Novel Approach for Job Offloading Technique in Mobile Cloud Computing

User preferences for computing have evolved as a result of recent advancements in mobile computing technologies. Mobile devices (SMDs) are still low potential computer platforms with capped memory sizes, CPU speeds, and battery life. Computationally heavy mobile apps may be run on SMDs thanks to Mobile Cloud Computing (MCC), which uses computational offloading. In both the business and academic worlds, there is rising interest in the mobile cloud computing is a new computing paradigm. Lack of a comprehensive experimental framework to use in their experiments and to evaluate their proposed work is a serious issue for mobile cloud computing researchers. Through MCC, mobile devices will be able to serve a wider variety of resource-intensive tasks while maintaining and growing their resource pools. In order to improve the mobile user experience, it places a high priority on improving energy efficiency, storage capacity, computational power, and data security. Since both the mobile device and the Cloud must determine energy-time trade-offs and decisions made on one side have an influence on the performance of the other, designing MCC systems is a challenging task. According to an examination of the MCC literature, all present models are centred on mobile devices, with the Cloud viewed as a system with infinite resources. Furthermore, no MCC-specific simulation tool is currently known to exist. To fill this need, we present in this study a Novel Approach for Job Offloading in Cloud Environments such as Google Cloud, using OCR application, while attempting to reduce energy use (Power). We are measuring the results of this experiment on both Cloud Computing and Mobile Device Computing. &nbsp

Experimental Comparison of Simulation Tools for Efficient Cloud and Mobile Cloud Computing Applications

Cloud computing provides a convenient and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is an emerging technology that integrates cloud computing technology with mobile devices. MCC provides access to cloud services for mobile devices. With the growing popularity of cloud computing, researchers in this area need to conduct real experiments in their studies. Setting up and running these experiments in real cloud environments are costly. However, modeling and simulation tools are suitable solutions that often provide good alternatives for emulating cloud computing environments. Several simulation tools have been developed especially for cloud computing. In this paper, we present the most powerful simulation tools in this research area. These include CloudSim, CloudAnalyst, CloudReports, CloudExp, GreenCloud, and iCanCloud. Also, we perform experiments for some of these tools to show their capabilities

Mobile Big Data Analytics in Healthcare

Mobile and ubiquitous devices are everywhere around us generating considerable amount of data. The concept of mobile computing and analytics is expanding due to the fact that we are using mobile devices day in and out without even realizing it. These mobile devices use Wi-Fi, Bluetooth or mobile data to be intermittently connected to the world, generating, sending and receiving data on the move. Latest mobile applications incorporating graphics, video and audio are main causes of loading the mobile devices by consuming battery, memory and processing power. Mobile Big data analytics includes for instance, big health data, big location data, big social media data, and big heterogeneous data. Healthcare is undoubtedly one of the most data-intensive industries nowadays and the challenge is not only in acquiring, storing, processing and accessing data, but also in engendering useful insights out of it. These insights generated from health data may reduce health monitoring cost, enrich disease diagnosis, therapy, and care and even lead to human lives saving. The challenge in mobile data and Big data analytics is how to meet the growing performance demands of these activities while minimizing mobile resource consumption. This thesis proposes a scalable architecture for mobile big data analytics implementing three new algorithms (i.e. Mobile resources optimization, Mobile analytics customization and Mobile offloading), for the effective usage of resources in performing mobile data analytics. Mobile resources optimization algorithm monitors the resources and switches off unused network connections and application services whenever resources are limited. However, analytics customization algorithm attempts to save energy by customizing the analytics process while implementing some data-aware techniques. Finally, mobile offloading algorithm decides on the fly whether to process data locally or delegate it to a Cloud back-end server. The ultimate goal of this research is to provide healthcare decision makers with the advancements in mobile Big data analytics and support them in handling large and heterogeneous health datasets effectively on the move