Network is an essential tool today, and the Intrusion Detection System (IDS) can ensure the safe operation. However, with the explosive growth of data, current methods are increasingly struggling as they often detect based on a single scale, leading to the oversight of potential features in the extensive traffic data, which may result in degraded performance. In this work, we propose a novel detection model utilizing multi-scale transformer namely IDS-MTran. In essence, the collaboration of multi-scale traffic features broads the pattern coverage of intrusion detection. Firstly, we employ convolution operators with various kernels to generate multi-scale features. Secondly, to enhance the representation of features and the interaction between branches, we propose Patching with Pooling (PwP) to serve as a bridge. Next, we design multi-scale transformer-based backbone to model the features at diverse scales, extracting potential intrusion trails. Finally, to fully capitalize these multi-scale branches, we propose the Cross Feature Enrichment (CFE) to integrate and enrich features, and then output the results. Sufficient experiments show that compared with other models, the proposed method can distinguish different attack types more effectively. Specifically, the accuracy on three common datasets NSL-KDD, CIC-DDoS 2019 and UNSW-NB15 has all exceeded 99%, which is more accurate and stable.

Resource over-provisioning in cloud computing consumes energy excessively. Energy-aware dynamic Virtual Machine consolidation (DVMC) reduces energy consumption without compromising Service Level Agreement (SLA). In this paper, we put forward a new framework of DVMC for green cloud computing. In particular, we propose a new VM placement policy, namely Space Aware Best Fit Decreasing (SABFD) and a new migration VM selection policy, namely high CPU utilization based migration VM selection (called HS). Thorough simulations are carried out to evaluate the performances of different energy-aware DVMC plans based on real-world workload traces, with DVMC plans as various combinations of host overload detection, migration VM selection and VM placement policies. The simulation results show that DVMC plans with SABFD policy or with HS policy outperform alternative DVMC plans. What's more, a DVMC plan with both SABFD and HS policies makes the best performance.

Resource allocation is fundamental function of cloud computing. Different auction models offer a variety of market driven resource allocation mechanisms. This paper studies auction based resource allocation for cloud computing. First, a framework of

RocksDB is a powerful database engine that offers a wide range of adjustable knobs, which greatly influence its performance. However, configuring RocksDB manually for optimal performance is challenging due to the large number of available knobs and their complex settings. To address this issue, we propose Transformer Adaptive Gentic Algorithm Tune(TATune), an auto-tuning system for RocksDB knobs. In TATune, knob configuration files for RocksDB are randomly generated and executed at different preset workloads first. Subsequently, the correlation between the knob and RocksDB performance is learned by the prediction model based on Transformer. Finally, an adaptive genetic algorithm that utilizes the prediction model as a fitness function to recommend the RocksDB knob setting. Additionally, a novel optimization metric is also proposed to evaluate the performance of the auto-tuning RocksDB knob system. TATune is compared with other approaches to configure RockDB knobs on six distinct workloads. The results indicate that TATune is effective and achieves significant performance improvement across various target workloads. The final average optimization performance is 26% better than K2vTune and 72% better than RTune.