最近发表成果 Publications

Title: Cost-aware optimal data allocations for multiple dimensional heterogeneous memories using dynamic programming in big data

Authors: Hui Zhao, Meikang Qiu, Min Chen, Keke Gai
Journal: Journal of Computational Science
Date of Publication: 06-12-2016
Abstract: Multiple constraints in SPMs are considered a problem that can be solved in a nondeterministic polynomial time. In this paper, we propose a novel approach solving the data allocations in multiple dimensional constraints. For supporting the approach, we develop a novel algorithm that is designed to solve the data allocations under multiple constraints in a polynomial time. Our proposed approach is a novel scheme of minimizing the total costs when executing SPM under multiple dimensional constraints. Our experimental evaluations have proved the adaptation of the proposed model that could be an efficient approach of solving data allocation problems for SPMs. More

Title: Secure cyber incident analytics framework using Monte Carlo simulations for financial cybersecurity insurance in cloud computing

Authors: Keke Gai, Meikang Qiu, Houcine Hassan
Journal: Concurrency and Computation: Practice and Experience
Date of Publication: 05-27-2016
Abstract: The remarkable increasing demands of mitigating losses from cyber incidents for financial firms have been driving the rapid development of the Cybersecurity Insurance (CI). The implementations of CI have covered a variety of aspects in cyber incidents, from hacking to frauds. However, CI is still at its exploring stage so that there are a number of dimensions that are uncovered by the current applications. The cyber attack on critical infrastructure is one of the serious issues that prevents the expansions of CI. This paper addresses CI implementations focusing on cloud-based service offerings and proposes a secure cyber incident analytics framework using big data, named as Cost-Aware Hierarchical Cyber Incident Analytics (CA-HCIA). The approach is designed for matching different cyber risk scenarios, which uses repository data. We use Monte Carlo simulations for extracting the incident features based on the training datasets. The main algorithms in CA-HCIA include Monte Carlo Cyber Feature Extraction (MC2FE) and Optimal Cost Balance (OCA) Algorithms. Our experimental evaluation has provided the theoretical proof of the adoptability and feasibility. Results show that our proposal improves the cost of existing techniques in 7.98% and 15.39%. More

Title: A novel pre-cache schema for high performance Android system

Authors: Hui Zhao, Min Chen, Meikang Qiu, Keke Gai, Meiqin Liu
Journal: Future Generation Computer Systems
Date of Publication: 03-31-2016
Abstract: As a mobile operating system framework, Android plays a significant role in supporting mobile apps. However, current Android application model is not efficient by using current two common approaches, including Activity+XML Layout Files (AXLF) and HTML+WebKit (HWK) models. In this paper, we propose a novel middleware service solution that overcomes the drawbacks with using the pre-cache approach, PrecAche Technology of Android System (PATAS). The proposed method uses HTML to design the application interface and separately store the Page Framework (PF) and Page Data (PD). We create a new middleware of web pages, Version Flags, to indicate whether PF and PD are expired. Our experimental results represent that the proposed approach can improve the execution efficiency as well as reduce the networking costs, which can be broadly used in cloud-based distributed systems. More

Title: Supporting high-quality video streaming with SDN-based CDNs

Authors: Longbin Chen, Meikang Qiu, Wenyun Dai, Ning Jiang
Journal: The Journal of Supercomputing
Date of Publication: 03-02-2016
Abstract: Videos and other multimedia contents become increasing popular among users of the Internet nowadays. With the improvement of underlying infrastructure of the Internet, users are allowed to enjoy video contents with much higher quality than last decade. Content delivery networks (CDNs) are a type of content hosting solution that widely used across the Internet. Content providers offload the task of content hosting to CDN providers and redirect users’ requests to CDNs. Video contents, especially high quality videos at real-time has occupying a major part of the Internet traffic. It is challenging to handle such workloads even for a large- scale CDN. Load balancing algorithms are critical to address this issue. However, traditional load balancing algorithms such as round-robin and randomization are unaware of user side requirements. Therefore, it is not uncommon that requests for high-quality videos at real-time are not satisfied. In this paper, we try to fulfill such requests by integrating software-defined networking technology with CDN infrastructure. We also propose revised load balancing algorithms and develop simulations to verify our approaches. The results show that the proposed algorithms achieve much higher user satisfaction in bandwidth-idle environments. More

Title: Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry

Authors: Meikang Qiu, Keke Gai, Bhavani Thuraisingham, Lixin Tao, Hui Zhao
Journal: Future Generation Computer Systems
Date of Publication: Feb. 2016
Abstract: As one of the most significant issues in the financial industry, customers’ privacy information protection has been considered a challenging research over years. The constant emergence of the novel technologies often leads to dynamic threats from both internal and external service providers. We consider the implementations of mobile cloud-based financial services an important approach of service provisions, which also causes risks to privacy protections due to the data sharing with the unknown third parties. The data generated by mobility are usually associated with mobile users’ personal privacy information. This paper addresses this issue and proposes an approach proactively protect financial customers’ privacy information using Attributed-Based Access Control (ABAC) as well as data self-deterministic scheme. The proposed approach is called Proactive Dynamic Secure Data Scheme (P2DS), which aims to guarantee the unanticipated parties cannot reach the privacy data. There are two main algorithms supporting the proposed scheme, which are Attribute-based Semantic Access Control (A-SAC) Algorithm and Proactive Determinative Access (PDA) Algorithm. The main contributions of this paper have three aspects. First, we propose a semantic approach for constraining data accesses. Second, we propose a user-centric approach that proactively prevents users’ data from unexpected operations on the cloud side. Finally, the proposed scheme has a higher-level secure sustainability since it can deal with dynamic threats, including the emerging and future hazards. We have examined that our proposed scheme had a quality performance matching our expected goal. More

Title: Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing

Authors: Keke Gai, Meikang Qiu, Hui Zhao, Lixin Tao, Ziliang Zong
Journal: Journal of Network and Computer Applications
Date of Publication: 01-30-2016
Abstract: Employing mobile cloud computing (MCC) to enable mobile users to acquire benefits of cloud computing by an environmental friendly method is an efficient strategy for meeting current industrial demands. However, the restrictions of wireless bandwidth and device capacity have brought various obstacles, such as extra energy waste and latency delay, when deploying MCC. Addressing this issue, we propose a dynamic energy-aware cloudlet-based mobile cloud computing model (DECM) focusing on solving the additional energy consumptions during the wireless communications by leveraging dynamic cloudlets (DCL)-based model. In this paper, we examine our model by a simulation of practical scenario and provide solid results for the evaluations. The main contributions of this paper are twofold. First, this paper is the first exploration in solving energy waste problems within the dynamic networking environment. Second, the proposed model provides future research with a guideline and theoretical supports. More

Title: Privacy protection for preventing data over-collection in smart city

Authors: Yibin Li, Wenyun Dai, Zhong Ming, Meikang Qiu
Journal: IEEE Transactions on Computers
Date of Publication: 08-19-2015
Abstract: In smart city, all kinds of users' data are stored in electronic devices to make everything intelligent. A smartphone is the most widely used electronic device and it is the pivot of all smart systems. However, current smartphones are not competent to manage users' sensitive data, and they are facing the privacy leakage caused by data over-collection. Data over-collection, which means smartphones apps collect users' data more than its original function while within the permission scope, is rapidly becoming one of the most serious potential security hazards in smart city. In this paper, we study the current state of data over-collection and study some most frequent data over-collected cases. We present a mobile-cloud framework, which is an active approach to eradicate the data over-collection. By putting all users' data into a cloud, the security of users' data can be greatly improved. We have done extensive experiments and the experimental results have demonstrated the effectiveness of our approach. More

Title: Phase-change memory optimization for green cloud with genetic algorithm

Authors: Meikang Qiu, Zhong Ming, Jiayin Li, Keke Gai, Ziliang Zong
Journal: IEEE Transactions on Computers
Date of Publication: 03-04-2015
Abstract: Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell(SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time. More