The advent of deep learning (DL) has transformed signal interpretation, enabling more efficient solutions to complex signal processing problems. DL workloads in signal processing typically share the computational resources of GPU clusters. However, the unpredictable nature of the duration of the DL job introduces a dynamic environment in these clusters. Existing GPU cluster schedulers rely heavily on prior knowledge, overlooking the system’s evolving dynamism. Balancing individual job quality of service (QoS) with overall cluster efficiency is critical from a fairness perspective. Unfortunately, most current schedulers prioritize minimizing job completion time (JCT) at the expense of fairness. This paper introduces Dike, an information-agnostic framework. We propose discrete mixed-priority feedback queues and a communication-aware job placement strategy to balance fairness and efficiency. Large-scale trace-driven experiments demonstrate that Dike improves cluster efficiency by over 1.65 × compared to state-of-the-art resource schedulers.

Autonomous vehicles (AVs) are expected to handle complex tasks in dynamic environments. However, the limited onboard computing capacity of AVs and heterogeneous tasks under strict delay constraints bring performance bottlenecks. To efficiently complete the onboard tasks, we formulate a dynamic Vehicle-to-Vehicle Task Offloading Problem (V2TOP) in Vehicular Ad Hoc Network (VANET), aiming to jointly minimize end-to-end task delay and maximize the average speed of connected vehicles subject to safety constraints. Then, we design a Mobility-Aware Distributed Primal-Dual Offloading Algorithm (MDPDOA) to optimize global resource utilization, explicitly modeling both task diversity and heterogeneous computing capabilities of participating vehicles. Furthermore, a dynamic triggering mechanism is introduced to timely re-allocation in response to topology or resource variations, guaranteeing consistent performance in realtime vehicular networks. Simulation results demonstrate that the V2V collaboration policy efficiently utilizes vehicle computing resources and enhances driving safety.

Federated learning (FL) enables a great deal of distributed independent participants to collaborate in training without sharing data. Malicious adversary can poison the local model by backdoor poisoning attacks and utilize the characteristic that server cannot trace original data to make the poisoned model directly aggregated. Especially in the AIoT-FL network that generates large amounts of data in real-time, such an attack is more powerful. In this paper, we design a sybil-based backdoor poisoning attacks (SBPA) against the above vulnerability. Malicious participants inject backdoor triggers into distributed Big Data to covertly complete data poisoning. During subsequent iterative aggregation, the joint model activates the backdoor during testing to achieve misclassification for the backdoor images. Besides, malicious participants create some sybil nodes to join the aggregation by taking advantage of the vulnerability of system devices to be easily disconnected. They are committed to making the poisoned local models aggregated with higher probability. Their goal works on making the final global model misclassify backdoor images while keeping high classification accuracy on the other non-backdoor samples. We conduct extensive experiments on multiple datasets and exhibit more robust performance than the state-of-the-art in various metrics under both data distribution including i.i.d. and non-i.i.d. scenarios.

Recently, with the large-scale outbreak of the global financial crisis and public safety incidents (such as COVID-19), high-performance computing has been widely applied to risk prediction, vaccine development, and other fields. In scenarios where high-performance computing infrastructure responds to the instantaneous explosion of computing demands, a crucial issue is to provide large-scale flexible allocation and adjustment of computing capability by rapidly constructing computing clusters. Existing large-scale computing cluster deployment solutions usually utilize source code deployment or other deployment tools. The great challenge of existing deployment methods is to reduce excessive image distribution time and refrain from configuration defects. In this article, we design an intelligent distributed registry deployment (IDRD) architecture based on the OpenStack cloud platform, which adaptively places distributed image repositories using the containerized deployment of multiple registries. We propose a server load priority algorithm to solve multiple registries placement problems in IDRD. Furthermore, we devise a clustering algorithm based on demand density that can optimize the global performance of IDRD and improve large-scale cluster load balancing capabilities, which has been implemented in the TianHe Supercomputing environment. Extensive experimental results demonstrate that IDRD can effectively reduce$30\%$-$50\%$of the distribution time of component images and significantly improve the efficiency of large-scale cluster deployment.