Dynamic Provisioning of Network Services on Heterogeneous Resources

5G communications have become an enabler for the creation of new and more complex networking scenarios, bringing together different vertical ecosystems. Such behavior has been fostered by the network function virtualization (NFV) concept, where the orchestration and virtualization capabilities allow the possibility of dynamically supplying network resources according to its needs. Nevertheless, the integration and performance of heterogeneous network environments, each one supported by a different provider, and with specific characteristics and requirements, in a single NFV framework is not straightforward. In this work we propose an NFV-based framework capable of supporting the flexible, cost-effective deployment of vertical services, through the integration of two distinguished mobile environments and their networks: small sized unmanned aerial vehicles (SUAVs), supporting a flying ad hoc network (FANET) and vehicles, promoting a vehicular ad hoc network (VANET). In this context, a use case involving the public safety vertical will be used as an illustrative example to showcase the potential of this framework. This work also includes the technical implementation details of the framework proposed, allowing to analyse and discuss the delays on the network services deployment process. The results show that the deployment times can be significantly reduced through a distributed VNF configuration function based on the publish–subscribe model.

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An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks

Applied Sciences ◽

10.3390/app11062530 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2530

Author(s):

Minsoo Lee ◽

Soyeon Oh

Keyword(s):

Social Networks ◽

Social Network ◽

Recommendation Systems ◽

Network Services ◽

The Past ◽

User Behaviors ◽

User Influence ◽

User Activity ◽

Information Recommendation ◽

Social Network Services

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.

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Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

Applied Sciences ◽

10.3390/app11010149 ◽

2020 ◽

Vol 11 (1) ◽

pp. 149

Author(s):

Wu-Chun Chung ◽

Tsung-Lin Wu ◽

Yi-Hsuan Lee ◽

Kuo-Chan Huang ◽

Hung-Chang Hsiao ◽

...

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Data Stream ◽

Limited Attention ◽

Heterogeneous Servers ◽

Resource Allocation Algorithm ◽

System Utilization ◽

Allocation Algorithms ◽

Considerable Resource ◽

Heterogeneous Resources

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

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Entropy Based Features Distribution for Anti-DDoS Model in SDN

Sustainability ◽

10.3390/su13031522 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1522

Author(s):

Raja Majid Ali Ujjan ◽

Zeeshan Pervez ◽

Keshav Dahal ◽

Wajahat Ali Khan ◽

Asad Masood Khattak ◽

...

Keyword(s):

Network Security ◽

False Positive ◽

Denial Of Service ◽

Network Services ◽

Detection Accuracy ◽

Data Sets ◽

Traffic Patterns ◽

Average Accuracy ◽

Ddos Detection ◽

Quantitative Results

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.

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