Abstract
Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include storing and accessing user data in commercial clouds, mining of social data, and analysis of large-scale simulations and experiments such as the Large Hadron Collider. An increasing number of such data—intensive applications and services are relying on clouds in order to process and manage the enormous amounts of data required for continuous operation. It can be difficult to decide which of the many options for cloud processing is suitable for a given application; the aim of this paper is therefore to provide an interested user with an overview of the most important concepts of cloud computing as it relates to processing of Big Data.
Highlights
Attempting to define cloud computing can be as nebulous an activity as the term itself implies
We review the most common approaches and aspects in an attempt to provide researchers a tool to guide them in the selection process when considering cloud applications for processing of Big Data
In this paper we reviewed the most common approaches and aspects of Cloud Computing in an attempt to provide researchers a tool to guide them in the selection process when considering cloud applications for processing of Big Data
Summary
Attempting to define cloud computing can be as nebulous an activity as the term itself implies. According to the National Institute of Standards and Technology (NIST), the definition of cloud computing is “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [1]. (2014) Cloud Computing and Big Data: A Review of Current Service Models and Hardware Perspectives.
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