Thermotemporalities, thermomasculinities: Uptime, downtime, and server heat in the digital Anthropocene

Amid a warming planet and a surge in digital activity precipitated by COVID-19 lockdowns, the ecological impacts of cloud infrastructures are of increasing interest to scholars and publics. Deemed "essential workers," data center operators maintain server uptime by keeping equipment cool (via air conditioning). Failure results in overheating and a state of service interruption called downtime. Drawing on ethnographic research in data centers, this article introduces the concept of thermotemporalities to illustrate how time, temperature, and expertise converge in novel formations. By attending to the embodied practices and discursive pronouncements of data center operators, I reveal how uptime (cold) and downtime (hot), a binary opposition, are performative genres rather than discrete referents. Emerging out of this dyadic interplay, I locate a species of aspirational identity I call thermomasculinities. [ FROM AUTHOR] Copyright of New Media & Society is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Tools Towards the Sustainability and Circularity of Data Centers.

We are living in an age when data centers are expanding, require abundant spaces, and are an integral part in the urban communities, using massive amounts of environmental resources, and remains in the foreseeable future as the primary driver of the global energy consumption. This demand is disruptive and at times of both peril and opportunity due to impacts such as the COVID-19 pandemic, which is altering the demand of digital infrastructure around the world. With the global call for zero carbon emissions, there needs to be solutions put in place for the de-carbonization of data centers. New innovations are made available, which will have an economic, social, and environmental impact on data centers. Concepts such as circular economy and fourth industrial revolution technologies are useful procedural tools that can be used to systematically analyze data centers, control their mining and critical raw materials, can be utilized in the transition towards a sustainable and circular data center, by objectively assessing the environmental and economic impacts, and evaluating alternative options. In this paper, we will look at the current research and practice, the impact on the United Nations Sustainable Development goals, and look at future strides being taken towards more sustainable and circular data centers. We had discovered that decreasing the environmental effect and energy consumption of data centers is not sufficient. When it comes to data center architecture, both embodied and operational emissions are critical. Data centers also have a vital societal role in our daily lives, enabling us to share data and freely communicate via social media, transacting on the blockchain with cryptocurrencies, free online education, and job creation. As a result, sustainability and efficiency measures have expanded in a variety of ways, including circularity and its associated tools, as well as newer technologies.

MAG-D: A multivariate attention network based approach for cloud workload forecasting.

The Coronavirus pandemic and the work-from-home have drastically changed the working style and forced us to rapidly shift towards cloud-based platforms & services for seamless functioning. The pandemic has accelerated a permanent shift in cloud migration. It is estimated that over 95% of digital workloads will reside in cloud-native platforms. Real-time workload forecasting and efficient resource management are two critical challenges for cloud service providers. As cloud workloads are highly volatile and chaotic due to their time-varying nature; thus classical machine learning-based prediction models failed to acquire accurate forecasting. Recent advances in deep learning have gained massive popularity in forecasting highly nonlinear cloud workloads; however, they failed to achieve excellent forecasting outcomes. Consequently, demands for designing more accurate forecasting algorithms exist. Therefore, in this work, we propose 'MAG-D', a Multivariate Attention and Gated recurrent unit based Deep learning approach for Cloud workload forecasting in data centers. We performed an extensive set of experiments on the Google cluster traces, and we confirm that MAG-DL exploits the long-range nonlinear dependencies of cloud workload and improves the prediction accuracy on average compared to the recent techniques applying hybrid methods using Long Short Term Memory Network (LSTM), Convolutional Neural Network (CNN), Gated Recurrent Units (GRU), and Bidirectional Long Short Term Memory Network (BiLSTM).

The increasing importance of the alternate real estate sectors for institutional investors

The alternate real estate sectors (including healthcare, data centres, self-storage, university student accommodation and infrastructure) have taken on increased importance in recent years with institutional investors, as they have sought to broaden their real estate sector exposure. This has been driven by key real estate investment factors, including the changing global demographics, advances in technology and the impact of COVID-19. Importantly, this trend is expected to continue and has a major influence on real estate management and strategies by institutional investors going forward. Using a range of alternate real estate sectors across several countries (US, UK and globally) in the direct, non-listed and listed real estate spaces, this paper examines the risk-adjusted performance and portfolio diversification benefits of these alternate real estate sectors compared to the standard asset classes in the portfolios of institutional investors. The real estate management and strategic implications for institutional investors going forward are also assessed.

The Changes in the Demographic Characteristics and Spatial Structure of Tourism Demand in the West Balaton Region’s Spa Cities

The last two years, the period of the pandemic, have brought a significant change in the tourism of Hungary, which has been developing unbroken until then. The year 2019 broke all the peaks that were interrupted by the pandemic. This particularly affected our spa towns of international significance, including the examined settlements, Hévíz and Zalakaros. The aim of the study is to show what changes have taken place in the development of the number of visitors in the cities that have been based mainly on foreign traffic until then, what territorial reorganization has taken place in terms of sending areas, and what new target groups with modified attitude have emerged. In this study, we analyzed in detail the databases of the National Tourist Data Center, which has been operating since July 2019, and the monthly database of the Hungarian Central Statistical Office. The special, so-called unconventional tourism is carried out on the one hand by the methodology of statistical data collection and on the other hand by the explored tourism behavior. According to our results, it is clear that due to the domestic traffic, a completely new target group (age group and status) appeared in the two spa towns, their sending areas affected the metropolitan suburban zones, and the target group was high-status, younger guests. In our opinion, this offers a new opportunity for spa towns to generate more sustainable, future-oriented guests with a focus on local values, creating a new supply structure and image, as well as messages.

Observational data from uncrewed systems over Southern Great Plains

Uncrewed Systems (UxS), including uncrewed aerial systems (UAS) and tethered balloon/kite systems (TBS), are significantly expanding observational capabilities in atmospheric science. Rapid adaptation of these platforms and the advancement of miniaturized instruments have resulted in an expanding number of datasets captured under various environmental conditions by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. In 2021, observational data collected using ARM UxS platforms, including seven TigerShark UAS flights and 133 tethered balloon system (TBS) flights, were archived by the ARM Data Center (https://adc.arm.gov/discovery/#/, last access: 11 February 2022) and made publicly available at no cost for all registered users (10.5439/1846798) (Mei and Dexheimer, 2022). These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research. This paper describes the DOE UAS/TBS datasets, including information on the acquisition, collection, and quality control processes, and highlights the potential scientific contributions using UAS and TBS platforms.

A Sustainability Improvement Strategy of Interconnected Data Centers Based on Dispatching Potential of Electric Vehicle Charging Stations

With the rapid development of information technology, the electricity consumption of Internet Data Centers (IDCs) increases drastically, resulting in considerable carbon emissions that need to be reduced urgently. In addition to the introduction of Renewable Energy Sources (RES), the joint use of the spatial migration capacity of IDC workload and the temporal flexibility of the demand of Electric Vehicle Charging Stations (EVCSs) provides an important means to change the carbon footprint of the IDC. In this paper, a sustainability improvement strategy for the IDC carbon emission reduction was developed by coordinating the spatial-temporal dispatch flexibilities of the IDC workload and the EVCS demand. Based on the Minkowski sum algorithm, a generalized flexible load model of the EVCSs, considering traffic flow and Road Impedance (RI) was formulated. The case studies show that the proposed method can effectively increase the renewable energy consumption, reduce the overall carbon emissions of multi-IDCs, reduce the energy cost of the DCO, and utilize the EV dispatching potential. Discussions are also provided on the relationship between workload processing time delay and the renewable energy consumption rate.

A Fuzzy Grouping Genetic Algorithm for Solving a Real-World Virtual Machine Placement Problem in a Healthcare-Cloud

Due to the large-scale development of cloud computing, data center electricity energy costs have increased rapidly. Energy saving has become a major research direction of virtual machine placement problems. At the same time, the multi-dimensional resources on the cloud should be used in a balanced manner in order to avoid resources waste. In this context, this paper addresses a real-world virtual machine placement problem arising in a Healthcare-Cloud (H-Cloud) of hospitals chain in Saudi Arabia, considering server power consumption and resource utilization. As a part of optimizing both objectives, user service quality has to be taken into account. In fact, user quality of service (QoS) is also considered by measuring the Service-Level Agreement (SLA) violation rate. This problem is modeled as a multi-objective virtual machine placement problem with the objective of minimizing power consumption, resource utilization, and SLA violation rate. To solve this challenging problem, a fuzzy grouping genetic algorithm (FGGA) is proposed. Considering that multiple optimization objectives may have different degrees of influence on the problem, the fitness function of the proposed algorithm is calculated with fuzzy logic-based function. The experimental results show the effectiveness of the proposed algorithm.

Sistema para a identificação de aglomerações operando em Redes IoT e Fog Computing

: This paper presents an IoT network system, using fog computing to identify agglomerations from IP camera images, processing for pattern recognition and distance calculations. [...]monitoring is done efficiently, as there is no need to send the images to be processed by a centralized system (data center or cloud), bringing savings in terms of sending and storing data. Having said that, situations that need attitude from a monitoring manager to avoid breaks in social distance can easily be managed. O sistema é composto por por dispositivos inteligentes, ou SBC (Single Board Computers), que sao responsáveis por processar e identificar as aglomeraçoes nas imagens enviadas pelas câmeras IP, assim como também um computador, que é responsável por receber todo o fluxo de imagens das possíveis aglomeraçoes detectadas pelos dispositivos, validar as imagens e notificar as quebras de distanciamento social. 3.1.Arquitetura do Projeto O sistema foi construido em cima de paradigmas IoT, Fog/Cloud Computing e Vis&acaron;o Computacional.

A flexible approach for virtual machine selection in cloud data centers with AHP: Practice & Experience

Increasing resource efficiency and reducing the energy consumption of cloud data centers is critical, especially during the global CORONA virus pandemic. Virtual machines' consolidation using live migration maximizes the hosts' and the reduction of energy consumption. An increase in the host's virtual machines in the consolidation process and the dynamic workload of the virtual machines may cause the overloading in the hosts. One approach to overcome this problem is reducing the hosts' virtual machines. One crucial issue to improve the quality of the consolidation process's quality is determining the best virtual machine for the migration process. Although the selection process has lower computational complexity than other challenges (like placement and overload prediction) in the consolidation process, this issue has received less attention. This article aims to present an efficient algorithm for the selection process. We first considered five main criteria for the selection process: migration time, migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation. Then, we propose an algorithm based on analytic hierarchy process multi‐criteria decision‐making technique. Next, to determine the weight of the proposed criteria, we simulate thousands of virtual machines of the PlanetLab workloads. These weights are tunable based on the data center preferences. The results of the suggested approach results show 23% reduction in the hosts' energy consumption, 49% reduction in the number of migrations, and 18% reduction in the SLA violation compared with other techniques. So, using the proposed method may significantly reduce the overall cost of the data centers.

Data-Center Farming: Exploring the Potential of Industrial Symbiosis in a Subarctic Region

As our world becomes increasingly digitalized, data centers as operational bases for these technologies lead to a consequent increased release of excess heat into the surrounding environment. This paper studies the challenges and opportunities of industrial symbiosis between data centers’ excess heat and greenhouse farming, specifically utilizing the north of Sweden as a case study region. The region was selected in a bid to tackle the urgent urban issue of self-sufficiency in local food production. A synergetic approach towards engaging stakeholders from different sectors is presented through a mix of qualitative and quantitative methods to facilitate resilient data-center-enabled food production. The paper delivers on possible future solutions on implementing resource efficiency in subarctic regions.

Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser

Cloud computing has been a dominant computing paradigm for many years. It provides applications with computing, storage, and networking capabilities. Furthermore, it enhances the scalability and quality of service (QoS) of applications and offers the better utilization of resources. Recently, these advantages of cloud computing have deteriorated in quality. Cloud services have been affected in terms of latency and QoS due to the high streams of data produced by many Internet of Things (IoT) devices, smart machines, and other computing devices joining the network, which in turn affects network capabilities. Content delivery networks (CDNs) previously provided a partial solution for content retrieval, availability, and resource download time. CDNs rely on the geographic distribution of cloud servers to provide better content reachability. CDNs are perceived as a network layer near cloud data centers. Recently, CDNs began to perceive the same degradations of QoS due to the same factors. Fog computing fills the gap between cloud services and consumers by bringing cloud capabilities close to end devices. Fog computing is perceived as another network layer near end devices. The adoption of the CDN model in fog computing is a promising approach to providing better QoS and latency for cloud services. Therefore, a fog-based CDN framework capable of reducing the load time of web services was proposed in this paper. To evaluate our proposed framework and provide a complete set of tools for its use, a fog-based browser was developed. We showed that our proposed fog-based CDN framework improved the load time of web pages compared to the results attained through the use of the traditional CDN. Different experiments were conducted with a simple network topology against six websites with different content sizes along with a different number of fog nodes at different network distances. The results of these experiments show that with a fog-based CDN framework offloading autonomy, latency can be reduced by 85% and enhance the user experience of websites.

Load Aware Hotspot Selection for SLA Improvement in Cloud Computing and Protect Environment by Reduction In CO2 Emissions

Internet of Things (IoT) is a leading concept that envisions everyday objects around us as a part of internet. In order to accomplish this attribution, cloud computing provides a pathway to deliver all the promises with IoT enabled devices. The outbreak of COVID-19 coronavirus, namely SARS-CoV-2, acts as feather to the cap for the growth of Cloud users. With the increasing traffic of applications on cloud computing infrastructure and the explosion in data center sizes, QoS along with energy efficiency to protect environment, reducing CO2 emissions is need of the hour. This strategy is typically achieved using Three Layer upper Threshold (TLTHR) policy to analyze and perform VM consolidation. The proposed model controls number of migrations by placement of virtual machines, based on VMs and their utilization capacity on host. The efficacy of the proposed technique is exhibited by comparing it with other baseline algorithms using computer based simulation. Hence better QoS and energy efficiency has been obtained than other classical models.