Santonu Sarkar

Bio: Santonu Sarkar is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Software as a service & Software system. The author has an hindex of 22, co-authored 125 publications receiving 2048 citations. Previous affiliations of Santonu Sarkar include Jadavpur University & Accenture.

Fitness-Aware Containerization Service Leveraging Machine Learning

Abstract: Containerized deployment of microservices has gained immense traction across industries. To meet demand, traditional cloud providers offer container-as-a-service, where selection of the container and containerization of workloads remain developer's responsibility. This task is arduous for a developer since the choice of containers across different cloud providers is many. Furthermore, there does not exist any mechanism using which one can compare and contrast the capabilities of containers across different providers. In this scenario, we envisage the need for a smart cloud broker that can automatically deploy a chosen IT service into the best-fit container environment mapped to performance requirements, from among the set of available underpinning brokered container hosting systems spread across multiple cloud providers. We propose a novel fitness-aware containerization-as-a-service to achieve this. We show why a best-fit container selection process is operationally complex and time consuming, and how we heuristically prune the associated decision tree in two phases so that it becomes viable to implement this as an on-demand service. We propose a new metric called fitness quotient (FQ) to evaluate containers obtained from heterogeneous providers. We leverage machine learning techniques to inject automation into these two phases: unsupervised K-Means clustering in the first-level build-time phase to accurately classify IaaS cost and performance data, and polynomial regression during the second-level provisioning-time phase to discover relationships between SaaS performance and container strength.

Empirical study of performance benefits of hardware assisted virtualization

Abstract: An application's performance can suffer from significant computational overheads when it is moved from a native to a virtualized environment. Adoption of virtualization without understanding such overheads in detail can dramatically impact the overall performance of hosted applications. The rapid adoption of virtualization has fueled the development of new hardware technologies, which promise to optimize the performance and scalability of processor and network I/O virtualization. However, no comprehensive empirical study of the effectiveness of these hardware assistance technologies is publicly available. In this paper we focus on x86 architectures and study empirically the performance improvements introduced by Intel's VT and PCI-SIG's SR-IOV on a Xen-based hypervisor. Using a range of benchmark programs, we compare benchmark scores and resource utilization between native and virtual environments for two different testbeds, one with hardware assistance and one without. The results indicate that hardware assistance indeed eliminates most overheads, especially those relating to network I/O, but non-negligible CPU overheads still remain. Also, there is no hardware technology with specifically deals with disk I/O virtualization, and significant overheads do arise in workloads requiring intensive disk usage.

Identification of critical parameters for MapReduce energy efficiency using statistical Design of Experiments

Abstract: Energy efficiency is an important concern for data centers today. Most of these data centers use MapReduce frameworks for big data processing. These frameworks and modern hardware provide the flexibility in form of parameters to manage the performance and energy consumption of system. However tuning these parameters such that it reduces energy consumption without impacting performance is challenging since - 1) there are a large number of parameters across the layers of frameworks, 2) impact of the parameters differ based on the workload characteristics, 3) the same parameter may have conflicting impacts on performance and energy and 4) parameters may have interaction effects. To streamline the parameter tuning, we present the systematic design of experiments to study the effects of different parameters on performance and energy consumption with a view to identify the most influential ones quickly and efficiently. The final goal is to use the identified parameters to build predictive models for tuning the environment. We perform a detailed analysis of the main and interaction effects of rationally selected parameters on performance and energy consumption for typical MapReduce workloads. Based on a relatively small number of experiments, we ascertain that replication-factor has highest impact and, surprisingly compression has least impact on the energy efficiency of MapReduce systems. Furthermore, from the results of factorial design we infer that the two-way interactions between block-size, Map-slots, and CPU-frequency, parameters of Hadoop platform have a high impact on energy efficiency of all types of workloads due to the distributed, parallel, pipe-lined design.

Synchronization of communicating modules and processes in high level synthesis

Abstract: In ASIC designs, reuse of already available components is often preferred. Synthesis systems catering to this need must ensure proper synchronization among the communicating modules. This paper proposes an object oriented design framework to support reuse. The steps to be taken for synchronization of communicating hardware entities through a non-blocking channel have been analyzed. The synthesis system ensures synchronization among the communicating modules before scheduling. The scheme has been tested on a few real life image processing examples.

A Profile Guided Approach to Optimize Branch Divergence While Transforming Applications for GPUs

Abstract: GPUs offer a powerful bulk synchronous programming model for exploiting data parallelism; however, branch divergence amongst executing warps can lead to serious performance degradation due to execution serialization. We propose a novel profile guided approach to optimize branch divergence while transforming a serial program to a data-parallel program for GPUs. Our approach is based on the observation that branches inside some data parallel loops although divergent, exhibit repetitive regular patterns of outcomes. By exploiting such patterns, loop iterations can be aligned so that the corresponding iterations traverse the same branch path. These aligned iterations when executed as a warp in a GPU, become convergent. We propose a new metric based on the repetitive pattern characteristics that indicates whether a data-parallel loop is worth restructuring. When tested our approach on the well-known Rodinia benchmark, we found that it is possible to achieve upto 48% performance improvement by loop restructuring suggested by the patterns and our metrics.

Using Multivariate Statistics

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Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Handbook of software reliability engineering

Abstract: Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.