Center Operators Research Articles

Modeling of Traffic Information and Services for the Traffic Control Center in Autonomous Vehicle-Mixed Traffic Situations

Achieving fully autonomous driving requires seamless collaboration between advanced autonomous driving and road infrastructure technologies. As the proportion of autonomous vehicles (AVs) increases, challenges may arise from their insufficient knowledge of the behavior of traffic objects and inability to effectively drive short distances. Therefore, traffic control centers that can proactively control these issues in real time are essential. In this study, first, the terminology is defined and the types of AV-mixed Traffic Information that a traffic control center needs to efficiently collect, store, and analyze to accommodate the coexistence of AVs and conventional vehicles are identified. Second, a generic notation for an AV-mixed Traffic Information model is defined and the results of modeling each AV-mixed Traffic Information type are presented. Third, an AV-mixed Traffic Information services model that included the names, operations, input/output messages, and relationships of all services is suggested. Finally, the importance of the service functionalities is evaluated through a survey. This study will serve as an initial guideline for the design, construction, and operation of traffic control centers and will help proactively address issues that may arise from the interaction between AVs and conventional vehicles on the road. Moreover, it contributes to identifying the types of traffic information and services that traffic control centers must provide in the era of AV-mixed traffic and suggests future directions for analysis and utilization of traffic information.

A study on demand management plans for National Supercomputer resources

In response to the rapid surge in AI computing demand at a large scale, the requirement for effective management of national supercomputing resources has emerged in the Republic of Korea. The objective is to not just amplify supply in accordance with demand but also regulate demand based on supply availability. The primary rationale behind this pertains to our complete reliance on purchasing foreign computing resources. Given that all expenditures for resource development are drawn from the government budget, numerous economic constraints come into play. Therefore, this study outlines a plan for managing the demand of national supercomputing resources and assesses its impact across various scenarios. By utilizing operational data from the National Center's supercomputing operations, we evaluated the potential and degree of enhancement in operational efficiency using Data Envelopment Analysis. Additionally, we scrutinized the effects of enhancing operational efficiency for each scenario by applying the Valley-Filling demand-management approach. To quantitatively compare operational effects in each scenario, load factor metrics were employed. The analysis revealed both the tangible improvement in operational efficiency due to demand management and crucial factors pivotal for formulating future demand management strategies. The outcomes are expected to serve as a primary guide for shaping a demand management system and devising prospective action plans.

FUNCTIONS OF THE DISTRIBUTED SYSTEM OPERATOR IN PEER-TO-PEER TRADING IN THE PROSUMER GROUP

The article discusses the topic of mutual trade between prosumers at the same level. By increasing the amount of electricity distributed in the grid, it becomes possible to reduce the flow of electrical power in the grid, which in turn helps to reduce power losses and reduce voltage. This opens up an opportunity for prosumers to become participants in the Energy Market, and for ordinary consumers to receive electricity at prices that are lower than the basic tariffs set by energy supply companies. The article pays attention to the analysis of electricity trade between prosumers and ordinary consumers, as well as the role of distributed system operators in this process. The concept of peer-to-peer trading and the system model are described. The influence of different types of prosumers, their economic incentives and sanctions for violation of the rules is studied. The prosumer model is discussed in detail, including loads, renewable energy sources, battery system for energy storage, and switching equipment. It is noted that the purpose of market regulation is to solve a centralized problem that can be solved by a central operator with access to consumers' private information. The proposed method uses a low-voltage flexibility market in which consumers are connected to a single feeder. They form a community of prosumers among themselves and participate in the flexibility market. Limiting consumers to the level of low-voltage networks reduces flexibility, but allows to increase the efficiency of calculations. In addition, prosumers at the connection point can compete with each other to provide aggregate flexibility at low voltage levels. ORG provides the necessary flexibility of the peer-to-peer market in view of voltage limitations and congestion of distribution networks.

Esports in Emerging Markets: A Balanced Scorecard Approach to LAN Gaming Centers in Iran

Esports, competitive video gaming competitions, are becoming increasingly popular worldwide, with their growth in emerging markets contingent on local area network (LAN) gaming centers. LAN gaming centers are integral in emerging economies as they provide access to gaming technology and infrastructure that may be otherwise unavailable. Given their importance, the purpose of our study is to identify characteristics relevant to LAN gaming center operations within the context of an emerging market. We conducted a two-phase, multimethod analysis of LAN gaming centers in Iran, an emerging yet dynamic market. In the first phase, we used a balanced scorecard (BSC) approach to identify business factors salient to LAN gaming center operations. In the second phase, we collected data from LAN gaming center consumers (N = 330) to rank the factors by importance using a multi attribute decision-making (MADM) model. We found that business factors relevant to LAN gaming centers operations include: offering a variety of services, providing attractive and modern facilities, employing sports marketing methods, monitoring competition, and developing multiple streams of revenue. via the BSC, our research offers strategic recommendations for LAN gaming centers. LAN gaming centers provide access to modern gaming technology and help consumers overcome technological limitations by providing a physical space for esports. LAN gaming centers help create a sense of community and social connection for players and fans alike and facilitate how consumers in emerging markets connect with the global esports community.

First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. We present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool for quantitative structure-activity/property relationship (QSAR/QSPR) modelling. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets. We describe an end-to-end implementation at the H3D Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. By leveraging in-house data collected over a decade, we have developed a virtual screening cascade for malaria and tuberculosis drug discovery comprising 15 models for key decision-making assays ranging from whole-cell phenotypic screening and cytotoxicity to aqueous solubility, permeability, microsomal metabolic stability, cytochrome inhibition, and cardiotoxicity. We show how computational profiling of compounds, prior to synthesis and testing, can inform progression of frontrunner compounds at H3D. This project is a first-of-its-kind deployment at scale of AI/ML tools in a research centre operating in a low-resource setting.

Dualities of adjoint SQCD and supersymmetry enhancement

We propose a new dual description of four-dimensional N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 1 SU(N) gauge theory with one adjoint (X) and Nf fundamental matters with a superpotential W = TrXp+1. The dual theory consists of the D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{D} $$\\end{document}p[SU(N)] Argyres-Douglas theory coupled to SU(N) gauge theory and Nf fundamentals with a superpotential deformation. We study renormalization group fixed points of the Argyres-Douglas dual theories with and without superpotential deformations, and identify the conditions for them to be dual to the fixed points of adjoint SQCD. We check our proposal via matching central charges, chiral operators and superconformal indices. We find that when Nf = 2N and p = 2, the dual theory flows to N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 2 SU(N) superconformal QCD with 2N flavors upon suitable superpotential deformation, exhibiting supersymmetry enhancement.

Congestion control analysis of optical packet switch for optical data center applications

Abstract Optical data centers serve as the backbone of modern networking, facilitating seamless connectivity for users across the globe. The connection between users and the optical data centers is established through various network topologies, which play a critical role in determining the traffic characteristics. The design and implementation of these network topologies, along with the assortment of applications hosted on optical data centers, significantly influence the flow of data within the network. The advent of cloud computing has further revolutionized optical data center operations, leading to the coexistence of a wide range of applications on different optical data center switches. As a result, the traffic characteristics observed on each optical data center switch vary significantly. This diversity in traffic patterns necessitates a comprehensive understanding of how data arrival is managed and handled by the networking infrastructure. In this paper, we explore the concept of a random traffic model for data arrival on Top of Rack (ToR) switches, which represent a crucial component of optical data center networking. In the modeling, small world model is considered. The effect of buffering and packet priorities is observed on traffic shaping. Finally, to evaluate the effectiveness of the traffic shaping techniques, we measure the packet loss performance of ToR switches and found to be as low as 10−4 even at the higher loads. Blocking performance provides valuable insights into how effectively the optical data center network manages incoming data and avoids congestion or bottlenecks.

Nominal and Real Shocks in the EURALL Exchange Rate. (A SVAR Guide)

Exchange rates are one of the most important topics in the economic and financial sectors. Considering the exchange rate as the price between currencies, financial theories aim to understand the behavior of exchange rates from a more rational viewpoint. Domestic and foreign inflation has a direct impact on the real interest rates and the exchange rates. This paper aims to analyze the nominal and real shocks of the exchange rate between the Euro and Albanian lek [EURALL]. As the most important foreign currency used in the Republic of Albania, the euro is becoming more and more a stable determinant of prices and asset values in the Albanian economy. The vast majority of import and export is dependent on the EURALL trend. That is one of the main reasons why it is so important to study the shocks in the EURALL exchange rate. The Structural Vector Autoregressive (SVAR) models can be used to understand the effects of imposing some long-run restrictions. In addition, some short-run shocks play a major role and continue to have effects even in the long run. This paper takes into consideration the monthly data of exchange rates and inflation of Albania and the Eurozone from 2016 – 2022 (the last 7 years). As one of the most important models, SVAR models can be used in both fiscal and monetary policymaking. Studying nominal and real exchange rates means studying inflation in the eurozone and Albania. Real shocks have a significant impact on both nominal and real exchange rates. Meanwhile, there is no evidence that the nominal shocks (such as institutional interventions or central bank operations) have an important impact on the nominal and real exchange rates. Referring to that, we can conclude that policymaking institutions should be cautious about the real exchange rate.

Power data integrity verification method based on chameleon authentication tree algorithm and missing tendency value

Abstract The power system operation and control data are from a wide range of sources. The relevant data acquisition equipment is disturbed by the complex electromagnetic environment on the power system operation and control lines, resulting in data errors and affecting the application and analysis of data. Therefore, a power data integrity verification method based on chameleon authentication tree algorithm and missing trend value is proposed. Get 2D data from different sensors and place it in the space environment. After data conversion, convert heterogeneous data into the same structure, expand the scope of power data acquisition, and conduct power system operation and control node layout and integrity data acquisition; The chameleon authentication tree algorithm is used to deal with the heterogeneous information of the power data, and the true value of the data is determined in the heterogeneous conflict of the power data at the same site; Query the integrity data based on the power system operation and control positioning node, creatively calculate the missing trend value of power data, evaluate the importance of data integrity, obtain the priority of power data integrity verification, and complete the integrity verification of power data. The experimental results show that the optimal clustering number is 9.05, the distribution coefficient is 16.30, the absolute error of validity analysis is 2.80, all test indicators are close to the preset standard, and the trend of the validation curve is close to the trend of the set demand covariance curve. Ensuring the integrity of power data and determining the important indicators of power lines are more conducive to the safe and stable operation of the power data center.

A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships

Shore control center (SCC) operators of maritime autonomous surface ships (MASS) may be allocated with complex responsibilities within different degrees of autonomy (DoA), from remote control to remote supervision, and the role of human in-the-loop is significant. In the current research on MASS, however, the safety of interactions between humans and systems are not sufficiently considered. Hence, this paper proposes a system-theoretic approach to safety analysis for human-system collaboration. The novelty of the approach is the definition of operational contexts of MASS and the integration of a human cognitive model into the system theoretic process analysis (STPA), called STPA-Cog. The method is demonstrated in two case studies of MASS: (i) remote control mode (RCM) and (ii) remote supervision mode (RSM), focusing on two types of navigational accidents (i.e., collision and grounding). The analysis results are derived by comparing the human-related and technical-related causal scenarios in RCM and in RSM. The findings can assist in human-oriented design and operational planning for SCC of MASS. Further, the proposed approach also has the potential to be used and extended to systemic safety analysis in other intelligent transportation systems.

Improvements for Efficient Operation of the Fire Safety Experience Center

Through the systematic operation of the experience center through the safety management manual, the awareness of the need to prevent safety accidents, acquire basic knowledge about major disasters, social disasters, and various accidents, and learn how to deal with disasters and accidents has spread. Fire safety education is a device that can prevent unsafe conditions and dangerous behaviors in advance and minimize the damage caused by similar sudden accidents even if they occur, and although there are differences in the experience and need for safety education among people, most people feel the need for fire safety education. Therefore, since the 2000s, ministries such as the Ministry of Education, Ministry of Land, Infrastructure and Transport, Ministry of Oceans and Fisheries, and Ministry of Employment and Labor have been expanding safety experiential education facilities in specific fields through their affiliated organizations, starting with the Seoul Civil Safety Laboratory. As such, we would like to explore ways to prevent safety accidents caused by duplication of experience contents and inability to deploy specialized personnel due to indiscriminate construction and operation of safety experience education facilities by city, province, and management entity, and to explore ways to systematically and effectively operate them.

Study on Effects of Operating Parameters on a Water-Cooled Loop Thermosyphon System under Partial Server Utilization

During the operation of a data center, servers are gradually installed in racks, causing most racks to work under a low heating load for a long time and affecting the cooling efficiency of the loop thermosyphon system (LTS). Thus, the effects of operating parameters on the thermal performance should be investigated. In this study, a water-cooled LTS was experimentally investigated under different airflow rates and heating loads. The results show that the additional liquid refrigerant reduced the heat transfer performance and aggravated a drop in cooling capacity when the airflow rate and heating load were decreased. To further reveal the effects of the operating parameters on the thermal performance and cooling efficiency, a steady-state distributed-parameter model was developed and validated based on the experimental data. The results show that the excessive cooling capacity was reduced by decreasing the airflow rate according to the upper limit of the server exhaust air temperature under partial server utilization. The excessive cooling capacity was reduced by 14.5–52.1% under 5–56.5% server utilization. To further reduce the excessive cooling capacity while ensuring thermal security, the water side operating parameters (including the supply chilled water temperature and water flow rate) were adjusted according to the upper limit of the rack’s average outlet air temperature, which reduced the excessive cooling capacity by more than 23.8% under partial server utilization.

Improving the operational efficiency of control centers for emergency events by using GIS technologies

The object of the study is the functioning of control centers during emergencies. Ukraine has faced large-scale emergency events caused by Russian aggression. The experience of crisis management in emergency events showed the need to find new solutions and improve the activities of emergency management centers at the State Emergency Service of Ukraine and GIS technologies in decision-making support systems. The curent paper has formulated a conceptual model of the work of emergency management centers at the Emergency Service of Ukraine; based on it, their main functions were formed. Identification of risks for objects that carry potential danger was carried out and a block diagram of object risk assessment was developed to improve the work of emergency control centers at the State Emergency Service of Ukraine. An approximate list of software and online resources for the operation of emergency management centers at Emergency Service of Ukraine is provided, which makes it possible to summarize the risks that are present in the given territory, isolate them, and pay special attention to prevent possible consequences from them. Based on the computer software MARPLOT, CAMEO, and ALOHA, an example of response to an emergency event was described. In this case, the weather conditions, physico-chemical parameters of the dangerous substance, and the radii of the danger zones of its distribution and the number of people who need to be evacuated were taken into account. In practice, having this knowledge, a head of the emergency management center at the State Emergency Service of Ukraine will be able to effectively manage people without exposing them to danger, as well as conduct timely evacuation. The use of these programs makes it possible to reduce the time of hazard assessment and visualize them, which creates advantages over the existing methodology of the State Emergency Service of Ukraine in terms of speed of hazard assessment. The results have made it possible to solve the problem of increasing the efficiency of the emergency management centers at the Emergency Service of Ukraine and to create the prerequisites for building a geo-information portal that could combine various methods and techniques on one platform

A diversified portfolio of tokenised revenue streams can provide hedging opportunities for renewable electricity generators

AbstractThe revenue streams of renewable energy generators are subject to both price and volumetric risks, owing to the variable nature of weather patterns. This negatively impacts viability of the generation projects. Blockchain‐based decentralised finance methods may present new means for generators to hedge against such volatility. Tokenised revenue streams (RevToks) as a novel tool for electrical generators is proposed. By holding a RevTok, a participant can directly claim a portion of that generator's revenue. To articulate how such exotic financial arrangements may benefit renewable generators, a case study market simulation is performed. Generators can trade RevToks to diversify their cash flows, decreasing their variance and thus overall risk exposure. The simulation uses Multiportfolio Theory — an extension of Modern Portfolio Theory — to optimise the RevTok holdings of all generators simultaneously. Examining the results show that RevTok trades occur between generators of varying technology and remuneration schema. By trading RevToks amongst themselves, all generators achieve far less volatile revenue streams, while maintaining constant expected revenues. Thus, the RevTok paradigm potentially offers improved revenue hedging when compared to established methods for energy firms. Results show that implementing such a blockchain‐based arrangement for existing central pool operators unlocks downstream opportunities for renewable generators.

VRLA battery fault prediction for data center based on random forest model and feature enhancement method

The Valve-Regulated Lead-Acid (VRLA) battery is an important part of data center power supply system. Battery failure will threaten the safe operation of the data center. How to predict the impending battery failure in advance is an urgent problem to be solved to ensure the operation safety of the data center. However, batteries in such precise data centers rarely fail. Few faulty samples cause extreme imbalance between normal samples and faulty samples. In the data center site, the battery is usually in a floating state and the battery charge-discharge cycle times are less. As a result, the obtained battery data has a single working condition. In addition, VRLA batteries are faced with the problems of limited observation information (large amount of data but low data dimension, only voltage, resistance and temperature collected directly from sensors).In this paper, a feature enhancement method is proposed by analyzing the working characteristics of VRLA batteries in the data center. This method extends the two-dimensional characteristics (voltage, resistance) of battery to nine-dimensional characteristics to solve the problem of limited observation information of VRLA battery. The problem of extreme imbalance between normal samples and faulty samples of battery is solved based on clustering undersampling method. Based on the above two methods, a VRLA battery fault classification prediction model is proposed. The nine-month operation data of 1000 VRLA battery were randomly selected from a data center and combined with the simulated fault samples to form a test set. The test results show that the F-score value of the model is increased from 54.5 % to 97.5 % after the clustering undersampling method and the feature enhancement method proposed in this paper. Compared with the VRLA battery replacement strategy recommended in IEEE STD 1188-2005 on this test set, the method can predict the impending battery failure at least 3 days in advance.

A fuzzy cognitive map-based algorithm for predicting water consumption in Spanish healthcare centres

The management of water consumption in healthcare centres can have positive impacts on both the environmental performance and profitability of health systems. Computational tools assist in the decision-making process of managing the operation and maintenance of healthcare centres. This research aimed to integrate the empirical knowledge of experts in Healthcare Engineering and the historical data from 66 healthcare centres in a Fuzzy Cognitive Map. The outputs of the predictive model included water consumption, water cost, and CO2 emissions in healthcare facilities, along with eleven variables to discover the causes and consequences of water consumption in healthcare centres. A healthcare centre with about 12 350 users, located in a city that experiences an average of 1100 heating degree days, whose facilities be moderately energy-efficient contributing over 50% with renewable energies is expected to consume 8.4 dam3 of water with 32.1 k€ of cost, and contribute realising 30.8 ton CO2eq emissions. The use of Fuzzy Cognitive Maps for prediction can provide a high level of effectiveness in identifying the factors that contribute to water consumption and in designing key performance indicators to manage the environmental performance of healthcare buildings. This tool is extremely effective in enhancing the performance of the management division of health systems.

Contribution of the Landscape Evaluation in the Study of the Impact on Environment: Application of the Hydro-Québec Method on the Technical Landfill Center of Hamici, Tipaza (Algeria)

The development and operation of Technical Landfill Centers (TLC) for urban solid waste lead to significant landscape alterations and have a negative impact on its overall image. However, the Environmental Impact Assessment (EIA) conducted for these TLCs in Algeria do not currently consider the impact of this activity on the landscape as a determining factor for validating the implementation of the landfill site. It is limited, instead, to some mitigation measures.
 This work addresses the importance of taking into account the impact of TLCs on the visible landscape through EIAs. In this context, the planned landscape integration measures within the scope of the EIA conducted by the Ministry of Environment for the urban solid waste landfill site of Hamici, situated 29 km from the capital Algiers, were examined. In addition, an evaluation the TLC’s impact on the visible landscape after its construction and operation was implemented using the Hydro-Québec method.
 The results show a high visual impact of the TLC on the landscape unit receiving the landfill cells, a moderate impact on the unit receiving the TLC buildings, and a minor impact on the unit hosting the human settlement, which has the largest number of potential observers in the area. In the light of these findings, it is imperative to integrate landscape evaluation as an operational phase through EIAs before making decisions regarding the siting of landfill sites. This is essential for the purpose of preserving the image of the environment conveyed by the visible landscape.

A robust higher-order finite difference technique for a time-fractional singularly perturbed problem

A higher-order finite difference method is developed to solve the variable coefficients convection–diffusion singularly perturbed problems (SPPs) involving fractional-order time derivative with the order α∈(0,1). The solution to this problem class possesses a typical weak singularity at the initial time t=0 and an exponential boundary layer at the right lateral surface as the perturbation parameter ɛ→0. Alikhanov’s L2−1σ approximation is applied in the temporal direction on a suitable graded mesh, and the spatial variable is discretized on a piecewise uniform Shishkin mesh using a combination of midpoint upwind and central finite difference operators. Stability estimates and the convergence analysis of the fully discrete scheme are provided. It is shown that the fully discrete scheme is uniformly convergent with a rate of O(M−p+N−2(logN)2), where p=min{2,rα}, r is the graded mesh parameter and M,N are the number of mesh points in the time and space direction, respectively. Two numerical examples are taken in counter to confirm the sharpness of the theoretical estimates.

PENGARUH ROLLING MASSAGE TERHADAP KELANCARAN PRODUKSI ASI IBU POST PARTUM

<p><strong><em>At Begins at the 5th and 6th nerves and ends at the scapula, speeding up the action of the parasympathetic nerves in conveying signals to the anterior and posterior brain, allowing oxytocin and prolactin to be released. </em></strong><strong><em>T</em></strong><strong><em>he researchers want to see how the husband's Rolling Massage affects the smooth production of postpartum mother's milk in the Mercu Public Health Center's working region in Sangir Balai Janggo District, South Solok Regency. A quasi-experimental design with a post-test only control group is used in this sort of study. The research was carried out between November 202</em></strong><strong><em>2</em></strong><strong><em> and January 202</em></strong><strong><em>3</em></strong><strong><em>. The participants in this study were all postpartum women who were in the Mercu Health Center's operating region, and a purposive selection approach was used to choose a sample of 32 persons. The observation sheet for the smooth production of breast milk on days 1-4 was employed as the instrument. The Mann-Whitney Test was used to analyze the data. The study's findings revealed that p = 0.000, indicating that there is an effect of rolling massage by the husband on the smooth production of breast milk for postpartum mothers, with smooth production of breast milk in the treatment group on days 1-4, namely 18.8 percent, 62,5 percent, 93.8 percent, 93.8 percent, and smooth production of breast milk in the control group on days 1-4, namely 37.5 percent, 50 percent, and smooth production of breast milk on days 2-3, namely 37.5 percent. The conclusion is that the husband's Rolling Massage has a substantial influence on the smooth production of postpartum mother's milk. Husbands should be allowed to perform Rolling Massage on postpartum women to ensure that milk supply continues smoothly after childbirth, according to researchers.</em></strong></p><p><strong><em>Keywords : Rolling massage, milk production that is smooth</em></strong></p>

Operations of data centers with onsite renewables considering greenhouse gas emissions

In a highly technology-dependent society, processing large amounts of data has become essential, resulting in a growing number of data centers. The global data center industry accounts for a significant proportion of the world’s energy consumption. A partial or total transition of the energy sources that power the operation of data centers to cleaner energy, particularly onsite renewable technologies, will help to achieve the greenhouse gas emission reduction goals established in international agreements. However, integrating such technologies is difficult due to the intermittency and availability of renewable sources. Nevertheless, temporal and geographical flexibility for processing part of the data centers’ workload provides an opportunity to efficiently integrate these renewable energy sources. In this paper, we develop an operational scheme for a network of interconnected data centers with a significant electricity supply from onsite technologies consisting of (i) an optimal job migration and scheduling policy for data centers to match their operation and the availability of renewable energy under an emissions cap-and-trade program; (ii) an energy management system to control the data centers’ onsite renewable and grid energy resources; and (iii) a robust optimization approach to account for the intermittency of the renewables and to identify the optimal migration and scheduling policies under the worst-case scenario. A case study is carried out to assess the economic and environmental impacts. The results indicate that the installation of onsite technologies and an optimal migration policy reduce the operational costs and the greenhouse gas emissions of the networks of data centers.