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This paper mainly studies the financial data management method and edge computing platform based on intelligent edge computing and big computing. This paper first introduces the theoretical basis of edge computing and big data computing, analyzes the collaborative mode of edge computing model and cloud computing, and discusses the cloud data storage scheme. In this paper, edge computing platform is built, and EC Master and Docker private image warehouse are mainly deployed in the data center. In this paper, the edge computing platform constructed was tested. In the mobile edge computing business processing time test, when there was a big gap in computing amount between business 1 and business 2, the total service time of MEC instances was around 0.3 s, showing a good user experience. In the edge acceleration effect test, before and after the adoption of CDN edge acceleration, from the perspective of the overall web page, the average time consumed to open a web page decreased from 1615 ms to 403 ms. From requesting a single HTTP access request, the average time decreased from 520 ms to 74 ms. The financial data management edge computing platform designed in this paper can effectively manage financial data and make an effective contribution to enterprise data management. [ABSTRACT FROM AUTHOR]

Due to the unprecedented growth of data, edge computing is transforming our lives in a better manner by increasing the performance and reducing the latency. It is the key framework deployed close to the data for processing effectively by connecting millions of devices together. Though it handles accelerated growth of data efficiently in real-time, data security and edge device authentication are still a nightmare. On the contrary, it is not possible to use complex protocols for authentication of edge devices due to its resource-constrained in nature. To address this issue, a state-of-art Hyperelliptic Curve Diffie–Hellman-based two-server Password-Only Authenticated Key Exchange Protocol is proposed in this paper by utilizing the properties of a geometrical shape, namely, tetrahedron. As a primary mandate, edge devices are authenticated to centralize cloud servers that store password information as tetrahedron properties using Hyperelliptic Curve Cryptography and Diffie–Hellman key exchange mechanisms. The proposed work is compared with similar existing works and proves its strength by considering communication cost, computational cost, security defense rate, and attack resistance rate as its core evaluation parameters. [ABSTRACT FROM AUTHOR]

Following the considerable development of big data and the need for real-time information processing, the recent computing paradigm changed from centralized intelligence on cloud computing to the distributed intelligence on Edge Computing. On the algorithm layer, Artificial Intelligence (AI) has been applied in different areas related to intelligent-edge applications. In this type of application, consumption and speed present essential challenges when designing hardware-level architecture on the embedded systems. In particular, an Edge Computing System (ECS) must be an energy efficient, compact, and fast component integrated into edge devices. Miniaturization has been happening at a steady pace for the last years. However, the MOS transistor size has been slowly reaching the physical limit. In this paper, we propose a Quantum Cellular Automata clock-phase-based technique for the design of the proposed ALU module. The obtained results using the QCA architecture designer exhibit better performances over the existing designs in terms of size, cell number, cost, latency, and power consumption. We have also used QCAPro software for power consumption estimation. The results show the effectiveness of the design in terms of cost and power. [ABSTRACT FROM AUTHOR]

The article titled "An Edge Computing Unloading Algorithm for IIoT-based Mobile and Internet of Vehicles (IoV) Applications" has been retracted by the Editor, Institution, and Publisher of the IETE Journal of Research. This retraction is a result of concerns raised about the peer review and decision-making processes for the special issue in which the article was published. An investigation by the Taylor & Francis Publishing Ethics & Integrity team confirmed that the articles in the special issue were not reviewed appropriately. The journal is committed to correcting the scientific record and will cooperate with any institutional investigations. The retracted articles will remain online but will be digitally watermarked as "Retracted." [Extracted from the article]

In the present literature, various algorithms are available for computing the shortest path between two objects. The maximum number of these algorithms compute the shortest path either between two static objects or one static object and one dynamic object. This article presents an insight to integrated Mobile Edge Computing (MEC) based smart devices for tracking mobile rogue objects based on dynamic source and dynamic destination optimal cost estimation. This device considers any two mobile objects to estimate the shortest path between them. The proposed Ant Colony Optimization (ACO) based algorithm considers the property of dead-end removal and nth path exploration with efficient self-loop removal strategy. To review the performance of the proposed algorithm, experimentations are carried out and compared with several well-established shortest cost estimation techniques available in the literature – Floyd Warshall, Bellman Ford, Dijkstra, A* algorithms and the only dynamic shortest path algorithm. The detailed algorithmic comparisons clearly indicate the superiority of the proposed one over the existing dynamic and present state-of-the-art shortest path estimation methodologies. [ABSTRACT FROM AUTHOR]

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled "Federated Learning for Blockchain Systems and Industrial Internet of Things": Wenfan Wang & Junzheng Li (2022) An Edge Computing Unloading Algorithm for IIoT-based Mobile and Internet of Vehicles (IoV) Applications, IETE Journal of Research, DOI: Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as "Retracted". [ABSTRACT FROM AUTHOR]

Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for analysis. However, this process can worsen the network route to the traffic management centre. A two-tier mechanism has been developed to address this issue, which performs vehicle speed estimation and traffic congestion detection for efficient traffic management. The real-time traffic video data are captured and the video frames are initially processed through a foreground extraction process, which extracts the temporarily stopped vehicles on the road by removing background pixels from the frames. The video frames are then wrapped in an up-down view to remove the influence of the observation angle. The traffic congestion is then detected accurately based on the traffic characteristics using the proposed Ensemble Random Forest-based Gradient Optimization (ERF-GO) algorithm. The generalization error occurs when learning complex features on frames is minimized using a gradient-based optimization (GO) algorithm. Finally, the learned information on traffic conditions is forwarded to the cloud and edge computing environments based on network connection speed. The efficiency of the proposed ERF-GO is investigated in terms of performance metrics, namely root mean square error, speed detection error, execution time, computational cost, accuracy, latency, workload balance, precision, recall, f-measure, and congestion detection error rate. The analytic result displays that the proposed ERF-GO algorithm attains a greater accuracy rate of about 98.65% in detecting traffic congestion which is comparably higher than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The communication services of multimedia, such as live online and short videos, gained significant attention and have been prominent in daily social interactions due to the fast development of 5G technology. However, the increase in multimedia communication requirements presents further difficulties for both the communication capacity of the wireless network and its processing capability. Mobile Edge Computing (MEC) is considered an effective technology to overcome the mentioned challenges. The subject of long-term task allocation including resource coordination is the primary focus of this study, with a special emphasis on multimedia services that must be processed, uploaded, and shared in network. The optimization issue is formulated as a stochastic optimization issue to reduce the system's time-average energy consumption. An online energy-efficient task allocation and computing offloading method is proposed to determine task allocation, coordinate and optimize wireless, computing resource allocation, considering dynamic wireless state and service delay constraints. The proposed method is implemented in MATLAB and the efficacy of the MCO-VEA framework is estimated and performance metrics, such as minimum user rate, sum rate, energy, and spectral performance are analyzed. Then, the performance of MEC-MCO-VEA provides 22.35%, 25.84% and 36.58% higher throughput and 25.45%, 41.28%, and 28.56% higher energy efficiency compared to the existing models, such as an energy-effective multimedia task assignment with computing offloading for mobile edge computing (BA-LO-EE), energy efficiency using a communication deep neural network optimized with power allocation optimization (EE-CDNN-PAO), and energy efficiency using deep transfer deterministic policy gradient with deep deterministic policy gradient algorithm (DT-DPG-EE), respectively. [ABSTRACT FROM AUTHOR]

This study presents an innovative architecture leveraging Deep Reinforcement Learning (DRL) to optimize online service delivery in Mobile Edge Computing (MEC) networks. Our proposed method, the DRL-based dynamic Service Function Chain (SFC) placement with parallelized Virtual Network Functions (VNFs), aims to maximize Long-Term Cumulative Reward (LTCR). By parallelizing VNFs, our approach achieves computational acceleration, enhancing the efficiency of online service provisioning. Furthermore, we enhance future request processing capabilities by extracting the distribution of initialized VNFs. Through comprehensive simulations, our proposed architecture demonstrates a notable improvement, with approximately a 9.5% increase in the average number of accepted requests. [ABSTRACT FROM AUTHOR]

Accurately identifying all objects of interest inside the specified frame of reference is crucial for object identification techniques to allow machine vision to interpret images successfully. Theoretical frameworks from computer vision and deep learning have informed many potential solutions to this problem. However, current approaches frequently fail when faced with objects going through random geometric changes and continually show shortcomings when identifying small, dense objects. This research looks at state-of-the-art object detection methods, compares them, and then suggests a convolutional object detection network that has been tweaked to fix the problems with the existing methods. Comparing our research to existing approaches, we find that they perform better. We accomplish this by training deep convolutional networks to detect geometric transformations and adjusting the networks to handle multi-scaled features. The results achieved after the optimization of the You only look once (YOLO) V5 & V7 are stated in this paper. The experiments demonstrated that YOLO v8 achieved higher accuracy than the hyper-tuned YOLO V5 and V7, with an average Precision (mAP) score of 52.8% on the customized military dataset running at 60 Frames Per Second (FPS). Object detection using deep neural networks (DNNs) poses a significant challenge due to the high computational and power requirements, mainly when deployed on general-purpose platforms such as CPUs and GPUs. However, addressing these limitations becomes crucial for efficient edge computing tasks like object detection, where faster, smaller, and energy-efficient solutions are essential. To overcome these challenges, system-on-chip (SoC) designs emerge as a promising solution. [ABSTRACT FROM AUTHOR]

Conventional lifestyles and working methods are motivated by rising trends and new technologies to form smart cities. Distributed chains are growing to computerize with a highly complicated framework. It is becoming an essential source of additional advantages in today's digital and smart city. Customers or users of smart cities are highly concerned about the quality of food and farm products. On the other hand, tracking the source information is challenging and, preserving its provenance throughout the supply chain network. Traditional supply chain networks are centralized, where a third party does the trading. These centralized systems lack of quality transparency and distribution accountability. Therefore, we provide a promising approach with a unique strategy for converting conventional agriculture to smart agriculture, concerning smart cities, considering blockchain technology and the Internet of Things (IoT) features. The blockchain technique is responsible for maintaining the immutability of the existing network. Still, it faces problems resolving fundamental issues in the distribution chain, like the trustworthiness of the different involved members, transaction responsibility, and real-time tracking. As a result, a trustworthy organization with traceability, conviction, confirmed transportation, and transparent mechanisms in the e-farm supply chain is proposed. Ethereum is used to deploy the proposed smart contract. Testing with the Hyperledger caliper measuring tool shows performance evaluation of several factors such as latency, throughput, resource consumption transaction per second, and so on are assessed. The findings support the effectiveness of the recommended strategy. [ABSTRACT FROM AUTHOR]

In this article, a smart city architecture with dew computing and machine-to-machine (M2M) communication for the Internet of Things (IoT) is proposed. IoT devices are directly connected to the machines and these machines contain a small amount of computing unit, called dew. The machines combine to form clusters based on distances through a coalition formation game and the machines within the cluster perform the tasks of the IoT nodes. The dew located within each machine can complete a maximum threshold amount of task. If the task exceeds that, then it is transferred to another machine connected to it via M2M communication. In DewCityGame, delay and power consumption of the proposed network are calculated. The delay and power consumption of the proposed optimized dew-enabled M2M communication network has been compared with other non-optimized dew-enabled M2M communication and non-M2M communication network. The proposed optimized dew-enabled M2M communication network has generated ∼15% to 42% less delay and ∼20% to 50% less power consumption than non-optimized dew-enabled M2M communication and non-M2M communication network. [ABSTRACT FROM AUTHOR]

Power load forecasting in Data Analytics is an emerging technology. In this paper, we have proposed the Generative Adversarial Networks (GANS) neural network model as the classifier for probabilistic electricity price forecasting. To assess the performance of these frameworks, we apply our models on the dataset cater by (IESO) in Ontario, Canada. We have compared our proposed model with Random Forest, Support vector machine (SVM), and XG-Boost. MSE, RMSE, MAE metrices are considered for the evaluation of the model's performance. The outcome indicates that the mean squared error (MSE) of our proposed model is 687.513 whereas the MSE of existing methodologies is 830.15, 746.812, and 776.201 which is more than our proposed methodology. Mean absolute error (MAE) of SVM and our proposed GANS Neural Network (EPFEG) have the lowest MAE that is 8%. Furthermore, EPFEG achieved almost 7% better accuracy than existing schemes. [ABSTRACT FROM AUTHOR]

Cloud computing uses a network of remote servers hosted on the Internet to store, manage, and process data. As the cloud is explicitly unsafe, from the time the cloud platform was utilized for storage many researchers and analysts have been trying to develop many encryption algorithms to provide excellent security on the data. The algorithms' weaknesses, performance and strength should be known to apply a suitable cryptography algorithm to an application. This paper evaluates the efficiency of the encryption techniques AES, DES, 3DES, Blowfish, RC4 and RSA on the basis of image data security and performance in the cloud environment. The algorithms have been subjected to different analytic tests such as differential cryptanalysis, statistical analysis, key sensitivity analysis, and performance analysis to analyses the efficiency of each algorithm for security and performance. [ABSTRACT FROM AUTHOR]

Fourier analysis is well known to provide complete information of the frequencies present in a signal. But in the process, time information is lost. Therefore, its time-frequency representation is required for depicting both time and frequency information simultaneously. Therefore in this paper, fractional wavelet transform (FrWT) is proposed to be used for the first time for extracting the features of various datasets in a standard ECG database by combining the advantages of both fractional domain techniques and wavelets as case-II. Afterwards, Probabilistic Principal Component Analysis (PPCA) is used for detecting R-peaks for diagnosing heart abnormalities in various morphologies of the ECG signal. The proposed technique has been evaluated on the basis of sensitivity (SEN), detection error rate (DER), and positive predictivity (PPR) (of the detected ECG beats) for MIT-BIH Arrhythmia database (M/B Ar DB). Even though both FrFT and FrWT techniques exhibit a high degree of robustness, but SEN of 99.99%, DER of 0.026%, & PPR of 99.99% obtained by latter in case-II are better than SEN of 99.97%, DER of 0.053%, & PPR of 99.98% obtained by the former in case-I for M/B Ar DB. In this paper, average time error (ATE) is also obtained for the considered datasets establishing the effectiveness of the proposed technique further. These encouraging results suggest that the proposed methodology will go a long way in assisting the cardiologists to detect temporal patterns in a wide variety of electrophysiological cases, which is important for improved management of healthcare system. [ABSTRACT FROM AUTHOR]

The evolution of wireless networks towards high mobility has led to the concept of connecting vehicles. Wireless link quality is a vital attribute of a dynamic vehicular environment and a backbone for successful communication among vehicles. The vehicles are expected to be equipped with a number of sensors and measurement devices, making a lot of data available for research. To solve the challenges in the context of the connectivity of vehicles, data-driven approaches are recently applied to estimate the dynamic attributes of vehicular networks. In this paper, a deep learning model based on an artificial neural network technique is proposed to predict wireless link quality in vehicular networks. The proposed algorithm uses the essential features, including the current location of a vehicle, to estimate the quality of a wireless link. [ABSTRACT FROM AUTHOR]

Monitoring the farm field data parameters is one of the important criteria for farmers to keep track of the cultivations and its progress. Internet of Things (IoT) powered system can be implemented in the farm field, with the help of sensors to sense the data from the farm field in the Real-Time cloud. The data collected at the farm field can be accepted after proper authentication from the farmer. Because of heterogeneous and dynamic nature of IoT network, it poses a tremendous threat to security and privacy. Authentication is one of the IoT environment's most demanding security requirements, where users can straightforwardly get to data from any sort of gadgets; we need to implement a mutual authentication mechanism between user and the gadgets. This paper illustrates how the protection of the message stage is achieved in IoT associations and presents an algorithm to identify the right cryptographic algorithm based on the value of device parameters. Various cryptographic algorithms are also assessed which are appropriate for the task and also assesses the algorithm operation time when processing larger messages. Tests with respect to pre-defined measurements are conducted and the measurements are analyzed by comparing the outcomes. At the end of the paper, all outcomes and analyzes are deciphered. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) paved the way for devices and machine communication using TCP/IP protocol. Lightweight and stateless communication is imperative especially in a situation requiring conservation of energy usage, e.g. wireless sensor network. Representational State Transfer (REST) API method is based on web communication protocol, Hyper-Text Transfer Protocol (HTTP), and is widely used in IoT messaging. Some of these protocols are DPWS, XMPP, MQTT, COaP, AMQP. Among these protocols, MQTT is the most preferred protocol and is expected to be the de facto messaging IoT standard. MQTT uses a publisher/subscriber model to facilitate messaging between devices making messaging lightweight. Nevertheless, there are a number of security issues due to the design of the protocol itself. Some of the issues are denial of service, identity spoofing, information disclosure, elevation of privileges and data tampering. These issues can be caused by both internal and external perpetrators. Researchers have proposed various security techniques and mechanisms to address these issues. Incorporation of security has added processing overhead to the devices and this will have a bearing on IoT devices that are powered by a battery. This issue has opened up new research challenges in making the protocols more lightweight and at the same time not compromising the level of security provided. [ABSTRACT FROM AUTHOR]

Facial Emotion Recognition (FER) is the approach of detecting emotions of humans from facial expressions. Emotions are detected automatically by the human brain and hence, many computer aided techniques are implemented for recognizing emotions. Recognition of natural emotions is an exciting area with a broad range of real time applications like automated tutoring models, smart environments computer–human interaction, driver warning systems, and video and image retrieval. The major goal of this paper is to implement an intelligent model for FER. The proposed model involves few steps: (a) Face extraction (b) Image filtering, (c) Extraction of facial components, (d) Descriptor selection, and (d) Classification. Initially, the face is extracted from the input image by the Viola–Jones method, which is generally employed for the detection of object. Further, the noise of the image is filtered by the Gabor Filtering. Further, the facial components are extracted as features via the Affine-Scale-Invariant Feature Transform (ASIFT), which is the modified version of SIFT method. Since the length of the descriptors generated from ASIFT is high; the number of descriptors is reduced by the optimal descriptor selection approach with the aid of hybrid meta-heuristic algorithm termed as MV-WOA. The extracted descriptors are subjected to Neural Network (NN). As a modification to the existing machine learning algorithms, the number of hidden neurons in the NN is optimized by the proposed WOA+MVO algorithm. By conducting experiments, the results demonstrate that the developed system can perform better the recent traditional approaches for emotion recognition for classifying seven emotions like normal, smile, sad, surprise, anger, fear, and disgust. [ABSTRACT FROM AUTHOR]

Memristor technology is becoming an attractive option for memory architectures, in-memory computing, and logic applications due to their non-volatility, high density, and low power operation. However, these memristor-based devices are prone to defects because of the non-deterministic nature of nano-scale fabrication. This research describes a methodology for testing memristor circuits for fault detection and fault diagnosis using a unique property of memristor crossbar circuits – sneak paths. This research focuses on the stuck-at low resistance and stuck-at high resistance faults for our analysis. A 3 × 3 crossbar array was used as an example to demonstrate our fault dictionary-based diagnosis approach with improved test time. Our results show that fault diagnosis can be achieved only in three test vectors for the best case and a worst-case of m + 1 test vectors for an m × n array where m > n. [ABSTRACT FROM AUTHOR]

Advancements in sensor and hardware technology have surged the growth of smart devices (SDs), including smartphones, and wearable devices. The data generated by the built-in sensors are utilized by different applications such as health-care, smart-city, and connected-vehicles. However, due to the computation and energy limitations of the SDs, they often need to offload the computation-intensive tasks for processing to the remote server. The cloud-based offloading can meet various applications' demands, but due to high network latency, it is inefficient for real-time applications. Fog computing provides an alternative for the same, as it aggregates the fog nodes' resources at the edge of the network to meet the computational requirements of the real-time applications. In this paper, we consider a Fog-Cloud architecture consisting of multiple SDs, fog nodes, and the cloud. We use appropriate queuing models to simulate the traffic delay at different network elements and formulate a non-linear multi-objective optimization problem to minimize the energy consumption, execution delay, and cost of remote execution. Finally, the Stochastic Gradient descent (SGD) algorithm based solution approach is proposed that jointly optimizes offloading probability and transmission power to find the optimal trade-off between the offloading objectives. Simulation results show the effectiveness and the efficiency of the proposed system validated by the results. [ABSTRACT FROM AUTHOR]

VANET enables communication between the vehicles and Road Side units (RSUs). With the ever fast growth in industry and research necessitates the design of smart transportation system. Moreover, VANET success lies in the security features provided and it cannot be achieved precisely due to all type of security attacks. To secure the VANET against these types of security attacks, most of the research persons have proposed different security mechanisms based on lightweight and one way hash function. While both lightweight and one way hash function are efficient to certain degree, each method has some weakness to entirely secure the network. In this paper, we propose a list-based malicious attack detection scheme based on Zobrist Hash, called, Secured Zobrist Hash Symmetric Attack Detection (SZ-HSAD) to secure VANET to a larger extent. A system initialization is an essential part of trust establishment and secure communications between vehicles, the proposed SZ-HSAD integrates symmetric cryptography based initialization for efficient and robust trust management scheme. The system initialization includes Zobrist Hash Optimal Distance Registration. The trusted authority (TA) evaluates key for both the Road Side Unit (RSU) and vehicles based on the identity. Next, a list-based malicious attack detection using symmetric and sentinel node is designed for timely and early attack detection in VANET. The results are acquired with immense simulations to validate the proposed scheme. The results show that the proposed scheme converge security prerequisites, therefore reducing the average traffic overhead with minimum execution time and verification delay. [ABSTRACT FROM AUTHOR]

Authors propose an improved orthogonal frequency division multiplexing (OFDM) scheme in which the information samples are transmitted in otherwise discarded negative frame of asymmetrically clipped OFDM (ACO–OFDM). ACO–OFDM has two similar positive and negative OFDM envelopes so that clipping does not result in loss of signal information. The flipped polarity negative samples are inserted at the zeros of positive frame or back to back with positive frame, in time-compressed frame such that composite signal has the same frame duration as ACO–OFDM but improved transmission power, bandwidth efficiency. Simulation results for bit error rate with respect to number of subcarriers and optical launch power show that the proposed time-compressed OFDM frames perform better in high-dispersion condition over long distance, high data rate, and high transmission power over single-mode fiber. It has been demonstrated that the proposed time-compressed frame structure also performs well with the 2*2 MIMO setup for multimode fiber link. [ABSTRACT FROM AUTHOR]

Automated physiological signal monitoring to elderly sick patient is not only for fast access of data but also to get reliable service by accurate prediction by healthcare service provider. To address this challenge, this research focuses on novel Internet of Things (IoT) application-based physiological signal monitoring system to advance e-healthcare system. For the realization of the proposed system, Deep Neural Network-based accurate Signal Prediction and estimation algorithm was employed. The proposed system is prototyped as an advanced electronics component by using an intelligent sensor for signal measurement, National Instrument myRIO for smart data acquisition. Smart-Monitor is designed with intelligent sensor as the consumer product. To validate the proposed Smart-Monitor system, four physiological signal prediction accuracies for two users were computed. In prototype experimental set-up, an average accuracy of 97.2% was obtained. This shows that the proposed automated system is reliable and accurate monitoring is possible. From the experimental result, we validate the proposed system can provide reliable assist and accurate signal prediction. [ABSTRACT FROM AUTHOR]

Hypervisor-enabled virtualization is a promising technology to deploy applications and implement Virtual Machine (VM) migration on cloud data centers. However, one critical aspect in hypervisor-based solution is that to deploy applications each VM is installed with a dedicated guest OS along with the requirement of binaries and library files and substantially large VM's image size. Therefore, it is a significant concern for data center administrators because it causes performance overheads. To solve these issues, a new paradigm of virtualization technology that has attracted considerable attention is containerization. Although, progress has been made to address container-based virtualization, there has been less attention to explore the issue of performance evaluation of container and VM in a migration environment. The main contribution of this research study is to develop an experimental setup to implement and evaluate the performance of LXD/CR container-based migration technique and compared it with the pre-copy VM migration scheme. To conduct this performance evaluation, first we modified Linux kernel and implemented LXD which uses liblxc to launch container, then migration of the running container is facilitated using a checkpoint/restore mechanism of CRIU technique. To ensure the applicability and validity for real-time cloud infrastructure, we executed a wide variety of benchmarks such as web server, CPU-intensive, disk I/O and database. From the results obtained, it is found that compared to pre-copy VM migration scheme, LXD/CR container migration technique reduces the downtime, migration time, amount of data transferred and the number of pages transferred by 75.66%, 65.55%, 76.63%, and 76.78%, respectively. In addition, the migration overhead caused is reduced by 55.89% w.r.t. CPU utilization and 76.52% w.r.t. RAM utilization. This being an important contribution for migrating the running applications efficiently and consequently increase the business value of the cloud infrastructure providers. [ABSTRACT FROM AUTHOR]

Graph B Sreenath J G b received the BTech degree in electrical and electronics engineering from the College of Engineering, Trivandrum, India, in 2009, and the MTech and PhD degrees in electrical engineering from the Indian Institute of Technology Kanpur, Kanpur, India, in 2015 and 2019, respectively. Graph B Pathipati Srihari b (Senior Member, IEEE) received the BTech degree in electronics and communication engineering from Sri Venkateswara University, the master's degree in communications engineering and signal processing from the University of Plymouth, UK, and the PhD degree in the field of radar signal processing from Andhra University, in 2012. Graph B Pushpendra Singh b earned BE (Hons) from Govt Engineering College Rewa, MP (India), MTech from Indian Institute of Technology Kanpur (IITK), and PhD from Indian Institute of Technology Delhi (IITD). Graph B Bhim Singh b (Fellow, IEEE, National Science Chair) has received his BE (Electrical) from the University of Roorkee (Now IIT Roorkee), India, in 1977 and his MTech (Power Apparatus & Systems) and PhD from the IIT Delhi, India, in 1979 and 1983, respectively. Graph B Debabrata Sikdar b received the BE degree (Hons) in electronics & instrumentation engineering and the MSc degree (Hons) in physics in 2008, the ME degree in communication engineering from BITS, Pilani, India, in 2012, and the PhD degree from Monash University, Australia, in 2015. [Extracted from the article]