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FORK-JOIN QUEUE MODELING AND OPTIMAL SCHEDULING IN PARALLEL PROGRAMMING FRAMEWORKS ABSTRACT 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 equil...

FORK-JOIN QUEUE MODELING AND OPTIMAL SCHEDULING IN PARALLEL PROGRAMMING FRAMEWORKS ABSTRACT 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 equ...

Diman Zad Tootaghaj 's publications 1. D. Z. Tootaghaj , F. Farhat. Optimal placement of Cores, Caches and Memory controllers in NoC. arXiv, 2016. [ link ] [ pdf ] 2. F. Farhat,  D. Z. Tootaghaj , M. Arjomand. Towards optimizing data computing in the cloud. arXiv, 2016. [ link ] [ pdf ] 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. D. Z. Tootaghaj ,  Evaluating cloud workload characteritics. Master’s thesis, The Pennsylvania State University, 2015. [ link ] [ pdf ] 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 ] [ pd...

Towards Stochastically Optimizing Data Computing Flows 

Optimal Placement of Cores, Caches and MemoryControllers in NoC

تحليل پنهان سازي تصوير با نرخ درج پايين در حوزه مكان فرحت، فرشيد Farhat, Farshid شماره پایان نامه: 45945 کد دانشکده: 05 پديدآور: فرحت، فرشيد عنوان: تحليل پنهان سازي تصوير با نرخ درج پايين در حوزه مكان. نام دانشگاه/پژوهشگاه: صنعتي شريف سال اخذ مدرك: 1392. نام دانشکده: مهندسي برق مقطع: دكتري گرایش: مخابرات سيستم توصیف ظاهری: 89ص.: جدول، نمودار، كتابنامه؛ چكيده به فارسي و انگليسي توصیفگر: نهان نگاري تصوير Image Watermarking توصیفگر: تحليل نهان نگاري Steganalysis توصیفگر: تجزيه مقادير ويژه Eigenvalue Decomposition توصیفگر: جاسازي كم ارزش ترين بيت Least Significant Bit (LSB)Embedding توصیفگر: خودهمبستگي نسبي Relative Autocorrelation توصیفگر: مجموع كمترين جزء ها Parts Min Sum توصیفگر: تخمين نرخ جاسازي Embedding Rate Estimation توصیفگر: مجموع درگاشت هاي محلي Local Entropies Sum استاد راهنما. قائم مقامي، شاهرخ استاد مشاور. عارف، محمد رضا