An integrated framework for evaluating big-data storage solutions - IDA case study

The amount of data stored is rapidly increasing due to consumer, business, scientific, and government generated content. In addition to keeping pace with storing generated data, there is a need to comply with laws and Service Level Agreements (SLA) to protect and preserve stored data. The issues of capacity and scale in data storage are of constant concern to ensure the ability to absorb data growth, to manage existing data and to analyze the data. Evaluating data storage for IT infrastructure is a complex task that has multiple variables. These variables include capacity, scalability, financial, workload requirements, security, privacy, availability, reliability, analytics and operational. Current frameworks that attempt to address data storage evaluations focus either on a single aspect of these variables or use generic IT frameworks to evaluate data storage as a sub-component. The complexity of data storage requirements merits a holistic framework in the data storage domain. The contribution of this paper is an integrated framework to evaluate and assist in selecting optimum storage solution for multi-variable requirements. This paper examines Information Dispersal Algorithm (IDA) storage technology using this framework and is the first in a series to examine four different big-data storage technologies using this framework.