Enterprise Data Centers Research Articles

H[formula omitted]-RAID: Improving the reliability of SSD RAID with unified SSD and HDD hybrid architecture

With the increasing development of SSD (Solid-State Drives) technology, SSD RAID (Redundant Arrays of Independent Disks) has been widely deployed in enterprise data centers. However, the inherent write endurance issue of SSD seriously affects the reliability of the array. Meanwhile, compared with conventional HDD-based RAID, SSD RAID exhibits very different failure characteristics, such as correlated failure (Balakrishnan et al., 2010) under RAID-5. In this paper, we present a Hybrid High reliability RAID architecture, named H2-RAID, by equipping each SSD with an extra HDD as the backup to improve the reliability of SSD RAID. Considering the relatively longer write latency of HDD, in H2-RAID, we first propose an HDD-aware backup mechanism to smartly aggregate random writes into sequential writes to decrease performance degradation. In addition, to cope with the scenarios of SSD failure, an HDD-aware reconstruction method is further proposed to guarantee the reliability and the online transaction processing performance. We build a novel Markov process-based mathematical model to analyze the reliability of different architectures, and the theoretical results prove the reliability of H2-RAID is much higher than that of traditional SSD RAID. To more accurately evaluate the performance influence of HDD on H2-RAID, we develop a simulator based on Disksim and the experimental results show H2-RAID significantly increases the reliability compared with SSD array (under RAID-5) while with little performance loss on average.

DMoiSDN: A defensive mechanism of object integrity for SDN

SummarySoftware‐defined network (SDN) technology is widely used for computer networks, especially in enterprise data centers and virtualized networking. However, SDN networks encounter severe challenges to security. One such challenge comes from third‐party applications that contain malicious logic and security vulnerabilities, resulting in controller integrity attacks. In this paper, we propose a defensive mechanism of object integrity for SDN (DMoiSDN) to mitigate the issue known as Cross‐App Poisoning (CAP). Our results contribute to increasing the integrity level of the controller's resources by conducting a potential risk analysis, which showed a decrease of 57% in the risk factor for potential attacks. We further examined the results of comparing DMoiSDN's performance with related work that uses information flow control (IFC) policies. The best results among the three conducted scenarios were as follows: we found that decreased latency in our system ranged from 12% to 90%, with an average of 59%, when encountering an increase in requests. It ranged from 78% to 49%, with an average of 63%, when receiving a variable number of total permissions for each application. DMoiSDN is expected to show a perceptible but reasonable latency and, to some extent, be able to avoid a critical impact on performance.

Advancing Enterprise Data Strategies: Integration and Optimization of SAP HANA for Backup and Disaster Recovery

This research paper delves into the integration and optimization of SAP HANA, in enterprise data centers with a focus on strategies to ensure backup and disaster recovery (DR). SAP HANA, which is an in memory database brings about a transformation in how data's stored and processed. It offers real time analytics, predictive modeling and advanced analytics capabilities. The paper discusses the utilization of SAP HANA in both on premise and cloud environments while highlighting the need to strike a balance between speed, scalability and challenges such as data security and system integration. Emphasizing the importance of backup and DR protocols it underscores their role in protecting data integrity and ensuring uninterrupted business operations even during high risk situations. Through exploration of methods and technologies for backup and recovery this study aims to enhance efficiency in handling data within the realm of SAP HANA. By shedding light on implications related to deployment of SAP HANA – regarding data security measures and operational resilience – this research contributes to the ongoing discourse surrounding, in memory databases within enterprise settings.

An In-depth Comparative Analysis of Cloud Block Storage Workloads: Findings and Implications

Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their input/output (I/O) activities is critical for guiding better system designs and optimizations. In this article, we present an in-depth comparative analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from two production systems, Alibaba Cloud and Tencent Cloud Block Storage. We study their characteristics of load intensities, spatial patterns, and temporal patterns. We also compare the cloud block storage workloads with the notable public block-level I/O workloads from the enterprise data centers at Microsoft Research Cambridge, and we identify the commonalities and differences of the three sources of traces. To this end, we provide 6 findings through the high-level analysis and 16 findings through the detailed analysis on load intensity, spatial patterns, and temporal patterns. We discuss the implications of our findings on load balancing, cache efficiency, and storage cluster management in cloud block storage systems.

Retrieval Technology of Enterprise Data Center Resources Based on Density Peak Clustering Algorithm

Retrieval Technology of Enterprise Data Center Resources Based on Density Peak Clustering Algorithm

Workforce capacity planning for proactive troubleshooting in the Network Operations Center

Modern data centers require that a Network Operations Center is continuously monitoring network health, desirably in order to take proactive action before potential trouble occurs. In this paper, we contribute to the capacity planning of the workforce in charge. To this end, we have extensively analyzed, with real-world data, behavioral changes in a large server population in a data center. Our findings allow classifying such behavioral changes, which may be indicative of potential trouble, into relevance regions using a ranking mechanism. Then, the proposed methodology allows, together with an estimation of the time to analyze, assessing the workforce necessary to proactively tackle the behavioral changes observed. We conclude with a case study from a working data center, including a hands-on implementation of a traffic analysis solution to detect such behavioral changes and an estimation of the needed workforce to analyze them. Our results show that between 4 and 5 network managers are an adequate number for handling behavioral-changes analysis in a large enterprise data center.

Network Topology Classification in SDN Ecosystem using Machine Learning

To meet the increasing network demands of enterprise environments and data centers, traditional network architectures have been replaced by software-enabled hardware devices for developing agile, dynamic, and programmable networks. Software Defined Networking (SDN) is a new paradigm shift that abstracts network design and infrastructure in the software and then implements it by using software across hardware devices. SDN is used to build and manage the network in a customized way. SDN architecture offers network virtualization, network programmability, and flexibility by decoupling control and data planes which further enriches the network performance. As such, the SDN controller is a tactical control point in SDN. An SDN normally allows data flow control to the switches and/or routers and the rationale of the application's logic for deploying intelligent networks. SDN with Machine Learning (ML) and Artificial Intelligence (AI) techniques build network models, which essentially can take decisions based on self-learning and self-management capabilities. Accurate classification of topology is of prime importance to satisfy future network prerequisites like unpredictable traffic patterns, dynamic scaling, flexibility, and centralized control. The controller needs to have exact information about the topology of the network in order to configure and manage the network. Subsequently, topology classification is an important component of any Software Defined Network architecture. This paper presents the classification of topologies using different supervised ML algorithms. The accuracy obtained from Support Vector Machine (SVM) and Classification and Regression (CART) is 95% and 90% respectively. The experimental results show that according to the k Cross-Fold validation technique, SVM algorithm has been found to be the most accurate amongst the other ML algorithms with a mean accuracy value of 85%.

ERP System Design for Hydrogen Equipment Manufacturing Industry Based on Low Code Technology

Research Problem. Aiming at the problems such as fast update and iteration speed, high development cost, and isolated enterprise data in hydrogen equipment manufacturing industry, a low code programming system based on enterprise data center and business center was proposed and designed. Research Method. This paper designs the data center service and business center architecture of hydrogen equipment manufacturing industry. The data center collects, mines, and integrates massive data into unified standard structured data and stores them. The business center provides architectural support for the establishment of enterprise resource management system. With the data center service as the system data interaction center and the business center as the model to build the system architecture, the hydrogen equipment manufacturing industry resource management system is constructed through the steps of data interaction, process modeling, and UI editing. Research Results. According to the specific requirements of hydrogen equipment manufacturing industry, the business process of hydrogen enterprise resource management system is constructed, and the construction and development of low code system are studied and discussed by integrating data center and business center. Conclusions. Low code is not just a tool to increase productivity, it has the potential to be a catalyst for technology and culture to promote enterprise innovation and business agility, and ERP systems for hydrogen equipment manufacturing based on low coding technology are needed for the industry of the future. This article contributes to the hydrogen equipment manufacturing industry of low code programming system, users can according to their own needs, through the combination of components, quickly generate a complete management system, update system iteration, greatly reduce the development cost, solve the problem of enterprise data isolation, reduce the development cost, and realize the seamless integration of modern software development practice, so as to promote enterprise innovation and business agility.

Security Framework for Storage Area Network (SAN)

Over the years, storage network technology has been faced with significant changes and there are recent innovations trying to improve the level of service and reliability in storage area network. The need for storage of data and information as well as the increase of security awareness in the general population has brought the concept of Storage Area Network to the forefront. Fibre Channel SANs have become the backbone for serving the information needs of enterprise data centers. This paper presents an overview of SAN technology which is implemented using fibre channel technology, securing a Storage Area Network by using best practices in setting up a SAN. New security protocol called DH CHAP (Diffie Hellman challenge handshake authentication protocol) are implemented to counter the threats and protect data authenticity, confidentiality and integrity.

Implementation Of A Hybrid Color Image Compression Technique Using Principal Component Analysis And Discrete Tchebichef Transform

The proliferation of scientific and technological demands of high-resolution multimedia contenthas extremely increased the data volume in enterprise data centers and servers on the internet.The characterization of high-resolution picturescreates a great challenge to transfer files with the immensely colossal volume of image data over communication networks. The uploading/downloading time of large images has always been a keyproblemon the Internet. In addition to data communicationissues, high-resolution photo consumes larger storage capacity. Therefore, compression is an almost inevitable process towards the reduction of the transmission time and/or storage capacity requirements of images.Principle component analysis (PCA) and Discrete Tchebichef Transform (DTT) algorithmsare often employed for image compression in several references. In this paper, we propose a hybrid color image compression approachbased on PCA and DTT algorithms (PCADTT), which integrates the benefits of both PCA and DTT algorithms.Our hybrid approach exploits (i) PCA to reduce the dimensionality of the image; and (ii) DTT algorithm to enhance the image quality.The proposed technique has been assessed and related to a compression method that integrates DTT with a singular value decomposition (SVD) scheme (DTTSVD)using different performancemeasures including compression time (CT),compression ratio (CR), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and universal quality index (UQI). The experimental results reveal that our proposed method outperformsthe existing method for all kinds of image content at a high compression ratio with lower computational complexity and retaining thequality of imagedata.

A semantic-aware log generation method for network activities

Context-aware network logging is becoming more prevalent for enterprise networks, data centers, and forensics. Monitoring agents are strategically placed to generate log files from the activity of interests from various network points. In a distributed architecture, these agents are scattered across multiple nodes, and they have limited network visibility. Consequently, the resulting logs become fragmented and less perceptible without a unified network context. Besides, aggregating useful information from a diverse management protocol with various languages, syntax styles, and notations requires complex semantic understanding to synthesize these log files. Currently, general-purpose logs like SNMP's logs only provide parametric values at connection levels but lacks incident-specific information. Meanwhile, proprietary services like AWS CloudTrail identify more contexts at the incident-level, but they only work on selected products and infrastructure. This paper proposed a platform-agnostic log decoding and generation algorithm (SAG) for network logging that is semantic aware using context aggregation. Firstly, a protocol-agnostic controller acts as a master to collect logs from agents running in routers, firewall, IDS/IPS, load balancers, managed switches, and servers. From these logs, three traffic models, namely (1) service-activity model (SaM), (2) general-activity model (GaM), and (3) device-activity model (DaM), are trained using artificial neural network (ANN). The log generator then uses the context-filling technique to resolve and construct log entries using a generic sentence template while inferring from these machine-learning models. A sentence smoothing technique is designed to restructure entities in the logs based on traffic directionality for semantic correctness. The experimental result shows that SAG's logs have 1.8 times more contexts resolved for improved log's perceptibility.

Big Data Analysis Supports the Research and Pilot Application of Distribution Network Planning Technology

The construction of the enterprise data center has met the automatic access of the massive data generated by the operation and management of the distribution network, and realized the integration and unification of the professional data of dispatching, operation inspection, and marketing, providing big data technology to support the planning and management of the distribution network The application of new technical conditions such as broadband carrier has greatly improved the authenticity, reliability and timeliness of big data in the distribution network. Through the design of distribution network monitoring and early warning, diagnosis management, quality assessment, portrait research, power outage optimization, load forecasting and other intelligent application models, the accurate processing of distribution data is realized, and the state of the distribution network can be accurately evaluated. Provide technical support for grid structure optimization. Based on this research background, papers, articles have studied and proposed big data-based distribution network planning, planning auxiliary decision-making system: Based on unified technical principles, a unified planning platform is adopted to realize unified project construction. Practice shows that the plan, the auxiliary decision-making system has improved the quality of planning scheme preparation, improved the review effect, and raised the lean level of investment decision-making.

Control Plane Reflection Attacks and Defenses in Software-Defined Networks

Software-Defined Networking (SDN) continues to be deployed spanning from enterprise data centers to cloud computing with the proliferation of various SDN-enabled hardware switches and dynamic control plane applications. However, state-of-the-art SDN-enabled hardware switches have rather limited downlink message processing capability, especially for Flow-Mod and Statistic Query, which may not suffice the huge need of dynamic control plane applications. In this paper, we systematically study the interactions between the control plane applications and the data plane switches, and present two new attacks, namely Control Plane Reflection Attacks, to exploit the limited processing capability of SDN-enabled hardware switches. The reflection attacks adopt direct and indirect data plane events to force the control plane to issue massive expensive downlink messages towards SDN switches. Moreover, we propose a two-phase probing-triggering attack strategy, which makes the reflection attacks much more efficient and powerful. Experiments on a testbed with 3 different physical OpenFlow switches demonstrate that the attacks can lead to catastrophic results such as hurting the establishment of new flows and even disruption of connection between SDN controller and switches. To mitigate such attacks, we present several countermeasures from different perspectives. In particular, we propose a novel, systematical defense framework, SwitchGuard, to detect anomalies of downlink messages and prioritize these messages based on a novel monitoring granularity, i.e., host-application pair (HAP). Implementations and evaluations demonstrate that SwitchGuard can effectively reduce the latency for legitimate hosts and applications under the control plane reflection attacks with only minor overheads.

Service Chaining Placement Based on Satellite Mission Planning in Ground Station Networks

As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtual network function (VNF) placement and routing traffic problem for enterprise data center networks, the issue needs to further study in satellite communication scenarios. In this article, we investigate the VNF placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer nonlinear programming (INLP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on Greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube, and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks.

Discuss the Application and Presentation of 3D Visualization Technology in Data Center

The 3D visualization virtual simulation technology performs professional 3D modeling of scenes and equipment at all levels in an enterprise data center. It is driven by the bottom of the 3D engine technology and presents data center infrastructure information on a large screen. Seamlessly integrate various monitoring systems and asset libraries, diversified charts customized to show the running trend and health status. With the new characteristic concept of operation and maintenance combined with 3D scene display and 2D data elements, we strive to make 3d visual virtual simulation technology better serve the operation and maintenance personnel of the data center. Supports the highlighting of alarm special effects in the equipment room scene, click the device alarm quick positioning, and handles the alarm event. Output the real-time operation data of the equipment in the form of three-dimensional curve charts to intuitively grasp the operating trend of the equipment. IT equipment loading and unloading in the cabinet does not require operation and maintenance personnel to visit the equipment room to check the distribution of equipment. 3D visualization technology supports the retrieval of the space, electricity, load bearing and information points in the cabinet as query conditions to generate construction orders, Solve the inefficient way of inquiring account books and planning by operation and maintenance personnel through scientific and technological means. Through human-computer interaction, the traditional tedious and boring single operation mode of operation and maintenance has been changed, and the operation and maintenance management level of enterprise data centers has been effectively improved.

Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers

Enterprise cloud data centers consume a tremendous amount of energy due to the large number of physical machines (PMs). These PMs host a huge number of virtual machines (VMs), on which a vast number of applications are deployed. Existing research uses two separate layers to manage data center resources: application assignment to VMs, and VM placement to PMs, each of which is a bin packing problem. While this consecutive two-layer bin packing (Consec2LBP) makes the problems easier to solve, it also limits further improvement in the quality of solution. To address this issue, an integrated any colony optimization approach is proposed in this paper to deal with both layers simultaneously. It formulates the two-layer resource management into an integrated two-layer bin packing (Int2LBP) optimization problem. Then, an integrated first fit-decreasing (FFD) algorithm Int2LBP_FFD is proposed to solve this optimization problem. Using the result of Int2LBP_FFD as an initial solution, an integrated ant colony system (ACS) algorithm Int2LBP_ACS is further developed to improve the quality of solution. Simulation experiments are conducted to demonstrate the effectiveness of our integrated approach.

Optimization analysis of online marketing strategy based on the regression prediction model

In response to the three new product sales recommendations of the Amazon online market “Sunshine”, based on the data set provided by the enterprise data center, the data in the NLP feature package is preprocessed in Python, and the AHP model is established to measure and establish the data. This experiment according to the quarterly time series ARIMA model to forecast the product sales, and then through the standardization of OLS regression model to predict the trend of the development of products. The results showed that customer text reviews were an important reference for the sunshine company and found that microwave oven sales increased by an average of 6.28%, while sales of baby pacifiers and hair dryers increased by an average of 1.02%. This experiment based on this, puts forward the corresponding marketing strategy. Online marketing strategy can pay more attention to the customer’s comments, make updates on the features of the product, the use of to meet customer demand. The study enriches the existing research results, and the sun of the company’s sales strategy provides feasibility suggestions, thus has important theoretical significance and practical value.

Software Defined Networks using Mininet

A software defined network (SDN) is combined with centralized management by providing separation of the network, data and control planes. Different cloud computing environments, enterprise data centers and service providers are using this important feature. By implementing software defined data centers. Here in this paper we have used Mininet to demonstrate the applicability of SDN for different scalability. We study the performance of two SDN controllers – RYU and POX, that are implemented in Python using Mininet and D-ITG, Distributed Internet Traffic Generator. During this study we have used two network topologies, single and linear. The performance parameters used are maximum delay, average jitter, average bitrate. Experimental results demonstrated that the linear topology with RYU controller performs better as compared to single topology with POX controller for different network sizes.

A Global Data Model for Electric Power Data Centers

With the development of large-scale regional interconnected power grid, smart electronic devices and phasor measurement units have been widely used. How to realize high-speed processing of global data in power enterprise data center has become the key to real-time computing. This paper presents a data center global data model (DCGDM) for power enterprises. According to the computational coupling between tasks, the global data model of data center is established. With the constraints of CPU utilization and memory utilization of virtual machine and the goal of energy saving, the optimal configuration of the global data model of power enterprise data center is established. The results show that DCGDM is superior to traditional single-machine multi-threaded parallel computing in terms of time and system consumption. The calculation time of IEEE118 nodes is shortened by 42.32%, and the actual calculation time of 1133 nodes is shortened by 75.8% with the increase of system scale.

Shared Virtual Function Orchestration Technique

Network function virtualization (NFV) is a promising technique of high quality, flexible and scalable service for telecommunication companies clients and for enterprise data center clients. One of the important capabilities of this technique is providing a virtual service as a combination of multiple virtual functions. There are two types of virtual functions: those intended for a single customer (su-VF) and those that can serve multiple users (mu-VF). In case when output of mu-VF is chained with inputs of several different su-VFs, there is a need for a mechanism of identification and separation of users network flows passing through mu-VF to allocate them correctly between inputs of su-VFs in the NFV infrastructure. In the cloud environment, it is not always possible to use VLAN tags, IP and MAC addresses for that. In this paper, we consider the problem of identification of network traffic coming from a certain user inside an NFV platform and present a solution implemented in C2 MANO-platform.