Tag Archives: Dissertation

Big Data Computing: Modeling and Optimization

MapReduce framework is widely used to parallelize batch jobs since it exploits a high degree of multi-tasking to process them. However, it has been observed that when the number of servers increases, the map phase can take much longer than expected. This thesis analytically shows that the stochastic behavior of the servers has a negative effect on the completion time of a MapReduce job, and continuously increasing the number of servers without accurate scheduling can degrade the overall performance. We analytically model the map phase in terms of hardware, system, and application parameters to capture the effects of stragglers on the performance. Mean sojourn time (MST), the time needed to sync the completed tasks at a reducer, is introduced as a performance metric and mathematically formulated. Following that, we stochastically investigate the optimal task scheduling which leads to an equilibrium property in a datacenter with different types of servers. Our experimental results show the performance of the different types of schedulers targeting MapReduce applications. We also show that, in the case of mixed deterministic and stochastic schedulers, there is an optimal scheduler that can always achieve the lowest MST.

• Farshid Farhat, Diman Zad Tootaghaj, Anand Sivasubramaniam, Mahmut Kandemir, and Chita R. Das are with the school of electrical engineering and computer science, the Pennsylvania State University, University Park, PA, 16802, USA. Email: {fuf111,dxz149,anand,kandemir,das}@cse.psu.edu.

• Yuxiong He is with the Cloud Computing Futures group, the Microsoft Research, Redmond, WA 98052 USA. Email: yuxhe@microsoft.com.

• The work was done during my visit from MSR in June 2016 in Redmond WA.

Image Steganalysis of Low Rate Embedding in Spatial Domain


LSB embedding in spatial domain with very low rate can be easily performed and its detection in spite of many researches is very hard, while BOSS competition has been held to break an adaptive embedding algorithm with low rate. Thus, proposing powerful steganalyzer of very low rate in spatial domain is highly requested. In this thesis it has been tried to present some algorithms to detect secret message with very low rate in spatial domain using eigenvalues analysis and relative auto-correlation of image.

First approach is based on the analysis of the eigenvalues of the cover correlation matrix that we used for the first time. Image partitioning, correlation function computation, constellation of the correlated data, and eigenvalues examination are major challenging parts of our analysis method. The proposed method uses the LSB plane of images in spatial domain, extendable to transform domain, for detecting low embedding rates that is a major concern in the area of the LSB steganography. Simulation results show that the proposed approach improves over some well-known LSB steganalysis methods, specifically at low embedding rates.

Our second image steganalysis method suggests analysis of the relative norm of the image parts manipulated in an LSB embedding system. Image partitioning, multidimensional cross-correlation, feature extraction, and rate estimation, as the major steps of the main analysis procedure. We then extract and use new statistical features, Parts-Min-Sum and Local-Entropies-Sum, to get a closer estimate of the embedding rate and the detection performance. Our simulation results, as compared to some recent steganographic methods show that our new approach outperforms some well-known, powerful LSB steganalysis schemes, in terms of true and false detection rates.

Keywords: Image Steganalysis, Eigenvalues Analysis, LSB Embedding, Relative Autocorrelation, Parts Min Sum, Embedding Rate Estimation, Local Entropies Sum.

Image Steganalysis of Low Bit-rate Embedding