Resource allocation in mobile edge cloud computing for data-intensive applications

Rapid advancement in the mobile telecommunications industry has motivated the development of mobile applications in a wide range of social and scientific domains. However, mobile computing (MC) platforms still have several constraints, such as limited computation resources, short battery life and high sensitivity to network capabilities. In order to overcome the limitations of mobile computing and benefit from the huge advancement in mobile telecommunications and the rapid revolution of distributed resources, mobile-aware computing models, such as mobile cloud computing (MCC) and mobile edge computing (MEC) have been proposed. The main problem is to decide on an application execution plan while satisfying quality of service (QoS) requirements and the current status of system networking and device energy. However, the role of application data in offloading optimisation has not been studied thoroughly, particularly with respect to how data size and distribution impact application offloading. This problem can be referred to as data-intensive mobile application offloading optimisation. To address this problem, this thesis presents novel optimisation frameworks, techniques and algorithms for mobile application resource allocation in mobile-aware computing environments. These frameworks and techniques are proposed to provide optimised solutions to schedule data intensive mobile applications. Experimental results show the ability of the proposed tools in optimising the scheduling and the execution of data intensive applications on various computing environments to meet application QoS requirements. Furthermore, the results clearly stated the significant contribution of the data size parameter on scheduling the execution of mobile applications. In addition, the thesis provides an analytical investigation of mobile-aware computing environments for a certain mobile application type. The investigation provides performance analysis to help users decide on target computation resources based on application structure, input data, and mobile network status

Evidence-Aware Mobile Computational Offloading

Non Peer reviewe

A data quarantine model to secure data in edge computing

Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, linear discriminant analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data