Exploring Wireless Data Center Networks: Can They Reduce Energy Consumption While Providing Secure Connections?

Data centers have become the digital backbone of the modern world. To support the growing demands on bandwidth, Data Centers consume an increasing amount of power. A significant portion of that power is consumed by information technology (IT) equipment, including servers and networking components. Additionally, the complex cabling in traditional data centers poses design and maintenance challenges and increases the energy cost of the cooling infrastructure by obstructing the flow of chilled air. Hence, to reduce the power consumption of the data centers, we proposed a wireless server-to-server data center network architecture using millimeter-wave links to eliminate the need for power-hungry switching fabric of traditional fat-tree-based data center networks. The server-to-server wireless data center network (S2S-WiDCN) architecture requires Line-of-Sight (LoS) between servers to establish direct communication links. However, in the presence of interference from internal or external sources, or an obstruction, such as an IT technician, the LoS may be blocked. To address this issue, we also propose a novel obstruction-aware adaptive routing algorithm for S2S-WiDCN. S2S-WiDCN can reduce the power consumption of the data center network portion while not affecting the power consumption of the servers in the data center, which contributes significantly towards the total power consumption of the data center. Moreover, servers in data centers are almost always underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. To address the high power consumption of the servers, we proposed a network-aware bandwidth-constrained server consolidation algorithm called Network-Aware Server Consolidation (NASCon) for wireless data centers that can reduce the power consumption up to 37% while improving the network performance. However, due to the arrival of new tasks and the completion of existing tasks, the consolidated utilization profile of servers change, which may have an adverse effect on overall power consumption over time. To overcome this, NASCon algorithm needs to be executed periodically. We have proposed a mathematical model to estimate the optimal inter-consolidation time, which can be used by the data center resource management unit for scheduling NASCon consolidation operation in real-time and leverage the benefits of server consolidation. However, in any data center environment ensuring security is one of the highest design priorities. Hence, for S2S-WiDCN to become a practical and viable solution for data center network design, the security of the network has to be ensured. S2S-WiDCN data center can be vulnerable to a variety of different attacks as it uses wireless links over an unguided channel for communication. As being a wireless system, the network has to be secured against common threats associated with any wireless networks such as eavesdropping attack, denial of services attack, and jamming attack. In parallel, other security threats such as the attack on the control plane, side-channel attack through traffic analysis are also possible. We have done an extensive study to elaborate the scope of these attacks as well as explore probable solutions against these issues. We also proposed viable solutions for the attack against eavesdropping, denial of services, jamming, and control-plane attack. To address the traffic analysis attack, we proposed a simulated annealing-based random routing mechanism which can be adopted instead of default routing in the wireless data center

Improved self-management of datacenter systems applying machine learning

Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the world and belong to different entities and authorities. Self-management lets these distributed computing systems improve their resource and energy management, a very important issue when resources have a cost, by obtaining, running or maintaining them. Here we propose to improve Autonomic Computing techniques for resource management by applying modeling and prediction methods from Machine Learning and Artificial Intelligence. Machine Learning methods can find accurate models from system behaviors and often intelligible explanations to them, also predict and infer system states and values. These models obtained from automatic learning have the advantage of being easily updated to workload or configuration changes by re-taking examples and re-training the predictors. So employing automatic modeling and predictive abilities, we can find new methods for making "intelligent" decisions and discovering new information and knowledge from systems. This thesis departs from the state of the art, where management is based on administrators expertise, well known data, ad-hoc studied algorithms and models, and elements to be studied from computing machine point of view; to a novel state of the art where management is driven by models learned from the same system, providing useful feedback, making up for incomplete, missing or uncertain data, from a global network of datacenters point of view. - First of all, we cover the scenario where the decision maker works knowing all pieces of information from the system: how much will each job consume, how is and will be the desired quality of service, what are the deadlines for the workload, etc. All of this focusing on each component and policy of each element involved in executing these jobs. -Then we focus on the scenario where instead of fixed oracles that provide us information from an expert formula or set of conditions, machine learning is used to create these oracles. Here we look at components and specific details while some part of the information is not known and must be learned and predicted. - We reduce the problem of optimizing resource allocations and requirements for virtualized web-services to a mathematical problem, indicating each factor, variable and element involved, also all the constraints the scheduling process must attend to. The scheduling problem can be modeled as a Mixed Integer Linear Program. Here we face an scenario of a full datacenter, further we introduce some information prediction. - We complement the model by expanding the predicted elements, studying the main resources (this is CPU, Memory and IO) that can suffer from noise, inaccuracy or unavailability. Once learning predictors for certain components let the decision making improve, the system can become more ¿expert-knowledge independent¿ and research can focus on an scenario where all the elements provide noisy, uncertainty or private information. Also we introduce to the management optimization new factors as for each datacenter context and costs may change, turning the model as "multi-datacenter" - Finally, we review of the cost of placing datacenters depending on green energy sources, and distribute the load according to green energy availability

VihreäIT metriikoiden analysointi sekä mittausviitekehyksen luonti Sonera Helsinki Datakeskus (HDC) projektille.

The two objectives of this thesis were to investigate and evaluate the most suitable set of energy efficiency metrics for Sonera Helsinki Data Center (HDC), and to analyze which energy efficient technologies could be implemented and in what order to gain most impact. Sustainable IT is a complex matter, and it has two components. First and the more complex matter is the energy efficiency and energy-proportionality of the IT environment. The second is the use of renewable energy sources. Both of these need to be addressed. This thesis is a theoretical study, and it focuses on energy efficiency. The use of off-site renewables is outside of the scope of this thesis. The main aim of this thesis is to improve energy efficiency through effective metric framework. In the final metric framework, metrics that target renewable energy usage in the data center are included as they are important from CO2 emission reduction perspective. The selection of energy efficient solutions in this thesis are examples from most important data center technology categories, and do not try to cover the whole array of different solutions to improve energy efficiency in a data center. The ontological goal is to present main energy efficiency metrics available in scientific discourse, and also present examples of energy efficient solutions in most energy consuming technology domains inside the data center. Even though some of the concepts are quite abstract, realism is taken into account in every analysis. The epistemology in this thesis is based on scientific articles that include empirical validation and scientific peer review. This forms the origin of the used knowledge and the nature of this knowledge. The findings from this thesis are considered valid and reliable based on the epistemology of scientific articles, and by using the actual planning documents of Sonera HDC. The reasoning in this thesis is done in abstracto, but there are many empirical results that qualify the results also as ´in concreto´. Findings are significant for Sonera HDC but they are also applicable for any general data center project or company seeking energy efficiency in their data centers.Lopputyöllä on kaksi päätavoitetta. Ensimmäinen tavoite on löytää sopivin mittausviitekehys energiatehokkuuden osoittamiseksi Sonera Helsinki Datakeskukselle (HDC). Toisena tavoitteena on analysoida, mitä energiatehokkaita ratkaisuja tulisi implementoida ja missä järjestyksessä, saavuttaakseen mahdollisimman ison vaikutuksen. Vihreä IT on monimutkainen asia ja samalla siihen liittyy kaksi eri komponenttia. Ensimmäisenä komponenttina, ja merkityksellisempänä sekä monimutkaisempana, on energiatehokkuus ja energian kulutuksen mukautuvuus suhteessa työkuormaan. Toinen komponentti vihreän IT:n osalta on uusiutuvien energialähteiden käyttäminen. Molemmat komponentit on huomioitava. Lopputyö on teoreettinen tutkimus. Lopputyön ontologinen tavoite on esittää keskeisimmät energiatehokkuusmittarit, jotka ovat saatavilla tieteellisessä keskustelussa, ja esittää myös esimerkkejä energiatehokkaista ratkaisuista teknologia-alueisiin, jotka kuluttavat eniten energiaa data keskuksissa. Vaikka osa esitetyistä ratkaisuista on melko abstraktissa todellisuudessa, realismi on pyritty ottamaan huomioon arvioita tehdessä. Epistemologisesti tämä lopputyö perustuu tieteellisiin artikkeleihin, joissa on tehty empiiristä validointia ja tiedeyhteisön vertaisarviointia tiedon totuusarvosta. Kirjoittaja pyrkii välttämään oman arvomaailman ja subjektiivisen näkemyksen tuomista analyysiin pyrkimällä enemmänkin arvioimaan ratkaisuja perustuen päätavoitteeseen, joka on sekä lisätä energiatehokkuutta että vähentää CO2 -päästöjä datakeskuksessa. Lopputyön löydökset todetaan valideiksi ja luotettaviksi, koska ne perustuvat tieteellisten artikkeleiden epistemologiaan ja siihen, että arvioinnin pohjana on käytetty todellisia Sonera HDC -projektin suunnitteludokumentteja. Päätelmät ja analyysit ovat abstrahoituja, mutta perustuvat empiirisiin tuloksiin, jotka koskevat käytännön tekemistä sekä valintoja. Löydökset ovat merkittäviä Sonera HDC -projektin kannalta, ja myös muille datakeskuksille, jotka haluavat toimia kestävän kehityksen pohjalta

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Increasing service visibility for future, softwarised air traffic management data networks

Air Traffic Management (ATM) is at an exciting frontier. The volume of air traffic is reaching the safe limits of current infrastructure. Yet, demand for more air traffic continues. To meet capacity demands, ATM data networks are increasing in complexity with: greater infrastructure integration, higher availability and precision of services; and the introduction of unmanned systems. Official recommendations into previous disruptive outages have high-lighted the need for operators to have richer monitoring capabilities and operational systems visibility, on-demand, in response to challenges. The work presented in this thesis, helps ATM operators better understand and increase visibility into the behaviour of their services and infrastructure, with the primary aim to inform decision-making to reduce service disruption. This is achieved by combining a container-based NFV framework with Software- Defined Networking (SDN). The application of SDN+NFV in this work allows lightweight, chain-able monitoring and anomaly detection functions to be deployed on-demand, and the appropriate (sub)set of network traffic routed through these virtual network functions to provide timely, context-specific information. This container-based function deployment architecture, allows for punctual in-network processing through the instantiation of custom functionality, at appropriate locations. When accidents do occur, such as the crash of a UAV, the lessons learnt should be integrated into future systems. For one such incident, the accident investigation identified a telemetry precursor an hour prior. The function deployment architecture allows operators to extend and adapt their network infrastructure, to incorporate the latest monitoring recommendations. Furthermore, this work has examined relationships in application-level information and network layer data representing individual examples of a wide range of generalisable cases including: between the cyber and physical components of surveillance data, the rate of change in telemetry to determine abnormal aircraft surface movements, and the emerging behaviour of network flooding. Each of these examples provide valuable context-specific benefits to operators and a generalised basis from which further tools can be developed to enhance their understanding of their networks

Enabling sustainable power distribution networks by using smart grid communications

Smart grid modernization enables integration of computing, information and communications capabilities into the legacy electric power grid system, especially the low voltage distribution networks where various consumers are located. The evolutionary paradigm has initiated worldwide deployment of an enormous number of smart meters as well as renewable energy sources at end-user levels. The future distribution networks as part of advanced metering infrastructure (AMI) will involve decentralized power control operations under associated smart grid communications networks. This dissertation addresses three potential problems anticipated in the future distribution networks of smart grid: 1) local power congestion due to power surpluses produced by PV solar units in a neighborhood that demands disconnection/reconnection mechanisms to alleviate power overflow, 2) power balance associated with renewable energy utilization as well as data traffic across a multi-layered distribution network that requires decentralized designs to facilitate power control as well as communications, and 3) a breach of data integrity attributed to a typical false data injection attack in a smart metering network that calls for a hybrid intrusion detection system to detect anomalous/malicious activities. In the first problem, a model for the disconnection process via smart metering communications between smart meters and the utility control center is proposed. By modeling the power surplus congestion issue as a knapsack problem, greedy solutions for solving such problem are proposed. Simulation results and analysis show that computation time and data traffic under a disconnection stage in the network can be reduced. In the second problem, autonomous distribution networks are designed that take scalability into account by dividing the legacy distribution network into a set of subnetworks. A power-control method is proposed to tackle the power flow and power balance issues. Meanwhile, an overlay multi-tier communications infrastructure for the underlying power network is proposed to analyze the traffic of data information and control messages required for the associated power flow operations. Simulation results and analysis show that utilization of renewable energy production can be improved, and at the same time data traffic reduction under decentralized operations can be achieved as compared to legacy centralized management. In the third problem, an attack model is proposed that aims to minimize the number of compromised meters subject to the equality of an aggregated power load in order to bypass detection under the conventionally radial tree-like distribution network. A hybrid anomaly detection framework is developed, which incorporates the proposed grid sensor placement algorithm with the observability attribute. Simulation results and analysis show that the network observability as well as detection accuracy can be improved by utilizing grid-placed sensors. Conclusively, a number of future works have also been identified to furthering the associated problems and proposed solutions

Demystifying Internet of Things Security

Break down the misconceptions of the Internet of Things by examining the different security building blocks available in Intel Architecture (IA) based IoT platforms. This open access book reviews the threat pyramid, secure boot, chain of trust, and the SW stack leading up to defense-in-depth. The IoT presents unique challenges in implementing security and Intel has both CPU and Isolated Security Engine capabilities to simplify it. This book explores the challenges to secure these devices to make them immune to different threats originating from within and outside the network. The requirements and robustness rules to protect the assets vary greatly and there is no single blanket solution approach to implement security. Demystifying Internet of Things Security provides clarity to industry professionals and provides and overview of different security solutions What You'll Learn Secure devices, immunizing them against different threats originating from inside and outside the network Gather an overview of the different security building blocks available in Intel Architecture (IA) based IoT platforms Understand the threat pyramid, secure boot, chain of trust, and the software stack leading up to defense-in-depth Who This Book Is For Strategists, developers, architects, and managers in the embedded and Internet of Things (IoT) space trying to understand and implement the security in the IoT devices/platforms