Browsing by Author "Ghosh, S.K."

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Automating the Selection of Container Orchestrators for Service Deployment
(Institute of Electrical and Electronics Engineers Inc., 2022) Chaurasia, P.; Nath, S.B.; Addya, S.K.; Ghosh, S.K.
With the ubiquitous usage of cloud computing, the services are deployed as a virtual machine (VM) in cloud servers. However, VM based deployment often takes more amount of resources. In order to minimize the resource consumption of service deployment, container based lightweight virtualization is used. The management of the containers for deployment is a challenging problem as the container managers need to consume less amount of resources while also catering to the needs of the clients. In order to choose the right container manager, we have proposed an architecture based on the application and user needs. In the proposed architecture, we have a machine learning based decision engine to solve the problem. We have considered docker containers for experimentation. The experimental results show that the proposed system can select the proper container manager among docker compose based manager and Kubernetes. Â© 2022 IEEE.
CoMCLOUD: Virtual Machine Coalition for Multi-Tier Applications over Multi-Cloud Environments
(Institute of Electrical and Electronics Engineers Inc., 2023) Addya, S.K.; Satpathy, A.; Ghosh, B.C.; Chakraborty, S.; Ghosh, S.K.; Das, S.K.
Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model called CoMCLOUD to select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines. © 2013 IEEE.
Container-based Service State Management in Cloud Computing
(Institute of Electrical and Electronics Engineers Inc., 2021) Nath, S.B.; Addya, S.K.; Chakraborty, S.; Ghosh, S.K.
In a cloud data center, the client requests are catered by placing the services in its servers. Such services are deployed through a sandboxing platform to ensure proper isolation among services from different users. Due to the lightweight nature, containers have become increasingly popular to support such sandboxing. However, for supporting effective and efficient data center resource usage with minimum resource footprints, improving the containers' consolidation ratio is significant for the cloud service providers. Towards this end, in this paper, we propose an exciting direction to significantly boost up the consolidation ratio of a data-center environment by effectively managing the containers' states. We observe that many cloud-based application services are event-triggered, so they remain inactive unless some external service request comes. We exploit the fact that the containers remain in an idle state when the underlying service is not active, and thus such idle containers can be checkpointed unless an external service request comes. However, the challenge here is to design an efficient mechanism such that an idle container can be resumed quickly to prevent the loss of the application's quality of service (QoS). We have implemented the system, and the evaluation is performed in Amazon Elastic Compute Cloud. The experimental results have shown that the proposed algorithm can manage the containers' states, ensuring the increase of consolidation ratio. Â© 2021 IFIP.
Containerized deployment of micro-services in fog devices: a reinforcement learning-based approach
(Springer, 2022) Nath, S.B.; Chattopadhyay, S.; Karmakar, R.; Addya, S.K.; Chakraborty, S.; Ghosh, S.K.
The real power of fog computing comes when deployed under a smart environment, where the raw data sensed by the Internet of Things (IoT) devices should not cross the data boundary to preserve the privacy of the environment, yet a fast computation and the processing of the data is required. Devices like home network gateway, WiFi access points or core network switches can work as a fog device in such scenarios as its computing resources can be leveraged by the applications for data processing. However, these devices have their primary workload (like packet forwarding in a router/switch) that is time-varying and often generates spikes in the resource demand when bandwidth-hungry end-user applications, are started. In this paper, we propose pick–test–choose, a dynamic micro-service deployment and execution model that considers such time-varying primary workloads and workload spikes in the fog nodes. The proposed mechanism uses a reinforcement learning mechanism, Bayesian optimization, to decide the target fog node for an application micro-service based on its prior observation of the system’s states. We implement PTC in a testbed setup and evaluate its performance. We observe that PTC performs better than four other baseline models for micro-service offloading in a fog computing framework. In the experiment with an optical character recognition service, the proposed PTC gives average response time in the range of 9.71 sec–50 sec, which is better than Foglets (24.21 sec–80.35 sec), first-fit (16.74 sec–88 sec), best-fit (11.48 sec–57.39 sec) and mobility-based method (12 sec–53 sec). A further scalability study with an emulated setup over Amazon EC2 further confirms the superiority of PTC over other baselines. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
CSMD: Container state management for deployment in cloud data centers
(Elsevier B.V., 2025) Nath, S.B.; Addya, S.K.; Chakraborty, S.; Ghosh, S.K.
As the containers are lightweight in resource usage, they are preferred for cloud and edge computing service deployment. Containers serve the requests whenever a user sends a query; however, they remain idle when no user request comes. Again, improving the consolidation ratio of container deployments is essential to ensure fewer servers are used in a cloud data center with an optimal resource balance. To increase the consolidation ratio of a cloud data center, in this paper, we propose a system called Container State Management for Deployment (CSMD) to manage the container states. CSMD uses an algorithm to checkpoint the idle containers so that their resources can be released. The new containers are deployed using the released resources in a server. In addition, CSMD uses an algorithm to check the container status periodically, and the containers are resumed from the checkpoint state when the user requests them. We evaluate CSMD in Amazon Elastic Compute Cloud (Amazon EC2) by performing efficient state management of the containers. The experiments in the Amazon cloud show that the proposed CSMD system is superior to the existing algorithms as the proposed system increases the consolidation ratio of data centers. © 2024 Elsevier B.V.
Deep learning-based multi-view 3D-human action recognition using skeleton and depth data
(Springer, 2023) Ghosh, S.K.; Rashmi, M.; Mohan, B.R.; Guddeti, R.M.R.
Human Action Recognition (HAR) is a fundamental challenge that smart surveillance systems must overcome. With the rising affordability of capturing human actions with more advanced depth cameras, HAR has garnered increased interest over the years, however the majority of these efforts have been on single-view HAR. Recognizing human actions from arbitrary viewpoints is more challenging, as the same action is observed differently from different angles. This paper proposes a multi-stream Convolutional Neural Network (CNN) model for multi-view HAR using depth and skeleton data. We also propose a novel and efficient depth descriptor, Edge Detected-Motion History Image (ED-MHI), based on Canny Edge Detection and Motion History Image. Also, the proposed skeleton descriptor, Motion and Orientation of Joints (MOJ), represent the appropriate action by using joint motion and orientation. Experimental results on two datasets of human actions: NUCLA Multiview Action3D and NTU RGB-D using a Cross-subject evaluation protocol demonstrated that the proposed system exhibits the superior performance as compared to the state-of-the-art works with 93.87% and 85.61% accuracy, respectively. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Effect of electrolyte temperature on the formation of highly ordered nanoporous alumina template
(2016) Sellarajan, B.; Sharma, M.; Ghosh, S.K.; Nagaraja, H.S.; Barshilia, H.C.; Chowdhury, P.
In this work, we present a systematic influence of electrolyte temperature along with anodizing potential on the pore parameters during two-step anodization of Al in H2SO4 electrolyte. Top surface morphology of the nanoporous templates was examined with the help of field emission scanning electron microscope and atomic force microscope. Three-dimensional (3D) Fast Fourier Transform (FFT) image analysis was then employed to quantify pore regularity and pore periodicity as a function of both the bath temperature (1-15 C) and the anodic potential (15-25 V). A highest pore regularity ratio of 5 108 was obtained at 3 C and 25 V with a pore diameter of 32 3 nm and inter-pore distance of 65 nm. With further increase in temperature, the pore regularity ratio was found to decrease drastically. It was found that higher temperature favored the dissolution of compact aluminum oxide layer isotropically along the pore length. This process in effect enhanced the pore size, growth rate, and template top surface roughness without affecting much inter-pore distance. Self-ordering of the pores was found to improve with increasing anodizing potential with a critical influence of the current density along with inter-pore distance. The mechanism of pore growth was discussed in terms of temperature-dependent activation energy controlled dissolution of aluminum. The typical activation energy evaluated at 25 V was 72.8 kJ/mol at 3 C. 2015 Elsevier Inc. All rights reserved.
Effect of electrolyte temperature on the formation of highly ordered nanoporous alumina template
(Elsevier B.V., 2016) Boominathasellarajan, B.; Sharma, M.; Ghosh, S.K.; Nagaraja, H.S.; Barshilia, H.C.; Chowdhury, P.
In this work, we present a systematic influence of electrolyte temperature along with anodizing potential on the pore parameters during two-step anodization of Al in H2SO4 electrolyte. Top surface morphology of the nanoporous templates was examined with the help of field emission scanning electron microscope and atomic force microscope. Three-dimensional (3D) Fast Fourier Transform (FFT) image analysis was then employed to quantify pore regularity and pore periodicity as a function of both the bath temperature (1-15 °C) and the anodic potential (15-25 V). A highest pore regularity ratio of 5 × 108 was obtained at 3°C and 25 V with a pore diameter of 32 ± 3 nm and inter-pore distance of 65 nm. With further increase in temperature, the pore regularity ratio was found to decrease drastically. It was found that higher temperature favored the dissolution of compact aluminum oxide layer isotropically along the pore length. This process in effect enhanced the pore size, growth rate, and template top surface roughness without affecting much inter-pore distance. Self-ordering of the pores was found to improve with increasing anodizing potential with a critical influence of the current density along with inter-pore distance. The mechanism of pore growth was discussed in terms of temperature-dependent activation energy controlled dissolution of aluminum. The typical activation energy evaluated at 25 V was 72.8 kJ/mol at 3°C. © 2015 Elsevier Inc. All rights reserved.
ESMA: Towards elevating system happiness in a decentralized serverless edge computing framework
(Academic Press Inc., 2024) Datta, S.; Addya, S.K.; Ghosh, S.K.
Due to the rapid growth in the adoption of numerous technologies, such as smartphones and the Internet of Things (IoT), edge and serverless computing have started gaining momentum in today's computing infrastructure. It has led to the production of huge amounts of data and has also resulted in increased network traffic, which if not managed well can cause network congestion. To address this and maintain the quality of service (QoS), in this work, a novel dispatch (destination selection) algorithm called Egalitarian Stable Matching Algorithm (ESMA) for faster data processing has been developed while also considering the best use of server resources in a decentralized Serverless-Edge environment. This will allow us to effectively utilize the enormous volumes of data that are generated. The proposed algorithm has been able to achieve lower overall dissatisfaction scores for the entire system. Individually, the client's happiness as well as the server's happiness have improved over the baseline. Moreover, there has been a drop of 25.7% in the total execution time and the total network resources consumed are lower as compared to the baseline algorithm as well as random-allocation algorithm. © 2023 Elsevier Inc.
Fully Automated Waste Management System Using Line Follower Robot
(Springer Science and Business Media Deutschland GmbH, 2022) Geetha, V.; Salvi, S.; Ghosh, S.K.; Ahmed, S.S.; Meshram, R.S.
With a population of over seven billion which generates waste of more than two billion metric tons a year, waste management is a serious issue that needs to be addressed. All this waste needs to be managed so that there will not be an overflow at the waste disposal bins in a locality as that might lead to deadly diseases and pollution. To overcome this problem, in this paper, we propose a way to collect the waste automatically using a line follower robot and dump it in the dumping ground. The proposed system uses an Arduino Yun which is installed on top of the line follower and a NodeMCU, which is installed at the garbage disposal sites for communication and collection of garbage. Both these components communicate over the â€œThingSpeakâ€ Cloud. These bins continuously send the percentage of waste that is in the bin. When the percentage reaches a certain threshold, the line follower goes to the site and collects the garbage and dumps it at a nearby dumping yard. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets
(Institute of Electrical and Electronics Engineers Inc., 2023) Addya, S.K.; Satpathy, A.; Ghosh, B.C.; Chakraborty, S.; Ghosh, S.K.; Das, S.K.
Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is $\mathcal {NP}$NP-Hard, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by 25%-30% and electricity cost by approximately 25% compared to the baseline. © 2008-2012 IEEE.
LCS : Alleviating Total Cold Start Latency in Serverless Applications with LRU Warm Container Approach
(Association for Computing Machinery, 2023) Sethi, B.; Addya, S.K.; Ghosh, S.K.
Serverless computing offers "Function-as-a-Service"(FaaS), which promotes an application in the form of independent granular components called functions. FaaS goes well as a widespread standard that facilitates the development of applications in cloud-based environments. Clients can solely focus on developing applications in a serverless ecosystem, passing the overburden of resource governance to the service providers. However, FaaS platforms have to bear the degradation in performance originating from the cold starts of executables i.e. serverless functions. The cold start reflects the delay in provisioning a runtime container that processes the functions. Each serverless platform is handling the problem of cold start with its own solution. In recent times, approaches to deal with cold starts have received the attention of many researchers. This paper comes up with an extensive solution to handle the cold start problem. We propose a scheduling approach to reduce the cold start occurrences by keeping the containers alive for a longer period of time using the Least Recently Used warm Container Selection (LCS ) approach on Affinity-based scheduling. Further, we carried out an evaluation and compared the obtained results with the MRU container selection approach. The proposed LCS approach outperforms by approximately 48% compared to the MRU approach. Â© 2023 ACM.
Shape induced magnetic vortex state in hexagonal ordered cofe nanodot arrays using ultrathin alumina shadow mask
(2018) Sellarajan, B.; Saravanan, P.; Ghosh, S.K.; Nagaraja, H.S.; Barshilia, H.C.; Chowdhury, P.
The magnetization reversal process of hexagonal ordered CoFe nanodot arrays was investigated as a function of nanodot thickness (td) varying from 10 to 30 nm with fixed diameter. For this purpose, ordered CoFe nanodots with a diameter of 80 4 nm were grown by sputtering using ultra-thin alumina mask. The vortex annihilation and the dynamic spin configuration in the ordered CoFe nanodots were analyzed by means of magnetic hysteresis loops in complement with the micromagnetic simulation studies. A highly pinched hysteresis loop observed at 20 nm thickness suggests the occurrence of vortex state in these nanodots. With increase in dot thickness from 10 to 30 nm, the estimated coercivity values tend to increase from 80 to 175 Oe, indicating irreversible change in the nucleation/annihilation field of vortex state. The measured magnetic properties were then corroborated with the change in the shape of the nanodots from disk to hemisphere through micromagnetic simulation. 2017 Elsevier B.V.
Shape induced magnetic vortex state in hexagonal ordered cofe nanodot arrays using ultrathin alumina shadow mask
(Elsevier B.V., 2018) Boominathasellarajan, B.; Saravanan, P.; Ghosh, S.K.; Nagaraja, H.S.; Barshilia, H.C.; Chowdhury, P.
The magnetization reversal process of hexagonal ordered CoFe nanodot arrays was investigated as a function of nanodot thickness (td) varying from 10 to 30 nm with fixed diameter. For this purpose, ordered CoFe nanodots with a diameter of 80 ± 4 nm were grown by sputtering using ultra-thin alumina mask. The vortex annihilation and the dynamic spin configuration in the ordered CoFe nanodots were analyzed by means of magnetic hysteresis loops in complement with the micromagnetic simulation studies. A highly pinched hysteresis loop observed at 20 nm thickness suggests the occurrence of vortex state in these nanodots. With increase in dot thickness from 10 to 30 nm, the estimated coercivity values tend to increase from 80 to 175 Oe, indicating irreversible change in the nucleation/annihilation field of vortex state. The measured magnetic properties were then corroborated with the change in the shape of the nanodots from disk to hemisphere through micromagnetic simulation. © 2017 Elsevier B.V.
Shipping code towards data in an inter-region serverless environment to leverage latency
(Springer, 2023) Sethi, B.; Addya, S.K.; Bhutada, J.; Ghosh, S.K.
Serverless computing emerges as a new standard to build cloud applications, where developers write compact functions that respond to events in the cloud infrastructure. Several cloud service industries started adopting serverless for deploying their applications. But one key limitation in serverless computing is that it disregards the significance of data. In the age of big data, when applications run around a huge volume, to transfer data from the data side to the computation side to co-allocate the data and code, leads to high latency. All existing serverless architectures are based on the data shipping architecture. In this paper, we present an inter-region code shipping architecture for serverless, that enables the code to flow from computation side to the data side where the size of the code is negligible compared to the data size. We tested our proposed architecture over a real-time cloud platform Amazon Web Services with the integration of the Fission serverless tool. The evaluation of the proposed code shipping architecture shows for a data file size of 64 MB, the latency in the proposed code shipping architecture is 8.36 ms and in existing data shipped architecture is found to be 16.8 ms. Hence, the proposed architecture achieves a speedup of 2x on the round latency for high data sizes in a serverless environment. We define round latency to be the duration to read and write back the data in the storage. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Skeleton-Based Human Action Recognition Using Motion and Orientation of Joints
(Springer Science and Business Media Deutschland GmbH, 2022) Ghosh, S.K.; Rashmi, M.; Mohan, B.R.; Guddeti, R.M.R.
Perceiving human actions accurately from a video is one of the most challenging tasks demanded by many real-time applications in smart environments. Recently, several approaches have been proposed for human action representation and further recognizing actions from the videos using different data modalities. Especially in the case of images, deep learning-based approaches have demonstrated their classification efficiency. Here, we propose an effective framework for representing actions based on features obtained from 3D skeleton data of humans performing actions. We utilized motion, pose orientation, and transition orientation of skeleton joints for action representation in the proposed work. In addition, we introduced a lightweight convolutional neural network model for learning features from action representations in order to recognize the different actions. We evaluated the proposed system on two publicly available datasets using a cross-subject evaluation protocol, and the results showed better performance compared to the existing methods. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Spatio-temporal Analysis and Modeling of Coastal areas for Water Salinity Prediction
(Institute of Electrical and Electronics Engineers Inc., 2023) Sudhakara, B.; Priyadarshini, R.; Bhattacharjee, S.; Kamath Sâ€¤, S.; Umesh, P.; Gangadharan, K.V.; Ghosh, S.K.
Salinity is an important parameter affecting the quality of water, and excessive amounts adversely affect vege-tation growth and aquatic organism populations. Natural factors like tidal waves, natural calamities etc., and man-made factors like unchecked disposal of industrial wastes, domestic/ urban sewage, and fish hatchery activities can cause significant increases in water salinity. In this article, an approach that utilizes multimodal data like Landsat 8 optical observations and the SMAP salinity data product for predicting water salinity indices in the coastal region is proposed. Machine Learning models such as K-nearest neighbor (KNN), Gradient Boost (GB), Extremely Randomized Tree (ERT), Random Forest Regression (RFR), Decision Tree (DT), Multiple Linear Regression (MLR), Lasso Regression (LR), and Ridge Regression (RR) are used for salinity prediction. Empirical experiments revealed that the ERT model outperformed other ML models, with a R2 of 0.92 and RMSE of 0.25 psu. Â© 2023 IEEE.