Satellite systems have emerged as a key component of future 6G networks, where beam hopping is widely adopted to improve resource utilization under limited onboard power and bandwidth. However, due to the inherent timescale mismatch between beam scheduling and resource allocation, as well as the increased system complexity caused by wide-area coverage and flexible resource demands, efficient resource scheduling requires coordinated decision-making across multiple timescales while ensuring user-level QoS provisioning. This paper aims to address the three-dimensional coupling of resources in the beam, power, and frequency domains while satisfying heterogeneous user QoS requirements. The key idea is to establish a multitime-scale framework that decouples the original optimization problem in the temporal domain. Specifically, beam scheduling is performed over beam hopping time slots(BHTSs), while resource allocation are conducted over finer-grained user scheduling time slots. A verification approach is developed to provide theoretical justification for the proposed timescale decomposition and to ensure the consistency. Based on this framework, a weight-based search strategy is employed for beam hopping pattern (BHP) selection, and a block coordinate descent (BCD) method is adopted to iteratively optimize user-level resource allocation. The simulation results demonstrate that the proposed scheme significantly improves system throughput and service satisfaction quality compared to existing methods, while maintaining robust performance under heterogeneous traffic and delay-sensitive scenarios.

An effective Pod scheduling strategy is crucial for maintaining optimal performance and resource utilization in distributed satellite clusters. The inherent complexity of the satellite cluster environment, combined with the sequential decision-making nature of Pod scheduling, makes it challenging to develop a strategy that meets multiple optimization objectives. Previous studies addressing multi-objective Pod scheduling often rely on DQN-based algorithms that model cumulative returns as expectation values$(V(s)\ \mathbf{or}\ Q(s,\ a.))$leading to significant loss of detailed distributional information. Furthermore, traditional DQN methods struggle with the dynamic variability of multi-objective reward functions, making weighted summation an unstable approach for achieving load balance in satellite clusters. To overcome these limitations, we propose a novel Pod scheduling strategy based on distributional reinforcement learning (DRL), which models the distribution$Z(s,\ a)$of cumulative returns, thereby preserving detailed distributional information. To enhance learning in satellite cluster environments, we employ multi-dimensional reward functions capturing the joint distributions of source-specific rewards. Our strategy effectively integrates optimization objectives including CPU, memory, inter-satellite links, and load balancing, ensuring robust and efficient service quality. Experimental results demonstrate that our proposed strategy significantly improves scheduling success rates, resource utilization efficiency, and load balancing in complex satellite cluster environments, outperforming traditional scheduling algorithms and DQN-based methods.

With the rapid development of Internet of Things (IoT) technology, the number of nodes in wireless sensor networks (WSNs) is explosively increasing, and the scale of network is increased gradually. Traditional single-layer non-clustering network is no longer suitable for current WSNs, which results in high maintenance cost and fast deterioration of network performance. By analyzing the impact of existing static and dynamic clustering schemes on network performance, it is concluded that additional factors need to be considered to improve the overall performance of the network, such as residual energy of nodes, number of neighbor nodes and load balancing. Therefore, an adaptive multi-layer clustering networking strategy based on capability weights is proposed. Based on the real-time changes of each cluster density, node load and residual energy, the node capacity weights are updated dynamically according to the actual network performance, then the cluster heads are renewed adaptively. By comparing the performance metrics in the experiments, proposed strategy can effectively reduce the load of key nodes such as cluster head, and improves the network performance metrics such as average transmission delay, average transmission hops and load balancing.

Wireless Sensor Networks (WSNs) are mainly used to collaboratively sense and process information in the monitoring area. Sensor nodes have limited energy and are inconvenient to be replaced when the monitoring area is remote or dangerous. That makes the energy consumption an important problem for WSNs. For heterogeneous network, where sensor nodes have different energy, existing clustering methods only consider single factor, heterogeneity of node energy, for head selection. This paper applies fuzzy logic to consider multiple factors for clustering with the purpose of prolonging the lifetime of the network. Important factors, such as relative density of nodes in the network and the relative distance from the nodes to the base station, are considered together with the initial energy to select the cluster head dynamically. Simulation results demonstrated that proposed clustering algorithm can balance the energy consumption of the nodes in network and effectively prolong the survival time of the network, thus ensuring the accuracy of data aggregation.

Local learning has been proposed as a common framework to predict both application run times and queue wait times based on workload traces [8]. The queue wait time is shown to be more difficult and expensive to predict because its distance calculations typically involve not only job attributes but also resource states. In this paper methods and algorithms are investigated to improve prediction accuracy and prediction performance for queue wait times. Firstly, the so-called "local tuning" is adopted to tune parameters for each training subset divided by a pivot attribute (e.g., group or queue name). Bias-variance analysis of error is conducted on local tuning and its global counterparts - tuning parameters on the whole training set. A method is then developed to select tuning type adaptively based on the generalization error and bias-variance decomposition. Secondly, an efficient search tree structure called "M-Tree" is integrated into our algorithm to speed up k-nearest neighbor search. Experimental studies are conducted to evaluate the proposed methods and algorithms using real-world workload traces, which are collected from the NIKHEF production cluster on the LHC Computing Grid and Blue Horizon in the San Diego Supercomputer Center (SDSC). The results show that adaptive tuning can reduce the average prediction error by 3 to 10 percents compared to global tuning, and that the M-Tree nearest neighbor search is up to 8 times faster than the sequential search.

MOTIVATION: Visualizing relationships among biological information to facilitate understanding is crucial to biological research during the post-genomic era. Although different systems have been developed to view gene-phenotype relationships for specific databases, very few have been designed specifically as a general flexible tool for visualizing multidimensional genotypic and phenotypic information together. Our goal is to develop a method for visualizing multidimensional genotypic and phenotypic information and a model that unifies different biological databases in order to present the integrated knowledge using a uniform interface. RESULTS: We developed a novel, flexible and generalizable visualization tool, called PhenoGenesviewer (PGviewer), which in this paper was used to display gene-phenotype relationships from a human-curated database (OMIM) and from an automatic method using a Natural Language Processing tool called BioMedLEE. Data obtained from multiple databases were first integrated into a uniform structure and then organized by PGviewer. PGviewer provides a flexible query interface that allows dynamic selection and ordering of any desired dimension in the databases. Based on users' queries, results can be visualized using hierarchical expandable trees that present views specified by users according to their research interests. We believe that this method, which allows users to dynamically organize and visualize multiple dimensions, is a potentially powerful and promising tool that should substantially facilitate biological research. AVAILABILITY: PhenogenesViewer as well as its support and tutorial are available at http://www.dbmi.columbia.edu/pgviewer/ CONTACT: [email protected].