With the exponential growth of data and demands for computing capabilities, optimizing resource utilization has become increasingly critical in cloud data centers. Employing virtual machine (VM) allocation technology to maintain hosts within an appropriate workload range holds substantial promise for improving workload balance, energy efficiency, and quality of service (QoS). Existing multi-objective VM allocation strategies based on greedy heuristics and reinforcement learning with predefined fixed objective weights lack generalizability and quick adaptability in dynamic workload scenarios. In this paper, we present MOVMA, based on a novel Multi-Objective Reinforcement Learning (MORL) algorithm to coordinately optimize three objectives, i.e., energy consumption, load balancing in multidimensional resource utilization, and service level agreement (SLA) violations. MOVMA adopts our proposed Sliding Time Window-based Dynamic Weight (STWDW) method to adaptively calculate the weights instantly based on the current system condition, ensuring the actual impact of the parameters. Furthermore, it integrates our proposed Priority-based Selection and Adjustment (PSA) scheme and a Near on-policy Experience Replay (NER) strategy in model training to accelerate convergence and avoid catastrophic forgetting. The experiments conducted on a real-world dataset demonstrate the superior performance of our MOVMA against state-of-the-art multi-objective optimization algorithms.

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, conventional FL approaches often struggle to deliver accurate and personalized models in the presence of non-IID data. Although model pruning has been proposed to improve model adaptability, existing methods relying solely on local data often yield sub-optimal sub-models due to limited task-specific information. To address this, we propose SAFL (Structure-Aware Federated Learning), a novel framework that enhances personalization by integrating client clustering with Similar Client Structure Information (SCSI)-guided pruning. SAFL adopts a two-stage process: it first clusters clients based on data similarity and uses aggregated structural insights to guide pruning; then, clients train the resulting sub-models and participate in heterogeneous model aggregation. Extensive experiments on benchmark datasets demonstrate that SAFL achieves superior accuracy and model compactness compared to existing methods, particularly under non-IID settings. These results highlight the effectiveness of structure-aware pruning and collaboration in advancing personalized federated learning.

Multi-objective optimization problems (MOPs), which involve the simultaneous optimization of multiple conflicting objectives, arise in various fields. A persistent challenge in this domain is achieving high computational efficiency while preserving a diverse and high-quality approximation of the Pareto front. Non-dominated sorting, as implemented in algorithms such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II), is a fundamental operation in evolutionary multi-objective optimization. However, the standard Fast Non-dominated Sorting (FNDS) suffers from a computational bottleneck of$O\left(M N^{2}\right)$, where$M$is the number of objectives and$N$is the population size, making it impractical for large-scale problems. To address this limitation, we introduce the Specialty Student Mechanism (SSM), a novel framework we propose for accelerating non-dominated sorting. Building on SSM, we design a heuristic approach,$H$-SSM, which first identifies extreme individuals (“specialty students”) and then employs a pre-sorting strategy to rapidly eliminate dominated solutions, reducing the time complexity to$O\left(M^{2} N \log N\right)$for$M \ll N$. This mechanism significantly improves scalability while preserving sorting accuracy. Extensive experiments on benchmark large-scale MOPs show that H-SSM consistently achieves substantial runtime reductions compared to conventional FNDS-based methods, without compromising solution quality or Pareto front diversity. The proposed method offers a practical and scalable solution for large-scale multi-objective optimization, striking a balance between computational efficiency and optimization performance.

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have available resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.