Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Privacy and trust in cloud database using threshold-based secret sharing(2013) Dutta, R.; Annappa, B.In today's cloud computing scenario, privacy of data and trust on the service provider have become a major issue and concern. Achieving trust and preserving the privacy of data stored in third-party cloud databases has emerged as a key research area. To achieve this, several different techniques have been proposed based on cryptography, auditing by a third party, etc. Secret sharing schemes have also been considered to address these issues of trust and privacy in databases by various researchers. In this paper, we propose a technique of using a well-known threshold-based visual secret sharing scheme to address the issue of privacy and trust in cloud databases and database-as-a-service offerings. We consider data records with at least one prime attribute and propose an indexing technique for the secret shares of records in a large database based on some properties of the secret sharing technique. Our technique is aimed at minimizing storage overhead of secret shares as well as high speed upload and retrieval of data. We discuss the results obtained from our implementation. Our implementation using Hadoop Distributed File System (HDFS) with Matlab shows that this technique minimizes storage overhead due to secret shares and ensures high speed data upload and retrieval. © 2013 IEEE.Item Load balancing strategy for optimal peak hour performance in cloud datacenters(Institute of Electrical and Electronics Engineers Inc., 2015) Kulkarni, A.K.; Annappa, B.Cloud computing is a growing computing model that is influencing every other entity in the global business industry. Efficient load balancing techniques plays a major role in cloud computing by allocating requests to computing resources efficiently to prevent under/over-allocation of Virtual Machines (VMs) and improve the response time to clients. It is observed that during peak hours when request frequency is high, active VM load balancer (packaged in cloudAnalyst) over-allocates initial VMs and under-allocates later ones creating load imbalance. In this paper we propose a novel VM load balancing algorithm that ensures uniform allocation of requests to virtual machines even during peak hours when frequency of requests received in data center is very high to ensure faster response times to users. The simulations results suggest that our algorithm allocates requests to VM uniformly even during peak traffic situations. © 2015 IEEE.Item Cloud performance evaluation using fuzzy logic(Institute of Electrical and Electronics Engineers Inc., 2015) Saxena, G.; Nanath, K.Cloud computing is the latest form of evolution of distributed computing which eradicates the need to own the expensive hardware resources, as the resources can be easily obtained on cloud in pay-per-usage manner. This proves to be very cost efficient to users as they no longer need to depend on the hardware resources to satisfy their needs. For measuring cloud performance there exist a few approaches, yet there is scope for experimenting and developing new approaches for evaluating cloud efficiency. This paper attempts to measure cloud performance by using fuzzy logic by taking into consideration different performance parameters of the cloud. A metric for analyzing the performance of the cloud is derived upon, by considering various performance parameters and proves to be helpful in comparing various cloud services. The method proposed in this paper is very flexible and easy to understand. The results provide useful information and directions for further research in this new emerging field. © 2015 IEEE.Item Virtual Machine Migration in Heterogeneous Clouds - A Practical Approach(Institute of Electrical and Electronics Engineers Inc., 2020) Raj, S.; Mangal, N.; Savitha, S.; Salvi, S.S.In modern times, Cloud Computing is viewed as more promising technology than any other traditional Information Technology Computing paradigms. It basically serves as an on-demand resource provisioning platform without any active intervention by its user. The resource provisioning strategies require appropriate load distribution management across the cloud network, without which the cloud would face biased workload performance. Virtualization is the backbone of Cloud Computing, which enables the distribution and management of data by initiating the Virtual Machines (VMs). Furthermore, a Cloud Service Provider(CSP) has to monitor, analyze, and manage the workload distribution for servers when VMs are migrated. It presents the need to consider VM migration as an important activity that would unload the cloud server that is overloaded to migrate it to the server that can handle the workload. This paper proposes a technique that initiates the migration of VMs between heterogeneous cloud environments that would lead to a stable and well-balanced cloud network. The process of VM migration is very intensive in terms of resources, and hence intelligent approaches are required. It should effectively reduce the utilization of network bandwidth by minimizing the downtime of the server. However, the migration of VMs between the heterogeneous cloud would be challenging, but the right solution would benefit the cloud network managers on a large scale. Our proposed technique demonstrates heterogeneous VM migration between various cloud platforms built on different architectures. Various parameters have to be technically tuned for the conversion of VM images according to the Cloud Architecture. The performance of the proposed technique is evaluated based on the time taken for migration. © 2020 IEEE.Item A Time Series Forecasting Approach to Minimize Cold Start Time in Cloud-Serverless Platform(Institute of Electrical and Electronics Engineers Inc., 2022) Jegannathan, A.P.; Saha, R.; Addya, S.K.Serverless computing is a buzzword that is being used commonly in the world of technology and among developers and businesses. Using the Function-As-A-Service (FaaS) model of serverless, one can easily deploy their applications to the cloud and go live in a matter of days, it facilitates the developers to focus on their core business logic and the backend process such as managing the infrastructure, scaling of the application, updation of software and other dependencies is handled by the Cloud Service Provider. One of the features of serverless computing is ability to scale the containers to zero, which results in a problem called cold start. The challenging part is to reduce the cold start latency without the consumption of extra resources. In this paper, we use SARIMA (Seasonal Auto Regressive Integrated Moving Average), one of the classical time series forecasting models to predict the time at which the incoming request comes, and accordingly increase or decrease the amount of required containers to minimize the resource wastage, thus reducing the function launching time. Finally, we implement PBA (Prediction Based Autoscaler) and compare it with the default HPA (Horizontal Pod Autoscaler), which comes inbuilt with kubernetes. The results showed that PBA performs fairly better than the default HPA, while reducing the wastage of resources. © 2022 IEEE.Item Enhancing Data Quality in Hybrid Cloud Architectures(Institute of Electrical and Electronics Engineers Inc., 2024) Fernandes, G.H.; Divakarla, U.; Chandrasekaran, K.The emergence of the hybrid cloud model has completely changed how businesses handle, store and use their data. A common option for businesses looking to combine the benefits of cloud services and on-premises infrastructure is the hybrid cloud strategy. However, because of the complexity of data management across contexts and the integration of disparate systems, this paradigm presents serious hurdles to preserving data quality. Since data is the primary source for many important business decisions, ensuring data quality - which includes correctness, consistency, completeness, security, and reliability - remains a top priority. This study presents a unique method that makes use of cloud computing and machine learning (ML) algorithms to improve data quality in hybrid cloud environments. The detection, prevention and remediation of data quality issues by integrating state-of-the-art machine learning techniques into hybrid cloud systems is thoroughly examined in this study. The suggested architecture seeks to deliver more dependable and trustworthy data for decision-making processes by offering real-time monitoring, analysis and quality enhancement of data throughout the hybrid infrastructure. The efficacy of methodology in tackling data quality issues in hybrid cloud settings is illustrated using experiments and case studies. © 2024 IEEE.