International Journal of Software Engineering and Knowledge Engineering

Mobile traffic is currently the most important traffic on the Internet, both domestically and internationally. This is predominantly attributable to widespread cloud computing-based services on mobile networks. Fifth generation (5G) mobile networks are expected to comfortably accommodate this type of traffic by supporting 1,000 times faster speeds than conventional 4G. However, commercial implementation of 5G mobile is targeted for the year 2020. Therefore, interim accommodation approaches are needed. In essence, 5G mobile networks are an evolution of 4G in which a licensed spectrum that generates revenue from user data transfer is being considered. Consequently, more cost-effective methods that utilize the unlicensed spectrum are also desired. Meanwhile, concomitant with pursuit of miniaturization and reduction in weight, the number and types of network interfaces in recent mobile devices are increasing. We propose a method for carrier aggregation of multiple heterogeneous wireless links based on software-defined networking (SDN) that controls traffic and manages the wireless interfaces of mobile devices effectively when multiple radio access technologies are available. Evaluations conducted of the proposed method on an experimental testbed developed using the OpenStack cloud computing platform with aggregated multiple radio access technology environments indicate that it is feasible and provides higher data rate.

The number and variety of applications for mobile devices continue to grow. However, the resources on mobile devices including computation and storage do not keep pace with the growth. How to incorporate the computation capacity on cloud servers into mobile computing has been desired and challenge issues to resolve. In this work, we design an elastic computation framework to take advantage the heterogeneous computation capacity on cloud servers, which consist of CPUs and GPGPUs, to meet the computation demands of ever growing mobile applications. The computation framework extends OpenCL framework to link remote processors with local mobile applications. The framework is flexible in the sense that the computation can be stopped at any time and gains results, which is called imprecise computation in real-time computing literature. The framework has been evaluated against OpenCL benchmark and physical computation engine for gaming. The results show that the framework supports OpenCL benchmark, RODINIA, without modifying the codes with few exceptions. The elastic computation framework allows the cloud servers to support more mobile clients without sacrificing their QoS requirements. The experiment results also show that IO intensive applications do not perform well when the network capacity is insufficient or unreliable.

Mobile cloud service is the most important traffic generator today, and high data rate services in this area are increasing. The Virtual Desktop Infrastructure (VDI) is a circuit-like service that is widely deployed owing to its security merits and the resource constraints of mobile devices. However, VDI requires a high and guaranteed virtually constant wireless data rate. Thus, we focus on the problems that have to be resolved in order for IEEE802.21 Media Independent Handover (MIH) to support a guaranteed wireless data rate and also enable it to offer optimized and maximized data rate support for mobile cloud services. We also consider a virtual machine (VM) migration scheme that is tightly coupled to mobile networks and can minimize the user's service delay and reduce the bandwidth requirements of the wired network. We then present a consistent control framework by extending the Global Environment for Network Innovations (GENI) control framework to provide a single integrated control framework for cloud computing and Future Internet compatibility. Finally, we develop an experimental testbed using a mobile cloud computing environment and evaluate the performance of the proposed scheme. We show that the proposed method can enhance the Quality of Experience (QoE) of mobile cloud computing services with increased data rate support and a single consistent control framework that is compatible with the GENI-based Future Internet architecture. The results of evaluations conducted indicate that our proposed method provides 24%–41% better data rate compared to conventional methods.

In this paper a systematic review is conducted to investigate the structure, components, techniques, evaluation procedure, and comparison scope related to prior ABE-based studies. The undeniable role of accurate development effort estimation in the success of software project management has attracted the attention of researchers over the past few years. Among various algorithmic and non-algorithmic estimation methods, analogy based estimation (ABE) is a widely accepted method due to its simplicity and estimation capability. This paper investigates the improvement process of ABE method during 2000 to 2012. Six research questions are defined to be addressed through evaluation of prior ABE-based studies. The review domain includes 24 papers selected through a tough filtration process. The results show that improvement of ABE can be performed through adjustment, grey theory, attribute weighting and attribute selection techniques. Moreover, ABE configurations can significantly affect the results.

Driven by mutual benefits, there is a demand for transactional data sharing among organizations or parties for research or business analysis purpose. It becomes an essential concern to provide privacy-preserving data sharing and meanwhile maintain data utility, due to the fact that transactional data may contain sensitive personal information. Existing privacy-preserving methods, such as k-anonymity and l-diversity, cannot handle high-dimensional sparse data well, since they would bring about much data distortion in the anonymization process. In this paper, we use bipartite graphs with node attributes to model high-dimensional sparse data, and then propose a privacy-preserving approach for sharing transactional data in a new vision, in which the bipartite graph is anonymized into a weighted bipartite graph by clustering node attributes. Our approach can maintain privacy of the associations between entities and resist certain attackers with knowledge of partial items. Experiments have been performed on real-life data sets to measure the information loss and the accuracy of answering aggregate queries. Experimental results show that the approach improves the balance of performance between privacy protection and data utility.