Modeling and Simulation of Data Center Networks

Data centers (DCs), owing to the exponential growth of Internet services, have emerged as an irreplaceable and crucial infrastructure to power this ever-growing trend. A DC typically houses a large number of computing and storage nodes, interconnected by a specially designed network, namely, DC network (DCN). The DCN serves as a communication backbone and plays a pivotal role in optimizing DC operations. However, compared to the traditional network, the unique requirements in the DCN, for example, large scale, vast application diversity, high power density, and high reliability, pose significant challenges to its infrastructure and operations. We have observed from the premium publication venues (e.g., journals and system conferences) that increasing research efforts are being devoted to optimize the design and operations of the DCN. In this paper, we aim to present a systematic taxonomy and survey of recent research efforts on the DCN. Specifically, we propose to classify these research efforts into two areas: 1) DCN infrastructure and 2) DCN operations. For the former aspect, we review and compare the list of transmission technologies and network topologies used or proposed in the DCN infrastructure. For the latter aspect, we summarize the existing traffic control techniques in the DCN operations, and survey optimization methods to achieve diverse operational objectives, including high network utilization, fair bandwidth sharing, low service latency, low energy consumption, high resiliency, and etc., for efficient DC operations. We finally conclude this survey by envisioning a few open research opportunities in DCN infrastructure and operations.

Data centres are large energy consumers, which have become more energy efficient in recent years due to increased awareness of opportunities, increasing energy costs and corporate social responsibility pressures. A lifecycle assessment approach identifies two other significant areas of impact: the embodied impact of IT equipment and mechanical and electrical plant, and the electricity source used in operational and embodied processes. Many data centre operators publicise their energy efficiency, some also report on their carbon footprint. However, there is a need for simple tools in order to help operators better understand and quantify the embodied impact and inform green procurement. Focussing purely on energy efficiency may cause a burden shift, e.g. by replacing equipment with more efficient equipment but increasing the embodied impact. The total environmental impact could remain the same/increase, but with the perception of a ‘greener’ data centre. Although currently there is limited data available for data centre life cycle assessments and the process is resource intensive, research has identified which factors significantly impact a facility’s environmental impact. This knowledge should be used in the design process and throughout the data centre lifecycle to minimise data centre environmental impact. Practical application: As the data centre industry continues to grow and its sustainability receives closer scrutiny, it is important to increase awareness of where its highest environment impacts lie and analyse the factors which influence this. This can help inform policy and decision-making to support the design and operation of data centres which are truly more sustainable. It is important that this research is not just theoretical but can translate into practical actions which can be implemented in a cost-effective manner for the benefit of everyone. The current focus in industry has been energy consumption and energy efficiency; many practices which were considered innovative a few years ago have now become standard best practice. It is time to examine which other areas should be prioritised for improvement.

Reliability, availability, and life-cycle cost (LCC) analysis of combined cooling, heating and power (CCHP) integration to data centers considering electricity and cooling supplies

Cooling systems play a vital role in the design and operation of Data Centers (DCs), as they consume a considerable amount of energy. Hot air infiltration from the servers' outlets into their inlets, which creates hot spots and flow short-circuiting, is one of the main sources of thermal inefficiency inside air-cooled DCs. This inefficiency increases the total energy consumption of DCs especially when the allocated workload and heat dissipation of the servers are increased. Therefore, efficient thermal management of DCs is considered as one of the main challenges of the DC industry. In order to highlight the importance of this inefficiency, an operational DC has been considered in this study. The thermal behaviour of the DC has been evaluated by conducting a numerical analysis of the flow and temperature fields based on the experimental measurements in a real DC. Numerical simulation highlighted a number of undesirable hot spots near the racks. Cold aisle containment, which is one of the efficient and industrially affordable methods of thermal inefficiency reduction, has been considered. The effectiveness of this method has been evaluated using three non-dimensional metrics known as Supply Heat Index (SHI), Rack Cooling Index (RCI) and the Coefficient of Performance (COP) of the cooling system. In this case study, by applying the proposed method, SHI, RCI and COP of the cooling system have been improved by more than 0.45, 17% and 19.5%, respectively. These results demonstrate the effectiveness of cold aisle containment for energy efficiency enhancement of DCs.

The number of data centers has been increased for various reasons such as cloud computing, big-data analysis, multimedia service, etc. With public interests on data center, many novel technologies for data center networks have been proposed and deployed to support data center operations more efficiently and effectively. However, the construction of data center network incurs significant costs. Moreover, various technologies interplay each other to achieve multiple objectives, and it makes difficult to validate and/or verify characteristics of data center network. In addition, it difficult to perform experiments with a number of hosts and switches. Therefore, it is necessary to observe the characteristics of target data center network before building it. A common approach to evaluate data center is to run simulations that should be similar with real-world data center environment. However, generating traffic with the characteristics of data center networks is not matured yet. People still employ a traffic generator based on the characteristics of Internet traffic. We design a traffic generator that shows more accurate characteristics of data center network traffic. Various traffic characteristics exploited explored by several studies are considered. The proposed method generates flow-level network traffic matrix based on Poisson Shot-Noise model. We implemented the traffic generator using Python programming language to create traffic matrix. To evaluate the proposed method, we compare the results with real data center network traffic. Our results show that the generated traffic owns similar characteristics with the real network traffic in terms of flow size, duration, and the mean and variance of total traffic rate.

The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model’s output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model’s output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features’ impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature’s impact on the model’s output.

IPv6 for Enterprise Networks The practical guide to deploying IPv6 in campus, WAN/branch, data center, and virtualized environments Shannon McFarland, CCIENo. 5245 Muninder Sambi, CCIE No. 13915 Nikhil Sharma, CCIE No. 21273 Sanjay Hooda, CCIE No. 11737 IPv6 for Enterprise Networks brings together all the information you need to successfully deploy IPv6 in any campus, WAN/branch, data center, or virtualized environment. Four leading Cisco IPv6 experts present a practical approach to organizing and executing your large-scale IPv6 implementation. They show how IPv6 affects existing network designs, describe common IPv4/IPv6 coexistence mechanisms, guide you in planning, and present validated configuration examples for building labs, pilots, and production networks. The authors first review some of the drivers behind the acceleration of IPv6 deployment in the enterprise. Next, they introduce powerful new IPv6 services for routing, QoS, multicast, and management, comparing them with familiar IPv4 features and behavior. Finally, they translate IPv6 concepts into usable configurations. Up-to-date and practical, IPv6 for Enterprise Networks is an indispensable resource for every network engineer, architect, manager, and consultant who must evaluate, plan, migrate to, or manage IPv6 networks. Shannon McFarland, CCIE No. 5245, is a Corporate Consulting Engineer for Cisco serving as a technical consultant for enterprise IPv6 deployment and data center design with a focus on application deployment and virtual desktop infrastructure. For more than 16 years, he has worked on large-scale enterprise campus, WAN/branch, and data center network design and optimization. For more than a decade, he has spoken at IPv6 events worldwide, including Cisco Live. Muninder Sambi, CCIE No. 13915, is a Product Line Manager for Cisco Catalyst 4500/4900 series platform, is a core member of the Cisco IPv6 development council, and a key participant in IETFs IPv6 areas of focus. Nikhil Sharma, CCIE No. 21273, is a Technical Marketing Engineer at Cisco Systems where he is responsible for defining new features for both hardware and software for the Catalyst 4500 product line. Sanjay Hooda, CCIE No. 11737, a Technical Leader at Cisco, works with embedded systems, and helps to define new product architectures. His current areas of focus include high availability and messaging in large-scale distributed switching systems. n Identify how IPv6 affects enterprises n Understand IPv6 services and the IPv6 features that make them possible n Review the most common tranisition mechanisms including dual-stack (IPv4/IPv6) networks, IPv6 over IPv4 tunnels, and IPv6 over MPLS n Create IPv6 network designs that reflect proven principles of modularity, hierarchy, and resiliency n Select the best implementation options for your organization n Build IPv6 lab environments n Configure IPv6 step-by-step in campus, WAN/branch, and data center networks n Integrate production-quality IPv6 services into IPv4 networks n Implement virtualized IPv6 networks n Deploy IPv6 for remote access n Manage IPv6 networks efficiently and cost-effectively This book is part of the Networking Technology Series from Cisco Press, which offers networking professionals valuable information for constructing efficient networks, understanding new technologies, and building successful careers.

Data centers are integral part of cloud computing that support Web services, online social networking, data analysis, computation intensive applications and scientific computing. They require high performance components for their inter-process communication, storage and sub-communication systems. The performance bottleneck that used to be the processing power has now been shifted to communication speed within data centers. The performance of a data center, in terms of throughput and delay, is directly related to the performance of the underlying internal communication network. In this paper, we introduce an analytical model that can be used to evaluate the underlying network architecture in data centers. The model can further be used to develop simulation tools that extend the scope of performance evaluation beyond what it can be achieved by the theoretical model in terms of various network topologies, different traffic distributions, scalability, and load balancing. While the model is generic, we focus on its implementation for fat-tree networks that are widely used in data centers. The theoretical results are compared and validated with the simulation results for several network configurations. The results of this analysis provide a basis for data center network design and optimization.

Energy efficiency is one of the most important issues in the design and operation of data centers. Energy efficiency implies efficient and environmentally friendly use of energy, namely heat recovery, flexible cooling management, use of terrain climate to beneData centers should not have transit communications (heating, water, sewer). However, for rooms containing servers, there are requirements for the level of humidity, so the presence of water is necessary at all times. The risk of flooding can be reduced by waterproofing. Y Servers require constant, round-the-clock cooling and heat removal, even if the data center is located on a pole. Therefore, the room must be equipped with an artificial climate system. Temperature and humidity must be monitored. Cabinets and racks should be arranged in an alternating pattern, so that the front side of the cabinets/racks in each row faces the front side of the other row, creating “hot” and “cold” passages. Cold air comes from under the raised floor, hot air goes to the ceiling: this is how air circulation occurs.fit infrastructure, carbon reduction, solar panels, etc.KeywordsData centerEnergy efficiencyPerspetrons

In this paper, starting with the current security issues of the Ethernet data center, the author put forward the solutions for the secure data centers. The solutions are based on a security infrastructure, framed according to border protection, and The depth detection as the core of the secure data center solutions. It is apparent to penetrate the concept of security to the entire data center network design, deployment, and operation and maintenance.

In this research, the escalating energy consumption challenges in data centers are addressed by optimizing airflow organization designs. Through the use of computational fluid dynamics (CFD) simulations, three different airflow strategies were evaluated and improved: underfloor precision air conditioning, inter-column air conditioning, and backplane air conditioning. These cooling systems, which are usually considered in isolation, were compared in a comprehensive manner to get a full picture of their efficiency and effectiveness. The findings reveal that the implementation of cold aisle containment (CAC) or hot aisle containment (HAC) significantly improves air supply efficiency (ASE) and reduces the supply heat index (SHI), leading to a more uniform temperature distribution and enhanced cooling performance. Specifically, the ASE increased from 65.69% to 85.57% and 90.25% for underfloor precision air conditioning and from 71.29% to 92.16% and 92.17% for inter-column air conditioning, with corresponding reductions in SHI. The backplane cooling system offered consistent ambient temperatures throughout the room, eliminating thermal hotspots without the need for aisle containment. This study offers a comparative analysis of different airflow organization schemes, highlighting the benefits of aisle containment in precision and inter-column air conditioning and the suitability of backplane air conditioning for high-density cooling without the need for traditional aisle separation. The results are crucial for informing energy-efficient cooling strategies in data center design and operation.

For addressing problems such as long delays, latency fluctuations, and frequent timeouts in conventional Transmission Control Protocol (TCP) in a data center environment, Data Center TCP (DCTCP) has been proposed as a TCP replacement to satisfy the requirements of data center networks. It is gaining more popularity in academic as well as industry areas due to its performance in terms of high throughput and low latency, and is widely deployed in data centers. However, according to the recent research about the performance of DCTCP, authors have found that most times the sender’s congestion window reduces to one segment, which results in timeouts. In addition, the authors observed that the nonlinear marking mechanism of DCTCP causes severe queue oscillation, which results in low throughput. To address the above issues of DCTCP, we propose multiple congestion points using double threshold and congestion reaction using window adjustment (DT-CWA) mechanisms for improving the performance of DCTCP by reducing the number of timeouts. The results of a series of simulations in a typical data center network topology using Qualnet network simulator, the most widely used network simulator, demonstrate that the proposed window-based solution can significantly reduce the timeouts and noticeably improves the throughput compared to DCTCP under various network conditions.

Virtualization technologies have been widely deployed in data centers to improve the system utilization. However, they cause increased workload for operators to clarify the structure of virtual networks in data centers. To reduce the operation time, this paper provides the virtual-network management system which automates the integration of the configurations of the virtual networks. The proposed system collects the configurations from server virtualization platforms and VLAN-supported switches, and integrates these configurations according to the newly developed XML-based management information model for virtual-network configurations. The preliminary evaluations show that the proposed system helps to reduce the time to collect and update the configurations by about 40 percent. This result implies that the proposed system is effective for improving the configuration management process for virtual networks in data centers.

The rise of Cloud Computing and Big Data has created the need for morepowerful warehouse-scale cloud data centers, which usually comprise hundreds of thousands ofservers with highaccess bandwidth (>10Gb/s) or more, and cost-effective intra- and inter- data center networks(DCNs) to provide switching and interconnect among all servers. However, the current DCNs sufferfrom limited throughput, large latencies and highpower consumption, and will not be able to sustainthe projected future DCN traffic demandswithout consuming excessive amounts of power. Optical interconnection networks capable of supporting high throughput, high bandwidth, low latency, low power consumptionand full-bandwidth communication among all servers have been proposedas a promising solution by performing switchingat the optical domain, thus greatly improving the performance of the cloud data centers.

With ubiquitous presence and perpetual growth of software applications and computing devices connected to the Internet, organizations and businesses are faced with increasing challenges in operating and maintaining technologies that support their IT infrastructure. Among the goals of technology management are cost-effectiveness, energy efficiency, and secure management of information technologies, which are expected to produce maximum utility value. Virtualization has been proposed as a promising solution to the challenges facing today's IT managers. From virtualized servers to virtualized networks to virtual data centers, various virtualized systems have been explored as means of easing IT operation challenges. One of the major benefits of virtualization is the possibility of building a network of virtual machines and storage infrastructure between the virtual data center and external physical networks. A data center involves a large number of networking devices such as switches, routers, and firewalls. With traditional networking, managing networks in data centers is a complex task due to the closed architecture and vertical integration among the networking devices. Software-defined networking (SDN) is a new architectural paradigm, enabling flexible and dynamic management of networks and associated services. This paper presents the benefits of virtualization in general, the advantages of adopting virtualization in data centers, a practical implementation of virtual data centers on OpenStack, and implementing SDN to manage networks in the virtual data center.