Data Center Network Architecture Research Articles

Green Data Center : Reviews And Challenges.

Data is undoubtedly the currency of the 21st century. The data that we are providing to or retrieving from the users has a very important role to play in the evolution of services in both the computing and non-computing spheres. Various internet services, such as web hosting, e-commerce, and Social Networking, find their roots in the Data centers. With the ever-increasing concerns about carbon emissions, reliance on non-renewable energy resources, and energy consumption by the cooling systems in conventional Data centers, it is imperative to shift our focus to newer, greener, and cleaner technologies that will aim to reduce the overall carbon footprint, technologies that will reduce energy consumption, and the integration of renewable energy resources into the existing setup. There are several studies that have highlighted challenges regarding the adoption of technologies in the hardware and software domains and the standardization of performance metrics, which are generally used to study trends in energy consumption and net emissions and, therefore, assess the sustainability and efficiency of Data center operations. This shift to Green Data centers will certainly involve both standardized performance metrics, effective strategies to reduce energy consumption, and the integration of renewable energy resources to reduce the carbon footprint. Furthermore, we are going to discuss data center network architecture that will make our future Green Data centers scalable, fault-tolerant, agile, and energy efficient. This paper reviews various strategies for designing an efficient data center, ways to incorporate renewable energy resources, standardization and benchmarking of performance metrics, and optimizing the cooling systems to improve energy efficiency. Finally, we are going to outline the future directions and opportunities for further research and innovation.

ODRAD: An optical wireless DCN dynamic-bandwidth reconfiguration with AWGR and deep reinforcement learning

The rapid growth of Data Center Network (DCN) traffic has brought new challenges, such as limited bandwidth, high latency, and packet loss to existing DCNs based on electrical switches. Because of its theoretically unlimited bandwidth and faster data transmission speeds, optical switching can overcome the problems of electrically switched DCNs. Additionally, numerous research works have been devoted to optical wired DCNs. However, static and fixed-topology DCNs based on optical interconnects significantly limit their flexibility, scalability, and reconfigurability to provide adaptive bandwidth for traffic with heterogeneous characteristics. In this study, we propose and conduct performance evaluations on a reconfigurable optical wireless DCN architecture based on distributed Software-Defined Networking (SDN), Deep Reinforcement Learning (DRL), Semiconductor Optical Amplifier (SOA), and Arrayed Waveguide Grating Router (AWGR). Our architecture is called ODRAD (which stands for Optical Wireless DCN Dynamic-bandwidth Reconfiguration with AWGR and Deep Reinforcement Learning). A Mininet simulation model is established to further verify the reconfigurability of the ODRAD network for various server scales. Based on experimental verification, ODRAD achieves an average end-to-end server latency of 5.2μs under a load of 99%. Compression results demonstrate a 17.36% improvement in packet rate latency performance compared to RotorNet and a 15.21% improvement compared to OPSquare at a load of 99% as the ODRAD network scales from 2,560 to 40,960 servers. Furthermore, ODRAD exhibits effective throughput across different routing protocols, DCN scales and loads.

Re-routable and Low-latency All Optical Switching Algorithm for Next Generation DCN

Digital infrastructure is now indispensable to the function of society and quality of life of the citizen. All over the world there is leveraging of digital infrastructure that comprises use of data, computerized devices, methods and systems. During COVID-19 pandemic it comes more prominently in front. India being the most popular and populated country in the world, comprises nearly half billion internet users expected to transform the digital ecosystem. With the increasing demand of smartphone application, online activities gigantic amount of data has been generated, so evolution and development of data centers be the high importance not only for India but as well as for the world. Thus, it is the need to promote and create robust data center network infrastructure that supports emerging technologies such as 5G, IoT, AI, machine learning, adaptive manufacturing, smart agriculture, and automation and so on. In this paper we propose an all Optical re-routable, low latency Data Center Network (DCN) architecture using Passive Optical Datacenter Switch (PODS). The PODS consists of an Arrayed Waveguide Grating Router (AWGR) in association with a controller unit. An efficient wavelength assignment algorithm is integrated in PODS, to reroute the packet for congestion avoidance and packet loss.. This feature makes the DCN architecture more scalable, proficient with high throughput, low latency, power efficient under high traffic and bursty traffic conditions. The performance of the proposed PODS-based DCN is compared with existing passive optical DCN architectures PODCA and finds improved characteristics in terms of delay, throughput and blocking probability. Simulation of the framework is done in Python platform and executed on google COLAB in the Windows environment and the result shows 46.3% improvement in latency and throughput in terms of 90% network load compared with PODCA architecture.

Scalable and low server-to-server latency data center network architecture based on optical packet inter-rack and intra-rack switching

In this work, we introduce an efficient data center network (DCN) architecture using optical packet switching for the inter-rack and intra-rack packet networks. We investigate the end-to-end communication in a server-to-server (S2S) base, implementing the east-west networking scenario across the whole intra- and inter-rack DCN. As opposed to other optical or hybrid optical–electrical DCN studies that focus on either the intra-rack or inter-rack part of the DCN, our study proposes and investigates a unified DCN architecture that consists of three separate optical network models: the intra-rack, the inter-rack, and the bridge that connects the intra- and inter-rack networks. Particularly, the intra-rack optical network is a passive-coupler-based single-hop wavelength division multiplexing (WDM) network for the communication among servers of the same rack, following bandwidth-efficient synchronous transmission WDM access (WDMA) and time division multiplexing access (TDMA) rules. The bridge optical network is designed as a passive-optical-network-based network to connect the rack servers with the above-placed top-of-rack (ToR) switch, bridging the intra- and inter-rack optical networks and following a greedy TDMA scheme. Finally, the inter-rack optical network connects the different ToRs in a 2D torus topology over optical fibers, offering all-to-all connectivity via lightpaths that rely on a combination of spatial and wavelength paths. In our study, the DCN traffic is classified into several priority classes, each representing distinct service delay requirements, as occurs in existing DCNs. The DCN architecture design, i.e., the server and ToR switch architectures as well as the TDMA/WDMA algorithms for the intra-rack, inter-rack, and bridge optical networks, takes into consideration the traffic variability aiming to serve it into a considerably low end-to-end latency time of the order of few µs, even under high congestion conditions. The proposed DCN performance is evaluated under the scenario of 400 Gbps and 8 Tbps total capacity in the intra-rack and whole end-to-end networks, respectively, while its limitations are extensively explored. Simulation results demonstrate that our proposal achieves 90% and 100% bandwidth utilization in the optical intra- and inter-rack networks, respectively, and 91% for end-to-end S2S communication across the whole DCN. Also, the maximum end-to-end packet latency experienced across the whole DCN under highly loaded conditions is only 0.98 µs, 27 µs, and 218 µs for the highest, medium, and lower priority traffic classes, respectively, fully complying with the rigid latency requirements of various modern cloud applications such as Industry 4.0 and the Internet of Things. The proposed DCN architecture is scalable and can accommodate more than 10,000 servers. In addition, it provides a low energy footprint ensuring up to 50% power consumption reduction as compared to existing Fat-Tree DCN architectures. Finally, it provides lower end-to-end latency across the whole DCN up to high loads, as compared with other relative studies.

Fault-tolerant unicast using conditional local safe model in the data center network BCube

As an essential infrastructure to support cloud computing services, data center networks (DCNs) are primarily used for transmitting and storing data. BCube is a large-scale DCN architecture that can effectively handle the massive data generated by network end devices due to the explosive growth of the Internet. However, as server failures in DCNs become more frequent, ensuring reliable data communication in BCube is critical. In this paper, we first establish a conditional local safe model in BCube, which divides the fault-free nodes in sub-BCube by adding some restrictions, and effectively avoids communication obstacles that may arise from faulty nodes. Then, based on the proposed model, a fault-tolerant unicast path algorithm is designed, which can construct a reliable data transmission path in the local safe sub-BCube of BCube and can tolerate more faulty nodes than existing algorithms. Finally, we conduct simulation experiments to verify that our algorithm can construct the shortest path with a high probability and achieve almost complete success in data transmission when the number of faulty nodes does not exceed half of the total nodes in BCube.

RibsNet: A Scalable, High-Performance, and Cost-Effective Two-Layer-Based Cloud Data Center Network Architecture

Today, we witness an unprecedented surge of data traffic due to the explosive growth of smart devices and intelligent applications. Hence, supporting the Data Center Networks (DCNs) infrastructure by accommodating myriads of servers becomes natural and inevitable. This vision can be realized by providing an interconnecting architecture with higher scalability and remarkable capacity while maintaining fault tolerance, minimum latency, and cost and power efficiency. Motivated by this trend, this paper proposes a new network based on dual-centric dubbed RibsNet, which is a symmetric network composed of two layers of switches and dual-port servers. The proposed network enables the servers to establish all kinds of connections (server-switch and server-server), thus enhancing network fault tolerance with high bisection width and maximizing its performance and reliability. RibsNet allows the gradual addition of servers while preserving all its topological properties; it has a stable and low diameter, which does not exceed six regardless of the DCN’s size. Furthermore, an efficient and fault tolerant routing scheme, exclusively designed for this network, is proposed to enhance the routing efficiency and address different types of failures. Theoretical analysis proves that RibsNet strikes a good balance among several connections within the network and brings outstanding gains in terms of incremental scalability, bisection width, and cost and power savings against various cutting-edge DCNs architectures. Simulation results demonstrate RibsNet as a promising structure that outperforms BCube, DCell, HSDC, and FiConn in terms of latency and throughput by approximately 8%, 6%, 7%, and 26%, respectively.

Reclaiming Light Dropped During Optical Modulation to Bias Avalanche Photodetectors

The introduction of avalanche photodetectors in optical interconnects can extend the unallocated optical budget while unleashing further opportunities to migrate to novel datacenter network architectures. The required high-voltage rail that biases the photodiodes would involve a specialized electronic circuitry but will be instead harvested directly at the optical layer, taking advantage of the dropped power during optical modulation at local transmitters. Rather than dumping light resulting from extinct space bits, we will collect this contribution and convert it to a high-voltage bias by means of a photovoltaic power conversion circuit that is shared among and powered from all constituent data lanes of the optical interconnect. We will experimentally demonstrate that this energy reclamation circuit can sustain the sourced current during avalanche photodetection whilst maintaining a bias rail at ∼25 V with continuity, as will be proven for up to 64 data lanes. We demonstrate an optical budget of ∼30 dB for 10 Gb/s/lane transmission, with a reception penalty as small as 0.2 dB with respect to an electrically biased photoreceiver. We will further elaborate on the limitations linked to the proposed concept, such as in terms of dynamic range.

GHDC: a dual-centric data center network architecture by using multi-port servers with greater incremental scalability

As the volume of data keeps growing rapidly, more and more storage devices, servers, and network devices are continuously added into data center networks (DCNs) to store, manage, and analyze the data. The industry experience indicates that, instead of adding a huge number of servers into the DCNs at a time, the DCN can also be expanded gradually by adding a small number of servers from time to time. This paper proposes a new type of dual-centric data center network structure, called GHDC (Generalized Hypercube Data Center network architecture), which is constructed by using commodity switches and multi-port servers. After analyzing the shortest distance between any two vertices, a routing algorithm for GHDC is developed. To achieve incremental scalability, two incomplete GHDC structures are proposed. A small number of servers can be added into the incomplete GHDC structures while their topological properties are maintained. The analysis and experiment results show that GHDC significantly outperform the other DCN structures, such as FatTree, BCube, Platonica, FCell, FSquare, and FRectangle, in terms of the incremental scalability and robustness. The average throughput of GHDC is approximately comparable to that of FatTree, BCube, and FSquare, and is higher than that of Platonica, FCell, and FRectangle by about 130.2%, 17.45% and 25.5%. Compared with the FatTree, BCube, FCell, FRectangle, and FSquare, GHDC reduces the cost by about 68.49%, 78.04%, 10.84%, 22.85%, and 29.55%, and reduces the max energy consumption by about 69.45%, 34.48%, 11.55%, 24.31%, and 29.58%, respectively. The actual energy consumption of GHDC is much lower than that of FatTree, BCube, FCell, FRectangle, and FSquare, and is little higher than that of Platonia.

Exploring High-Performance Architecture for Data Center Networks

As a critical infrastructure of cloud computing, data center networks (DCNs) directly determine the service performance of data centers, which provide computing services for various applications such as big data processing and artificial intelligence. However, current architectures of data center networks suffer from a long routing path and a low fault tolerance between source and destination servers, which is hard to satisfy the requirements of high-performance data center networks. Based on dual-port servers and Clos network structure, this paper proposed a novel architecture to construct high-performance data center networks. Logically, the proposed architecture is constructed by inserting a dual-port server into each pair of adjacent switches in the fabric of switches, where switches are connected in the form of a ring Clos structure. We describe the structural properties of in terms of network scale, bisection bandwidth, and network diameter. architecture inherits characteristics of its embedded Clos network, which can accommodate a large number of servers with a small average path length. The proposed architecture embraces a high fault tolerance, which adapts to the construction of various data center networks. For example, the average path length between servers is 3.44, and the standardized bisection bandwidth is 0.8 in (32, 5). The result of numerical experiments shows that enjoys a small average path length and a high network fault tolerance, which is essential in the construction of high-performance data center networks.

Series Editorial: Cloud and Edge Computing

Cloud and Edge Computing are currently undergoing a sub-stantive transformation on several fronts, from applications to hardware, and from architectures to devices. This issue of the Cloud and Edge Computing Series solicited articles in the area of Cloud and Edge Computing to address the main issues concerned with evolving processes, supporting pedagogies, and applications in cloud computing, networking, and storages technologies. There have been advances on both the cloud and edge computing fronts that have affected this line of technology. Paradigms, such as computing in virtualization-based architectures, issues on geo-graphical constraints for deploying clouds, and the use of SDN/NFV in clouds, for example, were of special interest for this issue of the Series. Nevertheless, our attention was also focused on data center network (DCN) architectures, security, load balancing, and application data streaming supported by evidence from simulations, analysis, or experiments. Cloud and Edge Computing are also incorporated with the Internet of Things (IoT) ecosystem. IoT requiring multiple access edge computing systems is gaining momentum and considered to be deployed in smart cities, public safety, and e-health. The last, but not least important, factor we expected to see in articles was the standardization aspects of Cloud and Edge Computing technology. We were especially interested in articles that could address communications standards in networking for clouds, fogs, and edge computing.

Scaling Optical Interconnects for Hyperscale Data Center Networks

Due to widespread applications and rapid development of cloud computing and Internet services, hyperscale data centers have experienced an unprecedented demand for networking. Data center networks are required not only to handle fast-growing traffic, which doubles almost every one to two years, but also to provide high flexibility and availability to support rapidly changing businesses as well. Therefore, hyperscale data center networks have become one of the main drivers for optical interconnect technologies in recent years. In this article, technologies for scaling optical interconnects for inter-data center interconnects (DCIs) are presented. We first describe hyperscale data center network architectures and requirements, as well as the differences between carrier’s optical transport networks and DCI optical networks, with the main focus on metro-DCI and campus-DCI networks. The scale and fast growth rates of DCI networks require innovations not only in data plane technologies but also in control and management planes as well. In data planes, high-capacity flexible coherent technology, pluggable wavelength-division-multiplexing (WDM) optical transponders, including 100G direct-detection four-level pulse-amplitude modulation (PAM4) and coherent 400ZR/800ZR, and flex-grid and disaggregated reconfigurable optical add-drop multiplexer (ROADM) optical networks are described. Control and management planes are an integral part of optical transport networks and crucial for effectively operating DCI networks. We show that standard protocols, data models, and modular design of a software platform are essential to building a scalable open and disaggregated optical network, which is fundamental to enabling high automation and intelligence in an optical network. An example control and management platform is discussed in detail. With current fiber capacity approaching the nonlinear Shannon limit, challenges to further scale DCI networks and some potential technologies to support ever-increasing traffic growth are discussed at the end.

Collision-free distributed MAC protocol for passive optical intra-rack data center networks

In this paper, we present a distributed medium access control (MAC) protocol and a network architecture suitable for optical intra-rack data center networks (DCNs). The intra-rack communication is performed using passive optical components, over four data wavelength division multiplexing (WDM) channels of either 40 or 100 Gbps each, keeping low power consumption. On the other hand, the inter-rack communication is performed over a separate network through upper layer routers. In this study, we focus only on the intra-rack communication. We introduce an intra-rack DCN (IR-DCN) architecture that works in the optical domain, and two IR-DCN configurations with different total nominal capacity: 160 and 400 Gbps, respectively. Also, we propose a synchronous pre-transmission coordination fair access intra-rack MAC (intra-MAC) protocol taking into account the traffic characteristics and priority classes within existing DCNs. The proposed intra-MAC protocol totally eliminates packet collisions, achieving high performance. Particularly, it reaches high bandwidth utilization even under heavy loads: 90% and 87.5% for the two IR-DCN configurations of 160 and 400 Gbps total capacity, respectively. Also, it achieves low mean end-to-end (e2e) packet delay, lower than 0.25 and 0.12 ms, respectively, providing a reliable solution for time-sensitive DCN traffic. Specifically, simulation results demonstrate that the highest priority traffic experiences e2e delay lower than 1.9 and 1.1 µs, respectively, which is sufficient for the service of the strictest delay requirements of time-sensitive cloud applications. The intra-MAC protocol is decentralized, without the need for a network controller, providing high flexibility. Our IR-DCN proposal is studied in comparison to other currently dominant intra-rack/cluster DCNs, and it achieves from 6% to 57% higher throughput and from 20% to 99% lower e2e delay at high loads. Comparatively, it is on average 80% and 68% more energy and cost efficient, respectively.

DROAD: Demand-aware reconfigurable optically-switched agile data center network

We present a Demand-aware Reconfigurable Data Center Network architecture design (DROAD) with integrated fast-switching optics and space switches that allows dynamic reconfiguration and separation of intra- and inter-cluster connections. The performance analysis results show a 64% improvement in average Flow Completion Time and a significant reduction in TCP session time, as well as a reduced number of sessions needed to be opened compared to traditional electrically-switched leaf-spine networks.

Hamiltonian Properties of the Data Center Network HSDC with Faulty Elements

Abstract The data center network HSDC is a superior candidate for building large-scale data centers, and strikes a good balance among diameter, bisection width, incremental scalability and other important characteristics in contrast to the state-of-the-art data center network architectures. The Hamiltonian property is an important indicator to measure the reliability of a network. In this paper, we study the Hamiltonian properties of HSDC’s logic graph $H_n$. Firstly, we prove that $H_n$ is Hamiltonian-connected for $n\geq 3$. Secondly, we propose an $O(NlogN)$ algorithm for finding a Hamiltonian path between any two distinct nodes in $H_n$, where $N$ is the number of nodes in $H_n$. Furthermore, we consider the Hamiltonian properties of $H_n$ with faulty elements, and prove that $H_n$ is $(n-3)$-fault-tolerant Hamiltonian-connected and $(n-2)$-fault-tolerant Hamiltonian for $n\geq 3$.

Neural network-assisted decision-making for adaptive routing strategy in optical datacenter networks

To improve the blocking probability (BP) performance and enhance the resource utilization, a correct decision of routing strategy which is most adaptable to the network configuration and traffic dynamics is essential for adaptive routing in optical datacenter networks (DCNs). A neural network (NN)-assisted decision-making scheme is proposed to find the optimal routing strategy in optical DCNs by predicting the BP performance for various candidate routing strategies. The features of an optical DCN architecture (i.e., the rack number N, connection degree D, spectral slot number S and optical transceiver number M) and the traffic pattern (i.e., the ratio of requests of various capacities R, and the load of arriving request) are used as the input to the NN to estimate the optimal routing strategy. A case of two-strategy decision in the transparent optical multi-hop interconnected DCN is studied. Three metrics are defined for performance evaluation, which include (a) the ratio of the load range with wrong decision over the whole load range of interest (i.e., decision error E), (b) the maximum BP loss (BPL) and (c) the resource utilization loss (UL) caused by the wrong decision. Numerical results show that the ratio of error-free cases over tested cases always surpasses 83% and the average values of E, BPL and UL are less than 3.0%, 4.0% and 1.2%, respectively, which implies the high accuracy of the proposed scheme. The results validate the feasibility of the proposed scheme which facilitates the autonomous implementation of adaptive routing in optical DCNs.

Low-Latency Optical Wireless Data-Center Networks Using Nanoseconds Semiconductor-Based Wavelength Selectors and Arrayed Waveguide Grating Router

In order to meet the massively increasing requirements of big-data applications, data centers (DCs) are key infrastructures to cope with the associated demands, such as high performance, easy scalability, low cabling complexity and low power consumption. Many research efforts have been dedicated to traditional wired data center networks (DCNs). However, DCNs’ static and rigid topology based on optical cables significantly limits their flexibility, scalability, and even reconfigurability. The limitations of this wired connection can be addressed with optical wireless technology, which avoids cable complexity problems while allowing dynamic adaption and fast reconfiguration. Here, we propose and investigate a novel optical wireless data-center network (OW-DCN) architecture based on nanoseconds semiconductor optical amplifier (SOA)-based wavelength selectors and arrayed waveguide grating router (AWGR) controlled by fast field-programmable gate array (FPGA)-based switch schedulers. The full architecture, including the design, packet-switching strategy, contention solving methodology, and reconfiguration capability, is presented and demonstrated. Dynamic switch scheduling with a FPGA-based switch scheduler processing optical label and software-defined network (SDN)-based reconfiguration were experimentally confirmed. The proposed OW-DCN was also achieved with a power penalty of less than 2 dB power penalty at BER < 1 × 10−9 for a 50 Gb/s OOK transmission and packet-switching transmission.

Secure and pervasive communication framework using Named Data Networking for connected healthcare

Connected healthcare system is one of the most critical Internet of Things (IoT) applications offering numerous healthcare services. But it requires the next-generation technologies to collude with the IoT standards to provide prompt end-user services. Thus, a data-centric network architecture like, Named Data Network (NDN) can be effective to tackle the concerns related to communication in the connected healthcare. In this article, we propose a Secure and Pervasive Health Care Framework (SecPHCF) that attempts to process healthcare data securely and transmit it using NDN thereby aiding faster healthcare support. The data dissemination model in SecPHCF contains two operating modes, (a) a consumer-based model where the end-user initiates the data communication process and (b) a producer-based model where alert messages indicating critical conditions trigger the transmission. SecPHCF is validated through extensive simulations on different network scenarios by considering evaluation parameters such as throughput, network delay, packet delivery ratio, and computation overheads.

A Novel Addressing and Routing Architecture for Cloud-Service Datacenter Networks

Datacenter networks (DCNs) play a key role in providing cloud services. The energy consumption and cost of a DCN are growing sharply with the extensions of network bandwidth and network size. The energy consumption, complexity and cost of a DCN depend on some design factors such as the topology structure, addressing scheme and routing mechanism. A novel addressing and routing architecture for cloud-service DCNs with regular topologies is proposed in this paper. First of all, we propose a port-based source-routing addressing (PSRA) scheme, which makes the table-lookup operation unnecessary and decreases the switch complexity. Next, leveraging the characteristics of PSRA and the regularity of DCN topologies, an extremely simple routing mechanism is designed, without switch involvement, control message interaction and topology information storage. Lastly, a high-efficiency fault-tolerance mechanism is proposed for the addressing and routing architecture. The analysis, implementation and simulation results indicate that the proposed architecture not only decreases the energy consumption and thus the cost of a DCN, but also enhances the routing performance and solves the fault-tolerance problem in a very efficient way.

DACON: a reconfigurable application-centric optical network for disaggregated data center infrastructures [Invited

To solve the issues of low resource utilization and performance bottleneck in current server-centric data center networks (DCNs), we propose and experimentally demonstrate a disaggregated application-centric optical network (DACON) for data center infrastructures based on hybrid optical switches. DACON achieves flexible provision and dynamic reconfiguration of hardware nodes exploiting the softwared-define networking (SDN) orchestration plane. A four-node SDN-enabled disaggregated prototype is implemented with a field-programmable-gate-array-based controller of hardware nodes and nanosecond optical switches, performing a minimal node-to-node network latency of 378.6 ns and zero packet loss. Based on the unmodified Linux kernel and two different applications (distributed computing and a Memcached database), the application runtime of the disaggregated prototype is investigated and compared with the server-centric architecture. Experimental results show that the disaggregated prototype performs better with Memcached database applications, achieving a1.46×faster runtime than the server-centric network at a memory node access ratio of 0.9. Based on the customized control plane orchestrator and dynamic resource reallocation, the node-to-node latency is reduced by 21% when CPU nodes access memory nodes. The scalability of DACON is then numerically assessed based on experimentally measured parameters. Results show that the intra-rack node-to-node latency is less than 404.8 ns with a 6144-node network and memory node access ratio of 0.9. Finally, the cost and power consumption are also studied and compared with current DCN architectures. Results indicate DACON saves 13.4% of the cost of an interconnect network compared with current disaggregated architecture and consumes up to 31.1% less power with respect to server-centric DCN architectures.

(ITMP) – Intelligent Traffic Management Prototype using Reinforcement Learning approach for Software Defined Data Center (SDDC)

Software defined network architecture offers scalability and resilience as the significant advantages to data center networks. This increases the fault tolerance ability of traditional data center network architectures. Massive amounts of mobile network data as well as e-commerce application data requests are the key sources for data centers which recurrently desire attention. Researchers are yet to design a suitable prototype with functional intelligence to support traffic optimization techniques in SDDC. In this research work, we are proposing an intelligent traffic management prototype for software defined data center by means of reinforcement learning approach through the integration of the functionalities such as controller positioning, traffic load balancing, routing and energy efficiency. These are the key areas where traffic optimization becomes essential to improve network performance. The proposed prototype provides a complete framework for enterprises to deploy applications in an efficient manner. We model the prototype to handle dynamic network data applications such as information retrieval, communication and banking applications. We focus in this article on how communication happens among the data center nodes as an inter-data center communication process upon receiving requests from the applications considered. To further enhance the novelty and efficiency of our research work, we adopt multiple reinforcement learning agents to lever load balancing and routing functionalities. Moreover, to assess and ensure the optimized network performance, we evaluate the energy consumption of the network achieved through our proposed prototype.