Sanjukta Pal

Bio: Sanjukta Pal is an academic researcher from National Institute of Technology, Durgapur. The author has contributed to research in topics: Software as a service & Multitenancy. The author has an hindex of 1, co-authored 2 publications receiving 10 citations.

Application Multi-Tenancy for Software as a Service

Abstract: Software as a service has evolved as a new software deployment paradigm in the cloud, which offers information technology services dynamically, "on-demand". Application Multi-tenancy in SaaS leads to improved resource utilization and reduces overall application costs by sharing the same applications, resources and data services through multiple tenants. In this paper a graph-based approach called SaaS Level Application Multi Tenancy graph is proposed to represent the multi-tenant aspects of the SaaS model of cloud environment formally. The proposed approach will facilitate the analysis of service interaction paths for accessing shared sets of services in SaaS by the multiple tenants. The proposed approach is capable enough to model several kinds of tenant like isolated tenant, shared tenants with single or multiple data services and multiple tenants in SaaS. Further, several metrics are defined for the proposed approach to describe the essential features of multi-tenant SaaS applications. The expressiveness of the proposed approach is illustrated using several examples and a detailed case study.

Dynamic service composition towards database virtualization for efficient data management

Abstract: Automated data centric technology of Cloud computing facilitate the end users through the service module, named SaaS. Where, these group of end users are either be skilled or unskilled. Recently, the most intellectual decision is to retrieve the requested data from the enormous flooded data storage through the service based cloud architecture by any type of cloud users through the remarkably efficient way using the methodologies like DBaaS, multi-tenancy, database integration. Among them, multi-tenancy and database integration can be applicable in the SaaS service model through the tightly coupled nature of service composition. But, this static service composition suffers from implementation complexity, cost factor, flexibility and scalability for further database adaptability and efficient data availability. Here, the proposed Dynamic Service Composition (abbreviated as DSC) methodology is sophisticated enough to retrieve different types of data from the multiple heterogeneous cloud databases after connectivity setup with new databases at runtime and on-demand basis. This dynamic database connectivity through the loosely coupled service composition is able to supply the requested data within a revolutionary computational speed. This methodology is able to overcome the challenges introduced by static service composition. DSC can govern multiple cloud databases through the flexible services connectivity without any information about their position in the cloud. This concept can be termed as database virtualization. Overall, the proposed DSC mechanism can monitor heterogeneous cloud databases and is responsible for significant growth over computational power for efficient data availability within a remarkable lower cost in a flexible and scalable way.

Availability Aware VNF Deployment in Datacenter Through Shared Redundancy and Multi-Tenancy

Abstract: By means of network function virtualization (NFV), dedicated proprietary network devices can be implemented as software and instantiated flexibly on common-off-the-shelf servers, in the form of virtual network functions (VNF). NFV can bring great cost reduction as well as operation flexibility. However, it also brings new problems, one of which is how to meet the availability of network services in the VNF deployment process, because of the error prone nature of software. The availability aware VNF deployment problem has attracted attention by academics, and reserving redundancy has been treated as the de facto technology. Compared with traditional backup schemes for physical machines, resource orchestration in NFV is more flexible and the characteristics of software should be considered to improve resource utilization efficiency. Based on the above considerations, in this paper we further study the availability aware VNF deployment problem in datacenter networks. To improve the resource utilization efficiency, the sharing mechanism of redundancy and multi-tenancy technology are taken into account. Then we formulate the problem mathematically and propose a joint deployment and backup scheme (JDBS). Finally, we conduct a numerical simulation in detail and compare it with four contrasting schemes in the existing literature. The simulation results show that JDBS is obviously superior to the contrasting schemes and can save about 40% resources at most.

An Intelligent Cloud Service Composition Optimization Using Spider Monkey and Multistage Forward Search Algorithms

Abstract: Web service composition allows developers to create and deploy applications that take advantage of the capabilities of service-oriented computing. Such applications provide the developers with reusability opportunities as well as seamless access to a wide range of services that provide simple and complex tasks to meet the clients’ requests in accordance with the service-level agreement (SLA) requirements. Web service composition issues have been addressed as a significant area of research to select the right web services that provide the expected quality of service (QoS) and attain the clients’ SLA. The proposed model enhances the processes of web service selection and composition by minimizing the number of integrated Web Services, using the Multistage Forward Search (MSF). In addition, the proposed model uses the Spider Monkey Optimization (SMO) algorithm, which improves the services provided with regards to fundamentals of service composition methods symmetry and variations. It achieves that by minimizing the response time of the service compositions by employing the Load Balancer to distribute the workload. It finds the right balance between the Virtual Machines (VM) resources, processing capacity, and the services composition capabilities. Furthermore, it enhances the resource utilization of Web Services and optimizes the resources’ reusability effectively and efficiently. The experimental results will be compared with the composition results of the Smart Multistage Forward Search (SMFS) technique to prove the superiority, robustness, and effectiveness of the proposed model. The experimental results show that the proposed SMO model decreases the service composition construction time by 40.4%, compared to the composition time required by the SMFS technique. The experimental results also show that SMO increases the number of integrated ted web services in the service composition by 11.7%, in comparison with the results of the SMFS technique. In addition, the dynamic behavior of the SMO improves the proposed model’s throughput where the average number of the requests that the service compositions processed successfully increased by 1.25% compared to the throughput of the SMFS technique. Furthermore, the proposed model decreases the service compositions’ response time by 0.25 s, 0.69 s, and 5.35 s for the Excellent, Good, and Poor classes respectively compared to the results of the SMFS Service composition response times related to the same classes.

Cloud enterprise resource planning development model based on software factory approach

Abstract: Literature reviews revealed that Cloud Enterprise Resource Planning (Cloud ERP) is significantly growing, yet from software developers’ perspective, it has succumbed to high management complexity, high workload, inconsistency software quality, and knowledge retention problems. Previous researches lack a solution that holistically addresses all the research problem components. Software factory approach was chosen to be adapted along with relevant theories to develop a model referred to as Cloud ERP Factory Model (CEF Model), which intends to pave the way in solving the above-mentioned problems. There are three specific objectives, those are (i) to develop the model by identifying the components with its elements and compile them into the CEF Model, (ii) to verify the model’s deployment technical feasibility, and (iii) to validate the model field usability in a real Cloud ERP production case studies. The research employed Design Science methodology, with a mixed method evaluation approach. The developed CEF Model consists of five components; those are Product Lines, Platform, Workflow, Product Control, and Knowledge Management, which can be used to setup a CEF environment that simulates a process-oriented software production environment with capacity and resource planning features. The model was validated through expert reviews and the finalized model was verified to be technically feasible by a successful deployment into a selected commercial Cloud ERP production facility. Three Cloud ERP commercial deployment case studies were conducted using the prototype environment. Using the survey instruments developed, the results yielded a Likert score mean of 6.3 out of 7 thus reaffirming that the model is usable and the research has met its objective in addressing the problem components. The models along with its deployment verification processes are the main research contributions. Both items can also be used by software industry practitioners and academician as references in developing a robust Cloud ERP production facility.