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 Analyzing Information Flow of Hashtag Networks during Elections using Sentiment Analysis and Graph Algorithms(Institute of Electrical and Electronics Engineers Inc., 2022) Patra, C.; Shetty D, P.D.; Chakraborty, S.An exponential increase in the usage of social media across the world creates a lot of unstructured data and cross-communication between individuals. These platforms provides opportunity to the political parties to spread their word out. The information is spread using several hashtags in the form of user-generated tagging that facilitates cross-referencing of content. These hashtag-generated networks serve as a huge reservoir of data and if analyzed systematically can help in understanding the agenda-setting of each party and how successful or unsuccessful they are. This in turn helps in predicting the outcome of the election looking from the prism of social media. In the present study, a model is proposed by combining sentiment analysis and graph techniques to look into the trending hashtag networks propagated by political parties using Twitter. The sentiment analysis gives us a sense of inclination of each tweet and thereafter it's extrapolated onto the hashtag's user network to get insights as to how the information is diffusing and how one party propagates its favorable hashtag and how the others try to counter it. The major aim of the present work is to find out the intricacies that go on in the social media space before a major election. © 2022 IEEE.Item An Approach for Predicting Election Results with Trending Twitter Hashtag Information Using Graph Techniques and Sentiment Analysis(Springer Science and Business Media Deutschland GmbH, 2023) Patra, C.; Shetty D, D.; Chakraborty, S.India is one of the largest democracies in the world where the Lok Sabha and the Rajya Sabha elections are held every five years. Nowadays, social media acts as an important and inexpensive platform for propagating messages of the political parties. In the present study, a methodology is proposed by combining sentiment analysis and graph techniques to look into the trending hashtag networks propagated by the political parties using Twitter. The demonstration of the proposed methodology is done on the trending hashtag’s information collected from Twitter on the Uttar Pradesh (U.P) state elections, 2022. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item An Approach for Efficient Graph Mining from Big Data Using Spark(Springer Science and Business Media Deutschland GmbH, 2023) Gupta, R.K.; Shetty D, D.; Chakraborty, S.Huge amount of data is generated and accumulated over the last decade, and therefore, the use of data mining techniques is required to extract usable information from these massive data sets. Gaining important connections between data helps in getting useful insights. Depiction of relationships between the data using graphical approach is observed to be a helpful method. It provides an effective technique for demonstrating the working in a variety of situations, including biological networks, social networks, Web networks, and so on. Clustering techniques used in graph mining can be helpful for accumulating significant information. In this paper, an approach for graph mining from big data in Spark (AGMBS) is proposed on the basis of label propagation. The suggested technique enhances the efficiency of the conventional label propagation algorithm by making it more resilient. In addition to this, AGMBS employs a sparse matrix as its primary data structure, resulting in quicker performance. Thereafter, GraphX is used for managing the processing of the graphical data. The experiments were conducted on two graph data sets from the real world, and it is observed that the suggested AGMBS gives faster results as compared to the best available clustering algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Performance Analysis of Disruptive Instances in Cloud Environment(Institute of Electrical and Electronics Engineers Inc., 2024) Nandy, P.; Saha, R.; Satpathy, A.; Chakraborty, S.; Addya, S.K.Virtualization enables the service providers (SPs) to logically partition the resources into virtual machines (VM) instances. Real-world SPs such as Amazon, Google, Microsoft Azure, IBM, and Oracle provide different flavors of VM instances, such as on-demand, reserved, and low-cost or spot, depending on the type of application hosted. The on-demand instances are short-term and typically incur a higher cost than reserved instances that are provisioned for a longer duration at a discounted rate. Low-cost or spot instances are cost-effective compared to on-demand but are reclaimable by the SPs. The SPs often claim that the on-demand and spot instances achieve similar performance, but it is far from that. This paper studies the performance of spot instances via rigorous experimentation over commercial SPs such as Amazon AWS and Microsoft Azure. Real-world evaluations affirm that spot instances perform poorly compared to their on-demand counterpart concerning memory, CPU, disk read, and write operations. We identify such instances as disruptive and name them so because it does not fulfill the performance, durability, and flexibility expectations like an on-demand instance having the same configuration. We also perform hypothesis testing over the experimental data obtained to corroborate our claim further. © 2024 IEEE.Item A Comparative Study of Optimizers on Non-pretrained CNN Models for Stray Animal Surveillance System(Springer Science and Business Media Deutschland GmbH, 2025) Chakraborty, S.The stray animals sighted on the Indian vehicular roads cause traffic congestions and lead to major road accidents. Due to the menace, many people and animals get serious injuries and even lose their life. The attacks on the humans, damaging of properties and spreading of dangerous diseases such as rabies are the other major concerns due to the increase in stray animals. The present study performs detection and classification of the stray animals on the vehicular streets. The dataset comprises of 500 images of vehicular roads with and without stray animals. The classification accuracy of the surveillance system is compared between Stochastic Gradient Descent (SGD) and Adam optimizer for a 3-layer and 4-layer CNN having different batches. The study aims to act as a surveillance system on the roads for detecting the presence of stray animals. Timely detection and relocation of stray animals can help in preventing the fatalities and spread of diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.