TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines. Cloud computing, as the de-facto backbone of modern computing infrastructure for both enterprise and consumer applications, has to be able to handle user-defined pipelines of diverse DNN inference workloads while maintaining isolation and latency guarantees, and minimizing resource waste. The current solution for guaranteeing isolation within FaaS is suboptimal -- suffering from "cold start" latency. A major cause of such inefficiency is the need to move large amount of model data within and across servers. We propose TrIMS as a novel solution to address these issues. Our proposed solution consists of a persistent model store across the GPU, CPU, local storage, and cloud storage hierarchy, an efficient resource management layer that provides isolation, and a succinct set of application APIs and container technologies for easy and transparent integration with FaaS, Deep Learning (DL) frameworks, and user code. We demonstrate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x speedup in latency for image classification models and up to 210x speedup for large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201

Irregular sets for ratios of Birkhoff averages are residual

It follows from Birkhoff's Ergodic Theorem that the irregular set of points for which the Birkhff averages of a given continuous function diverge has zero measure with respect to any finite invariant measure. In strong contrast, for systems with the weak specification property, we show here that if the irregular set is nonempty, then it is residual. This includes topologically transitive topological Markov chains, sofic shifts and more generally shifts with the specfication property. We consider also the more general case of ratios of Birkhoff averages of continuous functions and the case when the set of accumulation points of the ratios of Birkhoff averages is a prescribed closed interval. Finally, we give an application of our work to the pointwise dimension of a Gibbs measure on a repeller of a conformal map

Full shifts and irregular sets

nuloBy Birkhoff´s ergodic theorem, the set of points X for which the Birkhoff averages of a continuous function diverge has zero measure with respect to any finite invariant measure. Thus, at least from the point of view of ergodic theory, this set could not be smaller. Nevertheless, it can be large from other points of view. For example, for subshifts with the weak specification property, we showed recently, that X is residual whenever it is nonempty (it is a simple exercise to show that X is dense whenever it is nonempty). The main purpose of this note is to convey in the simplest possible manner the proof of our result in the particular case of the full shift on a finite number of symbols. This has the advantage of avoiding some accessory technicalities that are necessary in the general case. In fact, we consider also the more general case when the set of accumulation points of the Birkhoff averages of a continuous function is prescribed closed interval and we show that it is residual whenever it is nonempty

Kelangsungan industri lada hitam

In current Cloud computing environments, management of data reliability has become a challenge. For data-intensive scientific applications, storing data in the Cloud with the typical 3-replica replication strategy for managing the data reliability would incur huge storage cost. To address this issue, in this paper we present a novel cost-effective data reliability management mechanism named PRCR, which proactively checks the availability of replicas for maintaining the reliability. Our simulation indicates that, comparing with the typical 3 replica replication strategy, PRCR can significantly reduce the storage space consumption, hence storage cost in the Cloud

China\u27s provincial carbon emission transfers and the effectiveness of mitigation polices

The complexity of shared emissions responsibility for carbon transfers in various regions of China has further raised additional challenges for energy savings and carbon mitigation efforts. This paper establishes an extended provincial input-output (IO) model for each province to calculate carbon emissions based on production, consumption, and transfers from 2005 through 2015, and examines whether carbon mitigation policies can effectively promote energy conservation and emissions reduction in the various provinces. The empirical analysis established that: (1) an increase in the implementation strength of mitigation policy can effectively reduce production-based carbon emissions amongst the different provinces; (2) stricter mitigation policy increases the possibility that a province will transfer more of their emissions to other areas, thus causing a net emissions outflow; and (3) subsequent policy enforcement will weaken once mitigation goals are accomplished. Therefore, this paper repudiates the accepted belief that mitigation policy effectively controls carbon emissions, especially for production-based emissions. More refined policy design and supplementation is needed when considering consumption-based emissions and related carbon transfers

Image Harmonization with Diffusion Model

Image composition in image editing involves merging a foreground image with a background image to create a composite. Inconsistent lighting conditions between the foreground and background often result in unrealistic composites. Image harmonization addresses this challenge by adjusting illumination and color to achieve visually appealing and consistent outputs. In this paper, we present a novel approach for image harmonization by leveraging diffusion models. We conduct a comparative analysis of two conditional diffusion models, namely Classifier-Guidance and Classifier-Free. Our focus is on addressing the challenge of adjusting illumination and color in foreground images to create visually appealing outputs that seamlessly blend with the background. Through this research, we establish a solid groundwork for future investigations in the realm of diffusion model-based image harmonization