Showing papers by "Parmeet Kaur published in 2021"
TL;DR: A comprehensive overview of fault tolerance-related issues in cloud computing is presented, emphasizing upon the significant concepts, architectural details, and the state-of-art techniques and methods.
84 citations
TL;DR: An architectural framework for an application to exploit heterogeneous databases with a polyglot approach is proposed and a working application to demonstrate the use of different databases for various modules of an application is presented.
Abstract: Traditionally, applications have used a single database to fulfill their storage requirements. However, limiting storage to a specific type of database system may result in a compromise in some functionalities of the application due to database features. This paper proposes an architectural framework for an application to exploit heterogeneous databases with a polyglot approach. A working application to demonstrate the use of different databases for various modules of an application is presented. Two instances of MongoDB and a single instance of MySQL have been used in the proposed application. Container technology is used to deploy the application's services like databases and web server. The use of microservices has resulted in a completely flexible and scalable application that utilizes the desired features of heterogeneous databases for its constituent modules. The proposed architecture is validated and compared with existing models. The performance comparison results are tabulated for six crucial parameters listed in the ISO/IEC 25010 standard.
5 citations
TL;DR: In this paper, the authors proposed a tenant-centric approach to develop an object-based software defined storage system for SaaS multi-tenant applications, which can efficiently meet the storage requirements of users or tenants with diverse needs.
Abstract: Enormous amounts of unstructured data such as images, videos, emails, sensors’ data and documents of multiple types are being generated daily by varied applications. Apart from the challenges related to collection or processing of this data, its efficient storage is also a significant challenge since this data do not conform to any predefined storage model. Therefore, any enterprise dealing with huge unstructured data requires a scalable storage system that can provide data durability and availability at a low cost. The paper proposes a tenant-centric approach to develop an object-based software defined storage system for SaaS multi-tenant applications. We present TOSDS (Tenant-centric Object-based Software Defined Storage), a system that can efficiently meet the storage requirements of users or tenants with diverse needs who are using a multitenant SaaS application. The experimental verification of TOSDS illustrates its effectiveness in storage utilization as well as tenant isolation.
1 citations
TL;DR: In this paper, the authors proposed an anonymization of diverse transactional data (ADT) based on slicing and generalization to achieve km-anonymity for transactional datasets.
Abstract: Data anonymization is commonly utilized for the protection of an individual's identity when his personal or sensitive data is published. A well-known anonymization model to define the privacy of transactional data is the km-anonymity model. This model ensures that an adversary who knows up to m items of an individual cannot determine which record in the dataset corresponds to the individual with a probability greater than 1/k. However, the existing techniques generally rely on the presence of similarity between items in the dataset tuples to achieve km-anonymization and are not suitable when transactional data contains tuples without many common values. The authors refer to this type of transactional data as diverse transactional data and propose an algorithm, anonymization of diverse transactional data (ADT). ADT is based on slicing and generalization to achieve km-anonymity for diverse transactional data. ADT has been experimentally evaluated on two datasets, and it has been found that ADT yields higher privacy protection and causes a lower loss in data utility as compared to existing methods.
01 Jan 2021
TL;DR: In this article, the authors proposed two fuzzy inference systems (FIS) for fault tolerance of VMs in cloud computing, namely physical machine FIS1 (PMFIS1) and PMFIS2, which use replication as a fault tolerance mechanism for VMs.
Abstract: Cloud computing provides various services and scalable computing resources through the internet. Due to the features that allow sharing and multiplexing computing resources across numerous tenants, cloud reliability has gained an extensive foothold in recent times. However, in a cloud computing-based environment, it is critical to enhance the reliability of cloud services such as the virtual machine (VM)-based services. To ensure the reliability of VMs in the Infrastructure as a Service (IaaS) cloud computing model, this paper proposes two fuzzy inference systems (FIS), namely Physical Machine FIS1 (PMFIS1) and PMFIS2 for fault tolerance of VMs in cloud computing. The proposed inference systems use replication as a fault tolerance mechanism for VMs. These systems aid in the selection of optimal physical machines (PMs) to place the replicas of virtual machines (VMs). Implementation of proposed FIS is performed in MATLAB to compare the FISs with each other in terms of complexity, flexibility, and better selection of PMs. However, from the simulation result, it is observed that the PMFIS1 is less complex than PMFIS2, but the PMFIS2 is more flexible and makes a better selection of PM than PMFIS1.