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Roadmap to greener computing
\"A concise and accessible introduction to green computing and green IT, this book addresses how computer science and the computer infrastructure affect the environment and presents the main challenges in making computing more environmentally friendly. The authors review the methodologies, designs, frameworks, and software development tools that can be used in computer science to reduce energy consumption and still compute efficiently. They also focus on Computer Aided Design (CAD) and describe what design engineers and CAD software applications can do to support new streamlined business directions and improve the environment\"-- Provided by publisher.
Book
Challenges and opportunities in migrating the CNAF data center to the Bologna Tecnopolo
The INFN Tier1 data center is currently located in the premises of the Physics Department of the University of Bologna, where CNAF is also located. Soon, it will be moved to the “Tecnopolo”, the new facility for research, innovation, and technological development in the same city area; it will follow the installation of Leonardo, the pre-exascale supercomputing machine managed by CINECA, co-financed as part of the EuroHPC JU (Joint Undertaking). The construction of the new CNAF data center will consist of two phases, corresponding to the computing requirements of LHC: Phase 1, starting from 2023, will involve an IT power of 3 MW, and Phase 2, starting from 2026, involving an IT power up to 10 MW. The primary goal of the new data center is to cope with the computing requirements of the data taking of the HL-LHC experiments, in the timeframe spanning from 2026 to 2040, providing, at the same time, computing services for several other INFN experiments, projects, and activities of interest, either they are currently in operation, under construction, in advanced design, or even not yet defined. The co-location with Leonardo will open new scenarios, with a close integration between the two systems able to share dynamically resources. In this contribution we describe the new center design, with a particular focus on the status of the migration, its schedule, and the technical challenges we will face moving the data center without service interruption. On top of this, we will analyse the opportunities that the new infrastructure will open in the context of the NRRP (National Resilience and Recovery Plan) funding and strategic plans, within and beyond the High Energy Physics domain.
Journal Article
Energy efficiency in cloud computing data centers: a survey on software technologies
Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage.
Journal Article
The New Normal in IT
Learn how IT leaders are adapting to the new reality of life during and after COVID-19 COVID-19 has caused fundamental shifts in attitudes around remote and office work. And in The New Normal in IT: How the Global Pandemic Changed Information Technology Forever, internationally renowned IT executive Gregory S. Smith explains how and why companies today are shedding corporate office locations and reducing office footprints. You'll learn about how companies realized the value of information technology and a distributed workforce and what that means for IT professionals going forward. The book offers insightful lessons regarding: * How to best take advantage of remote collaboration and hybrid remote/office workforces * How to implement updated risk mitigation strategies and disaster recovery planning and testing to shield your organization from worst case scenarios * How today's CIOs and CTOs adapt their IT governance frameworks to meet new challenges, including cybersecurity risks The New Normal in IT is an indispensable resource for IT professionals, executives, graduate technology management students, and managers in any industry. It's also a must-read for anyone interested in the impact that COVID-19 had, and continues to have, on the information technology industry.
eBook
Recalibrating global data center energy-use estimates
Growth in energy use has slowed owing to efficiency gains that smart policies can help maintain in the near term Data centers represent the information backbone of an increasingly digitalized world. Demand for their services has been rising rapidly ( 1 ), and data-intensive technologies such as artificial intelligence, smart and connected energy systems, distributed manufacturing systems, and autonomous vehicles promise to increase demand further ( 2 ). Given that data centers are energy-intensive enterprises, estimated to account for around 1% of worldwide electricity use, these trends have clear implications for global energy demand and must be analyzed rigorously. Several oft-cited yet simplistic analyses claim that the energy used by the world's data centers has doubled over the past decade and that their energy use will triple or even quadruple within the next decade ( 3 – 5 ). Such estimates contribute to a conventional wisdom ( 5 , 6 ) that as demand for data center services rises rapidly, so too must their global energy use. But such extrapolations based on recent service demand growth indicators overlook strong countervailing energy efficiency trends that have occurred in parallel (see the first figure). Here, we integrate new data from different sources that have emerged recently and suggest more modest growth in global data center energy use (see the second figure). This provides policy-makers and energy analysts a recalibrated understanding of global data center energy use, its drivers, and near-term efficiency potential.
Journal Article
Can Elon Musk Pull Off The Biggest IPO In History?, in Economist Video
Elon Musk’s rocket company, Space X, has filed to go public in what could be the largest public offering in history. But investors are being asked to back unproven technology with no guarantee of success. Will Musk make AI datacentres in space a reality?
Streaming Video
Big Data: A Survey
In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.
Journal Article
From distributed machine learning to federated learning: a survey
In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of laws or regulations, the distributed data and computing resources cannot be aggregated or directly shared among different regions or organizations for machine learning tasks. Federated learning emerges as an efficient approach to exploit distributed data and computing resources, so as to collaboratively train machine learning models. At the same time, federated learning obeys the laws and regulations and ensures data security and data privacy. In this paper, we provide a comprehensive survey of existing works for federated learning. First, we propose a functional architecture of federated learning systems and a taxonomy of related techniques. Second, we explain the federated learning systems from four aspects: diverse types of parallelism, aggregation algorithms, data communication, and the security of federated learning systems. Third, we present four widely used federated systems based on the functional architecture. Finally, we summarize the limitations and propose future research directions.
Journal Article
Task scheduling in edge-fog-cloud architecture: a multi-objective load balancing approach using reinforcement learning algorithm
The rapid development of internet of things (IoT) gadgets and the increase in the rate of sending requests from these devices to cloud data centers resulted in congestion and consequently service provisioning delays in the cloud data centers. Accordingly, fog computing emerged as a new computing model to address this challenge. In fogging, services are provisioned at the edge of the network using devices with computing and storage capabilities, which are located through the way to connect IoT devices to cloud data centers. Fog computing aims to alleviate the computing load in data centers and cut the delay of requests down, notably real-time and delay-sensitive requests. To achieve these goals, vitally important challenges such as scheduling requests, balancing the load, and reducing energy consumption, which affects performance and reliability in the edge-fog-cloud computing architecture, should be considered into account. In this paper, a reinforcement learning fog scheduling algorithm is proposed to address these challenges. The experimental results indicate that the proposed algorithm raises the load balance and diminishes the response time compared to the existing scheduling algorithms. Additionally, the proposed algorithm outperforms other approaches in terms of the number of used devices.
Journal Article