Simulation and Performance Evaluation of Hadoop Capacity Scheduler

MapReduce is a parallel programming paradigm used for processing huge datasets on certain classes of distributable problems using a cluster. Budgetary constraints and the need for better usage of resources in a MapReduce cluster often make organizations rent or share hardware resources for their main data processing and analysis tasks. Thus, there may be many competing jobs from different clients performing simultaneous requests to the MapReduce framework on a particular cluster. Schedulers like Fair Share and Capacity have been specially designed for such purposes. Administrators and users run into performance problems, however, because they do not know the exact meaning of different task scheduler settings and what impact they can have with respect to the resource allocation scheme across organizations for a shared MapReduce cluster. In this work, Capacity Scheduler is integrated into an existing MRPERF simulator to predict the performance of MapReduce jobs in a shared cluster under different settings for Capacity Scheduler. A few case studies on the behaviour of Capacity Scheduler across different job patterns etc. using integrated simulator are also conducted

Secure and Usable Behavioural User Authentication for Resource-Constrained Devices

Robust user authentication on small form-factor and resource-constrained smart devices, such as smartphones, wearables and IoT remains an important problem, especially as such devices are increasingly becoming stores of sensitive personal data, such as daily digital payment traces, health/wellness records and contact e-mails. Hence, a secure, usable and practical authentication mechanism to restrict access to unauthorized users is a basic requirement for such devices. Existing user authentication methods based on passwords pose a mental demand on the user's part and are not secure. Behavioural biometric based authentication provides an attractive means, which can replace passwords and provide high security and usability. To this end, we devise and study novel schemes and modalities and investigate how behaviour based user authentication can be practically realized on resource-constrained devices. In the first part of the thesis, we implemented and evaluated the performance of touch based behavioural biometric on wearables and smartphones. Our results show that touch based behavioural authentication can yield very high accuracy and a small inference time without imposing huge resource requirements on the wearable devices. The second part of the thesis focus on designing a novel hybrid scheme named BehavioCog. The hybrid scheme combined touch gestures (behavioural biometric) with challenge-response based cognitive authentication. Touch based behavioural authentication is highly usable but is prone to observation attacks. While cognitive authentication schemes are highly resistant to observation attacks but not highly usable. The hybrid scheme improves the usability of cognitive authentication and improves the security of touch based behavioural biometric at the same time. Next, we introduce and evaluate a novel behavioural biometric modality named BreathPrint based on an acoustics obtained from individual's breathing gestures. Breathing based authentication is highly usable and secure as it only requires a person to breathe and low observability makes it secure against spoofing and replay attacks. Our investigation with BreathPrint showed that it could be used for efficient real-time authentication on multiple standalone smart devices especially using deep learning models

Effect of osmo air drying method on nutritional quality of peach (Prunus persica (l) batsch.) cultivars during storage

The present investigation was carried out with the objective to develop value added products and to assess the quality parameters of osmo air dried peach slices. The peach slices were dried by osmo air drying method. Dehydrated peach (Prunus persica (L) Batsch.) slices prepared were stored under ambient conditions in polythenepacks and subjected to physico-chemical analysis at 45 days interval for a period of 135 days. The highest total sugars were observed in Flordasun 58.28 % and reducing sugars (39.35 %) in Shan-e-Punjab. The maximum acidity (1.84 %) in Shan-e-Punjab, ash content (4.43 %) in Early Grand were recorded. The maximum ascorbic acid content of 11.94 mg/100g was found in Shan-e-Punjab. During storage, an increasing trend was observed in total sugars (54.27-56.76%) and reducing sugars (38.08-39.38%), whereas, acidity (1.85-1.74), ascorbic acid (11.75-9.81mg/100g) , and ash content showed decreasing trend. It is thus concluded that Early Grand, Flordasun and Shan-e-Punjab, cultivars of peach can be suitably used for preparation of dehydrated peach product using osmo air drying methods

PassWalk: Spatial Authentication Leveraging Lateral Shift and Gaze on Mobile Headsets

Peer reviewe

Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks

Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice commands for smart homes. However, the feasibility and performance of these inferences on resource-constrained devices remain largely unexplored. The authors compare traditional machine-learning models with deep-learning RNN models for an end-to-end authentication system based on breathing acoustics

BreathPrint: Breathing acoustics-based user authentication

Ministry of Education, Singapore under its Academic Research Funding Tier 2; National Research Foundation (NRF) Singapore under IDM Futures Funding Initiativ