Software Defined Data Center Research Articles

User-Aware Software Defined Data Centers and Security against Authentication Attacks

User-Aware Software Defined Data Centers and Security against Authentication Attacks

An Architecture and Review of Intelligence Based Traffic Control System for Smart Cities

City traffic congestion can be reduced with the help of adaptable traffic signal control system. The technique improves the efficiency of traffic operations on urban road networks by quickly adjusting the timing of signal values to account for seasonal variations and brief turns in traffic demand. This study looks into how adaptive signal control systems have evolved over time, their technical features, the state of adaptive control research today, and Control solutions for diverse traffic flows composed of linked and autonomous vehicles. This paper finally came to the conclusion that the ability of smart cities to generate vast volumes of information, Artificial Intelligence (AI) approaches that have recently been developed are of interest because they have the power to transform unstructured data into meaningful information to support decision-making (For instance, using current traffic information to adjust traffic lights based on actual traffic circumstances). It will demand a lot of processing power and is not easy to construct these AI applications. Unique computer hardware/technologies are required since some smart city applications require quick responses. In order to achieve the greatest energy savings and QoS, it focuses on the deployment of virtual machines in software-defined data centers. Review of the accuracy vs. latency trade-off for deep learning-based service decisions regarding offloading while providing the best QoS at the edge using compression techniques. During the past, computationally demanding tasks have been handled by cloud computing infrastructures. A promising computer infrastructure is already available and thanks to the new edge computing advancement, which is capable of meeting the needs of tomorrow's smart cities.

RETRACTION: Design and Implementation of Software‐Defined Data Center (SDDC) for Medical Colleges and Universities

RETRACTION: Design and Implementation of Software‐Defined Data Center (SDDC) for Medical Colleges and Universities

SRetor: a semi-centralized regular topology routing scheme for data center networking

The performance of the data center network is critical for lowering costs and increasing efficiency. The software-defined networks (SDN) technique has been adopted in data center networks due to the recent emergence of advanced network control and flexibility demand. However, the rapid growth of data centers increases the complexity of control and management processes. With the rapid adoption of SDN, the following critical challenges arise in large-scale data center networks: 1) extra packet delay on the separated control plane and 2) controller bottleneck in large-scale topology. We propose sRetor in this paper, a topology-description-language-based routing approach for regular data center networks that leverages data center networks’ regularity. sRetor aims to reduce the packet waiting time and controller workload in software-defined data center networking. We propose to move partial forwarding decision-making from the controller to switches to eliminate unnecessary control plane delay and reduce controller workload. Therefore the sRetor controller is only responsible for troubleshooting complicated failures and on-demand traffic scheduling. Our numerical and experimental results show that sRetor reduces the flow start time by over 68% and the fail-over time by over 84%.

MiFi: Bounded Update to Optimize Network Performance in Software-Defined Data Centers

A controller needs to solve the multi-commodity flow problem and globally update the network under tight time constraints to maintain optimal network configurations. This centralized optimization in data centers involves many variables and constraints, which has a slow convergence speed and little scalability. In this paper, we propose MiFi, which aims to Minimize Flow cost or intuitively average transmission delay (delay or latency of flows), under reconfiguration budget constraints in data centers. Thus, we formulate this optimization problem as a constrained Markov Decision Process and propose a set of algorithms to solve it in a scalable manner. We first develop a propagation algorithm to identify the flows mostly affected in terms of latency and configuration in the next update. Then, we set a limitation range (the subset of switches requiring network updates) for updating them to improve adaptability and scalability by updating a less number of flows each time to achieve fast operations. Further, based on the Drift-Plus-Penalty method in Lyapunov theory, we propose a heuristic policy without prior information of flow demand and a renewal policy with a performance guarantee to minimize the additive optimality gap. To the best of our knowledge, MiFi is the first paper that studies the range and frequency of flow reconfigurations, which has both theoretical and practical significance in the area. Emulations and numerical simulations, which are much better than the estimated theoretical bound, show that MiFi outperforms the state of the art algorithms in terms of latency by over 45% while making improvements in adaptability and scalability.

RSLB: Robust and Scalable Load Balancing in Software-Defined Data Center Networks

Data center networks demand high-performance, robust, and scalable load balancing protocols. Despite progress, existing work still cannot meet these requirements well. Software defined networking (SDN) can bring considerable flexibility to the management of data center networks. In the software-defined data center network, we design, analyze, and evaluate RSLB, a robust and scalable load balancing protocol that overcomes these challenges. RSLB uses fine-grained flowcell as the transmission unit, and uses link delay as the congestion metric. It uses a three-step routing strategy to route flowcells to the path with the least congestion. Through global congestion awareness, RSLB reduces flow completion time (FCT), and is more robust to topological asymmetries compared to existing congestion-agnostic schemes. To collect and store congestion information, RSLB adopts a distributed control structure that monitors the congestion of the entire network through multiple controllers, which makes it much more scalable for implementation in large-scale networks compare to existing congestion-aware schemes. The simulation results show that RSLB can achieve lower FCT for mice flows and higher throughput for elephant flows than existing schemes, no matter in failure-free topology or asymmetric topology.

EVCT: An Efficient VM Deployment Algorithm for a Software-Defined Data Center in a Connected and Autonomous Vehicle Environment

Software-defined data centers (SDDC) are an emerging softwarized model that can monitor the virtual machines’ allocation atop the cloud servers. SDDC consists of softwarized entities like Virtual Machine (VM) and hardware entities like servers and connected switches. SDDCs apply VM deployment algorithms to preserve efficient placement and processing data traffic generated from the Connected and Autonomous Vehicles (CAV). To enhance user satisfaction, SDDC providers are always looking for an intellectual model to monitor large-scale incoming traffics, such as the Internet of Things (IoT) and CAV applications, by optimizing service quality and service level agreement (SLA). This paper is motivated by this, raising an energy-efficient VM cluster placement algorithm named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EVCT</i> to handle service quality and SLA issues in an SDDC in a CAV environment. EVCT algorithm leverages the similarity between VMs and models the problem of VM deployment into a weighted directed graph. Based on the amount of traffic between VM, EVCT adopts the “maximum flow and minimum cut theory” to cut the directed graph and achieve high energy-efficient placement for VMs. The proposed algorithm can efficiently reduce the energy consumption cost, provide a high quality of services (QoS) to users, and have good scalability for the variable workload. We have also carried out a series of experiments to use the real-world workload to evaluate the performance of the EVCT. The results illustrate that the EVCT surpasses the state-of-the-art algorithms in terms of energy consumption cost and efficiency.

Design and Implementation of Software-Defined Data Center (SDDC) for Medical Colleges and Universities

The current medical universities culture can reflect their professional nature, showing a strong development trend. The current socialist economy of China is rapidly developing along with cloud computing for data centers. For cultural and educational activities, virtualization and software-defined data center (SDDC) technologies are being used. The most widely used open-source virtualization technologies are SDDC and virtualization. Users may utilize OpenStack to establish private cloud computing environments. The separate control and forwarding architecture of SDDC also make it naturally suitable for the data center’s network environment. Ryu controller has become one of the most widely used SDDC controllers because of its lightweight, high efficiency, and modularity features. Due to the large variety of network operations required by the SDDC, appropriate management and control function modules must be developed on Ryu when it is used as the controller. This study investigates the construction of campus culture at medical colleges and universities using SDDC. Our main objective is to improve the efficiency of data construction in medical colleges and universities and creating a positive cultural environment. On the one hand, the addition of functional modules makes the management of the controller itself easier. Ryu lacks an intuitive interactive platform. Although OpenStack provides an interactive interface, it cannot meet the integration requirements of Ryu. The unified control and school scheduling system have some research and application potential. The experimental results show that our proposed approach of designing an SDDC for medical colleges and universities has a significant impact and has vast potential for future studies.

DRL-PLink: Deep Reinforcement Learning With Private Link Approach for Mix-Flow Scheduling in Software-Defined Data-Center Networks

In datacenter networks, bandwidth-demanding elephant flows without deadline and delay-sensitive mice flows with strict deadline coexist. They compete with each other for limited network resources, and the effective scheduling of such mix-flows is extremely challenging. We propose a deep reinforcement learning with private link approach (DRL-PLink), which combines the software-defined network and deep reinforcement learning (DRL) to schedule mix-flows. DRL-PLink divides the link bandwidth and establishes some corresponding private-links for different types of flows to isolate them such that the competition among different types of flows can decrease accordingly. DRL is used to adaptively and intelligently allocate bandwidth resources for these private-links. Furthermore, to improve the scheduling policy, DRL-PLink introduces the novel clipped double Q-learning, exploration with noise, and prioritized experience replay technology for DDPG to address function approximation error, to induce lager and more randomness for exploration, as well as more effective and efficient experience replay in DRL respectively. The experiment results under actual datacenter network workloads (including Web search and data mining workload) indicate that DRL-PLink can effectively schedule mix-flows at a small system overhead. Compared with ECMP, pFabric, and Karuna, the average flow completion time of DRL-PLink decreased by 77.79%, 65.61%, and 23.34% respectively, when the deadline meet rate is increased by 16.27%, 0.02%, and 0.836% respectively. Additionally, DRL-PLink can also well achieve load balance between paths.

ARP Overhead Reduction Framework for Software Defined Data Centers

ARP Overhead Reduction Framework for Software Defined Data Centers

Intelligent routing using convolutional neural network in software-defined data center network

Intelligent routing using convolutional neural network in software-defined data center network

SEMTE: scalable and extended modular traffic engineering in software-defined data center networks

SEMTE: scalable and extended modular traffic engineering in software-defined data center networks

(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.

Towards a unified ontology for IoT fabric with SDDC

The ever growing Internet of Things (IoT) paradigm aims to put people’s incorporation into the new phase of connectivity and sensor technology. While, IoT has the potential to deliver new value-added services in order to make life easier and healthier for people, there are still several issues to be addressed in order to harness the widespread dissemination and adoption of the IoT paradigm, considering its potential benefits. In this context, considering an IoT ecosystem holistically i.e. end-to-end which includes the proprietary Operational Technology (OT) comprising of software and hardware to monitor, detect and control the equipment through sensors and actuators and the general Information Technology (IT) which comprises of the data center infrastructure catering to the backend needs of IoT such as compute, storage and network, one major challenge is converging OT with IT. Such an OT/IT convergence has the potential to explore many problems that exist in the end-to-end IoT ecosystem. One such problem is related to drawing actionable insights through operational analytics. The data center paradigm which concerns this research is the Software-Defined Data Center (SDDC) which is touted as the preferred data center paradigm for IoT. While most of the research happening on IoT today deals with the data from the IoT applications and sensors, this research focuses on the plethora of the metadata contained in the end-to-end IoT ecosystem. This research demonstrates the modelling of the end-to-end IoT ecosystem using semantic-based approach such as ontology. We arrive at a cohesive unified ontology unifying the OT and the IT constituents of the IoT fabric with SDDC together with rich context aware attributes embedded in the unified ontology, thereby addressing the problem of OT/IT convergence as well as laying the foundation to explore many solutions to problems pertinent in such a converged OT/IT ecosystem.

RETRACTED ARTICLE: Proactive Load Balancing Strategy Towards Intelligence-Enabled Software-Defined Network

Software-defined network provides a greater solution to many of the complex network management functionalities in a data center network (DCN). In a software-defined data center network, it is essential to manage heavy traffic, to avoid data loss, and to avoid server unavailability during heavy flows. One of the major tasks in a network is the load balancing in the available links. Due to the dynamic data traffic in the network, it is necessary to perform a deep learning of the long-term and the short-term data. Hence, this paper proposes an intelligent load balancing scenario that includes link load balancing, server load balancing, and traffic classification. An artificial neural network is needed because the data are uncertain all the time. Thus, our method provides improved throughput, minimized the flow completion time, and minimizes data loss.

Consolidating Policy Chains Using One Pass Packet Steering in Software Defined Data Centers

In this paper, we address the problem of service function chaining in a network and present a solution that tackles this problem in Software Defined Data Centers. Service function chaining can broadly be categorized into middlebox placement in a network and packet steering through middleboxes. We address packet steering in this paper. In particular, we present a One Pass Packet Steering (OPPS) approach for use in multi-subscriber environments. We show algorithms and proof of concept implementation using emulation. We examine how OPPS can benefit from Software Defined Data Center architecture to overcome challenges that occur in simple linear networks. Our results show how OPPS can utilize a lesser number of middleboxes and achieve the same hop counts as a reference model which has been described in previous work as ideal, without violating the subscriber's policy chain.

Segment routing based energy aware routing for software defined data center

Despite the fact that most of the data centers are software-defined, the multifaceted network architecture and increase in network traffic make data centers to suffer from overhead. Multipath TCP supports multiple paths for a single routing session and ensures proper utilization of bandwidth over all available links. As rise in number of nodes in data center is frequent and drastic, scalability issue limits the performance of many existing techniques. Segment Routing is vibrant in reducing scalability disputes and routing overhead. Segment routing approach combined with MPTCP traffic result in efficient routing approach. The downfall of the link capacity due to drastic incoming traffic remains as a major concern in data center network which enforces preventing link energy depletion due to high network traffic. Our proposed work, segment routing based energy aware routing approach for software defined data center aims to achieve throughput maximization through preserving link residual capacity and proper utilization of links. As well, our approach shows a decrease in length of segment label stack with respect to maximum segment label depth. Analysis is done by comparing the executions of other existing approaches in a single-controller environment with our energy-aware routing approach in a distributed environment. Distributed controller setup prevents network from single point of failure. It helps to prevent controller overhead and provides improved network performance through throughput.

PARC: Placement Availability Resilient Controller Scheme for Software-Defined Datacenters

Software-Defined Datacenters (SDDC) have been widely used for load-aware data management for different applications across the globe. Due to its centralized architecture, the issues of scalability along with resilience (to overcome the failure of single or multiple controllers) are still challenging because of an exponential increase in the data generated from different smart devices. Most of the solutions reported in the literature for this problem use a single controller which may not address the scalability issues. However, the issues as mentioned above of scalability and resilience in SDDC can be solved by deploying multiple distributed controllers at the control plane. However, the primary concern in a network having various controllers is the optimal Controller Placement Problem (CPP) to resolve the issues of fault-tolerance, latency among controllers, availability, and placement. Hence, to resolve the issues described above, in this paper, we propose Placement Availability Resilient Controller (PARC) scheme. The PARC scheme works in the following four phases: (i) stable network partitioning (ii) localization of controllers using the cooperative game theory (iii) computation of an optimal number of multiple controllers and (iv) computation of minimal extra backup controllers to improve the overall network cost. The numerical results of the PARC scheme are evaluated on Internet2 OS3E topology using POCO-toolset simulated in Matlab. The experimental results demonstrated that the cost of deploying the number of controllers using the PARC scheme has reduced to 12.98%, 8.16%, and 6.25% as compared to the POCO-SA, POCO-MOALO, and CNCP schemes respectively. Moreover, the PARC scheme outperforms the existing state-of-the-art schemes (POCO-SA, POCO-MOALO, and CNCP) for inter-controller as well as switch-to-controller latency.

Fine-grained flow classification using deep learning for software defined data center networks

in a data center network, accurately classifying flow is the key to optimal schedule flow. However, the existing classification methods cannot meet the demand of real networks in terms of classification performance, detection latency, and control overhead. Thus, by combining the ability of deep learning to describe the multi-dimensional features, and the advantage of software-defined networking (SDN) in centrally controlling the network from a global viewpoint, this paper proposes a fine-grained flow classification method. This paper uses random forest technology to select eight important features in three dimensions—time distribution feature of flow, real-time feature of flow and packet header feature—for the classification model. First, this paper proposes a 2-classification scheme with a two-level architecture of pre-classification and exact-classification to detect elephant/mice flows. The pre-classification model using deep residual learning + A-Softmax with cost-sensitive is deployed on a SDN switch at the network edge to filter out a large number of mice flows. The exact-classification model using deep residual learning + AM-Softmax is deployed on the SDN controller to accurately identify the elephant flows. Second, this paper proposes a 4-classification scheme based on gated recurrent unit (GRU) to detect elephant/cheetah/tortoise/porcupine flows. Finally, the experiment results show that, when the 5th packet of a flow arrives, the 2-classification scheme can achieve a recall of up to 97.31%, an accuracy of up to 93.6%, a control overhead of 0.1kbps, and a detection latency of 7 ms. At the same time, the 4-classification scheme can achieve a recall, an accuracy, a false positive rate (FPR), a Kappa, a control overhead, and a detection latency of 83.58%, 86.53%, 5.02%, 0.797, 0.61kbps and 7.5 ms on an average, respectively. Compared with the existing methods (FlowSeer, NELLY and ESCA), all performance measures are improved to different degrees. At the same time, we also confirm the generalization of selected features and of the designed flow classification model.

REVERT: A Network Failure Recovery Method for Data Center Networks

As a repository that holds computing facilities, storage facilities, network facilities and other facilities, the Software Defined Data Center (SDDC) can provide computing and storage resources for users. For a SDDC, it is important to provide continuous services for users. Hence, in order to achieve high reliability in Software Defined Data Center Networks (SDDCNs), a network failure recovery method for software defined data center networks (REVERT) is proposed to recover failures in SDDCNs. In REVERT, the network failures that occurred in SDDCNs are classified into three types, which are switch failure, failure of links among switches and failure of links between switches and servers. Specially, except recovering the switch failure and failure of links between switches, REVERT can also recover the failures of links between the switches and servers. To achieve that, a failure preprocessing method used to classify the network failures, a data structure for storing and finding the affected flows, a server cluster agent for communicating with the server clustering algorithm and a routing path calculation method are designed in REVERT. Meanwhile, REVERT has been implemented and evaluated on RYU controller and Mininet using three routing algorithms. Compared with the link usage before recovering the network failures, when there are more than 200 flows in the network, the mean link usages only slightly increase at about 1.83 percent. More importantly, the evaluation results also demonstrate that except recovering switch failures, intra-topo link failures, REVERT has the ability of recovering failures of links between servers and edge switches successfully.