Data Center Management Research Articles

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer

BackgroundAndrogen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use.MethodsWe used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine–Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model–positive, i.e., benefited from ADT, and –negative subgroup treatment effects.ResultsOverall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit.ConclusionsOur AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.)

Modeling, evaluating, and orchestrating heterogeneous environmental leverages for large-scale data center management

Data centers are very energy-intensive facilities that can generate various environmental impacts. Numerous energy, power, and environmental leverages exist and can help cloud providers and data center managers to reduce some of these impacts. But dealing with such heterogeneous leverages can be a challenging task that requires some support from a dedicated framework. This article presents a new approach for modeling, evaluating, and orchestrating a large set of technological and logistical leverages. Our framework is based on leverages modeling and Gantt chart leverages mapping. First experimental results based on selected scenarios show the pertinence of the proposed approach in terms of management facilities and potential impacts reduction.

CASIE21-OBS: An Open-Access, OBS Controlled-Source Seismic Data Set for Investigating the Structure and Properties of the Cascadia Accretionary Wedge and the Downgoing Explorer-Juan de Fuca-Gorda Plate System

Abstract Geological processes at subduction zones and their associated geohazards (e.g., megathrust earthquakes, submarine landslides, tsunamis, and arc volcanism) are, to a large extent, controlled by the structure, physical properties and fluid content of the subducting plate, the accreted sediments, and the overriding plate. In these settings, modern seismic modeling and imaging techniques based on controlled-source, multicomponent ocean-bottom seismometer (OBS) data are some of the best tools available for determining the subseafloor elastic properties, which can be linked to the aforementioned properties. Here, we present CASIE21-OBS, a controlled-source marine wide-angle OBS data set recently collected across the Cascadia convergent margin as part of the larger CAscadia Seismic Imaging Experiment 2021 (CASIE21). The main component of CASIE21 is a long-offset multichannel seismic (MCS) survey of the Cascadia margin conducted in June–July 2021 onboard R/V M.G. Langseth (cruise MGL2104) aiming to characterize the incoming plate, the plate interface geometry and properties, and the overlying sediment stratigraphy and physical properties. CASIE21-OBS was conducted during R/V M.G. Langseth cruise MGL2103 (May 2021) and R/V Oceanus cruise OC2106A (June–July 2021). It consisted of 63 short-period four-component OBSs deployed at a total 120 stations along 10 across-trench profiles extending from ∼50 km seaward of the deformation front to the continental shelf, and from offshore northern Vancouver Island to offshore southern Oregon. The OBSs recorded the airgun signals of the CASIE21-MCS survey as well as natural seismicity occurring during the deployment period (24 May 2021 19:00 UTC–9 July 2021 09:00 UTC). The OBS data are archived and available at the Incorporated Research Institutions for Seismology Data Management Center under network code YR_2021 for continuous time series (miniSEED) and identifier 21-008 for assembled data set (SEG-Y).

Research on the Application of Blockchain Technology in the Design of Analgesic Drug Supply Chain

The supply chain of analgesic drugs has many circulation links and trading levels, and the trading channels are complex; Information asymmetry and low logistics efficiency; Wholesale links account for a large proportion of costs. Too many trading links and complicated trading channels make the trading information asymmetric, opaque and disorderly, which will inevitably lead to the loss of efficiency and benefit in the circulation process. In this paper, the application of blockchain technology in the design of analgesic drug supply chain is studied. The analgesic drug data management center is maintained by the pharmaceutical enterprises themselves or the third party, which is mainly responsible for distributing the unique identification code of drugs, backing up all records of the whole cycle of drug production, storage, circulation and sales, and realizing the two-way traceability function of drugs. In the production process, pharmaceutical enterprises attach electronic labels from single products and outer packaging boxes to realize the traceability and anti-counterfeiting of drugs throughout their life cycle. Correspondingly, it also integrates the advantages and disadvantages of self-management and outsourcing. At this stage, enterprises can choose to subcontract non-core logistics business first, so that enterprises can focus on their core business and avoid wasting too much money and energy in logistics.

Overview of ICARUS-A Curated, Open Access, Online Repository for Atmospheric Simulation Chamber Data.

Atmospheric simulation chambers continue to be indispensable tools for research in the atmospheric sciences. Insights from chamber studies are integrated into atmospheric chemical transport models, which are used for science-informed policy decisions. However, a centralized data management and access infrastructure for their scientific products had not been available in the United States and many parts of the world. ICARUS (Integrated Chamber Atmospheric data Repository for Unified Science) is an open access, searchable, web-based infrastructure for storing, sharing, discovering, and utilizing atmospheric chamber data [https://icarus.ucdavis.edu]. ICARUS has two parts: a data intake portal and a search and discovery portal. Data in ICARUS are curated, uniform, interactive, indexed on popular search engines, mirrored by other repositories, version-tracked, vocabulary-controlled, and citable. ICARUS hosts both legacy data and new data in compliance with open access data mandates. Targeted data discovery is available based on key experimental parameters, including organic reactants and mixtures that are managed using the PubChem chemical database, oxidant information, nitrogen oxide (NOx) content, alkylperoxy radical (RO2) fate, seed particle information, environmental conditions, and reaction categories. A discipline-specific repository such as ICARUS with high amounts of metadata works to support the evaluation and revision of atmospheric model mechanisms, intercomparison of data and models, and the development of new model frameworks that can have more predictive power in the current and future atmosphere. The open accessibility and interactive nature of ICARUS data may also be useful for teaching, data mining, and training machine learning models.

Optimization of financial market risk prediction system based on computer data simulation and Markov chain Monte Carlo

With the continuous development of the times, the business scope of the financial market is also expanding, and more and more unknown risks have emerged in the process of development, which has brought a certain impact to the financial market. The exchange rate fluctuation, interest rate and other factors related to the international financial market make financial market risk one of the important risks faced by securities companies. Therefore, securities companies need intuitive, accurate and effective quantitative management tools for financial market risk assessment to improve the overall risk management level of the company. Therefore, this paper constructs a financial market risk prediction system based on computer data simulation technology and Markov chain Monte Carlo algorithm. This system is mainly composed of three parts: financial risk management, data exchange management and background risk application services. The front end of the financial market risk prediction system is divided into risk monitoring, risk allocation, risk report, risk decision-making and system log modules according to functions. Data collection management functions mainly include data collection, data encryption, data conversion, data return and other functions. The measured results show that the system can predict the significant inflection points in most economic risks synchronously or in advance, and the forecast data show that the financial security index has an upward trend from bottom to top. In this paper, the Markov chain Monte Carlo algorithm is introduced into the financial market, through data simulation to predict the risks of companies with economic risks and make a small contribution to the market development.

Research on overall energy consumption optimization method for data center based on deep reinforcement learning

With the rapid development of cloud computing, there are more and more large-scale data centers, which makes the energy management of data centers more complex. In order to achieve better energy-saving effect, it is necessary to solve the problems of concurrent management and interdependence of IT, refrigeration, storage, and network equipment. Reinforcement learning learns by interacting with the environment, which is a good way to realize the independent management of the data center. In this paper, a overall energy consumption method for data center based on deep reinforcement learning is proposed to achieve collaborative energy saving of data center task scheduling and refrigeration equipment. A new multi-agent architecture is proposed to separate the training process from the execution process, simplify the interaction process during system operation and improve the operation effect. In the deep learning stage, a hybrid deep Q network algorithm is proposed to optimize the joint action value function of the data center and obtain the optimal strategy. Experiments show that compared with other reinforcement learning methods, our method can not only reduce the energy consumption of the data center, but also reduce the frequency of hot spots.

მონაცემთა ცენტრის მომავალი, მისი დადებითი და უარყოფითი მხარეები

Why are more and more large and mid-sized organizations now considering data center outsourcing? Are the pros of IT outsourcing overblown? Or do organizations want to embrace the trend for the financial and operational advantages that data center management outsourcing offers? Unfortunately, a simple yes or no will not be enough to answer the above questions. According to a recent survey of Fortune 500 companies by Forbes, most IT companies now prefer IT outsourcing over in-house data center operations due to the advantages that data center outsourcing offers. This includes minimal operational costs, better utilization of infrastructure, access to servers and compute/storage on demand, etc. Before you decide to outsource your data center, carefully research your options, analyze your business prospects, and conduct a careful examination of your assets, budgets, and requirements. Carefully evaluating the pros and cons of data center outsourcing and how it might affect your business is something you should consider before signing an outsourcing contract. Data centers are critical to today's business scene. Although their demands have always been on an upward trajectory, the situation created by the COVID-19 pandemic has pushed it further. Like all other segments in and around IT, data center technologies are rapidly changing to achieve a highly optimized and efficient system. Therefore, it is very important for you as a business owner to stay up to date with each of these technological changes and trends. After all, they are going to create the future of data centers. Advances in technology will help the overall performance of the data center. Predictive analytics tools can help security and real-time monitoring teams spot potential problems early and fix them before they impact operational performance. Data center AI can highlight areas that could work more efficiently, such as cooling or power consumption. Thus, data center composability means creating resources that can work with many different systems. Instead of dedicated or narrow-use resources providing only one service, data centers should use resource pools that can contribute to the deployment of different types of workloads, such as storage, networking, or server equipment. Likewise, the implementation of composite resources helps to avoid locked capacity in the data center, thereby improving efficiency and throughput. Above all, digital transformation will enable data centers to deliver additional value to customers. Edge computing will help providers run low-latency workloads with end users, while software virtualization will create more budget-friendly options for multiple workloads. As private and public clouds combine to create more flexible and robust hybrid cloud deployments, companies will leverage technology to do more with less.

Jointly Optimizing Job Assignment and Resource Partitioning for Improving System Throughput in Cloud Datacenters

Colocating multiple jobs on the same server has been widely applied for improving resource utilization in cloud datacenters. However, the colocated jobs would contend for the shared resources, which could lead to significant performance degradation. An efficient approach for eliminating performance interference is to partition the shared resources among the colocated jobs. However, this makes the resource management in datacenters very challenging. In this paper, we propose JointOPT, the first resource management framework that optimizes job assignment and resource partitioning jointly for improving the throughput of cloud datacenters. JointOPT uses a local search based algorithm to find the near optimal job assignment configuration, and uses a deep reinforcement learning (DRL) based approach to dynamically partition the shared resources among the colocated jobs. In order to reduce the interaction overhead with real system, it leverages deep learning to estimate job performance without running them on real servers. We conduct extensive experiments to evaluate JointOPT and the results show that JointOPT significantly outperform the state-of-the-art baselines, with an advantage from 13.3% to 47.7%.

Efficient online migration mechanism for memory write-intensive virtual machines

Online migration of virtual machines (VMs) is indispensable for system maintenance as it helps to achieve several resource management objectives such as load balancing, proactive fault tolerance, green operation, and resource management of data centers. The migration efficiency and reliability are two major challenges in the online migration of memory write-intensive VMs. For example, pre-copy migration transfers a large amount of data and takes a long time to migrate. This study proposes an efficient and reliable adaptive hybrid migration mechanism for memory write-intensive VMs. The mechanism optimizes the data transfer mode of the common migration method and improves the performance of conventional hybrid migration. First, the virtual machine (VM) memory data to be migrated are divided into dynamic and static data based on the bitmap marking method, and the migration efficiency is improved through parallel transmission based on different networks. Second, to accelerate the migration reliability, an iterative convergence factor is proposed to evaluate the current system load state and adaptively calculate the switching time of the migration mode for adaptive hybrid migration based on the convergence factor. Through adaptive hybrid migration can achieve migration completed successfully, shorten the post-copy migration duration, and minimize the impact on the performance of VMs. Finally, this paper implements the system prototype based on a kernel-based virtual machine (KVM), and experiments are performed using multiple memory write-intensive load VMs. The results show that the proposed migration algorithm can significantly improve migration performance and complete migration quickly to solve the pre-copy migration failure problem with a memory write-intensive load. Compared with the traditional hybrid migration with only one round of pre-copy, the proposed migration algorithm reduces the total migration time and transmits data by 23.2% and 26.7%, respectively.

Towards Software Defined Measurement in Data Centers: A Comparative Study of Designs, Implementation, and Evaluation

Cloud data centers are increasingly adopting the Software-Defined Networking (SDN) technologies for their underlying connection and communications. However, as a critical part of daily operations and management of such data centers, the network measurement is essential but has often been constrained by the available resources in the traditional network devices. Thus, how to properly balance the resource consumption while maintain timely and accurate measurement remains a challenge to data center systems. Recent advances in Software-Defined Networking (SDN) have enabled flexible and programmable network measurement, which is referred to as Software Defined Measurement (SDM). A promising trend for SDM is to conduct network traffic measurement on widely deployed Open vSwitches (OVS) in data centers. However, little attention has been paid to the design options for conducting traffic measurement on the OVS. In this study, we set to explore different designs and investigate the corresponding trade-offs among resource consumption, measurement accuracy, implementation complexity, and impact on switching speed. Through extensive experiments and comparisons, we quantitatively show the various trade-offs that the different schemes strike to balance, and demonstrate the feasibility of instrumenting OVS with monitoring capabilities. These results provide valuable insights into which design will best serve different measurement and monitoring needs.

Setting up a data management infrastructure for bioimaging.

While the FAIR (Findable, Accessible, Interoperable, and Re-usable) principles are well accepted in the scientific community, there are still many challenges in implementing them in the day-to-day scientific process. Datamanagement of microscopy images poses special challenges due to the volume, variety, and many proprietary formats. In particular, appropriate metadata collection, abasic requirement for FAIR data, is a real challenge for scientists due to the technical and content-related aspects. Researchers benefit here from interdisciplinary research network with centralized data management. The typically multimodal structure requires generalized data management and the corresponding acquisition of metadata. Here we report on the establishment of an appropriate infrastructure for the research network by a Core Facility and the development and integration of a software tool MDEmicthat allows easy and convenient processing of metadata of microscopy images while providing high flexibility in terms of customization of metadata sets. Since it is also in the interest of the core facility to apply standards regarding the scope and serialization formats to realize successful and sustainable data management for bioimaging, we report on our efforts within the community to define standards in metadata, interfaces, and to reduce thebarriers of daily data management.

PuzzleMesh: A Puzzle Model to Build Mesh of Agnostic Services for Edge-Fog-Cloud

This paper presents the design, development, and evaluation of PuzzleMesh, an agnostic service mesh composition model to process large volumes of data in edge-fog-cloud environments. This model is based on a puzzle metaphor where pieces, puzzles, and metapuzzles represent self-contained autonomous and reusable software artifacts encapsulated into containers and published as microservices. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">piece</i> represents the integration of apps with I/O interfaces ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">loops</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sockets</i> ), parallel processing, and management software. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">puzzle</i> represents a processing structure (e.g., workflows) built coupling pieces through loops and sockets. Puzzles integrate structures with a microservice architecture, implicit continuous dataflows, and transparent data exchange management software. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">metapuzzle</i> represents a recursive assemble of puzzles. A mesh represents a pool of pieces, puzzles, and metapuzzles available for designers to choose artifacts to build services. A prototype developed using PuzzleMesh model was evaluated through case studies about the automatic construction of processing services for the acquisition, pre-processing, manufacturing, preserving, and visualizing of satellite imagery. A qualitative comparison revealed that PuzzleMesh provides a flexible way to build reusable and portable services and to improve the usability of the services. The case study also revealed that PuzzleMesh yielded better performance results than other state-of-the-art tools.

Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers

This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management and hardware configurations. A Physics-Informed Data-Driven Recurrent Neural Network (PIDD RNN) is trained on a small scale-model experiment of a six-server data center to control cooling fans and maintain the exhaust chamber temperature within safe limits. The model features a hierarchical regularizing structure that reduces the degrees of freedom by connecting parameters for related modules in the system. With a RMSE value of 1.69, the PIDD RNN outperforms both a conventional RNN (RMSE: 3.18), and a State Space Model (RMSE: 2.66). We investigate how this design facilitates transfer learning when the model is fine-tuned over a few epochs to small dataset from a second set-up with a server located in a wind tunnel. The transferred model outperforms a model trained from scratch over hundreds of epochs.

RNASequest: An End-to-End Reproducible RNAseq Data Analysis and Publishing Framework

We present RNASequest, a customizable RNA sequencing (RNAseq) analysis, app management, and result publishing framework. Its three-in-one RNAseq data analysis ecosystem consists of (1) a reproducible, configurable expression analysis (EA) module, (2) multi-faceted result presentation in R Shiny, a Bookdown document and an online slide deck, and (3) a centralized data management system. In principle, following up our well-received omics data visualization tool Quickomics, RNASequest automates the differential gene expression analysis step, eases statistical model design by built-in covariates testing module, and further provides a web-based tool, ShinyOne, to manage apps powered by Quickomics and reports generated by running the pipeline on multiple projects in one place. Researchers can experience the functionalities by exploring demo data sets hosted at http://shinyone.bxgenomics.com or following the tutorial, https://interactivereport.github.io/RNASequest/tutorial/docs/introduction.html to set up the framework locally to process private RNAseq datasets. The source code released under MIT open-source license is provided at https://github.com/interactivereport/RNASequest.

Buried RF Sensors for Smart Road Infrastructure: Empirical Communication Range Testing, Propagation by Line of Sight, Diffraction and Reflection Model and Technology Comparison for 868 MHz-2.4 GHz.

Updating the road infrastructure requires the potential mass adoption of the road studs currently used in car detection, speed monitoring, and path marking. Road studs commonly include RF transceivers connecting the buried sensors to an offsite base station for centralized data management. Since traffic monitoring experiments through buried sensors are resource expensive and difficult, the literature detailing it is insufficient and inaccessible due to various strategic reasons. Moreover, as the main RF frequencies adopted for stud communication are either 868/915 MHz or 2.4 GHz, the radio coverage differs, and it is not readily predictable due to the low-power communication in the near proximity of the ground. This work delivers a reference study on low-power RF communication ranging for the two above frequencies up to 60 m. The experimental setup employs successive measurements and repositioning of a base station at three different heights of 0.5, 1 and 1.5 m, and is accompanied by an extensive theoretical analysis of propagation, including line of sight, diffraction, and wall reflection. Enhancing the tutorial value of this work, a correlation analysis using Pearson's coefficient and root mean square error is performed between the field test and simulation results.

A Trust-Based Hierarchical Consensus Mechanism for Consortium Blockchain in Smart Grid

As the smart grid develops rapidly, abundant connected devices offer various trading data. This raises higher requirements for secure and effective data storage. Traditional centralized data management does not meet the above requirements. Currently, smart grid with conventional consortium blockchain can solve the above issues. However, in the face of a large number of nodes, existing consensus algorithms often perform poorly in terms of efficiency and throughput. In this paper, we propose a trust-based hierarchical consensus mechanism (THCM) to solve this problem. Firstly, we design a hierarchical mechanism to improve the efficiency and throughput. Then, intra-layer nodes use an improved Raft consensus algorithm and inter-layer nodes use the Byzantine Fault Tolerance algorithm. Thirdly, we propose a trust evaluation method to improve the election process of Raft. Finally, we implement a prototype system to evaluate the performance of THCM. The results demonstrate that the consensus efficiency is improved by 19.8%, the throughput is improved by 12.34%, and the storage is reduced by 37.9%.

This site is a dead end? Employment uncertainties and labor in data centers

Would technological changes increase the need for human workers or eliminate them altogether? This uncertainty has produced an unresolved tension, from the industrial revolution to the rise of the information society. The data center industry has been largely invisible in public debates about this question. Yet the same tensions exist within the industry itself: Will automation create data center jobs or kill them? In this article, we work inside the “black box” – the data center, to examine uncertainties faced by those who work there. We do so through interviews and observations, first, of data center managers and executives at international trade expos, where anxieties about the shortage of data center workers but also their irrelevance were palpable. Then, we turn to a remote data center in Finland, where security guards and technical operators negotiate employment uncertainties through the biopolitics of their labor. In both sites, the uncertainties about data center employment are manifest and embodied, even if they are expressed and experienced in different ways. On both the top and bottom levels of data center hierarchies, people are discomfited by the possibility of their own redundancy. At the same time, they present the sunnier sides of data center work when they talked about their efforts to resolve ongoing issues of worker shortage, the lack of diversity in data centers, and the routines that could easily slide into boredom or anomie. We situate our findings on the long arc of capitalist transformations and discuss the insights they might provide for today’s data-driven economy in general.

High energy-density and power-density cold storage enabled by sorption thermal battery based on liquid-gas phase change process

Cold storage is essential for the preservation of food/medical goods, energy-saving of air conditioning, and emergency cooling. However, conventional cold storage in the form of sensible heat or solid-liquid latent heat suffers from the low energy density and large cold loss during long-term storage. To address the problem, a scalable sorption thermal battery (STB) with storage capacity of 30 kWh is designed and fabricated for realizing high energy-density and power-density cold storage by proposing sorption-induced liquid-gas phase change strategy. The STB utilizes the bonding energy of sorption working pair of zeolite 13X-water to achieve cold storage during charging phase, and utilizes the liquid-gas evaporation heat of water to realize cold production during discharging phase. The cold-storage performance of the STB prototype during charging/discharging processes is experimentally investigated at different operating conditions. By optimizing the assembled structure of solid-gas reactor and enhancing the heat transfer performance of liquid-gas evaporator, the STB exhibits high cold energy density up to 114.92 Wh/kg and 26.76 kWh/m3, and high power density of 455.62 W/kg and 106.10 kW/m3, over the conventional cold storage technologies. Correspondingly, the STB demonstrates a good performance in thermal management of data centers with cooling power as high as 150 kW. Moreover, the STB has the distinct advantage of near-zero cold energy loss in long-term cold storage. The proposed scalable sorption thermal battery based on sorption-induced liquid-gas evaporation heat of liquid offers a promising route to realize high energy density cold storage.

Carbon footprint and service coverage tradeoffs in geo-diverse sites

Data centers are growing hulks on energy consumption, thus carbon emissions. It is critical to maintain the services, i.e., data intensive computing, while preserving the greenness in the data center management. However, the more data centers required data replicated in, the faster exponential increase in the services’ carbon footprint. What is worse, the carbon coefficient of the data center is time-dependent as the power source varies from local renewable farms to power grids, making the problem dynamic. To address the challenges above, in this paper, we consider the temporal and spatial characteristics of cross-region data replication and propose a data replication framework (Carbon Aware Geo-diverse DAta Replication) CAGDAR to reduce carbon footprint and maximize service coverage simultaneously. CAGDAR balances the carbon footprint of data centers, i.e., sites, while respecting storage capacity, taking into account the coverage of data copies and the locations. We evaluate CAGDAR with real carbon traces and show that CAGDAR can rationally allocate data replicas in the temporal-spatio domain, reducing the services’ carbon footprint by 26% and improving the service coverage by 33.5%.