Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item 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. © 2021 IFIP.Item 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 “ThingSpeak†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. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item 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. © 2022 IEEE.Item 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. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item 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 S․, 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. © 2023 IEEE.Item 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. © 2023 ACM.