International Journal of High Performance Computing and Networking (8 papers in press)
Distributed software defined information centric networking
by Rihab Jmal, Lamia Chaari Fourati
Abstract: Recently, a new trend has emerged based on combining Software Defined Networking (SDN) and Information Centric Networking (ICN) as a promising approach for the future Internet. More serious control plane problems related to scalability, fault-tolerance and consistency may confront Software Defined Information Centric Networking (SD-ICN) compared with the traditional SDN environment, regarding new augmented features such as content name based communication and in-network caching. In this paper, we propose a Distributed Software Defined Information Centric Networking (DSD-ICN) that provides ICN features over SDN network with multiple controllers. We address in our design the fault-tolerant and strong consistency of the control plane, which allows the transparent distribution of the content over different network domains.
Keywords: software defined networking; information-centric networking; multiple controllers; inter-domain; distributed.
Multi-model coupling method for imbalanced network traffic classification based on clustering
by Zhengzhi Tang
Abstract: The identification of network traffic is of great significance for traffic management, billing and security detection. However, the imbalanced category of traffic in network poses a challenge to the current identification methods based on machine learning, because the unbalanced data structure affects the performance of machine learning algorithms. In this paper, we propose a multi-model coupling approach to address the imbalanced data problem in network traffic classification. In the training state, we used a clustering algorithm to process the major class and the major class can be categorised into some clusters. Then, we used these clusters and the minor class to form the training dataset for training machine learning model respectively, and finally the corresponding different trained models were obtained. In the test state, the test dataset was input into the previously trained models, and the identification results of the respective models are coupled to obtain the final identification result. We tested our method on two well-known network traffic datasets and the results showed that our proposed method achieved better performance and in shorter time compared with recent proposed methods for handling imbalance problem in network traffic classification in the case where the ratio of minor to major classes is very small.
Keywords: machine learning; imbalanced network traffic classification; clustering algorithm; multi-model coupling.
Device classification-based data encryption for the internet of things
by Rishabh Rishabh, T.P. Sharma
Abstract: The Internet of Things (IoT) has gained much popularity and has become an essential topic of research, because of its vast implementation in smart cities, smart homes, wearables, and smart appliances. It has emerged as a field of great potential, impact, and growth. Another reason for its popularity and growth is that it incorporates various heterogeneous sensors and objects to communicate with one another directly, without including the need for human intervention. As IoT enables the low resource and constrained devices to work, they also become more vulnerable to attacks than other endpoint devices. Although IoT has several privacy concerns and security issues, it still is growing in demand for large-scale deployment. In this paper, we propose class-specific data encryption/decryption techniques for heterogeneous IoT devices. Devices are classified into three classes, based on their computational and communication capabilities. Accordingly, different schemes for data encryption/decryption are proposed at different levels of interconnection across devices of different classes. The classification makes it easy to develop, study and analyse the behaviour of the devices, as the devices of the same class have similar properties and performance. It also helps to develop standards of security protocols, policies, and frameworks based on the device class. Simulation experiments reveal significant improvements in the solution of encryption/decryption techniques for given scenarios.
Keywords: internet of things; security; challenges; issues; threats; privacy.
PFSS: a privacy-friendly and secure smart metering for time of
use operational data collection in a smart grid network
by Oladayo Olakanmi
Abstract: Advanced metering infrastructure (AMI) is an integral part of a smart grid network which involves transmission of finest grain operational data of consumers' load profiles on wireless facilities. These operational data are used for effective load balancing, billing, and analysis of the smart grid network. Unauthorised access to these operational data can easily disrupt the smart grid network or create distrust among consumers. Meanwhile, the vulnerabilities of AMI wireless facilities encourage unauthorised access to this sensitive information, making AMI a soft target for adversaries in the smart grid network. Many security schemes have been proposed for AMI to detect and prevent attacks in order to achieve the required security and privacy properties expected. However, some of them only focus on specific attack(s), leaving other attacks, and most of them cannot be efficiently used to secure energy profiles from different time of use without increasing communication overhead. In this paper, a provably lightweight security scheme to secure metering and information exchange, and consumers privacy, irrespective of the attacking points and nature of the attacks is proposed for AMI. To achieve this, we developed a chain-based pseudonym and shadow key approach to preserve privacy. We also develop low-cost cryptographic approach using Shamir secret sharing to evolve effective grouping and authentication approaches that classify consumers energy profiles based on time of use. Our scheme ensures data security and cooperative aggregation among m number of consumers of the same service provider. The security analysis and performance evaluation of the scheme are also presented. The results illustrate that the approach secures metering at a low computational overhead.
Keywords: smart grid; operational data; time of use; aggregation; metering.
A rack-aware scalable resource management system for Hadoop YARN
by Timothy Moses, H.C. Inyiama, S.O. Anigbogu
Abstract: Big data has brought in an era of data exploration and use with MapReduce computational paradigm as its major enabler. Though great efforts through the implementation of Hadoop have made computation scale to tens of thousands of commodity cluster processors, the centralised architecture of the resource manager has adversely affected response time in large datacentres. Decentralising the responsibilities of the resource manager to address scalability issues of Hadoop for better response time and to eliminate single point of failure is, therefore, the concern of this work. The developed model decouples the responsibilities of the resource manager by providing another layer where each daemon called Rack_Unit Resource Manager (RU_RM) carries out the responsibility of allocating resources to compute nodes within its local rack. This ensures low latency for large files on compute nodes within the same local rack. The application was developed and tested using Java programming language with Hadoop workload benchmarks such as sort, WordCount, TeraSort, PageRank, Na
Keywords: MapReduce; Hadoop; framework; scalable; rack-aware; resource manager; big data; rack unit resource manager.
BAT algorithm used for load balancing purpose in cloud computing: an overview
by Arif Ullah
Abstract: Cloud computing is modern technology that has caused significant changes in different fields of life by providing different kinds of service, such as hardware and software on user demands based on pay and gain rule. Owing to the rapid growth of cloud computing, it faces different kinds of issue, and resource allocation is one of them. For the improvement of the resource allocation system in cloud computing, different kinds of technique are used, including the load balancing technique. In this paper, we discuss the resource allocation system in virtual machines (VM) because when a user sends data to a VM then the situation may occur that some VMs are overloaded and some become underloaded, which will cause the system to fail or delay the request. To improve this situation different researchers used different algorithms in the load balancing technique for cloud computing. This paper only focuses on the BAT algorithm, which is used for load balancing technique to improve resource allocation system for VMs and also to define those rules that are used for improvement of the load balancing technique in cloud data centres.
Keywords: load balancing; cloud computing; classification; BAT algorithm; virtual machine; virtualisation.
Cloud provider profit-aware and triadic concept analysis-based data replication strategy for tenant performance improvement
by Amel Khelifa, Tarek Hamrouni, Riad Mokadem, Faouzi Ben Charrada
Abstract: Effective data management is very challenging to cloud providers, whose business model relies on maintaining an economic profit while satisfying the tenants performance requirements. To address these challenges, many data replication strategies have been proposed. In this paper, we propose a new dynamic data replication strategy for cloud systems called RCPP.1 In order to satisfy performance requirements, the proposed strategy exploits the valuable knowledge extracted from the tenants past access history. Therefore, it uses the mathematical triadic concept analysis approach to determine correlated data to be replicated. Furthermore, the cloud providers profit is taken into account. Hence, an economic model is proposed to estimate the revenues and expenditures of the provider. Experimental studies show the efficiency and effectiveness of RCPP compared with state-of-the-art strategies. RCPP is indeed proven able to reduce the total expenditures of the cloud provider significantly while achieving better performance.
Keywords: replication; cloud provider; data correlation; profit; economic model; triadic concept.
XtremDew: a platform for cooperative tasks and data schedulers
by Mohamed Labidi, Oleg Lodygensky, Gilles Fedak, Maher Khemakhem, Mohamed Jemni
Abstract: With the emergence of Big Data, data scheduling is becoming an important field of research in distributed computing. Software data schedulers often rely on data management policies that can be defined by the user and provide high level features. Such advanced features become necessary nowadays to execute data-intensive applications, and this implies that data and task schedulers should cooperate closely to address the large data processing issue and ensure an optimal distribution of data-intensive applications. In this paper, we propose XtremDew, the data and task cooperative scheduler platform. We deal with the distribution of the optical character recognition (OCR) on a large scale. We show, in particular, the benefit of the focus on data scheduling to distribute our OCR application. We build the data-driven distributing platform by combining two existing middleware: BitDew, as the data scheduler, and XtremWeb-HEP, as the task scheduler. Taking advantage of both middleware, XtremDew provides new features. To evaluate the efficiency of our approach, we compare different strategies of scheduling tasks and data and we present several scenarios that illustrate the benefits of using XtremDew to execute data-intensive applications.
Keywords: Big Data; data-intensive application; cooperative middleware; complexity and performance of big data processing.