As software is getting more valuable, unauthorized users or malicious programmers illegally copies and distributes copyrighted software over online service provider (OSP) and P2P networks. To detect, block, and remove pirated software (illegal programs) on OSP and P2P networks, this paper proposes a new filtering approach using software birthmark, which is unique characteristics of program and can be used to identify each program. Software birthmark typically includes constant values, library information, sequence of function calls, and call graphs, etc. We target Microsoft Windows applications and utilize the numbers and names of DLLs and APIs stored in a Windows executable file. Using that information and each cryptographic hash value of the API sequence of programs, we construct software birthmark database. Whenever a program is uploaded or downloaded on OSP and P2P networks, we can identify the program by comparing software birthmark of the program with birthmarks in the database. It is possible to grasp to some extent whether software is an illegally copied one. The experiments show that the proposed software birthmark can effectively identify Windows applications. That is, our proposed technique can be employed to efficiently detect and block pirated programs on OSP and P2P networks.

Abstract.To realize real-time information sharing in generic platforms, it is especially important to support dynamic message structure changes. For the case of IDL, it is necessary to rewrite applications to change data sample structures. In this paper, we propose a dynamic reconfiguration scheme of data sample structures for DDS. Instead of using IDL, which is the static data sample structure model of DDS, we use a self describing model using data sample schema, as a dynamic data sample structure model to support dynamic reconfiguration of data sample structures. We also propose a data propagation model to provide data persistency in distributed environments. We guarantee persistency by transferring data samples through relay nodes to the receiving nodes, which have not participated in the data distribution network at the data sample distribution time. The proposed schemes can be utilized to support data sample structure changes during operation time and to provide data persistency in various environments, such as real-time enterprise environments and connection-less internet environments.

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The improvement of Hadoop performance has received considerable attention from researchers in cloud computing fields. Most studies have focused on improving the performance of a Hadoop cluster. Notably, various parameters are required to configure Hadoop and must be adjusted to improve performance. This paper proposes a mechanism to improve Hadoop, schedule jobs, and allocate and utilize resources. Specifically, we present an improved ant colony optimization method to schedule jobs according to the job size and the time expected for execution. Priority is given to the job with the minimum data size and minimum response time. The resource usage and running jobs by data node are predicted using an artificial neural network, and job activity and resource usage are monitored using the resource manager. Moreover, we enhance the Hadoop Name node performance by adding an aggregator node to the default HDFS framework architecture. The changes involve four entities: the name node, secondary name node, aggregator nodes, and data nodes, where the aggregator node is responsible for assigning the jobs among the data node, and the Name node keeps tracking only the aggregator nodes. We test the overall scheme among Amazon EC2 and S3, and show the results of throughput and CPU response time for different data sizes. Finally, we show that the proposed approach shows significant improvement compare to native Hadoop and other approaches.

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