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As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Shortcomings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature. [ABSTRACT FROM AUTHOR]

To address the current problem of high energy consumption in data centers, this paper proposes a data center heat pipe air-conditioning system based on the latent heat of water evaporation, which uses the latent heat of water evaporation for cooling by creating a low pressure environment to evaporate large amounts of water. In order to verify the effect of the system, a heat pipe test bench based on the latent heat of water evaporation was designed and built. Compared with the traditional heat pipe in the data center for heat dissipation, the performance and economy of the water evaporation latent heat pipe system designed in this paper are analyzed experimentally. A multi-physics coupled model of water evaporation latent heat pipe air-conditioning based on COMSOL Multiphysics was established to simulate and study the temperature field and velocity field distribution of water evaporation latent heat pipe air-conditioning system in data centers. The research shows that: -- Under the designed test conditions, compared with the traditional heat pipe system, the water evaporation latent heat pipe air conditioner can conduct 2540 kJ more heat in one day in an outdoor environment of 24 °C. -- At an ambient temperature of 3 5°C and an indoor temperature of 25.8 °C, the cooling capacity of the heat pipe in the data center water evaporation latent heat pipe air-conditioning system is twice the cooling capacity of the air conditioner, and the heat pipe can work efficiently regardless of the outdoor ambient temperature. -- The energy-saving effect of the latent heat pipe of water evaporation in the data center has a significant effect on air conditioners with an energy efficiency rating (EER) lower than 2.5-4.4. It can improve the energy efficiency of level 5 with an EER of 2.5 to level 2 with an EER of 3.22, greatly reducing the power consumption of the data center air-conditioning system. When the EER of the air conditioner exceeds 4.4, the coefficient of performance of the data center water evaporation latent heat pipe air-conditioning system will be lower than that of the air conditioner itself. [ABSTRACT FROM AUTHOR]

The number of data centres (DC) in recent years is growing rapidly, and with that, the share in total consumption of electricity is growing too. A significant amount of electricity is transformed into heat energy which increases the optimal temperature in DC for component operation. This heat needs to be removed and usually, it doesn't have any further application. Today it is recognized that waste heat can be integrated into district heating (DH) and by using it reduce the usage of conventional heat fuels. Integration of waste heat is possible in three ways: with a heat exchanger (HEX), with a booster heat pump, or with the combination of the HEX and heat pump. In this paper, a combination of HP and HEX was used. Although those utilizations are examined and implemented, there is a lack of research on the optimization of the integration of waste heat into the district heating network (DHN). To perform optimization, a thermodynamic model of the DC and a pinch analysis model were developed. In this study, a method for evaluating the economic feasibility of DC waste heat integration into DH systems is proposed. The most suitable integration technology of waste heat into DH systems by using the hourly merit order of waste heat utilization technologies based on pinch analysis is found. The connection pipe between DC and DHN is optimized, and the ideal diameter is determined considering different temperature regimes of the network: low-temperature, ultralow temperature, and neutral temperature networks. The methodology was tested using a case study of a DC in the City of Zagreb. [ABSTRACT FROM AUTHOR]

Owing to the high energy demand of buildings, which accounted for 36% of the global share in 2020, they are one of the core targets for energy-efficiency research and regulations. Hence, coupled with the increasing complexity of decentralized power grids and high renewable energy penetration, the inception of smart buildings is becoming increasingly urgent. Data-driven building energy management systems (BEMS) based on deep reinforcement learning (DRL) have attracted significant research interest, particularly in recent years, primarily owing to their ability to overcome many of the challenges faced by conventional control methods related to real-time building modelling, multi-objective optimization, and the generalization of BEMS for efficient wide deployment. A PRISMA-based systematic assessment of a large database of 470 papers was conducted to review recent advancements in DRL-based BEMS for different building types, their research directions, and knowledge gaps. Five building types were identified: residential, offices, educational, data centres, and other commercial buildings. Their comparative analysis was conducted based on the types of appliances and systems controlled by the BEMS, renewable energy integration, DR, and unique system objectives other than energy, such as cost, and comfort. Moreover, it is worth considering that only approximately 11% of the recent research considers real system implementations. [ABSTRACT FROM AUTHOR]

In recent times, a huge amount of data is being created from different sources and the size of the data generated on the Internet has already surpassed two Exabytes. Big Data processing and analysis can be employed in many disciplines which can aid the decision-making process with privacy preservation of users’ private data. To store large quantity of data, Geo-Distributed Data Centres (GDDC) are developed. In recent times, several applications comprising data analytics and machine learning have been designed for GDDC. In this view, this paper presents a hybrid deep learning framework for privacy preservation in distributed DCs. The proposed model uses Deep Neural Network (DNN) for the feature extractor and classifier operations. In addition, Siamese training method is applied to fine-tune the prevention of secondary inference on the data. Moreover, gradient descent approach is employed to reduce the loss function of the DNN model. Furthermore, Glow-worm Swarm Optimization (GSO) algorithm is utilized to fine tune the hyperparameters of the DNN model to improve the overall efficiency. The proposed model is executed on a Hadoop based environment, i.e., Hadoop Distributed File System (HDFS), which has two nodes namely master node and slave nodes. The master node is considered as the main user node to get the services from the service provider. Every slave node behaves as per master node’s instruction for data storage. In order to validate the enhanced performance of the proposed model, a series of simulations take place and the experimental results demonstrate the promising performance of the proposed model. The simple technique has reached a maximum gender recognition accuracy of 95, 90 and 79 on the applied data 1, 2 and 3 respectively. Also, the reduced simple approach has attained reduced gender recognition with the accuracy of 91, 84 and 74 on the applied data 1, 2 and 3 respectively. [ABSTRACT FROM AUTHOR]

The energy sector is essential in the transition to a more sustainable future, and renewable energies will play a key role in achieving this. It is also a sector in which the circular economy presents an opportunity for the utilisation of other resources and residual energy flows. This study examines the environmental and social performance of innovative energy technologies (which contribute to the circularity of resources) implemented in a demonstrator site in Luleå (Sweden). The demo-site collected excess heat from a data centre to cogenerate energy, combining the waste heat with fuel cells that use biogas derived from waste, meeting part of its electrical demand and supplying thermal energy to an existing district heating network. Following a cradle-to-gate approach, an environmental and a social life cycle assessment were developed to compare two scenarios: a baseline scenario reflecting current energy supply methods and the WEDISTRICT scenario, which considers the application of different renewable and circular technologies. The findings indicate that transitioning to renewable energy sources significantly reduces environmental impacts in seven of the eight assessed impact categories. Specifically, the study showed a 48% reduction in climate change impact per kWh generated. Additionally, the WEDISTRICT scenario, accounting for avoided burdens, prevented 0.21 kg CO2 eq per kWh auto-consumed. From the social perspective, the WEDISTRICT scenario demonstrated improvement in employment conditions within the worker and local community categories, product satisfaction within the society category, and fair competition within the value chain category. Projects like WEDISTRICT demonstrate the circularity options of the energy sector, the utilisation of resources and residual energy flows, and that these lead to environmental and social improvements throughout the entire life cycle, not just during the operation phase. [ABSTRACT FROM AUTHOR]

• An innovative dew point cooling system was designed, constructed, installed and real live tested in live data centre. • The system can provide continuously stable cooling air and maintain the required temperature and humidity in data centre. • The average COP of 29.7 was achieved during the testing period. • 90 % energy saving can be achieved for data centre cooling. • Suitability, stability and reliability of the system used in data centre environment were proved. This paper presents the development and application of a super performance dew point cooling technology for data centres. The novel super performance dew point cooler showed considerably improved energy saving and carbon reduction for data centre cooling. The innovations of this technology are built upon a series of technological breakthroughs including, a novel hybrid flat/corrugated heat and mass exchanging sheets, an innovative highly water absorptive and diffusive wet-material for the sheets which enable an intermittent water supply with well-tuned water pressure and flow rate, and the optimised fan configurations. Following a list of fundamental research including theoretical, numerical and lab experimental testing of a small scale prototype system, a specialist 100 kW rated data centre dew point cooling system was dedicated designed, constructed, installed and real life tested in an operational live data centre environment, i.e., Maritime Data Centre at Hull (UK) to investigate its dynamic performance, suitability and stability for application in operational data centre environment conditions. During the testing period, the system showed its reliability and capability to remove a tremendous amount of heat dissipated from the IT equipment and maintain an adequate space temperature in the operational live data centre. The dynamic data collection and analysis during the continuous testing and monitoring period showed the average COP of 29.7 with the maximum COP of 48.3. Compared to the existing traditional vapor compression air conditioning system in the data centre, the energy saving using the super performance dew point cooling system is around 90 %. The work presented in this paper include detailed innovation aspects of the technology and the system operation, as well as the established bridging knowledges, methodology and technical procedure for bringing this new technology into real life operation which involve in data centre survey, optimum design and modularization of the specialist cooling system for data centre application, proven system installation, operating method and cooling air management for data centre as well as the assurance of the continuous sufficient cooling supply to the data centre. [ABSTRACT FROM AUTHOR]

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As the global demand for data services expands, cooling in data centres continues to evolve towards more efficient and cost-effective systems. Incorporating active rear door heat exchangers has become a popular and reliable method that increases the capability of data centres to operate at higher power densities. This study conducts a thermal analysis of a data centre employing active rear door heat exchangers with the use of computational fluid dynamic (CFD) techniques. The data centre under investigation contains seventy-seven cooled racks with three additional uncooled racks operating in the centre of the hall. The main purpose of this study is to understand how the uncooled racks affect the temperature distribution in the data centre. This study presents a modelling technique which uses temperature and velocity field measurements to facilitate the modelling of rear door heat exchangers. Computer server modelling server was carried out at varying inlet temperature and load. Server simulation results have been utilized with field measurements to create four data centre scenarios. Scenarios were created to show how inlet temperature and load affect the temperature distribution in the data centre. Data centre scenarios have been used to validate and compare with field measurements performed. It was found that heat dissipation in the server was directly related to the server's velocity profile. From the data centre scenarios created it was found that when higher loaded racks are isolated amongst lower loaded racks the distribution of heat is less significant than if the higher loaded racks were situated in clusters of three or more. It was also found that higher loaded racks could be positioned strategically to diminish the effect of the untreated air produced by the uncooled racks in the data centre. The findings from this paper help to understand the thermal behaviour in data centres and suggests areas to consider when reviewing pre-existing data centre designs. [ABSTRACT FROM AUTHOR]

Mzuzu University lost its Library as a result of a fire that took place on December 18, 2015. In response, the university established two processes to ensure the library services were not interrupted. The first process was to restore information services within six months by creating an interim Library. The second was to design a new library in collaboration with Virginia Tech’s School of Architecture and Design in the United States. A total of three conceptual designs were developed, from which Mzuzu University selected a final design. One key aspect of each conceptual design was a dedicated space for a data centre. The initial concept was that the data centre would support research activities at the University, within Malawi, and with international partners outside Malawi, such as Virginia Tech. This paper captures the anticipations and aspirations of the key stakeholders involved with the library design project at Mzuzu University in Malawi and Virginia Tech in the USA. Data were captured by a survey that was shared via email with 29 stakeholders. A total of 10 responded at Mzuzu University, and 12 responded at Virginia Tech. A key finding from the survey was the need to create clear plans for each aspect of the project to ensure the effective implementation of the data centre. Critical aspects to the project include staffing, equipment procurement, the management of the data centre, data literacy programming, and the long-term sustainability of the data centre. Developing a policy/process to guide the operations of the data centre was also found to be critical. The library construction began in February 2021 and is expected to end in February 2023. Having a clear plan for how the data centre could be operationalized will be essential to ensuring the centre is successful. The data centre will be a new facility for the university and this paper is a first step towards shaping the requirements of, and potential for, this new facility.

This paper presents a new liquid-cooling technology for uninterruptible power supply (UPS) units in which an air-cooling system is combined with an indirect water-cooling system based on direct-chip cooling. This cooling architecture provides more opportunities to use free cooling as the main or only cooling system for optimal data centres (DCs). An experimental investigation was conducted using a 600 kW SOCOMEC UPS to identify the thermal behaviour of the system. Five thermal hydraulic tests were conducted under different thermal conditions, flow rates, and room temperatures. A 20 K temperature difference profile was validated with a safe operation of all UPS electronic equipment with a water flow rate of 13 l/min and inlet water and air room temperatures of 32 and 40 °C, respectively, resulting in a thermal efficiency of 82.27%. The experiments suggest that a DC-facility water-inlet temperature up to 41 °C is acceptable for UPS units. The effects of decreasing the water flow rate and increasing the water and air room temperatures were also analysed. A decrease in inlet water and air temperatures from 41 °C to 32 °C and from 47 °C to 40 °C, respectively, increased the thermal efficiency by 8.64%. A decrease in water flow rate from 20 l/min to 13 l/min reduced the thermal efficiency by 7.7%. Furthermore, an energy performance analysis comparison was performed between air- and water-cooled UPS units at both the UPS and infrastructure levels. A case study of a 600 kW UPS in a data centre in Roubaix, north of France, was investigated. The liquid-cooled UPS reduced the total annual energy consumption of the cooling system by at least 85% compared with an air-cooled UPS.

The rapid development of data center leads to a rapid increase in energy consumption. In this paper, the plate and tube evaporative condenser is proposed for the water-cooled chiller and the data center cooling system to improve the energy efficiency in hot and dry summer and avoid icing in cold winter. The chiller can operate in the wet mode for compressor refrigeration and the dry mode for free cooling with the coefficient of performance (COP) of 5.2 in wet mode and up to 33.3 in dry mode. Furthermore, the system is applied in a data center in Ulumiqi, which is located in the dry and cold region and its ambient air temperature can vary from −32 °C to 45 °C throughout the year. The actual operation results show that the power usage effectiveness (PUE) value of the whole data centre is 1.349 in June, 1.229 in December, and 1.320 as the annual average, which is stable and efficient for five consecutive years. These results ensure the proposed system is an important technical solution for energy saving and emission reduction in data centres in cold regions.

Energy efficiency is a critical issue in data centre management, which is the foundation for cloud computing. The VM placement has a considerable impact on a data centre's energy efficiency and resource utilisation. The assignment of VMs to PMs is an NP-hard problem without an easy way to find an optimal solution, particularly in large-scale data centres. In this study, the VM placement problem is formulated as a constrained optimisation problem. The Genetic Algorithm (GA) is a suitable method for solving this problem in terms of the quality of the solution. However, GA is time-consuming to obtain an optimal solution in the large scale optimisation problem. Therefore, this paper focuses on accelerated GA for energy-efficient VM placement. As the most time-consuming element of the GA is the calculation of its fitness function, this paper simplifies this calculation through a new fitness function in GA. Simulation results of small-, medium-, and large-scale data centres demonstrate that our accelerated GA is faster than the standard GA and gives better quality of solution than the First Fit Decreasing (FFD) algorithm, respectively. The findings of our GA with the new fitness function reveal an 8% energy saving for our GA compared to FFD and a 66% reduction in our GA execution time compared to the standard GA with standard energy formula as a fitness function. The number of generations in our GA is reduced by about 50% in comparison with the standard GA. Moreover, we started with 3000 PMs in the large-scale dataset, and only 1086 PMs were actually used after running our GA. Therefore, we may switch off far more PMs for energy savings from our GA results than those from the standard GA. • Virtual machine placement problem formulated as a constrained optimisation problem. •It focuses on accelerated genetic algorithm for virtual machine placement. •Our algorithm can minimise residual resources using Taylor extension. •Our solution quality is better than FFD and standard genetic algorithm. •It reduces execution time and energy in large scale data centres. [ABSTRACT FROM AUTHOR]

One of the most energy-intensive facilities requiring a comprehensive and well-optimised cooling system is the data centre. Air containment across the data centre is a key thermal management and energy-saving strategy that enhances the performance of data centres. The majority of modern energy-efficient data centres use some type of air containment. The primary advantage of aisle separation and containment is the decrease in the air temperature at the server inlet by reducing the mixing of hot air with cold air. In order to ascertain the volume of literature relating to corridor insulation, we conducted a literature review. Currently, there have been numerous articles regarding the application of computational fluid dynamics (CFD) analysis, however, publications delineating the integration of building information modelling (BIM) principles for corridor separation are still limited. Research specifically targeting data centre corridor insulation is somewhat limited. As a result of this analysis, the most common methods used to isolate hot or cold aisles within a data centre were identified. To determine the most effective type of corridor insulation, the BIM family was created in Autodesk Revit. The model includes 15 telecom cabinets containing information technology (IT) equipment, eight inter-row air conditioners, and one UPS. The model was used for the CFD analysis of the air temperature in different zones of the room. Visualisation of the results using gradient temperature distributions at different levels provides a complete picture of the microclimate formation in the room and allowed the advantage of the hot aisle isolation scheme to be demonstrated. [ABSTRACT FROM AUTHOR]

Data centres, though a necessary part of modern society, are being stigmatised for consuming vast amounts of electricity for their operational and cooling needs. Due to Ireland's reliance on fossil fuels to meet the increased energy demand of data centres, the data centres are contributing significantly to Ireland's total carbon emissions. As much of this energy is expelled from data centres as waste heat energy, the potential for recycling some of this wasted heat energy was explored using environmentally friendly systems from recent publications. The recovered waste heat energy was applied in a vertical farming system, and the benefits of this waste heat to the vertical farm were analysed and quantified in two scenarios. Using conservative estimates, it was predicted that each vertical farm could be between 5–23% the size of the data centre and produce enough food to feed between 14–61 adults their daily calorie needs, and between 13–58 people their daily fresh produce requirements, depending on the scenario applied. For a more accurate prediction, each vertical farm would have to be assessed on a case-by-case basis, as there is no current research in this area. However, there was not enough data available on Irish data centres to perform these calculations. [ABSTRACT FROM AUTHOR]

This paper examines the opportunities to reuse excess heat from direct free air-cooled data centres without incorporating heat pumps to upgrade the heat. The operation of a data centre in northern Sweden, Luleå, was simulated for a year. It was established that heat losses through the thermal envelope and from the humidification of the cooling airflow influenced the momentary energy reuse factor, iERF, with up to 7%. However, for the annual energy reuse factor, ERF, the heat losses could be neglected since they annually contributed to an error of less than 1%.It was shown that the ideal heat reuse temperature in Luleå was 13, 17, and 18 °C with an exhaust temperature of 30, 40 and 50 °C. The resulting ERF was 0.50, 0.59 and 0.66, meaning that a higher exhaust temperature resulted in potentially higher heat reuse. It could also be seen that raising the exhaust temperature lowered the power usage effectiveness, PUE, due to more efficient cooling.Using heat reuse applications with different heat reuse temperatures closer to the monthly average instead of an ideal heat reuse temperature for the whole year improved the ERF further. The improvement was 11–31% where a lower exhaust temperature meant a higher relative improvement.

This study examines the feasibility of utilising outside air for 'free cooling' in modular data centres through the implementation of an air-side economiser, as an alternative to traditional mechanical cooling systems. The objective is to offset the energy consumption associated with cooling by leveraging the natural cooling capacity of the ambient air. To investigate this potential, a 90-kW modular data centre is employed as the base case for model validation and analysis of energy reduction possibilities. The research employs dynamic thermal modelling techniques to assess the efficacy of the air-side economiser in four distinct climatic zones: Stockholm, Dubai, San Francisco, and Singapore, representing diverse worldwide climates. The model is meticulously calibrated and validated using power usage effectiveness (PUE) values obtained from the Open Compute Project. Simulation runs are conducted to evaluate the energy-reduction potential achievable with the air-side economiser compared to conventional mechanical air-conditioning systems. The results indicate significant energy reductions of up to 86% in moderate climates, while minimal reductions are observed in dry and hot climates. This comprehensive analysis offers valuable insights into the intricate relationship between modular data centres, their operational characteristics, and the viability of employing air-side economisers for free cooling and energy efficiency across different climatic conditions. The contribution of this publication to this field of science lies in its exploration of the practicality and energy-saving potential of air-side economisers in modular data centres. By utilising dynamic thermal modelling and empirical validation, this study provides evidence-based insights into the effectiveness of this cooling strategy, shedding light on its applicability in various climates. The findings contribute to the understanding of energy-efficient cooling solutions in data-centre design and operation, paving the way for more sustainable practices in the field. [ABSTRACT FROM AUTHOR]

Data centres are becoming an increasingly important part of our society’s infrastructure. The number of data centres is growing constantly, making growing the gross level of electrical energy consumption. At the same time, the rapid spread of sophisticated electrical devices as well as other automation systems in general produces an opportunity for making data centres an attractive player in the constantly designing energy market. But for this, new advanced technologies must be applied to solve the problems of complexity and heterogeneity in various types of data centre design. A new concept, which is based on the automated generation of a digital twin (DT) system, directly from its schematic representation is presented in this paper. A DT is a virtual version of an object or system, designed to aid decision-making and virtual commissioning through simulation, machine learning, and reasoning. In the scope of current work, the IEC 61850 standard is chosen as a starting point for a multi-step generation of the DT combining simulation model and decentralized control logic. As a result, the designed DT “clone” of an electrical system consists of the SIMULINK model of the electrical system plus the automatically generated control application (based on the IEC 61499 standard).

Software-defined networking (SDN) separates the forwarding plane and control plane of the network equipment, adopts a centralized control mode to simplify network deployment and improve network management efficiency and realizes the network flexible control and management of traffic through programmable open interfaces. At present, it has been widely used in domestic and international data centre networks. With the explosive growth of the scale of data centres and the increase in user requirements for service quality, load balancing and congestion control of data centres have become significant issues in current research. After studying and analysing data centre SDN architecture and load balancing problems in detail, certain experts proposed an SDN load balancing algorithm, based on improved ant colony optimization-load balancing (IACO-LB). Firstly, the overall framework of SDN load balancing in data centres is studied, which is mainly divided into three parts: basic network equipment, OpenFlow protocol and controller. Among them, the controller constitutes the core of the entire load balancing system, including four modules: network topology awareness, status collection, the core of the load balancing algorithm and the flow table distribution. Then, an SDN load balancing algorithm, based on the improved ant colony optimization (IACO) is proposed to achieve dynamic load balancing of the SDN. The algorithm fully considers the performance parameters of network links and servers, and its design is based on the principle of selecting links and servers with low utilization. The evaluation methods of server module and link module are designed and the ant colony algorithm is used to find the global optimal solution. In order to prevent the algorithm from falling into local optimum, the Kent chaotic model is adopted to disturb the transition probability of the ant colony, by improving the basic ant colony algorithm. Finally, a network topology model was established in MATLAB to carry out simulation experiments. The results show that, compared with the equivalent multi-path algorithm and path server traffic scheduling algorithm, IACO-LB can effectively solve the load balancing problem of SDN and can dynamically adjust the routing scheme, according to the changes in network link traffic and server utilization. The algorithm converges quickly and can achieve a better global load balancing scheme. [ABSTRACT FROM AUTHOR]

There has been a recent emphasis to establish and codify large-scale or national Trusted Research Environments (TREs) in the United Kingdom, with a view to limit smaller, local TREs. The basis for this argument is that it avoids duplication of infrastructure, information governance, privacy risks, monopolies and will promote innovation, particularly with commercial partners. However, the work around establishing TREs in the UK largely ignores the long-established local TRE landscape in Scotland, and the way in which local TREs can actually improve data quality, solve technical architecture challenges, promote information governance and risk minimisation, and encourage innovation and collaboration (both academic and commercial). This data centre profile focuses on the Grampian Data Safe Haven (DaSH),a secure, virtual healthcare data analysis and storage centre located in Aberdeen, Scotland. DaSH was co-established by the NHS Grampian Health Board and University of Aberdeen to allow for the secure processing and linking of health data for the Grampian and Scottish population when it is not practicable to obtain consent from individual patients. As an established trusted research environment now in its 10th operating year, DaSH technology ensures healthcare, social care data and other types of sensitive data, routinely collected and used without individual patient consent, are made accessible for both academic research and clinical service evaluation and improvements whilst protecting individuals' privacy at the local, national and international levels. DaSH has registered almost 600 projects and facilitated over 200 distinct research projects with data hosting, extraction, and novel linkages to completion. Ongoing innovation and collaboration between DaSH and the NHS Grampian Health Board continues to expand researcher access to new types of data and data linkages, introduce new technologies for advanced statistical research methods, and supports interdisciplinary research using population health and social care data for research, clinical and commercial advancements, and real-world practitioner applications. The purpose of this paper is to present DaSH's data population, operating model, architecture and information technology, governance, legislation and management, privacy-by-design principles and data access, data linkage methods, data sources, noteworthy research outputs, and further developments in order to demonstrate the value of local TREs within the data management and access debate.

Introduction: Care integration needs to take place on different levels, including that of infrastructure and especially data infrastructure. Only integrated data allow for policy making, care planning, research, and evaluation that spans across different sectors of care and support. Methods: In the course of an EU-funded reform initiative on integrated care, the Estonian government and various agencies have developed a concept for an integrated data centre, bringing together information from social, medical, and vocational support services. The concept was developed in co-production with many stakeholders. A test data set from all covered sectors, including the pseudonymised data of 17,945 citizens of an Estonian municipality, was created and analysed as a proof-of-concept exercise. Results: The co-production approach resulted in a set of requirements and use cases as well as a specification of premises, processes, and data flows for the data centre. The analysis of the test dataset showed the principal feasibility of the dataset for the intended purposes. Conclusion: The concept development phase showed that an integrated data centre for Estonia is feasible per se and helped to specify concrete actions required for its realisation. Strategic and financial decisions from the Estonian Reform Steering Committee are now needed to create the data centre. [ABSTRACT FROM AUTHOR]

Heat pipe, a high efficient, cost effective and reliable device, is considered one of the most promising passive technologies for cooling data centres. Aiming to provide comprehensive information and focused perspective on heat pipe system for cooling data centres, the literature reviewed in this review is obtained from the Web of Science by searching the topic "heat pipe" and "data centre" conducted on Sep. 1, 2019 for the decade from 2009 to 2018. In this paper, a comprehensive review on heat pipes for use in data centre cooling systems will be carried out, starting from the working principle, heat transfer analysis and applications. The review works will be analysed in terms of the system research methodology, energy performance evaluation, and IT tasks. From the system research methodology, the heat pipe structure, simulation and testing analyses were conducted. From the energy performance evaluation, the energy consumption, energy recovery and energy saving analyses were studied. From the IT tasks, the avoidance of hot spots and thermal management in the data centres were analysed. The opportunities for further works are revealed including: developing new and efficient data centre cooling systems employing advanced technology; optimising the heat pipe structure for the data centre cooling systems; testing the data centre cooling systems under actual conditions; and evaluating the economic feasibility and market potential of the data centre cooling systems. This review provides the guidelines for the development of data centre cooling system based on the heat pipe. • The heat pipe systems for cooling data centres are summarized from the research methodology, energy performance evaluation, and IT tasks. • Opportunities of data center cooling system based on the heat pipe are emphasized. • The evaluation of economic feasibility and market potential of the data centre cooling systems based heat pipe is needed. • Further tests of the data centre cooling systems in real environment should be carried out. [ABSTRACT FROM AUTHOR]

This paper presents a novel Marine Predators Algorithm for both training an Artificial Neural Network model used for predicting the energy demand and for solving a dynamic combined economic and emission dispatch of a data centre. The MPA is proposed for first finding the optimal weights and biases of the neural network based on a Mean Squared Error and Mean Absolute Error minimization objective function. Real life dataset obtained from an anonymous data centre operator in Cape Town, South Africa was used for the model implementation. The dataset was made up of a total of 564 samples and was split into training and testing set using an 80:20 ratio. The input variables contained in the dataset are the data centre’s ambient temperature, ambient relative humidity, chiller output temperature and Computer Room Air Conditioning supply temperature while the energy demand is the target variable. The optimal weights of the neural network model were analysed using a weights-based approach to determine the level of influence each input parameter of the model has on the data centre’s energy demand. Then based on the predicted energy demand of the data centre, a dynamic economic and emission dispatch problem is solved for the building while considering thermal and solar photovoltaic generations. A spinning reserve is also incorporated in the energy dispatch model to cater for any shortfall that may exist between the predicted and actual energy demand of the data centre due to possible inaccuracies in the energy demand prediction model. Results for the energy demand prediction task showed that the proposed method outperformed the state-of-the-art by producing the least Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and highest prediction accuracy for the training and testing sets. Further analyses also highlighted that the data centre’s ambient temperature has the highest influence of about 37.63% on its energy demand pattern. For the energy dispatch task, the proposed method also identified solar photovoltaic as the preferred energy source over conventional thermal generators in fulfilling the objective function, depending on its availability. Overall, the findings presented in this study emphasize the robustness of the proposed method in solving the problems considered and its potential application towards solving even more complex problems.

Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in cross-data-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine. [ABSTRACT FROM AUTHOR]

Cloud computing spreading in such a tremendous way that the energy consumption of the network and computing resources causes the emission of enormous quantities of CO2 to the environment that force to manage energy consumption. Here, we propose modified sun-and-wind energy-aware routing (MSWEAR) new cloud network model and routing algorithm to find the location of Data Center (DC) geographically. So, the data can be moved efficiently and effectively which will hamper the environment lesser than the usage of non-renewable energy sources. An effort has been put to balance the delay versus low energy consumption among DC of cloud to optimize the CO2 emission. The proposed mechanism improves the QoS through optimizing cost, load, and minimizing the reduction of carbon (emission of CO2). We have tried to derive an ideal network based on MSWEAR algorithm to maximize the DCs usage of renewable energy sources and studied the performances. Our proposed mechanism is compared with the benchmark mechanisms and found performing better in its class. [ABSTRACT FROM AUTHOR]

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