Diman Zad Tootaghaj 's publications

1. Diman Zad Tootaghaj 's publications
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1. 1.D. Z. Tootaghaj, F. Farhat. Optimal placement of Cores, Caches and Memory controllers in NoC. arXiv, 2016. [link] [pdf]
2. 2.F. Farhat, D. Z. Tootaghaj, M. Arjomand. Towards optimizing data computing in the cloud. arXiv, 2016. [link] [pdf]
3. 3. F. Farhat, D. Z. Tootaghaj, Y. He, A. Sivasubramaniam, M. T. Kandemir, C. R. Das. Stochastic modeling and optimization of stragglers. IEEE transaction on Cloud Computing (TCC), 2016. [link] [pdf]
   
4. 4.D. Z. Tootaghaj,  Evaluating cloud workload characteritics. Master’s thesis, The Pennsylvania State University, 2015. [link] [pdf]
5. 5.D. Z. Tootaghaj, F. Farhat, M. Arjomand, P. Faraboschi, M. T. Kandemir, A. Sivasubramaniam, C. R. Das, Evaluating the Combined Impact of Datacenter Architecture and Cloud Workload Characteristics on Performance, Network Traffic and Cost, IEEE International Symposium on Workload Characterization (IISWC) 2015. [link] [pdf]
6. 6.F. Farhat, D. Z. Tootaghaj, A. Sivasubramaniam, M. T. Kandemir, C. R. Das, Modeling and Optimization of Straggling Mappers.Technical Report CSE-14-006, Pennsylvania State University. [link] [pdf]

7. D. Z. Tootaghaj, F. Farhat, M. R. Pakravan, M. R. Aref, Risk of Attack Coefficient Effect on Availability of Ad-hoc Networks, IEEE CCNC Research Student Workshop, 2011. [link] [pdf]

8. D. Z. Tootaghaj, F. Farhat, M. R. Pakravan, M. R. Aref, Game-Theoretic Approach to Mitigate Packet Dropping in Wireless Ad-hoc Networks, IEEE CCNC Research Student Workshop, 2011. [link] [pdf]

9. M. A. Kashiha, D. Z. Tootaghaj, Partial Discharge Source Classification and De-noising in Rotating Machines Using Discrete Wavelet Transform and Directional Coupling Capacitor, Journal of Electromagnetic Analysis and Applications and submitted and accepted to Asia-Pacific Power and Engineering Conference (APPEEC), sponsored by IEEE Power & Energy Society (PES), March 2009. [link] [pdf]

Optimizing Data Computing Flows

Towards Stochastically Optimizing Data Computing Flows

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Abstract:
With rapid growth in the amount of unstructured data produced by memory-intensive applications, large scale data analytics has recently attracted increasing interest. Processing, managing and analyzing this huge amount of data poses several challenges in cloud and data center computing domain. Especially, conventional frameworks for distributed data analytics are based on the assumption of homogeneity and non-stochastic distribution of different data-processing nodes. The paper argues the fundamental limiting factors for scaling big data computation. It is shown that as the number of series and parallel computing servers increase, the tail (mean and variance) of the job execution time increase. We will first propose a model to predict the response time of highly distributed processing tasks and then propose a new practical computational algorithm to optimize the response time.

This entry was posted in Arxiv, Research and tagged Computing Flow, Data Computing Flow, Data Flow, Optimal Scheduling, Stochastic Optimization on July 1, 2015.

Optimal Placement of Cores Caches and MemoryControllers in NoC

Optimal Placement of Cores, Caches and MemoryControllers in NoC