Faculty Publications
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Item New sparse matrix storage format to improve the performance of total SPMV time(2012) Bayyapu, B.; Raghavendra, S.R.; Guddeti, G.Graphics Processing Units (GPUs) are massive data parallel processors. High performance comes only at the cost of identifying data parallelism in the applications while using data parallel processors like GPU. This is an easy effort for applications that have regular memory access and high computation intensity. GPUs are equally attractive for sparse matrix vector multiplications (SPMV for short) that have irregular memory access. SPMV is an important computation in most of the scientific and engineering applications and scaling the performance, bandwidth utilization and compute intensity (ratio of computation to the data access) of SPMV computation is a priority in both academia and industry. There are various data structures and access patterns proposed for sparse matrix representation on GPUs and optimizations and improvements on these data structures is a continuous effort. This paper proposes a new format for the sparse matrix representation that reduces the data organization time and the memory transfer time from CPU to GPU for the memory bound SPMV computation. The BLSI (Bit Level Single Indexing) sparse matrix representation is up to 204% faster than COO (Co-ordinate), 104% faster than CSR (Compressed Sparse Row) and 217% faster than HYB (Hybrid) formats in memory transfer time from CPU to GPU. The proposed sparse matrix format is implemented in CUDA-C on CUDA (Compute Unified Device Architecture) supported NVIDIA graphics cards. © 2012 SCPE.Item Implementation of comprehensive address generator for digital signal processor(2013) Ramesh Kini, R.M.; Sumam David, S.The performance of signal-processing algorithms implemented in hardware depends on the efficiency of datapath, memory speed and address computation. Pattern of data access in signal-processing applications is complex and it is desirable to execute the innermost loop of a kernel in a single-clock cycle. This necessitates the generation of typically three addresses per clock: two addresses for data sample/coefficient and one for the storage of processed data. Most of the Reconfigurable Processors, designed for multimedia, focus on mapping the multimedia applications written in a high-level language directly on to the reconfigurable fabric, implying the use of same datapath resources for kernel processing and address generation. This results in inconsistent and non-optimal use of finite datapath resources. Presence of a set of dedicated, efficient Address Generator Units (AGUs) helps in better utilisation of the datapath elements by using them only for kernel operations; and will certainly enhance the performance. This article focuses on the design and application-specific integrated circuit implementation of address generators for complex addressing modes required by multimedia signal-processing kernels. A novel algorithm and hardware for AGU is developed for accessing data and coefficients in a bit-reversed order for fast Fourier transform kernel spanning over log 2 N stages, AGUs for zig-zag-ordered data access for entropy coding after Discrete Cosine Transform (DCT), convolution kernels with stored/streaming data, accessing data for motion estimation using the block-matching technique and other conventional addressing modes. When mapped to hardware, they scale linearly in gate complexity with increase in the size. © 2013 Copyright Taylor and Francis Group, LLC.Item Trust models in cloud: A survey on pros and cons(Springer Verlag service@springer.de, 2015) Divakarla, U.; Chandra Sekaran, K.Cloud is the recent emerging technology in all aspects. The basic concern with the usage of this cloud technology is security. Security poses a major drawback with data storage, resource utilization, virtualization, etc. In the highly competitive environment the assurances are insufficient for the customers to identify the trust worthy cloud service providers. In a nut shell all the entities in cloud and cloud computing environment should be trusted by each other and the entities that have communication should be trusted by each other. This paper throws light on different Trust Models developed and their drawback with respect to resource security. A strong Trust Model is recommended to enhance the security of the resources in Cloud. © Springer International Publishing Switzerland 2015.Item Polarity dependent photoisomerization of ether substituted azodyes: Synthesis and photoswitching behavior(Elsevier, 2015) Gan, S.M.; Pearl, Z.F.; Yuvaraj, A.R.; Lutfor, M.R.; Hegde, H.Two new ether substituted azodyes were synthesized and characterized by different spectral analysis such as 1H NMR, 13C NMR, FTIR and UV/Vis. Synthesized compounds were used to study the photoisomerization phenomenon by using UV-Vis spectro-photometer. Interesting polarity dependent effect is observed for the first time on these materials. Trans-cis (E-Z) and cis-trans (Z-E) conversion occurred within 41 s and 445 min, respectively for both the compounds in solutions. Polarizing optical microscopy studies revealed that there is no liquid crystal phase for both the compounds. The dramatic variation in the optical property is speculated to be the polarity of the chemical species. These derivatives are useful to fabricate optical data storage devices. © 2015 Published by Elsevier B.V.Item A new control method to mitigate power fluctuations for grid integrated PV/wind hybrid power system using ultracapacitors(Walter de Gruyter GmbH info@degruyter.com, 2016) Sabhahit, N.S.; Gaonkar, D.N.The output power obtained from solar-wind hybrid system fluctuates with changes in weather conditions. These power fluctuations cause adverse effects on the voltage, frequency and transient stability of the utility grid. In this paper, a control method is presented for power smoothing of grid integrated PV/wind hybrid system using ultracapacitors in a DC coupled structure. The power fluctuations of hybrid system are mitigated and smoothed power is supplied to the utility grid. In this work both photovoltaic (PV) panels and the wind generator are controlled to operate at their maximum power point. The grid side inverter control strategy presented in this paper maintains DC link voltage constant while injecting power to the grid at unity power factor considering different operating conditions. Actual solar irradiation and wind speed data are used in this study to evaluate the performance of the developed system using MATLAB/Simulink software. The simulation results show that output power fluctuations of solar-wind hybrid system can be significantly mitigated using the ultracapacitor based storage system. © by De Gruyter 2016.Item Live migration of virtual machines with their local persistent storage in a data intensive cloud(Inderscience Enterprises Ltd. editor@inderscience.com, 2017) Modi, A.; Achar, R.; Santhi Thilagam, P.S.Processing large volumes of data to drive their core business has been the primary objective of many firms and scientific applications in these days. Cloud computing being a large-scale distributed computing paradigm can be used to cater for the needs of data intensive applications. There are various approaches for managing the workload on a data intensive cloud. Live migration of a virtual machine is the most prominent paradigm. Existing approaches to live migration use network attached storage where just the run time state needs to be transferred. Live migration of virtual machines with local persistent storage has been shown to have performance advantages like security, availability and privacy. This paper presents an optimised approach for migration of a virtual machine along with its local storage by considering the locality of storage access. Count map combined with a restricted block transfer mechanism is used to minimise the downtime and overhead. The solution proposed is tested by various parameters like bandwidth, write access patterns and threshold. Results show the improvement in downtime and reduction in overhead. © © 2017 Inderscience Enterprises Ltd.Item Perceptually lossless coder for volumetric medical image data(Academic Press Inc. apjcs@harcourt.com, 2017) Chandrika, B.K.; Aparna., P.; Sumam David, S.S.With the development of modern imaging techniques, every medical examination would result in a huge volume of image data. Analysis, storage and/or transmission of these data demands high compression without any loss of diagnostically significant data. Although, various 3-D compression techniques have been proposed, they have not been able to meet the current requirements. This paper proposes a novel method to compress 3-D medical images based on human vision model to remove visually insignificant information. The block matching algorithm applied to exploit the anatomical symmetry remove the spatial redundancies. The results obtained are compared with those of lossless compression techniques. The results show better compression without any degradation in visual quality. The rate-distortion performance of the proposed coders is compared with that of the state-of-the-art lossy coders. The subjective evaluation performed by the medical experts confirms that the visual quality of the reconstructed image is excellent. © 2017Item Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor(Elsevier Ltd, 2017) M.a, M.A.; Jaidhar, C.D.The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM security solution that detects malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS). Specifically, it functions by the Virtual Machine Monitor (VMM), or hypervisor. The reconstructed semantic details obtained by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, the existing out-of-VM security solutions require extensive manual analysis. In this paper, we propose an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor. Further, we use the VMI-based technique to introspect digital artifacts of the live guest OS to obtain a semantic view of the processes details. We implemented an Intelligent Cross View Analyzer (ICVA) and implanted it into our proposed A-IntExt system, which examines the data supplied by the VMI to detect hidden, dead, and dubious processes, while also predicting early symptoms of malware execution on the introspected guest OS in a timely manner. Machine learning techniques are used to analyze the executables that are mined and extracted using MFA-based techniques and ascertain the malicious executables. The practicality of the A-IntExt system is evaluated by executing large real-world malware and benign executables onto the live guest OSs. The evaluation results achieved 99.55% accuracy and 0.004 False Positive Rate (FPR) on the 10-fold cross-validation to detect unknown malware on the generated dataset. Additionally, the proposed system was validated against other benchmarked malware datasets and the A-IntExt system outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%. © 2017 Elsevier LtdItem Deterministic En-Route Filtering of False Reports: A Combinatorial Design Based Approach(Institute of Electrical and Electronics Engineers Inc., 2018) Kumar, A.; Pais, A.R.Wireless sensor networks are an easy target for report fabrication attack, where compromised sensor nodes can be used by an adversary to flood the network with bogus/false reports. En-route filtering is a mechanism where intermediate forwarding nodes identify and drop false reports while they are being forwarded toward the sink. Most of the existing en-route filtering schemes are probabilistic, where sensor nodes in each cell share secret keys with a fixed probability with intermediate nodes. Thus, forwarded reports are verified probabilistically by intermediate nodes, because of which false reports can travel several hops before being dropped. Few deterministic en-route filtering schemes have also been proposed in the literature, but all such schemes require a source to send the reports through a fixed path to reach the sink. In this paper, we propose a novel deterministic en-route filtering scheme based on a combinatorial design to overcome the above-mentioned limitations of the existing schemes. The use of combinatorial design-based keys ensures direct communication between all the sensor nodes while maintaining low key storage overhead in the network. We provide a comprehensive analysis of the proposed scheme. The proposed scheme notably performs better than the existing schemes in terms of the expected filtering position of false reports. Furthermore, the proposed scheme improves data authenticity in the network and is more buoyant to selective forwarding and report disruption attacks. © 2013 IEEE.Item Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMM(Elsevier B.V., 2018) M.a, A.K.; Jaidhar, C.D.In order to fulfill the requirements like stringent timing restraints and demand on resources, Cyber–Physical System (CPS) must deploy on the virtualized environment such as cloud computing. To protect Virtual Machines (VMs) in which CPSs are functioning against malware-based attacks, malware detection and mitigation technique is emerging as a highly crucial concern. The traditional VM-based anti-malware software themselves a potential target for malware-based attack since they are easily subverted by sophisticated malware. Thus, a reliable and robust malware monitoring and detection systems are needed to detect and mitigate rapidly the malware based cyber-attacks in real time particularly for virtualized environment. The Virtual Machine Introspection (VMI) has emerged as a fine-grained out-of-VM security solution to detect malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS) by functioning at the Virtual Machine Monitor (VMM) or hypervisor. However, the reconstructed semantic details by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, extensive manual analysis is required by the existing out-of-VM security solutions. To address the foremost issue, in this paper, we propose an advanced VMM-based guest-assisted Automated Multilevel Malware Detection System (AMMDS) that leverages both VMI and Memory Forensic Analysis (MFA) techniques to predict early symptoms of malware execution by detecting stealthy hidden processes on a live guest OS. More specifically, the AMMDS system detects and classifies the actual running malicious executables from the semantically reconstructed process view of the guest OS. The two sub-components of the AMMDS are: Online Malware Detector (OMD) and Offline Malware Classifier (OFMC). The OMD recognizes whether the running processes are benign or malicious using its Local Malware Signature Database (LMSD) and online malware scanner and the OFMC classify unknown malware by adopting machine learning techniques at the hypervisor. The AMMDS has been evaluated by executing large real-world malware and benign executables on to the live guest OSs. The evaluation results achieved 100% of accuracy and zero False Positive Rate (FPR) on the 10-fold cross-validation in classifying unknown malware with maximum performance overhead of 5.8%. © 2017 Elsevier B.V.