About
43
Publications
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Introduction
Chinmaya Dehury currently works at the Department and Graduate Institute of Computer Science and Information Engineering, FUN Lab, Chang Gung University. Chinmaya does research in Parallel Computing, Operating Systems and Algorithms. Their current project is 'Design and Implementation of Data Processing Algorithms for Analyzing Real Time Big Data in Cloud'.
Current institution
Additional affiliations
April 2019 - present
Position
- PostDoc Position
Description
- Working in RADON project (Horizon 2020 EU program) in Mobile and Cloud Lab.
September 2013 - January 2019
Position
- PhD Student
Description
- Artificial Intelligence & Big Data Computing Lab http://abc.csie.cgu.edu.tw/
Publications
Publications (43)
Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving demand for mobile device support. However, existing mobile inference frameworks often rely on a single processor per model, limiting hardware utilization and causing suboptimal performance and energy efficiency. Expanding DNN accessibility on mobile platforms r...
This special issue is a collection of emerging trends and challenges in applying learning-driven approaches to data fabric architectures within the cloud-to-thing continuum. As data generation and processing increasingly occur at the edge, there is a growing need for intelligent, adaptive data management solutions that seamlessly operate across dis...
Streaming applications are becoming widespread across an extensive range of business domains as an increasing number of sources continuously produce data that need to be processed and analysed in real time. Modern businesses are aggressively using streaming data to generate valuable knowledge that can be used to automate processes, help decision-ma...
Sharing Internet of Things (IoT) data across different sectors, such as in smart cities, becomes complex due to heterogeneity. This poses challenges related to a lack of interoperability, data quality issues and lack of context information, and a lack of data veracity (or accuracy). In addition, there are privacy concerns as IoT data may contain pe...
Deep neural networks (DNNs) have witnessed rapid advancements and remarkable success in recent years, leading to their increasingly widespread implementation on edge devices. However, the deployment, execution, and life cycle management of traditional artificial neural networks (ANNs) on resource-constrained edge devices present significant challen...
In intelligent transport systems (ITS), an interconnected Internet of things (IoT) device operates autonomously, collecting and exchanging data without human intervention. While the ITS offers numerous benefits, it also introduces various cybersecurity risks. These risks include the potential for undetected malicious IoT sensors, vulnerabilities in...
Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources. This transformation presents substantial challenges to network administration and service providers regarding sustainability and...
Ubiquitous edge computing facilitates efficient cloud services near mobile devices, enabling mobile edge computing (MEC) to offer services more efficiently by presenting storage and processing capability within the proximity of mobile devices and in general IoT domains. However, compared with conventional mobile cloud computing, ubiquitous MEC intr...
![Figure 1. The SDVN architecture [16].](publication/373093356/figure/fig1/AS:11431281181535664@1692058128411/The-SDVN-architecture-16_Q320.jpg)
![Figure 6. The general architecture for iSDHVN [112].](publication/373093356/figure/fig3/AS:11431281181543557@1692058128987/The-general-architecture-for-iSDHVN-112_Q320.jpg)
![Figure 7. Architecture for intelligent digital twin SDVN [62,70,72,73].](publication/373093356/figure/fig4/AS:11431281181514818@1692058129176/Architecture-for-intelligent-digital-twin-SDVN-62-70-72-73_Q320.jpg)
Digital twins and the Internet of Things (IoT) have gained significant research attention in recent years due to their potential advantages in various domains, and vehicular ad hoc networks (VANETs) are one such application. VANETs can provide a wide range of services for passengers and drivers, including safety, convenience, and information. The d...
By bringing computing capacity from a remote cloud environment closer to the user, fog computing is introduced. As a result, users can access the services from more nearby computing environments, resulting in better quality of service and lower latency on the network. From the service providers' point of view, this addresses the network latency and...
In recent years, renewable energies (RE) have gained more attention as they provide clean and sustainable energy. Affordable and clean energy is one of the sustainable development goals (SDG-7) of the UN. Solar energy is considered to be one of the world's most abundant renewable natural resources and can certainly contribute to meeting the SDGs. T...
Advancements in Internet and Telecommunications (ICT) accelerated the large-scale deployments of IoT applications including smart city, smart healthcare, and smart factory aiming for faster data processing. The latency and other challenges of cloud-centric IoT, driven edge computing-based data processing architectures. In precise, IoT data analytic...
The devices at the edge of a network are not only responsible for sensing the surrounding environment but are also made intelligent enough to learn and react to the environment. Clustered Edge Intelligence (CEI) emphasizes intelligence-centric clustering instead of device-centric clustering. It allows the devices to share their knowledge and events...
Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (self-monitoring, analysis, and reporting technology) hard drive metrics with other system metrics, such as...
The proposed edited book is going to cover the relationship of recent technologies (such as Blockchain, IoT, and 5G) with the cloud computing as well as fog computing, and mobile edge computing. The relationship will not be limited to only architecture proposal, trends, and technical advancements. The book will also explore power of predictive anal...
With the increasing number of Internet of Things (IoT) devices, massive amounts of raw data is being generated. The latency, cost, and other challenges in cloud-based IoT data processing have driven the adoption of Edge and Fog computing models, where some data processing tasks are moved closer to data sources. Properly dealing with the flow of suc...
The serverless platform allows a customer to effectively use cloud resources and pay for the exact amount of used resources. A number of dedicated open source and commercial cloud data management tools are available to handle the massive amount of data. Such modern cloud data management tools are not enough matured to integrate the generic cloud ap...
With the increasing number of Internet of Things (IoT) devices, massive amounts of raw data is being generated. The latency, cost, and other challenges in cloud-based IoT data processing have driven the adoption of Edge and Fog computing models, where some data processing tasks are moved closer to data sources. Properly dealing with the flow of suc...
Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (Self-Monitoring, Analysis, and Reporting Technology) hard drive metrics with other system metrics such as...
The applications that are deployed in the cloud to provide services to the users encompass a large number of interconnected dependent cloud components. Multiple identical components are scheduled to run concurrently in order to handle unexpected failures and provide uninterrupted service to the end user, which introduces resource overhead problem f...
The recent advances in virtualization technology have enabled the sharing of computing and networking resources of cloud data centers among multiple users. Virtual Network Embedding (VNE) is highly important and is an integral part of the cloud resource management. The lack of historical knowledge on cloud functioning and inability to foresee the f...
In Cloud Computing, the tenants opting for the Infrastructure as a Service (IaaS) send the resource requirements to the Cloud Service Provider (CSP) in the form of Virtual Network (VN) consisting of a set of inter-connected Virtual Machines (VM). Embedding the VN onto the existing physical network is known as Virtual Network Embedding (VNE) problem...
The serverless platform allows a customer to effectively use cloud resources and pay for the exact amount of used resources. A number of dedicated open source and commercial cloud data management tools are available to handle the massive amount of data. Such modern cloud data management tools are not enough matured to integrate the generic cloud ap...
The applications that are deployed in the cloud to provide services to the users encompass a large number of interconnected dependent cloud components. Multiple identical components are scheduled to run concurrently in order to handle unexpected failures and provide uninterrupted service to the end user, which introduces resource overhead problem f...
Fog computing is introduced by shifting cloud resources towards the users' proximity to mitigate the limitations possessed by cloud computing. Fog environment made its limited resource available to a large number of users to deploy their serverless applications, composed of several serverless functions. One of the primary intentions behind introduc...
Computer vision is a pioneering sub-field of artificial intelligence that is used in computers for throwing a light on the visual world and better understanding of it. In crucial times like COVID-19, computer vision is used to combat all the challenges that are been faced. In healthcare field, computer vision has been used to enhance the productivi...
Consider we are living in a remote place which is far away from any near-by hospitals or we don’t have enough time to take a leave from the work to visit a hospital or we can’t afford the rapidly increasing medical services costs. This is one of the few scenarios where we can relate to the fact that computer systems and algorithms if they may help...
To provide cost effective cloud resources with high QoS, serverless platform is introduced that allows to pay for the exact amount of resource usage. On the other hand, a number of data management tools are developed to handle the data from a large number of IoT sensing devices. However, the modern data-intensive cloud applications require the powe...
In Cloud Computing, the tenants opting for the Infrastructure as a Service (IaaS) send the resource requirements to the Cloud Service Provider (CSP) in the form of Virtual Network (VN) consisting of a set of inter-connected Virtual Machines (VM). Embedding the VN onto the existing physical network is known as Virtual Network Embedding (VNE) problem...
The amount of data generated by millions of connected IoT sensors and devices is growing exponentially. The need to extract relevant information from this data in modern and future generation computing system, necessitates efficient data handling and processing platforms that can migrate such big data from one location to other locations seamlessly...
Virtual Network Embedding (VNE) is the process of embedding the set of interconnected virtual machines onto the set of interconnected physical servers in the cloud computing environment. The level of complexity of VNE problem increases when large number of virtual machines with a set of resource demand need to be embedded onto a network of thousand...
Virtualization technology boosts up traditional computing concept to cloud computing by introducing Virtual Machines (VMs) over the Physical Machines (PMs), which enables the cloud service providers to share the limited computing and network resources among multiple users. Virtual resource mapping can be defined as the process of embedding multiple...
With advent of new technologies, we are surrounded by several tiny but powerful mobile devices through which we can communicate with the outside world to store and retrieve data from the Cloud. These devices are considered as smart objects as they can sense the medium, collect data, interact with nearby smart objects, and transmit data to the cloud...
We propose an implementation of Radix Sort algorithm with radix 2. In this paper we are trying to optimize the time as well as space taken by Radix sort algorithm to sort a large data set. Though the proposed algorithm and Radix sort both required a space complexity O(n), but implementation of the new proposed algorithm reduces the space by 10 time...
Questions
Questions (7)
Southbound interface is the bridge between Control plane and Data plane. C-DPI is also the interface between Control and Data plane. Are both the terms functionally synonym to each other?
Today I came across a different data center containing thousands of servers, like OVH from Europe containing more than 1 million physical servers spreaded over 8 DCs. So what actually do these servers consists of, in terms of hardware and software.
In many paper related to Cloud Computing, I come across the terms virtual link and virtual node. So what do these two terms refers to?
From the link "http://archives.opennebula.org/documentation:rel4.4:schg", I came to know that Match-making scheduler is used in OpenNebula for scheduling VMs onto physical processing nodes. But I am unable to find how a large number of small requests or data-intensive jobs or compute-intensive scientific requests are being served. In other words how are incoming jobs mapped to VMs?
What is the meaning of Cloudlet in Cloudsim? Is it similar to an incoming job from user?
Is there any similarity between a job scheduler and a parallel data processing framework? As I know a job scheduler receives a set of jobs and based on some algorithm sends them to start their execution. In contrast a data processing framework receives a job and executes in such a way that resources can be utilized efficiently and makespan of the job can be minimized. It seems there is a relation between the two.
Is there any alternative of MapReduce programming model? There are so many frameworks at present based on MapReduce like Google's MapReduce, Microsoft's Dryad, Yahoo's Map-Reduce-Merge. Is there any other framework available in Cloud industry?
