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Title: Dynamic and Scalable Data Access and Integration Services in Cloud Computing Environment
Authors: Kumar, Ajay
Supervisor: Bawa, Seema
Keywords: Cloud Computing;Scalability;Data Integration;Self Organizing;Meta-heuristic Algorithms
Issue Date: 11-Jun-2020
Abstract: Cloud computing has been emerging paradigm which operates on large scales thus requires different approaches and application architectures. This is a suitable option for bulk storage and reporting on the Internet. Cloud computing provides cost-effective business solutions, as it allows hosting of various services including computational power, storage services and network services on shared physical resources. Cloud computing empowers organizations/ enterprises to host scientific, engineering and different business applications. It gives high availability and more accessibility across the globe. However, there is a need to draw attention to the growing trends and growing data of online application services, as well as problems related to accommodating the continued expansion of cloud data centers. Particularly huge storage space and high bandwidth usage resulting in ultimately high operating costs. Now, Cloud-Oriented Storage as a Service (COSaaS) became one of the major aspects of cloud service model in which user can get access storage space dynamically. Here, Service Level Agreement (SLA) violation is one of the key issues of cloud computing. In general, SLA is a contract between service providers and consumers with some conditions like, service availability, service deadline, consistency of service, network congestion etc. In cloud environment, each user has apparent definition of SLA constraints and flexible conciliation procedures that can increase the trustworthiness relationship between users and providers. This thesis proposes a unique framework namely: Dynamic Access and Inte- gration Service (DAIS) that achieve the on-demand and integrated data access in cloud computing environments. The DAIS framework presents unique approach in cloud environment for large-scale storage management. It introduces new concepts including dynamic and scalable data access, self-organizing capability and well bal- anced multi-way COS overlay topology. It removes the necessity of centralized ap- proaches for traditional static storage management and adopts the dynamic and scal- able data access in the cloud computing environment. DAIS framework has four major components-Adjacency COS Overlay Topology (ACOT), Cloud Storage Resource Dis- covery (CSRD),COS Integration Services (CIS) and Cloud Usage Monitor (CUM). Each of these components have been explained thoroughly in this thesis. The proposed framework is tested in Hadoop 2.7 and Java 1.8.0 environments for all concerned ex- periments. WAN emulator has also been used to find the impact of dynamicity of DAIS framework. Several experiments including bandwidth evaluations (load vs. throughput and Delay vs. throughput), dynamicity and scalability evaluation, SLA optimization and energy efficiency evaluation have been conducted in this thesis. All test results are well explained and highlighted in different tables as well as visual graphs. Apart from this an effort has been made to propose a swarm-based meta-heuristic algorithm named Generalized Ant Colony Optimizer (GACO) for dynamic execution of cloud services. GACO has been experimentally demonstrated and compared with three well-known algorithms including Particle Swarm Optimization, Genetic Algo- rithm and Artificial Bee colony. As validated by experimental results, theproposed algorithms perform better in most of the cases.
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