Search

A heterogeneous cloud system, for example, a Hadoop 2.6.0 platform, provides distributed but cohesive services with rich features on large-scale management, reliability, and error tolerance. As big data processing is concerned, newly built cloud clusters meet the challenges of performance optimization focusing on faster task execution and more efficient usage of computing resources. Presently proposed approaches concentrate on temporal improvement, that is, shortening MapReduce time, but seldom focus on storage occupation; however, unbalanced cloud storage strategies could exhaust those nodes with heavy MapReduce cycles and further challenge the security and stability of the entire cluster. In this paper, an adaptive method is presented aiming at spatial-temporal efficiency in a heterogeneous cloud environment. A prediction model based on an optimized Kernel-based Extreme Learning Machine algorithm is proposed for faster forecast of job execution duration and space occupation, which consequently facilitates the process of task scheduling through a multi-objective algorithm called time and space optimized NSGA-II TS-NSGA-II. Experiment results have shown that compared with the original load-balancing scheme, our approach can save approximate 47-55i¾źs averagely on each task execution. Simultaneously, 1.254i¾ź of differences on hard disk occupation were made among all scheduled reducers, which achieves 26.6% improvement over the original scheme. Copyright © 2016 John Wiley & Sons, Ltd.

To prevent digital image from unauthorized use, image copy detection is an important technique in the field of copyright protection. The conventional methods of image copy detection concentrate on extracting global or local signatures to resist various kinds of copy attacks. However, the global signatures are sensitive to some geometric transformations, such as rotation and cropping, while the local signatures are not discriminative enough to identify copies from similar images. Considering both the robustness and discriminability, a novel image signature based on the combination of global and local signatures is proposed for image copy detection. Firstly, the interest points are detected from a given image by using the Hessian-Affine detector. Secondly, the image is divided into some circle tracks, and thus the interest points are distributed into these tracks. Finally, to combine the advantages of the circle-track-based global signature and the interest points, the global distribution characteristics of interest points based on circle tracks are used to generate our image signature. Experimental results demonstrate the effectiveness of our proposed method in the aspects of both robustness and discriminability. Copyright © 2013 John Wiley & Sons, Ltd.

A new steganographic method for data hiding in Microsoft Word 2007–2010 (Microsoft Corp., Redmond, WA, USA) files that use Office Open XML (OOXML) format is proposed. Secret information can be imperceptibly embedded into OOXML documents by splitting up the printable text, which is defined by the main document body of the OOXML format document. The number of printable words contained in each segment represents the secret message. Theoretical analysis demonstrates that embedding bit rate of the proposed method can take the maximum value (0.8) when 2 bits of secret message are embedded into each segment. Experiments show that 0.44 bit is embedded into each word and 1/151 bit is embedded into each bit of the document on average, which is higher than contemporary linguistic steganography approaches. The method can resist “Format”, “Impersonation”, “Save As”, “Copy”, and other active attacks, and all these changes will not be shown on the MS Office screen display. Therefore, the proposed method can apply to the fields of covert communication and security protection for OOXML format documents. Copyright © 2011 John Wiley & Sons, Ltd.

Summary Both mobile computing and cloud computing have experienced rapid development in recent years. Although centralized cloud computing exhibits abundant resources for computation-intensive tasks, the unpredictable and unstable communication latency between the mobile users and the cloud makes it challenging to handle latency-sensitive mobile computing tasks. To address this issue, fog computing recently was proposed by pushing the cloud computing to the network edge closer to the users. To realize such vision, we can augment existing access points in wireless networks with cloudlet servers for hosting various mobile computing tasks. In this paper, we investigate how to deploy the servers in a cost-effective manner without violating the predetermined quality of service. In particular, we practically consider that the available cloudlet servers are heterogeneous, ie, with different cost and resource capacities. The problem is formulated into an integer linear programming form, and a low-complexity heuristic algorithm is invented to address it. Extensive simulation studies validate the efficiency of our algorithm by it performs much close to the optimal solution.