A Survey of Large Scale Data Management Approaches in Cloud Environments

TLDR

The rapid growth of computational power and web technologies has generated massive data streams, prompting a shift toward cloud‑based architectures that relocate infrastructure to the network to lower hardware and software management costs. This survey reviews the emerging approaches and mechanisms for deploying data‑intensive applications in the cloud, identifying the most suitable application classes for such environments. The authors evaluate each approach’s design choices, assess their fit for specific application types, and outline open issues and future challenges related to scalability, consistency, and cost‑effective large‑scale data processing.

Abstract

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data. Moreover, the recent advances in Web technology has made it easy for any user to provide and consume content of any form. This has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. Cloud computing is associated with a new paradigm for the provision of computing infrastructure. This paradigm shifts the location of this infrastructure to the network to reduce the costs associated with the management of hardware and software resources. This paper gives a comprehensive survey of numerous approaches and mechanisms of deploying data-intensive applications in the cloud which are gaining a lot of momentum in both research and industrial communities. We analyze the various design decisions of each approach and its suitability to support certain classes of applications and end-users. A discussion of some open issues and future challenges pertaining to scalability, consistency, economical processing of large scale data on the cloud is provided. We highlight the characteristics of the best candidate classes of applications that can be deployed in the cloud.