
Anirban BhattacharjeeNational Institute of Standards and Technology | NIST · Engineering Laboratory
Anirban Bhattacharjee
Doctor of Philosophy
About
23
Publications
3,307
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385
Citations
Introduction
Interest and expertise lie in Cloud Computing, Distributed Systems, Machine Learning Infrastructure Designing, Cloud/Infrastructure Automation, Data Analytics, and Optimization techniques.
Additional affiliations
February 2020 - present
Education
August 2012 - February 2020
Publications
Publications (23)
An increasing number of interactive applications and services, such as online gaming and cognitive assistance, are being hosted in the cloud because of the elastic properties and cost benefits of distributed data centers. Despite these benefits, the longer and often unpredictable end-to-end network latencies between the end user and the cloud can b...
Deep Learning (DL) model-based AI services are increasingly offered in a variety of predictive analytics services such as computer vision, natural language processing, speech recognition. However, the quality of the DL models can degrade over time due to changes in the input data distribution, thereby requiring periodic model updates. Although clou...
Many IoT applications found in cyber-physical systems, such as smart grids, must take control actions in response to critical events, such as supply-demand mismatch, which requires low-latency processing of streaming data for rapid event detection and anomaly remediation. These streaming applications generally take the form of directed acyclic grap...
Services hosted in multi-tenant cloud platforms
often encounter performance interference due to contention for
non-partitionable resources, which in turn causes unpredictable
behavior and degradation in application performance. To grapple
with these problems and to define effective resource management
solutions for their services, providers often m...
With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task. Furthermore, with a plethora of cloud-based ML model development platforms, heterogeneity in hardware,...
Services hosted in multi-tenant cloud platforms often encounter performance interference due to contention for non-partitionable resources, which in turn causes unpredictable behavior and degradation in application performance. To grapple with these problems and to define effective resource management solutions for their services, providers often m...
Users of cloud platforms often must expend significant manual efforts in the deployment and orchestration of their services on cloud platforms due primarily to having to deal with the high variabilities in the configuration options for virtualized environment setup and meeting the software dependencies for each service. Despite the emergence of man...
With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task. Furthermore, with a plethora of cloud-based ML model development platforms, heterogeneity in hardware,...
Pre-trained deep learning models are increasingly being used to offer a variety of compute-intensive predictive analytics services such as fitness tracking, speech and image recognition. The stateless and highly parallelizable nature of deep learning models makes them well-suited for serverless computing paradigm. However, making effective resource...
Accurately analyzing the sources of performance anomalies in cloud-based applications is a hard problem due both to the multi tenant nature of cloud deployment and changing application workloads. To that end many different resource instrumentation and application performance modeling frameworks have been developed in recent years to help in the eff...
Although many provisioning tools are available for deployment and management of composite cloud services to overcome the manual efforts that are tedious and error-prone, users are often required to specify Infrastructure-as-Code (IAC) solutions via low-level scripting. IAC demands domain knowledge for provisioning the services across heterogeneous...
Users of cloud platforms often must expend significant manual efforts in the deployment and orchestration of their services on cloud platforms due primarily to having to deal with the high variabilities in the configuration options for virtualized environment setup and meeting the software dependencies for each service. Despite the emergence of man...
As distributed systems become more complex, understanding the underlying algorithms that make these systems work becomes even harder. Traditional learning modalities based on didactic teaching and theoretical proofs alone are no longer sufficient for a holistic understanding of these algorithms. Instead, an environment that promotes an immersive, h...
In modern networked control applications, confidentiality and integrity are important features to address in order to prevent against attacks. Moreover, network control systems are a fundamental part of the communication components of current cyber-physical systems (e.g., automotive communications). Many networked control systems employ Time-Trigge...
An important challenge in networked control systems is to ensure the confidentiality and integrity of the message in order to secure the communication and prevent attackers or intruders from compromising the system. However, security mechanisms may jeopardize the temporal behavior of the network data communication because of the computation and com...