Elhadj Benkhelifa

User Profiling for Energy Optimisation in Mobile Cloud Computing

Both mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality.Both mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality. This paper reviews current work in energy consumption of mobile cloud computing and then proposes a system whereby user applications may be profiled for their resource consumption locally and then if augmentation is required, they may negotiate with an external cloud for optimum energy consumption. Such a system is particularly useful for cloudlets which contain constrained resources so may need to choose between a number of clients. Whilst mobile computing enables a variety of feature rich functionality for users in a non-fixed location, cloud computing is revolutionising the way in which computing resources are being provisioned, used and optimised for both service providers and end users. These two fields are being combined in order to provide greater functionality for mobile devices in a number of different ways. Augmentation of mobile resources from the cloud has been shown as one way in which the energy consumption and power of mobile devices may be considerably enhanced. However, due to the resource constrained nature of the devices, in particular their power source and communication interfaces, there is often a fine line where offloading of these resources is economical.

SDIoT: a software defined based internet of things framework

The internet of things (IoT) represent the current and future state of the Internet. The large number of things (objects), which are connected to the Internet, produce a huge amount of data that needs a lot of effort and processing operations to transfer it to useful information. Moreover, the organization and control of this large volume of data requires novel ideas in the design and management of the IoT network to accelerate and enhance its performance. The software defined systems is a new paradigm that appeared recently to hide all complexity in traditional system architecture by abstracting all the controls and management operations from the underling devices (things in the IoT) and setting them inside a middleware layer, a software layer. In this work, a comprehensive software defined based framework model is proposed to simplify the IoT management process and provide a vital solution for the challenges in the traditional IoT architecture to forward, store, and secure the produced data from the IoT objects by integrating the software defined network, software defined storage, and software defined security into one software defined based control model.

SDStorage: A Software Defined Storage Experimental Framework

With the rapid growth of data centers and the unprecedented increase in storage demands, the traditional storage control techniques are considered unsuitable to deal with this large volume of data in an efficient manner. The Software Defined Storage (SDStore) comes as a solution for this issue by abstracting the storage control operations from the storage devices and set it inside a centralized controller in the software layer. Building a real SDStore system without any simulation and emulation is considered an expensive solution and may have a lot of risks. Thus, there is a need to simulate such systems before the real-life implementation and deployment. In this paper we present SDStorage, an experimental framework to provide a novel virtualized test bed environment for SDStore systems. The main idea of SDStorage is based on the Mininet Software Defined Network (SDN) Open Flow simulator and is built over of it. The main components of Mininet, which are the host, the switch and the controller, are customized to serve the needs of SDStore simulation environments.

The future of mobile cloud computing: Integrating cloudlets and Mobile Edge Computing

Extending the coverage area of mobile cloud computing services will allow new services to be provisioned to the mobile users. The main obstacle for achieving this goal is related to the deployments challenges and limitations of the Cloudlets system. Mobile Edge Computing (MEC) system emerged recently providing an opportunity to fill the gap of the Cloudlets system by providing resources-rich computing resources with proximity to the end users. In this paper, we are proposing a hierarchical model that is composed of MEC servers and Cloudlets infrastructures. The objective of the proposed model is to increase the coverage area for the mobile users in which the users can accomplish their requested services with minimal costs in terms of power and delay. An extensive experimental evaluation is conducted showing the superiority of the proposed model.

SDDC: A Software Defined Datacenter Experimental Framework

The rapid growth and the distributed sites of the datacenters increase the complexity of control and management processes. A new paradigm which is called Software Defined Systems (SDSys) comes as a solution to reduce the overhead of datacenters management by abstracting all the control functionalities from the hardware devices and setting it inside a software layer. These functionalities are responsible for organizing and controlling the main blocks of the datacenter; network, storage, compute and security. The Software Defined Datacenter (SDD) integrates the software defined concepts into all of these main blocks. Transferring these concepts into workable systems requires checking several performance aspects and testing its correctness before building real systems. In this paper we introduce a novel experimental framework (SDDC) to provide a novel virtualized testbed environment for SDD systems. This work builds on the Mininet simulator, where its core components, the host, the switch and the controller, are customized to build the proposed experimental simulation framework for SDD. This simulator lets the users develop and test their own SDD solutions, and at the same time gives the researchers an experimentation tool for benchmarking purposes. The developed simulator could also be used as an educational tool to train students and novice researchers.

Using big data analytics for authorship authentication of arabic tweets

Authorship authentication of a certain text is concerned with correctly attributing it to its author based on its contents. It is a very important problem with deep root in history as many classical texts have doubtful attributions. The information age and ubiquitous use of the Internet is further complicating this problem and adding more dimensions to it. We are interested in the modern version of this problem where the text whose authorship needs authentication is an online text found in online social networks. Specifically, we are interested in the authorship authentication of tweets. This is not the only challenging aspect we consider here. Another challenging aspect is the language of the tweets. Most current works and existing tools support English. We chose to focus on the very important, yet largely understudied, Arabic language. Finally, we add another challenging aspect to the problem at hand by addressing it at a very large scale. We present our effort to employ big data analytics to address the authorship authentication problem of Arabic tweets. We start by crawling a dataset of more than 53K tweets distributed across 20 authors. We then use preprocessing steps to clean the data and prepare it for analysis. The next step is to compute the feature vectors of each tweet. We use the Bag-Of-Words (BOW) approach and compute the weights using the Term Frequency-Inverse Document Frequency (TF-IDF). Then, we feed the dataset to a Naive Bayes classifier implemented on a parallel and distributed computing framework known as Hadoop. To the best of our knowledge, none of the previous works on authorship authentication of Arabic text addressed the unique challenges associated with (1) tweets and (2) large-scale datasets. This makes our work unique on many levels. The results show that the testing accuracy is not very high (61.6%), which is expected in the very challenging setting that we consider.

Power Optimization of Large Scale Mobile Cloud Computing Systems

The main objective of deploying large scale Mobile Cloud Computing (MCC) systems using the cloudlet based infrastructure is to reduce the total power consumption and the network delay while satisfying the Service Level Agreement (SLA). However, this objective is challenging to achieve due to the limited capabilities of the cloudlet system which limit the number of available services on each cloudlet system. This will force different cloudlets to cooperate in order to meet the users demands for different types of services. Moreover, the cooperation will require routing of user requests from the local cloudlet to a remote cloudlet that provide the requested service using a backbone network. This cooperation will increase the total power consumption per request which will include both the user to the local cloudlet power consumption and the power consumption for routing the request to a remote cloudlet. In this paper, we address the problem of optimizing the power consumption for large scale cooperative cloudlets deployments. The evaluation of the proposed model under different realistic scenarios shows that it can be used to accurately optimize power consumption in large scale MCC systems.

SDMEC: Software Defined System for Mobile Edge Computing

Mobile Edge Computing (MEC) promises a paradigm shift in enabling efficient Mobile Cloud Computing (MCC) services by providing storage and processing capacity within the access range of the mobile devices. In MEC, Mobile Edge (ME) servers are placed at the edge of the mobile networks eliminating the need to offload compute-/storage-intensive tasks of the mobile devices to the core of the network (the centralized cloud data center). This reduces the network latency and enhances the quality of service provided for the mobile end users. Different applications can benefit from the large scale deployments of ME servers such as smart grid applications, content delivery networks, content sharing, traffic management, and E-health applications. This promising paradigm comes with its own downside related to the management complexity of large scale deployments that offers hundreds of applications to millions of users. In this paper, we introduce a software defined based framework to enable efficient MCC services through the integration of different software defined system components with the MEC system.

Design and optimisation of microelectromechanical systems: a review of the state-of-the-art

This article provides an inclusive review on the field of design and optimisation (DO) of microelectromechanical systems (MEMS) since its emergence, about two decades ago. Fundamentals and applications of MEMS are presented herein, followed by a comprehensive review on the conventional tools and practices developed for MEMS DO throughout. The limitations of these techniques are identified, and the necessity for automated DO methods for MEMS technology is therefore justified and evaluated. A recent trend in DO of microsystems is inspired by the natural evolution and the survival of the fittest hypothesises. Motivated by its achievements in other engineering DO problems, evolutionary computation has also been adopted for the DO of MEMS, at different levels. A thorough review of this infant area of research is also presented in this article and highlights of the main challenges facing this field are discussed. Prior to a major research in the area, this article provides an update of the state-of-the-art on MEMS technology with a general interest in the automated DO techniques, particularly, the evolutionary methods.

A Conceptual Framework for Designing Data Governance for Cloud Computing

Data complexity and volume continue to explode; businesses have grown more sophisticated in their use of data which drives new demands that require different ways to combine, manipulate, store, and present information. Forward thinking companies have recognised that data management solutions on their own are becoming very expensive and not able to cope with business reality, and that they need to solve the data problem in a different way through the implementation of effective data governance. Attempts in governing data failed before, as they were driven by IT, and affected by rigid processes and fragmented activities carried out on system-by-system basis. Up to very recently governance is mostly informal, in siloes around specific enterprise repositories, lacking in structure and the in wider support by the organisation. With the emergence of cloud computing and the increased adoption, data governance is receiving an increasing interest amongst specialist, but still under researched. This paper presents initial research towards developing an effective data governance programmes for the cloud paradigm. The paper discusses why it is essential to do so from both the cloud consumer and provider perspectives and proposes a conceptual framework and a five-step procedure for designing data governance for cloud computing.