracy in hot key detection and high latency in cache updates due to the strict limitations on switch registers. We propose TurboCache, a switch-accelerated KV cache with accurate hot key detection and fast cache updates. Our key idea is to leverage the switch recirculation capability to build a novel data structure that caches hot KV pairs. With this hardware-compatible cache data structure, TurboCache designs efficient data plane algorithms that accurately detects new hot keys and quickly updates its cache entirely within switch ASIC pipelines. We have implemented TurboCache on a${64}\times {100}$Gbps Tofino switch. Testbed results indicate that TurboCache improves the hot key detection accuracy and decreases the cache update latency of existing KV caches by several orders of magnitude.

Conventional Self-Supervised Learning (SSL) exhibits notable limitations in fine-grained feature modeling due to pervasive issues in medical imaging, such as blurred organ boundaries, complex anatomical structures, and feature confusion caused by similar pathological patches, often leading to false positive sample interference. To address these challenges, this article proposes Gaze-Guided Semantic Contrastive Learning (G2Co), an innovative SSL algorithm inspired by visual diagnostic patterns of radiologists. At the semantic enhancement level, G2Co leverages a key information guidance mechanism to distinguish anatomical structures from background noise, thereby achieving fine-grained feature extraction. At the feature interaction level, G2Co introduces a cross-sample feature fusion strategy to extract discriminative features from potential positive samples, addressing feature confusion caused by visually similar patches. Furthermore, G2Co achieves refined modeling of tissue morphology and boundary characteristics by establishing inter-region mutual information maximization constraints. Finally, extensive experiments are conducted on the two widely used medical image datasets to demonstrate the effectiveness of our method.

Edge computing brings computing resources closer to the Internet of Things (IoT) devices, significantly reducing transmission latency and bandwidth usage. However, the limited resources of edge servers require efficient management. Serverless computing meets this demand through its elastic resource provisioning , leading to the emergence of serverless edge computing—a promising computing paradigm . Despite its potential, real-time task dispatching and scheduling in the highly complex and dynamic environment of serverless edge computing present significant challenges. On the one hand, task execution requires not only sufficient CPU resources but also free containers; on the other hand, tasks are typically event-driven, with strong burstiness and high concurrency, and impose stringent demands on fast decision-making. To address these challenges, we propose a real-time task dispatching and scheduling method, aiming to maximize the satisfaction rate of Service Level Objectives (SLOs) for tasks. First, we design a task dispatching algorithm named Adaptive Deep Reinforcement Learning (ADRL). This algorithm can quickly decide the execution position of tasks based on coarse information and effectively adapt to the changes in available servers in dynamic environments. Second, we propose a task scheduling algorithm named Warm-aware Shortest Remaining Idle Time (WSRIT), which guides the edge servers to schedule the tasks in the request queue based on the tasks’ remaining idle time and the state of the warm containers. Considering the limited storage space of the edge servers, we further introduce a container replacement algorithm named Low Priority First (LPF) to ensure smooth container launches. Extensive simulation experiments are conducted based on Azure datasets. The results show that our methodcan improve the satisfaction rate of SLOs by 12.57 ∼ 41.87% and achieve the lowest cold start rate compared to existing methods.

As a promising technique for offloading computation tasks from mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) utilizes UAVs as computational resources. A popular method for enhancing the quality of service (QoS) of UAV-assisted MEC systems is to jointly optimize UAV deployment and computation task offloading. This imposes the challenge of dynamically adjusting UAV deployment and computation offloading to accommodate the changing positions and computational requirements of mobile devices. Due to the real-time requirements of MEC computation tasks, finding an efficient joint optimization approach is imperative. This paper proposes an algorithm aimed at minimizing the average response delay in a UAV-assisted MEC system. The approach revolves around the joint optimization of UAV deployment and computation offloading through convex optimization. We break down the problem into three sub-problems: UAV deployment, Ground Device (GD) access, and computation tasks offloading, which we address using the block coordinate descent algorithm. Observing the$NP$-hardness nature of the original problem, we present near-optimal solutions to the decomposed sub-problems. Simulation results demonstrate that our approach can generate a joint optimization solution within seconds and diminish the average response delay compared to state-of-the-art algorithms and other advanced algorithms, with improvements ranging from 4.70% to 42.94%.

The programmable switch offers a limited capacity of packet header vector (PHV) words that store packet header fields and metadata fields defined by network functions. However, existing switch compilers employ inefficient strategies of encoding fields on PHV words. Their encoding wastes scarce PHV words and may result in failures when deploying network functions. In this paper, we propose Melody, a new framework that reuses PHV words for as many fields as possible to achieve resource-efficient PHV encoding. Melody offers a field analyzer and an optimization framework. The analyzer identifies which fields can reuse PHV words while preserving the original packet processing logic. The framework integrates analysis results into its encoding to offer the resource-optimal decisions. Also, to achieve timeliness at runtime, it provides a Greedy-based heuristic, which quickly solves PHV encoding and returns near-optimal results. We evaluate Melody with production-scale network functions. Our results show that Melody reduces the consumption of PHV words by up to 85%.

The programmable switch offers a limited capacity of packet header vector (PHV) words that store packet header fields and metadata fields defined by network functions. However, existing switch compilers employ inefficient strategies of encoding fields on PHV words. Their encoding wastes scarce PHV words and may result in failures when deploying network functions. In this paper, we propose Melody, a new framework that reuses PHV words for as many fields as possible to achieve resource-efficient PHV encoding. Melody offers a field analyzer and an optimization framework. The analyzer identifies which fields can reuse PHV words while preserving the original packet processing logic. The framework integrates analysis results into its encoding to offer the resource-optimal decisions. We evaluate Melody with production-scale network functions. Our results show that Melody reduces the consumption of PHV words by up to 85%.

Data‐hunger is a persistent challenge in machine learning, particularly in the field of image processing based on convolutional neural networks (CNNs). This study systematically investigates the factors contributing to data‐hunger in machine‐learning‐based image‐processing algorithms. The results revealed that the proliferation of model parameters, the lack of interpretability, and the complexity of model structure are significant factors influencing data‐hunger. Based on these findings, this paper introduces a novel semi‐white‐box neural network model construction strategy. This approach effectively reduces the number of model parameters while enhancing the interpretability of model components. It accomplishes this by constraining uninterpretable processes within the model and leveraging prior knowledge of image processing for model. Rather than relying on a single all‐in‐one model, a semi‐white‐box model is composed of multiple smaller models, each responsible for extracting fundamental semantic features. The final output is derived from these features and prior knowledge. The proposed strategy holds the potential to substantially decrease data requirements under specific data source conditions while improving the interpretability of model components. Validation experiments are conducted on well‐established datasets, including MNIST, Fashion MNIST, CIFAR, and generated data. The results demonstrate the superiority of the semi‐white‐box strategy over the traditional all‐in‐one approach in terms of accuracy when trained with equivalent data volumes. Impressively, on the tested datasets, a simplified semi‐white‐box model achieves performance close to that of ResNet while utilizing a small number of parameters. Furthermore, the semi‐white‐box strategy offers improved interpretability and parameter reusability features that are challenging to achieve with the all‐in‐one approach. In conclusion, this paper contributes to mitigating data‐hunger challenges in machine‐learning‐based image processing through the introduction of a novel semi‐white‐box model construction strategy, backed by empirical evidence of its effectiveness.

In the era of the Internet of Things (IoT), it is a promising way to improve system energy utility and better meet users’ requirements for Quality of Service (QoS) via integrating nonorthogonal multiple access (NOMA) and mobile-edge computing (MEC) technologies. In light of this idea, we investigate device access, subchannel division, and transmission power allocation for NOMA-enabled IoT systems. To maximize the energy utility of IoT systems while satisfying the minimum demands of IoT Devices (IoTDs) on achievable uplink data rate, a joint optimization problem is formulated with the consideration of device access, subchannel division, and transmission power allocation. Due to the nonconvexity of this problem, we propose an alternating optimization algorithm aiming to find the optimal solution. The proposed algorithm first decomposes the joint optimization problem into three subproblems through the block coordinate descent (BCD), and then obtains the near-optimal solution by solving the decomposed subproblems alternately. Extensive simulations validate our analysis for the convergence of the proposed algorithm. The numerical results demonstrate that the proposed algorithm significantly outperforms the benchmark algorithms in terms of improving system energy utility.

Abstract The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Service (QoS) of MEC systems, a multi-UAV-enabled MEC system model is designed. In the proposed model, UAVs are regarded as edge servers to offer computing services for MDs, aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading. Based on the problem definition, a two-layer joint optimization method (PSO-GA-G) is proposed. First, the outer layer utilizes a Particle Swarm Optimization algorithm combined with Genetic Algorithm operators (PSO-GA) to optimize UAV deployment. Next, the inner layer adopts a greedy algorithm to optimize computation offloading. The extensive simulation experiments verify the feasibility and effectiveness of the proposed PSO-GA-G. The results show that the PSO-GA-G can achieve a lower average task response time than the other three baselines.

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods.

Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge, and end devices, are adopted to offload DNN layers by combining the larger layers (more amount of data) in the cloud and the smaller layers (less amount of data) at the edge and end devices. A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints. In this article, a self-adaptive discrete particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators is proposed to reduce the system cost caused by data transmission and layer execution. This approach considers the characteristics of DNNs partitioning and layers off-loading over the cloud, edge, and end devices. The mutation operator and crossover operator of GA are adopted to avert the premature convergence of PSO, which distinctly reduces the system cost through enhanced population diversity of PSO. The proposed off-loading strategy is compared with benchmark solutions, and the results show that our strategy can effectively reduce the system cost of off-loading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the factors impacting transmission delay, such as the bandwidth between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover and mutation operators of the genetic algorithm were adopted to avoid the premature convergence of traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing.