3,027 research outputs found
Madhumakkhi palan ki uttam kriyayein
No full textNot AvailableMarboh, E.S., Gupta, A.K., Singh, S.K., Kumar, A., and Pongener, A. (2020).
Madhumakhi palan ke uttam kriyayein. NRCL-EB-12. ICAR-National Research Centre on Litchi,
Muzaffarpur (Bihar). Pp-8ICA
Supplemental Material, Appendix_RNR - Combating incumbency advantage of network effects: The role of entrant’s decisions and consumer preferences
No full textSupplemental Material, Appendix_RNR for Combating incumbency advantage of network effects: The role of entrant’s decisions and consumer preferences by Agam Gupta, Arqum Mateen, Divya Sharma, Uttam K. Sarkar, and Vinu Cheruvil Thomas in Competition and Regulation in Network Industries</p
Cost-Efficient Live VM Migration Based on Varying Electricity Cost in Optical Cloud Networks
No full textOn service-chaining strategies using Virtual Network Functions in operator networks
No full textNetwork functions (e.g., firewalls, load balancers, etc.) have been traditionally provided through proprietary hardware appliances. Often, hardware appliances need to be hardwired back-to-back to form a service chain providing chained network functions. These hardware appliances cannot be provisioned on-demand since they are statically embedded in the network topology, making creation, insertion, modification, upgrade, and removal of service chains complex, and also slowing down service innovation. Hence, network operators are starting to deploy Virtual Network Functions (VNFs), which are virtualized over commodity hardware. VNFs can be deployed in Data Centers (DCs) or in Network Function Virtualization (NFV)-capable network elements (nodes) such as routers and switches. NFV-capable nodes and DCs together form a Network-enabled Cloud (NeC) that helps to facilitate the dynamic service chaining required to support today's evolving network traffic and its service demands. In this study, we focus on the VNF service-chain placement and traffic routing problem, and build a model for placing a VNF service chain while minimizing network-resource consumption. Further, we study the network-resource consumption of various service-chaining strategies, which can be a NeC having a varying number of NFV-capable nodes or a proprietary hardware solution. Our results indicate that a NeC having a DC and NFV-capable nodes can significantly reduce network-resource consumption
Improving reliability and security monitoring in enterprise and cloud systems by leveraging information redundancy
No full textAs computing has become critical to all areas of modern life, the need to ensure the security and reliability of the underlying information technology infrastructures is greater than ever before. Large-scale enterprise and cloud systems, which form the backbone for the majority of computing activity, consist of many components and services interacting in complex and sometimes unpredictable ways. As such systems have grown in size, scale, and complexity, they have become increasingly difficult to protect against security and reliability incidents, resulting over recent years in ever more frequent service disruptions, failures, and data breaches, the financial and societal implications of which are massive. System owners have a strong desire to prevent such incidents.
Incident detection and response and compliance audit are the two primary mechanisms by which organizations enforce reliability and security policies and make their systems more resilient. Both the academic and professional communities have focused considerable attention on developing techniques to improve incident detection, incident root cause analysis, and compliance auditing, often with little consideration for the cost of the monitoring that is required to support them. Furthermore, as the scale and complexity of systems have increased, so too have the scale and complexity of their monitoring infrastructures. Monitors can fail or be compromised, and monitor data must be selectively collected to avoid exceeding storage and processing limits. Consequently, it has become increasingly important to explicitly consider the efficiency, efficacy, and resiliency of monitoring systems when one is designing large-scale enterprise and cloud systems.
In this dissertation, we address inefficiencies and inadequacies in reliability and security monitoring in enterprise and cloud systems by leveraging redundancy of information across diverse monitors. In particular, we use the redundancy of data generated by different monitors 1) to facilitate more effective and efficient use of the data in meeting reliability and security objectives, and 2) to improve the resiliency of the monitoring infrastructure itself against failures and attacks.
First, we present a framework for simplifying the complexity of data analysis for incident response in enterprise cloud systems. As a foundation for the framework, we define a general taxonomy for fields within monitor data that administrators can use to label both structured and unstructured components of data. We then present a method to automatically extract time series features based on labels from our taxonomy, remove uninformative features, and reduce the overall number of features by clustering together related and redundant features. We apply our framework to logs and metrics collected during reliability incidents from all levels of an experimental platform-as-a-service cloud at a large computing organization, and demonstrate that our approach enables efficient coordinated analysis of both metric data and log data. Such analysis typically presents a challenge to cloud support engineers, but can identify meaningful relationships between features that can aid in incident response.
Next, we present a systematic methodology that enables system administrators to design monitoring systems that are resilient to missing data. We develop a model-based approach to quantify the resilience of a system's monitoring and incident detection infrastructure design against missing data, using which we develop a method to find monitor deployments that maximize resilience subject to monitoring cost constraints. We illustrate how our approach can be applied to production systems by using a datacenter network case study model based on monitors employed in production systems, and we evaluate its scalability by using randomly generated models of varying sizes and structures. We compare our approach to the current state of the art and demonstrate that our approach consistently finds monitor deployments that are more resilient under the same constraints.
Finally, we address the inefficiencies faced by a cloud service provider (CSP) during audit evidence collection as a result of a poor understanding of evidence requirements. We motivate our analysis by developing a taxonomic framework for understanding the causes of and potential solutions to uncertainty in audit. We present a model-driven method to learn evidence sufficiency requirements directly from historical audit records. We then apply our cost-optimal resilient monitoring approach to the evidence sufficiency model to determine an efficient evidence collection strategy for the CSP. We apply our approach to the historical audit records from an enterprise infrastructure-as-a-service cloud system at a large computing organization and demonstrate how use of our approach could have enabled more efficient evidence collection.
We believe that our work clearly demonstrates the need to critically examine the resiliency and efficiency of monitoring infrastructures in enterprise and cloud systems. This dissertation presents solutions to specific challenges faced by practitioners when monitoring their systems for reliability and security objectives, but our work addresses only part of the larger problem space of resilient monitoring system design. We hope that this dissertation paves the way for future research that focuses on the resilience of the monitoring infrastructure itself.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01The student, Uttam Thakore, accepted the attached license on 2020-12-03 at 08:54.The student, Uttam Thakore, submitted this Dissertation for approval on 2020-12-03 at 08:58.This Dissertation was approved for publication on 2020-12-03 at 11:02.DSpace SAF Submission Ingestion Package generated from Vireo submission #16040 on 2021-03-04 at 16:33:21Made available in DSpace on 2021-03-05T21:47:28Z (GMT). No. of bitstreams: 2
THAKORE-DISSERTATION-2020.pdf: 2266283 bytes, checksum: b4cc0c2badfd2ca4a755495c13634459 (MD5)
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Previous issue date: 2020-12-03Embargo set by: Seth Robbins for item 117329
Lift date: 2023-03-05T21:47:41Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemAuthor requested closed access (OA after 2yrs) in Vireo ETD systemLimite
Data for Gupta et al., "Estimating the Meridional Extent of Adiabatic Mixing in the Stratosphere using Age-of-Air", JGR:Atmospheres,
No full textModel data and post-processed data supporting the creation of the manuscript "Estimating the Meridional Extent of Adiabatic Mixing in the Stratosphere using Age-of-Air" submitted to JGR:Atmospheres in August 2022.
1) The netCDF files created through post-processing of full model data in FORTRAN are shared in the /data/ directory. These file contains the zonal mean circulation statistics based on Gupta et al. (2020), age-of-air transport diagnostics based on Linz et al. (2021), and the novel \Gamma-\Theta circulation streamfunction introduced in this study. The /data/ directory also contains MATLAB .mat data files for the transport diagnostics obtained from WACCM. 150 days of actual GFDL-FV3 model data in the northern hemisphere, between 0.1 hPa-500 hPa pressure levels is also provided to support external computations and validation.
2) The Jupyter notebook used for final computation and figures production is provided in .ipynb, .html and .pdf formats in /code/. All the files referred to in the notebook are stored in the /data/ directory.
Corresponding author : Aman Gupta, [email protected], [email protected], [email protected]
Corrigendum: Capital Inflows and House Prices: Aggregate and Regional Evidence from China
No full textIn the paper ‘Capital Inflows and House Prices: Aggregate and Regional Evidence from China’ by H. An, et al., printed in the December 2016 issue, there was a missing acknowledgement section for funding resources.
On page 451, the acknowledgement section should appear after the corresponding information as:
“Correspondence: Rakesh Gupta, Department of Accounting, Finance and Economics, Griffith Business School, Griffith University, Nathan Campus QLD 4111. [email protected]
*This work was financially supported by the Humanities and Social Science Foundation of Ministry of Education of China (16YJA790001).”
The author apologises for this error and any confusion it may have caused.No Full Tex
First person – Akash Gupta
No full textFirst Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping early-career researchers promote themselves alongside their papers. Akash Gupta is first author on ‘A novel and cost-effective ex vivo orthotopic model for the study of human breast cancer in mouse mammary gland organ culture’, published in BiO. Akash conducted the research described in this article while a PhD Scholar in Rajendra Mehta's lab at IIT Research Institute, Chicago, USA. He is now an assistant research scientist in the lab of Syreeta L. Tilghman at the University of Arizona, Department of Medicine, Tucson, USA, investigating drug efficacy modeling using human organoids culture for the treatment of cancers
Engineering materials : research, applications and advances / author, K.M. Gupta.
No full text"A CRC title."Includes bibliographical references and index.596 p.
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