Yaser Jararweh

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.

Resource Efficient Mobile Computing Using Cloudlet Infrastructure

Mobile Cloud Computing (MCC) has been introduced as a viable solution to the inherited limitations of mobile computing. These limitations include battery lifetime, processing power, and storage capacity. By using MCC, the processing and the storage of intensive mobile device jobs will take place in the cloud system and the results will be returned to the mobile device. This will reduce the required power and time for completing such intensive jobs. However, connecting mobile devices with the cloud suffers from the high network latency and the huge transmission power consumption especially when using 3G/LTE connections. In this paper, we introduce a Cloudlet based MCC system aiming to reduce the power consumption and the network delay while using MCC. We merged the MCC concepts with the proposed Cloudlet framework and propose a new framework for the MCC model. Our practical experimental results showed that using the proposed model reduces the power consumption from the mobile device, besides reducing the communication latency when the mobile device requests a job to take place remotely while keeping high quality of service stander.

CloudExp: A comprehensive cloud computing experimental framework

Abstract Cloud computing is an emerging and fast-growing computing paradigm that has gained great interest from both industry and academia. Consequently, many researchers are actively involved in cloud computing research projects. One major challenge facing cloud computing researchers is the lack of a comprehensive cloud computing experimental tool to use in their studies. This paper introduces CloudExp , a modeling and simulation environment for cloud computing. CloudExp can be used to evaluate a wide spectrum of cloud components such as processing elements, data centers, storage, networking, Service Level Agreement (SLA) constraints, web-based applications, Service Oriented Architecture (SOA), virtualization, management and automation, and Business Process Management (BPM). Moreover, CloudExp introduces the Rain workload generator which emulates real workloads in cloud environments. Also, MapReduce processing model is integrated in CloudExp in order to handle the processing of big data problems.

TeachCloud: a cloud computing educational toolkit

Cloud computing is an evolving and fast-growing computing paradigm that has gained great interest from both industry and academia. Consequently, universities are actively integrating cloud computing into their IT curricula. One major challenge facing cloud computing instructors is the lack of a teaching tool to experiment with. This paper introduces TeachCloud, a modelling and simulation environment for cloud computing. TeachCloud can be used to experiment with different cloud components such as: processing elements, data centres, storage, networking, service level agreement (SLA) constraints, web-based applications, service oriented architecture (SOA), virtualisation, management and automation, and business process management (BPM). Also, TeachCloud introduces MapReduce processing model in order to handle embarrassingly parallel data processing problems. TeachCloud is an extension of CloudSim, a research-oriented simulator used for the development and validation in cloud computing.

Scalable Cloudlet-based Mobile Computing Model☆

Abstract Mobile Cloud Computing (MCC) has been introduced as a feasible solution to the inherited limitations of mobile computing. These limitations include battery lifetime, processing power and storage capacity. By using MCC, the processing and the storage of intensive mobile device jobs will take place in the cloud system and the results will be returned to the mobile device. This reduces the required power and time for completing such intensive jobs. However, connecting mobile devices with the cloud, suffers from the high network latency and the huge transmission power consumption especially when using 3G/LTE connections. On the other hand, multimedia applications are the most common applications in todays mobile devices; such applications require high computing resources. In this paper, a Cloudlet-based MCC system is introduced, aiming at reducing the power consumption and the network delay of multimedia applications while using MCC. The MCC concepts with the proposed Cloudlet framework are integrated and a new scalable framework for the MCC model is proposed. Our practical experimental results using multimedia applications show that using the proposed model reduces the power consumption of the mobile devices as well as reducing the communication latency when the mobile device requests a job to be performed remotely while satisfying the high quality of service requirements.

Internet of surveillance: a cloud supported large-scale wireless surveillance system

Large-scale video surveillance systems are among the necessities for securing our life these days. The high bandwidth demand and the large storage requirements are the main challenges in such systems. To face these challenges, the system can be deployed as a multi-tier framework that utilizes different technologies. In such a framework, technologies proposed under the umbrella of the Internet of Things (IoT) can play a significant rule in facing the challenges. In video surveillance, the cameras can be considered as “the things” that are streaming videos to a central processing and storage server (the cloud) through the Internet. Wireless technologies can be used to connect wireless cameras to the surveillance system more conveniently than wired cameras. Unfortunately, wireless communication in general tend to have limited bandwidth that needs careful management to achieve scalability. In this paper, we design and evaluate a reliable IoT-based wireless video surveillance system that provides an optimal bandwidth distribution and allocation to minimize the overall surveillance video distortion. We evaluate our system using NS-3 simulation. The results show that the proposed framework fully utilizes the available cloud bandwidth budget and achieves high scalability.

Cloudlet-based Efficient Data Collection in Wireless Body Area Networks

Abstract Wireless Body Area Networks (WBANs) have developed as an effective solution for a wide range of healthcare, military and sports applications. Most of the proposed works studied efficient data collection from individual and traditional WBANs. Cloud computing is a new computing model that is continuously evolving and spreading. This paper presents a novel cloudlet-based efficient data collection system in WBANs. The goal is to have a large scale of monitored data of WBANs to be available at the end user or to the service provider in reliable manner. A prototype of WBANs, including Virtual Machine (VM) and Virtualized Cloudlet (VC) has been proposed for simulation characterizing efficient data collection in WBANs. Using the prototype system, we provide a scalable storage and processing infrastructure for large scale WBANs system. This infrastructure will be efficiently able to handle the large size of data generated by the WBANs system, by storing these data and performing analysis operations on it. The proposed model is fully supporting for WBANs system mobility using cost effective communication technologies of WiFi and cellular which are supported by WBANs and VC systems. This is in contrast of many of available mHealth solutions that is limited for high cost communication technology, such as 3G and LTE. Performance of the proposed prototype is evaluated via an extended version of CloudSim simulator. It is shown that the average power consumption and delay of the collected data is tremendously decreased by increasing the number of VMs and VCs.

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.

Cloudlet-based for big data collection in body area networks

This paper presents a large scale BANs system in the presence of cloudlet-based data collection. The objective is to minimize end-to-end packet cost by dynamically choosing data collection to the cloud using cloudlet based system. The goal is to have the monitored data of BANs to be available to the end user or to the service provider in reliable manner. While reducing packet-to-cloud energy, the proposed work also attempts to minimize the end-to-end packet delay by choosing dynamically a neighbor cloudlet, so that the overall delay is minimized. Then, it will lead to have the monitored data in the cloud in real time manner. Note that, in the absence of network congestions in low data-rate BANs, the storage delays due to data collection manner are usually much larger compared to the congestion delay.