scholarly journals An Adaptive Authenticated Model for Big Data Stream SAVI in SDN-Based Data Center Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Qizhao Zhou ◽  
Junqing Yu ◽  
Dong Li
Keyword(s):  
Big Data ◽  
Data Center ◽  
Data Stream ◽  
Network Performance ◽  
Rapid Development ◽  
High Growth ◽  
Ratio Test ◽  
Data Center Networks ◽  
Time Data ◽  
Two Phase

With the rapid development of data-driven and bandwidth-intensive applications in the Software Defined Networking (SDN) northbound interface, big data stream is dynamically generated with high growth rates in SDN-based data center networks. However, a significant issue faced in big data stream communication is how to verify its authenticity in an untrusted environment. The big data stream traffic has the characteristics of security sensitivity, data size randomness, and latency sensitivity, putting high strain on the SDN-based communication system during larger spoofing events in it. In addition, the SDN controller may be overloaded under big data stream verification conditions on account of the fast increase of bandwidth-intensive applications and quick response requirements. To solve these problems, we propose a two-phase adaptive authenticated model (TAAM) by introducing source address validation implementation- (SAVI-) based IP source address verification. The model realizes real-time data stream address validation and dynamically reduces the redundant verification process. A traffic adaptive SAVI that utilizes a robust localization method followed by the Sequential Probability Ratio Test (SPRT) has been proposed to ensure differentiated executions of the big data stream packets forwarding and the spoofing packets discarding. The TAAM model could filter out the unmatched packets with better packet forwarding efficiency and fundamental security characteristics. The experimental results demonstrate that spoofing attacks under big data streams can be directly mitigated by it. Compared with the latest methods, TAAM can achieve desirable network performance in terms of transmission quality, security guarantee, and response time. It drops 97% of the spoofing attack packets while consuming only 9% of the controller CPU utilization on average.

scholarly journals HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiawei Huang ◽  
Shiqi Wang ◽  
Shuping Li ◽  
Shaojun Zou ◽  
Jinbin Hu ◽  
...  
Keyword(s):  
Data Center ◽  
Large Scale ◽  
Network Performance ◽  
Rooted Tree ◽  
Heterogeneous Traffic ◽  
Data Center Networks ◽  
Packet Reordering ◽  
Traffic Characteristics ◽  
Network Utilization ◽  
The Impact

AbstractModern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

scholarly journals Bandwidth-Aware Rescheduling Mechanism in SDN-Based Data Center Networks

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1774
Author(s):  
Ming-Chin Chuang ◽  
Chia-Cheng Yen ◽  
Chia-Jui Hung
Keyword(s):  
Data Center ◽  
Completion Time ◽  
Network Performance ◽  
Data Locality ◽  
Task Completion ◽  
Data Center Networks ◽  
Task Completion Time ◽  
Data Packets ◽  
Network Bandwidth ◽  
Data Process

Recently, with the increase in network bandwidth, various cloud computing applications have become popular. A large number of network data packets will be generated in such a network. However, most existing network architectures cannot effectively handle big data, thereby necessitating an efficient mechanism to reduce task completion time when large amounts of data are processed in data center networks. Unfortunately, achieving the minimum task completion time in the Hadoop system is an NP-complete problem. Although many studies have proposed schemes for improving network performance, they have shortcomings that degrade their performance. For this reason, in this study, we propose a centralized solution, called the bandwidth-aware rescheduling (BARE) mechanism for software-defined network (SDN)-based data center networks. BARE improves network performance by employing a prefetching mechanism and a centralized network monitor to collect global information, sorting out the locality data process, splitting tasks, and executing a rescheduling mechanism with a scheduler to reduce task completion time. Finally, we used simulations to demonstrate our scheme’s effectiveness. Simulation results show that our scheme outperforms other existing schemes in terms of task completion time and the ratio of data locality.

Performance Improvement IoT Applications Through Multimedia Analytics Using Big Data Stream Computing Platforms

2018 ◽  
pp. 200-221
Author(s):  
Rizwan Patan ◽  
Rajasekhara Babu M ◽  
Suresh Kallam
Keyword(s):  
Big Data ◽  
Real Time ◽  
Performance Improvement ◽  
Data Stream ◽  
Real Data ◽  
Stream Computing ◽  
Time Data ◽  
Real Time Data ◽  
Computing Platforms ◽  
Time And Energy

A Big Data Stream Computing (BDSC) Platform handles real-time data from various applications such as risk management, marketing management and business intelligence. Now a days Internet of Things (IoT) deployment is increasing massively in all the areas. These IoTs engender real-time data for analysis. Existing BDSC is inefficient to handle Real-data stream from IoTs because the data stream from IoTs is unstructured and has inconstant velocity. So, it is challenging to handle such real-time data stream. This work proposes a framework that handles real-time data stream through device control techniques to improve the performance. The frame work includes three layers. First layer deals with Big Data platforms that handles real data streams based on area of importance. Second layer is performance layer which deals with performance issues such as low response time, and energy efficiency. The third layer is meant for Applying developed method on existing BDSC platform. The experimental results have been shown a performance improvement 20%-30% for real time data stream from IoT application.

Dynamic Big-Data Broadcast in Fat-Tree Data Center Networks With Mobile IoT Devices

2019 ◽  
Vol 13 (3) ◽  
pp. 2898-2905 ◽  
Author(s):  
Sudip Misra ◽  
Ayan Mondal ◽  
Swetha Khajjayam
Keyword(s):  
Big Data ◽  
Data Center ◽  
Data Center Networks ◽  
Data Broadcast ◽  
Tree Data ◽  
Iot Devices

scholarly journals Layout-Independent Wireless Facility Constructing and Scheduling for Data Center Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xianglin Wei ◽  
Qin Sun
Keyword(s):  
Data Center ◽  
Network Performance ◽  
Data Center Networks ◽  
Data Packets ◽  
Typical Data ◽  
Calculation Step ◽  
Scheduling Method ◽  
Series Of Experiments ◽  
Traffic Estimation ◽  
And Control

The control packets in the data center networks (DCNs) have to contest with the data packets although they are usually much shorter in size and much more important in network management. Moreover, the uneven distribution of the packets may create potential traffic hotspots in the DCN which could degrade network performance drastically. To bridge these gaps, a layout-independent constructing algorithm and a scheduling method are put forward towards layout-independent wireless facility in data centers. First of all, a conflict aware spanning tree algorithm is developed to construct the wireless facility network (WFN). Secondly, a scheduling method which contains three steps, route calculation, traffic estimation, and flow scheduling, is presented. In the route calculation step, a route set between each node pair is calculated in advance for later usage. The scheduler estimates the traffic loads on the links on a regular basis in the traffic estimation step. Then, arrived data and control flows are scheduled according to multiple policies based on given route sets and scheduling objectives in the flow scheduling step. Finally, a series of experiments have been conducted on NS3 based on two typical data center layouts. Experimental results in both scenarios have validated our proposal’ effectiveness.

Big Data Analytics in Intra-Data Center Networks and Components of Data Mining

2016 ◽  
pp. 82-89
Author(s):  
Pushpa Mannava
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Center ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Center Networks ◽  
Advanced Analytics ◽  
Scalable Design ◽  
Data Collections

Data mining is considered as a vital procedure as it is used for locating brand-new, legitimate, useful as well as reasonable kinds of data. The assimilation of data mining methods in cloud computing gives a versatile and also scalable design that can be made use of for reliable mining of significant quantity of data from virtually incorporated data resources with the goal of creating beneficial information which is useful in decision making. The procedure of removing concealed, beneficial patterns, as well as useful info from big data is called big data analytics. This is done via using advanced analytics techniques on large data collections. This paper provides the information about big data analytics in intra-data center networks, components of data mining and also techniques of Data mining.

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