SPACE4AI-R: a Runtime Management Tool for AI Applications Component Placement and Resource Scaling in Computing Continua

The recent migration towards Internet of Things determined the rise of a Computing Continuum paradigm where Edge and Cloud resources coordinate to support the execution of Artificial Intelligence (AI) applications, becoming the foundation of use-cases spanning from predictive maintenance to machine vision and healthcare. This generates a fragmented scenario where computing and storage power are distributed among multiple devices with highly heterogeneous capacities. The runtime management of AI applications executed in the Computing Continuum is challenging, and requires ad-hoc solutions. We propose SPACE4AI-R, which combines Random Search and Stochastic Local Search algorithms to cope with workload fluctuations by identifying the minimum-cost reconfiguration of the initial production deployment, while providing performance guarantees across heterogeneous resources including Edge devices and servers, Cloud GPU-based Virtual Machines and Function as a Service solutions. Experimental results prove the efficacy of our tool, yielding up to 60% cost reductions against a static design-time placement, with a maximum execution time under 1.5s in the most complex scenarios

A Random Greedy based Design Time Tool for AI Applications Component Placement and Resource Selection in Computing Continua

Artificial Intelligence (AI) and Deep Learning (DL) are pervasive today, with applications spanning from personal assistants to healthcare. Nowadays, the accelerated migration towards mobile computing and Internet of Things, where a huge amount of data is generated by widespread end devices, is determining the rise of the edge computing paradigm, where computing resources are distributed among devices with highly heterogeneous capacities. In this fragmented scenario, efficient component placement and resource allocation algorithms are crucial to orchestrate at best the computing continuum resources. In this paper, we propose a tool to effectively address the component placement problem for AI applications at design time. Through a randomized greedy algorithm, our approach identifies the placement of minimum cost providing performance guarantees across heterogeneous resources including edge devices, cloud GPU-based Virtual Machines and Function as a Service solutions. Finally, we compare the random greedy method with the HyperOpt framework and demonstrate that our proposed approach converges to a near-optimal solution much faster, especially in large scale systems

Monitoramento ambiental e agropecuário do território de Frederico Westphalen (1998 - 2007).

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Persistent and transient productive inefficiency in a regulated industry: electricity distribution in New Zealand

The productive efficiency of a firm can be decomposed into two parts, one persistent and one transient. So far, most of the cost efficiency studies estimated frontier models that provide either the transient or the persistent part of productive efficiency. This distinction seems to be appealing also for regulators. During the last decades, public utilities such as water and electricity have witnessed a wave of regulatory reforms aimed at improving efficiency through incentive regulation. Most of these regulation schemes use benchmarking, namely measuring companies' efficiency and rewarding them accordingly. The purpose of this study is to assess the level of persistent and transient efficiency in an electricity sector and to investigate their implications under price cap regulation. Using a theoretical model, we show that an imperfectly informed regulator may not disentangle the two parts of the cost efficiency; therefore, they may fail in setting optimal efficiency targets. The introduction of minimum quality standards may not offer a valid solution. To provide evidence we use data on 28 New Zealand electricity distribution companies between 1996 and 2011. We estimate a total cost function using three stochastic frontier models for panel data. We start with the random effects model (RE) proposed by Pitt and Lee (1981) that provides information on the persistent part of the cost effciency. Then, we apply the true random effects model (TRE) proposed by Greene (2005a, 2005b) that provides information on the transient part. Finally, we use the generalized true random effects model (GTRE) that allows for the simultaneous estimation of both transient and persistent efficiency. We find weak evidence that persistent efficiency is associated to higher quality, and wrong efficiency targets are associated to lower quality compliance

Disentanglement of the electronic and lattice parts of the order parameter in a 1D Charge Density Wave system probed by femtosecond spectroscopy

We report on the high resolution studies of the temperature (T) dependence of the q=0 phonon spectrum in the quasi one-dimensional charge density wave (CDW) compound K0.3MoO3 utilizing time-resolved optical spectroscopy. Numerous modes that appear below Tc show pronounced T-dependences of their amplitudes, frequencies and dampings. Utilizing the time-dependent Ginzburg-Landau theory we show that these modes result from linear coupling of the electronic part of the order parameter to the 2kF phonons, while the (electronic) CDW amplitude mode is overdamped.Comment: 4 pages, 3 figures + supplementary material, accepted for publication in Phys. Rev. Let

Monitoramento Sócio Ambiental da Bacia da Lagoa Mirim (1997 - 2006).

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Presenting evidence-based health information for people with multiple sclerosis : the IN-DEEP project protocol

Background - Increasingly, evidence-based health information, in particular evidence from systematic reviews, is being made available to lay audiences, in addition to health professionals. Research efforts have focused on different formats for the lay presentation of health information. However, there is a paucity of data on how patients integrate evidence-based health information with other factors such as their preferences for information and experiences with information-seeking. The aim of this project is to explore how people with multiple sclerosis (MS) integrate health information with their needs, experiences, preferences and values and how these factors can be incorporated into an online resource of evidence-based health information provision for people with MS and their families.Methods - This project is an Australian-Italian collaboration between researchers, MS societies and people with MS. Using a four-stage mixed methods design, a model will be developed for presenting evidence-based health information on the Internet for people with MS and their families. This evidence-based health information will draw upon systematic reviews of MS interventions from The Cochrane Library. Each stage of the project will build on the last. After conducting focus groups with people with MS and their family members (Stage 1), we will develop a model for summarising and presenting Cochrane MS reviews that is integrated with supporting information to aid understanding and decision making. This will be reviewed and finalised with people with MS, family members, health professionals and MS Society staff (Stage 2), before being uploaded to the Internet and evaluated (Stages 3 and 4).Discussion - This project aims to produce accessible and meaningful evidence-based health information about MS for use in the varied decision making and management situations people encounter in everyday life. It is expected that the findings will be relevant to broader efforts to provide evidence-based health information for patients and the general public. The international collaboration also permits exploration of cultural differences that could inform international practice.<br /

Runtime Management of Artificial Intelligence Applications for Smart Eyewears

Artificial Intelligence (AI) applications are gaining popularity as they seamlessly integrate into end-user devices, enhancing the quality of life. In recent years, there has been a growing focus on designing Smart EyeWear (SEW) that can optimize user experiences based on specific usage domains. However, SEWs face limitations in computational capacity and battery life. This paper investigates SEW and proposes an algorithm to minimize energy consumption and 5G connection costs while ensuring high Quality-of-Experience. To achieve this, a management software, based on Q-learning, offloads some Deep Neural Network (DNN) computations to the user’s smartphone and/or the cloud, leveraging the possibility to partition the DNNs. Performance evaluation considers variability in 5G and WiFi bandwidth as well as in the cloud latency. Results indicate execution time violations below 14%, demonstrating that the approach is promising for efficient resource allocation and user satisfaction