Applying SDN based data network on HPC Big Data Computing – Design, Implementation, and Evaluation

As the infrastructure of cloud computing and big data, data centers have been deployed widely. Then, how to make full use of the computing and storage resources in data centers will be the focus. Data center networks are considered important solution for the problem above, which include intra-data center and inter-data center networks. Both of them will depend on the optical networking due to its advantages, such as high bandwidth, low latency, and low energy consumption. Data center interconnected by flexi-grid optical networks is a promising scenario to allocate spectral resources for applications in a dynamic, tunable and efficient control manner. Compared with inter-data center, optical interconnect in intra-data center networks is a more pressing need and promising scenario to accommodate these applications in a dynamic, flexible and efficient manner. OpenFlow based Software Defined Networking (SDN) is considered as a good technology, which is very suitable for data center networks. This paper mainly focuses on the data center optical networks based on software defined networking (SDN), which can control the heterogeneous networks with unified resource interface. Architecture and experimental demonstration of OpenFlow-based optical interconnects in intra-data center Networks and OpenFlow-based flexi-grid optical networks for inter-data center are presented in the paper respectively. Some future works are listed finally.

The rigidity of static configurations in the network devices, given by a list of commands or static scripts, is limiting current networks. They do not provide automatic resource provisioning and affects directly to the decrease of the efficiency. Furthermore, data centers have different traffic patterns because of the implementation of network virtualization and cloud computing technologies. Now the applications are executed in many virtual machines that must communicate, thus increasing the communication between servers. Software Defined Networks (SDNs) technology has appeared to allow higher programmable, automatic and flexible networks. They are able to divide the control plane from the data plane. In SDNs, the code is programmed centrally instead of programming node by node. The implementation can be done independently of the trademark or the model as long as it supports the communication protocol. SDN allows including new functions in the network devices. Moreover, it can easily include certain intelligent functions, in order to provide intelligence to the network. These features allow the system to self-adapt with the goal of improving it performance. The speech is focused on detailing and discussing some published Intelligent Systems for Multimedia delivery in SDN. Moreover, we will revise the most well-known artificial intelligence techniques that can be used in network data and communication networks. We will also revise the main network parameters and how they affect to the multimedia delivery. First we will detail the network architecture and the communication protocol design and development. We will also show how the information taken from the data frames, the users and servers behavior, and the traffic patterns (traffic changes, quality of service parameters, state of the frames, etc.) can be used to improve the multimedia delivery performance. It will self-adapt according to the state of the network. Network parameters and patterns are used by an intelligent network algorithm to estimate the behavior using the empirical data. Intelligent Systems for Multimedia delivery can be implemented in a wide range of multimedia applications.

This paper presents a novel method and system for flexible data storage management that incorporates dynamic encryption key techniques to ensure data security.The proposed approach determines storage operations and selects optimal storage resources based on predefined criteria such as performance, cost, and data access patterns.The integration of cryptographic keys ensures the confidentiality and integrity of data, addressing security concerns in diverse and large-scale data storage environments.By leveraging adaptive key management, the system can respond to changing security threats and operational requirements in real-time.This adaptability enhances the resilience of data storage solutions against emerging cyber threats and operational disruptions.The system leverages advanced algorithms to study data access patterns, optimizing storage operations for both rapid performance and efficiency.Additionally, the proposed method features a robust mechanism for key rotation and revocation, which reduces the risk of key compromise and bolsters overall security.The dynamic selection of storage resources considers multiple factors, ensuring a balanced approach that meets both performance and cost-efficiency criteria.This comprehensive strategy not only enhances security but also boosts the overall performance of data storage systems.Extensive testing in diverse environments has shown significant improvements in data retrieval times, reduced storage costs, and faster encryption and decryption processes.The results demonstrate the practical applicability of the proposed system in real-world scenarios, showcasing its potential to revolutionize data storage management.The proposed system also integrates seamlessly with existing infrastructure, making it a versatile solution for a wide range of applications.By providing a detailed analysis of key performance indicators, this paper offers valuable insights into the benefits of integrating adaptive cryptographic mechanisms in data storage management.The innovative combination of dynamic operations and adaptive security measures positions this system as a leading-edge solution in the field.Future research will focus on further enhancing these adaptive mechanisms and exploring their application in more diverse storage environments.Additionally, the integration of machine learning algorithms to predict and adapt to data access patterns will be investigated to further optimize system performance.The findings of this paper contribute to the ongoing discourse on secure and efficient data storage, offering a novel perspective that bridges the gap between performance optimization and robust security measures.

Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g., OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g., latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure.

Growing concern for reduced power dissipation, cost and latency demands in next generation Data Centers (DC) motivates us to revisit header optimizations. Headers contribute to about 30–40% of DC traffic and is responsible for equal proportion of consumed power. This amounts to significant overhead on per byte transfer of payload. In the past, highly inflexible switches have limited the focus of header optimizations primarily on end-host or edge network routers. Further, strict compliance to protocols by switches/routers along the path have limited the segments of headers that can be optimized. Recent evolution of Software Defined Network (SDN) brings in several opportunities to control over switches. We believe that if SDN's finer handle to the switching devices is exploited, it can help deal with header information. In this paper, we exploit the capabilities of SDN and introduce a new functionality that can effectively replace the redundant and repetitive header information with shorter unique identity. We present Scissor that trims the headers lower in the protocol stack. As a replacement for routing, we introduce the notion of Flow-ID, where all packets belonging to a flow are identified using this unique Flow-ID. We leverage the capabilities of the SDN to dynamically allow switching devices to route the packets based on the Flow-IDs. Our approach of trimming header at the switching devices leaves the hosts unmodified making it highly adoptable for DC environment. We show that our approach can decrease switch fabric power consumption by a factor of 2.5 compared to an existing L2 switch and reduce the latency by about 30% for a significant fraction (around 30–50%) of the network traffic.

Data centers are essential components of the modern digital world. They provide the necessary resources and infrastructure to enable access to online services and applications, enabling individuals and organizations to communicate, collaborate, and access information in real time. Data centers are an essential part of the internet and are used to manage and store data, providing access to a wide range of services. They provide organizations with the ability to store, access, and process data. Data centers are the backbone of the digital revolution, enabling organizations and individuals to connect with the world. Data centers are essential for providing secure access to data and resources. They are designed with security features such as firewalls, encryption, and authentication that protect data from unauthorized access. Data centers also provide advanced networking and storage solutions to ensure data is secure, reliable, and available for use. Data centers are also important for reducing operational costs. They provide the necessary resources and infrastructure to reduce hardware and software costs, as well as energy costs associated with running hardware and applications. This reduces the overall cost of doing business and allows organizations to focus on their core competencies.

Modern computer networks' increasing complexity and scale need serious attention towards network anomaly detection. Software‐defined networking (SDN) and graph neural networks (GNN) have emerged as promising approaches for anomaly detection due to their ability to capture dynamic network behavior and learn complex patterns from large‐scale network data. The amalgamation of SDN and GNN for network anomaly detection presents promising opportunities for improving the accuracy and efficiency of network anomaly detection. This paper focuses on various trends, issues, and challenges by integrating GNN on the top of SDN for network anomaly detection. The article highlights the advantages of using SDN for providing fine‐grained control and programmability in network monitoring. At the same time, GNN can model network behavior as a graph and learn representations from graph‐structured data. The authors also discuss the limitations of traditional anomaly detection methods in SDN, such as rule‐based approaches, and the potential of GNN to overcome these limitations by leveraging their ability to capture non‐linear and dynamic patterns in network data. This paper also presents a case study of DoS attack detection using SDN. The result shows that SDN based approach helps to detect attacks with an accuracy of 97% with future research directions.

The combination of Software-Defined Networking (SDN) with Network Functions Virtualization (NFV) approaches is gaining momentum in the Industry as a new way of implementing, managing and controlling telecommunications networks. This chapter aims to go through SDN and lightly over NFV, presenting main characteristics and the standardization work on that technologies. SDN enables programming networks together with the ability to adapt to applications requirements and network dynamics. NFV aims at virtualizing network services by merging several network equipment types onto standard Information Technologies (IT) high volume virtualization technology (switches, servers and storage) located either in data centres, customer premises or network nodes. SDN and NFV interworking ambition is to bring on-demand resource provisioning, resource elasticity, among others with a centralized view of the overall network, able to automatically and dynamically honor service requirements.

We present a control plane architecture to enable software-addressable optical acceleration from the application layer. The architecture is experimentally examined on a cluster-computing test-bed by enabling physical layer optical multicasting on-demand for the application layer to achieve non-blocking performance.

The volume of the coming data in HEP is growing. The volume of the data to be held for a long time is growing as well. Large volume of data – big data – is distributed around the planet. The methods, approaches how to organize and manage the globally distributed data storage are required. The distributed storage has several examples for personal needs like own-cloud.org, pydio.com, seafile.com, sparkleshare.org. For enterprise-level there is a number of systems: SWIFT - distributed storage systems (part of Openstack), CEPH and the like which are mostly object storage. When several data center’s resources are integrated, the organization of data links becomes very important issue especially if several parallel data links between data centers are used. The situation in data centers and in data links may vary each hour. All that means each part of distributed data storage has to be able to rearrange usage of data links and storage servers in each data center. In addition, for each customer of distributed storage different requirements could appear. The above topics are planned to be discussed in data storage proposal.

Software-Defined Networking (SDN) is now a well-established approach in 5G, Internet of Things (IoT) and Cloud Computing. The primary idea behind its immense popularity is the separation of its underlying intelligence from the data-carrying components like routers and switches. The intelligence of the SDN-based networks lies in the central point, popularly known as the SDN controller. It is the central control hub of the SDN-based network, which has full privileges and a global view over the entire network. Providing security to SDN controllers is one such important task. Whenever one wishes to implement SDN into their data center or network, they are required to provide the website to SDN controllers. Several attacks are becoming a hurdle in the exponential growth of SDN, and among all one such attack is a Distributed Denial of Service (DDoS) attack. In a couple of years, several new SDN controllers will be available. Among many, Open Networking Operating System (ONOS) and OpenDayLight (ODL) are two popular SDN controllers laying the foundation for many other controllers. These SDN controllers are now being used by numerous businesses, including Cisco, Juniper, IBM, Google, etc. In this paper, vulnerability analysis is carried out against DDoS attacks on the latest released versions of both ODL and ONOS SDN controllers in real-time cloud data centers. For this, we have considered distributed SDN controllers (located at different locations) on two different clouds (AWS and Azure). These controllers are connected through the Internet and work on different networks. DDoS attacks are bombarded on the distributed SDN controllers, and vulnerability is analyzed. It was observed with experimentation that, under five different scenarios (malicious traffic generated), ODL-3 node cluster controller had performed better than ONOS. In these five different scenarios, the amount of malicious traffic was incregradually increased. It also observed that, in terms of disk utilization, memory utilization, and CPU utilization, the ODL 3-node cluster was way ahead of the SDN controller.

The problem of energy consumption in data centers has attracted many researchers interest recently. This paper proposes an optimal energy consumption Software Defined Network (SDN) data center model using the dynamic activation of hosts and switches. We model switches and hosts as queues and formulate a Mixed Integer Linear Programming (MILP) model to minimize energy consumption while guaranteeing Quality of Service (QoS) of data center. Our purpose is minimizing static power, port power, and memory power of data centers. Since the problem is NP-hard, we adopt Simulated Annealing algorithm to obtain the solution. Through numerical experiment, we could observe that our model is able to save reasonable energy compared to the full operation data center model.

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in large scale sensor networks; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the aggregation and forwarding load across the network. With CDG, multiple forwarding trees are constructed, each for aggregating a coded or compressed measurement, and these measurements are collected at the sink for recovering the uncoded transmissions from the sensors. This paper studies the problem of constructing forwarding trees for collecting and aggregating sensed data in the network under the realistic physical interference model. The problem of gathering tree construction and link scheduling is addressed jointly, through a mathematical formulation, and its complexity is underlined. Our objective is to collect data at the sink with both minimal latency and fewer transmissions. We show the joint problem is NP-hard and owing to its complexity, we present a decentralized method for solving the tree construction and the link scheduling subproblems. Our link scheduling subproblem relies on defining an interference neighbourhood for each link and co-ordinating transmissions among network links to control the interference. We prove the correctness of our algorithmic method and analyse its performance. Numerical results are presented to compare the performance of the decentralized solution with the joint model as well as prior work from the literature.

Current research in data dissemination in Vehicular Ad Hoc Networks (VANETs) has examined different approaches and frameworks to enhance the effectiveness and dependability of information sharing between vehicles on the road. The integration of Machine Learning (ML) with Software-Defined Networking (SDN) has fundamentally transformed the field of network administration and security. This paper specifically addresses the challenges faced by traditional network architectures in effectively handling the increasing amount of data and complex applications. Software-Defined Networking (SDN), a cutting-edge framework, separates the control of network operations from the actual forwarding of data, offering a versatile and cost-effective solution. The combination of Software-Defined Networking (SDN) and Machine Learning (ML) allows for the extraction of valuable information from network data, leading to enhanced network management and the facilitation of predictive analytics. The aim of this study is to examine the feasibility and challenges of incorporating machine learning into software-defined networking (SDN), focusing particularly on practical applications. Integrating Machine Learning (ML) into Software-Defined Networking (SDN) presents challenges, including the requirement for robust algorithms to detect patterns and ensure security. It is crucial to carry out the tasks of developing and implementing machine learning models for real-time predictions and ensuring the robustness of the system. Research is essential to strike a balance between the transformative abilities of ML-SDN and the practical network environments. This helps to improve the resilience, security, and adaptability of network infrastructures in the digital age.