(EMC)-M-3: Improving Energy Efficiency via Elastic Multi-Controller SDN in Data Center Networks

Energy consumed by network constitutes a significant portion of the total power budget in modern data centers. Thus, it is critical to understand the energy consumption and improve the power efficiency of data center networks (DCNs). In doing so, one straightforward and effective way is to make the size of DCNs elastic along with traffic demands, i.e., turning off unnecessary network components to reduce the energy consumption. Today, software defined networking (SDN), as one of the most promising solutions for data center management, provides a paradigm to elastically control the resources of DCNs. However, to the best of our knowledge, the features of SDN have not been fully leveraged to improve the power saving, especially for large-scale multi-controller DCNs. To address this problem, we propose (EMC)-M-3, a mechanism to improve DCN\u27s energy efficiency via the elastic multi-controller SDN. In (EMC)-M-3, the energy optimizations for both forwarding and control plane are considered by utilizing SDN\u27s fine-grained routing and dynamic control mapping. In particular, the flow network theory and the bin-packing heuristic are used to deal with the forwarding plane and control plane, respectively. Our simulation results show that E3MC can achieve more efficient power management, especially in highly structured topologies such as Fat-Tree and BCube, by saving up to 50% of network energy, at an acceptable level of computation cost

Topological carbon materials: a new perspective

Carbon has numerous one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) allotropic structures. The study of carbon materials has been a major focus of material science and condensed matter physics. Previous studies have identified different classes of topological semimetallic carbon allotropes with different topological phases. In this review, we first give a brief summary of the development of carbon allotropes from 1D to 3D. Next, we discuss topological properties of carbon materials and their physical origin. Then, we consider possible expansion of the topological study of carbon materials to other light-element materials such as boron. Finally, we present future prospects in pursue of topological physics within carbon allotropes

Transcriptome-wide high-throughput deep m6A-seq reveals unique differential m6A methylation patterns between three organs in Arabidopsis thaliana

Proportion of two types of m6A distributing feature in mRNA. (DOC 30 kb

Utility of AD8 for Cognitive Impairment in a Chinese Physical Examination Population: A Preliminary Study

were subjected to AD8 scale. Individual information such as age, gender, and education was also collected. All data were analyzed by SPSS 19.0. Results. 1544 subjects were enrolled in this study with mean age 75.4 ± 10.6 years. The subjects who scored 0 to 8 of AD8 scale were 1015, 269, 120, 60, 30

FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion

The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these models, especially large models, poses a significant challenge when it comes to fine-tuning them for specific downstream tasks. As an alternative approach, retrieval-based methods have emerged as a promising solution, augmenting model predictions without the need for fine-tuning. Despite their potential, a significant challenge is that the designs of these methods often rely on heuristics, leaving critical questions about what information should be stored or retrieved and how to interpolate such information for augmenting predictions. To tackle this challenge, we first perform a theoretical analysis of the fine-tuning process, highlighting the importance of delta logits as a catalyst for improving model predictions. Building on this insight, we develop a novel retrieval-based method, FT2Ra, which aims to mimic genuine fine-tuning. While FT2Ra adopts a retrieval-based mechanism, it uniquely adopts a paradigm with a learning rate and multi-epoch retrievals, which is similar to fine-tuning.In token-level completion, which represents a relatively easier task, FT2Ra achieves a 4.29% improvement in accuracy compared to the best baseline method on UniXcoder. In the more challenging line-level completion task, we observe a substantial more than twice increase in Exact Match (EM) performance, indicating the significant advantages of our theoretical analysis. Notably, even when operating without actual fine-tuning, FT2Ra exhibits competitive performance compared to the models with real fine-tuning.Comment: ISSTA 202