Energy-Delay Tradeoffs of Virtual Base Stations With a Computational-Resource-Aware Energy Consumption Model

The next generation (5G) cellular network faces the challenges of efficiency, flexibility, and sustainability to support data traffic in the mobile Internet era. To tackle these challenges, cloud-based cellular architectures have been proposed where virtual base stations (VBSs) play a key role. VBSs bring further energy savings but also demands a new energy consumption model as well as the optimization of computational resources. This paper studies the energy-delay tradeoffs of VBSs with delay tolerant traffic. We propose a computational-resource-aware energy consumption model to capture the total energy consumption of a VBS and reflect the dynamic allocation of computational resources including the number of CPU cores and the CPU speed. Based on the model, we analyze the energy-delay tradeoffs of a VBS considering BS sleeping and state switching cost to minimize the weighted sum of power consumption and average delay. We derive the explicit form of the optimal data transmission rate and find the condition under which the energy optimal rate exists and is unique. Opportunities to reduce the average delay and achieve energy savings simultaneously are observed. We further propose an efficient algorithm to jointly optimize the data rate and the number of CPU cores. Numerical results validate our theoretical analyses and under a typical simulation setting we find more than 60% energy savings can be achieved by VBSs compared with conventional base stations under the EARTH model, which demonstrates the great potential of VBSs in 5G cellular systems.Comment: 5 pages, 3 figures, accepted by ICCS'1

Optimal Rate-Matrix Pruning For Large-Scale Heterogeneous Systems

We present an analysis of large-scale load balancing systems, where the processing time distribution of tasks depends on both the task and server types. Our study focuses on the asymptotic regime, where the number of servers and task types tend to infinity in proportion. In heterogeneous environments, commonly used load balancing policies such as Join Fastest Idle Queue and Join Fastest Shortest Queue exhibit poor performance and even shrink the stability region. Interestingly, prior to this work, finding a scalable policy with a provable performance guarantee in this setup remained an open question. To address this gap, we propose and analyze two asymptotically delay-optimal dynamic load balancing policies. The first policy efficiently reserves the processing capacity of each server for ``good" tasks and routes tasks using the vanilla Join Idle Queue policy. The second policy, called the speed-priority policy, significantly increases the likelihood of assigning tasks to the respective ``good" servers capable of processing them at high speeds. By leveraging a framework inspired by the graphon literature and employing the mean-field method and stochastic coupling arguments, we demonstrate that both policies achieve asymptotic zero queuing. Specifically, as the system scales, the probability of a typical task being assigned to an idle server approaches 1

MobileDiffusion: Subsecond Text-to-Image Generation on Mobile Devices

The deployment of large-scale text-to-image diffusion models on mobile devices is impeded by their substantial model size and slow inference speed. In this paper, we propose \textbf{MobileDiffusion}, a highly efficient text-to-image diffusion model obtained through extensive optimizations in both architecture and sampling techniques. We conduct a comprehensive examination of model architecture design to reduce redundancy, enhance computational efficiency, and minimize model's parameter count, while preserving image generation quality. Additionally, we employ distillation and diffusion-GAN finetuning techniques on MobileDiffusion to achieve 8-step and 1-step inference respectively. Empirical studies, conducted both quantitatively and qualitatively, demonstrate the effectiveness of our proposed techniques. MobileDiffusion achieves a remarkable \textbf{sub-second} inference speed for generating a $512\times512$ image on mobile devices, establishing a new state of the art

Reevaluation of carbonate concentration and oxygen isotope records from Lake Qinghai, the northeastern Tibetan Plateau

Lake Qinghai is the largest lake on the Tibetan Plateau, and it is also one of the important sites for studying global environmental changes. Over the past 30 years, many studies have used oxygen isotope of authigenic carbonates from the lake as the proxy to infer past environmental and climate changes on the Plateau. However, debate on interpretations of isotopic data and their environmental implications still exist, largely due to the complex arid environment settings and multiple sources/species for carbonate minerals within the lake. In this study, we systematically analyze delta O-18 values in different-type carbonates collected [i.e. bulk carbonates, ostracode shells, Chara encrustations, and fine-grain (< 63 mu m) carbonate minerals] from modern lake sediments and surrounding soils, as well as the down-core delta O-18 values of bulk/fine-grain carbonates since the Last Glacial Maximum. Together with previously published delta O-18 records from ostracode shells, we try to re-evaluate the controlling factors of variations in lacustrine carbonate delta O-18 data and to infer environmental changes on the northeastern Tibetan Plateau since the Last Glacial Maximum. Our results show that the lake depth, or the size of the water body, is an important factor to influence the lake water and carbonate delta O-18 values. A shallow and small lake would be more easily influenced by precipitation delta O-18 which is characterized by negative values at Lake Qinghai region, while a deep and large lake would be better to reflect environmental changes such as the precipitation-evaporation balance. The "lake volume" effect might be an explanation for the negative carbonate delta O-18 values during the early Holocene, which was likely caused by an increased influence of negative delta O-18 values in precipitation and glacial melt water under a small and shallow water body. The delta O-18 values of ostracode shells and bulk carbonates show similar variations since both of them are dominated by lake water oxygen isotopic composition, but they still have distinct geochemical information. The isotopic differences between ostracode and bulk carbonates probably reflect the temperature differences between the surface and the bottom of lake water. In addition, the delta O-18 values of evaporative induced carbonates may correlate with carbonate contents, while those of Chara encrustations do not show any correlation with carbonate contents. Our results suggest that special caution would be necessary when using lacustrine delta O-18 values of authigenic carbonates to infer past hydrological and climate changes in an arid environment