Problem Statement
Storage/Energy efficient Cloud Computing
Large infrastructure uses more energy and power because to the quickly rising demand for compute and the growing amount of data storage required to support high performance computing firms. The use of the cloud offers an energy-saving and solution to lessen negative environmental effects. The many energy and power models are examined in this study, and the key difficulties in developing a green cloud model with the use of SLAs are noted. Providing dependable QoS is one of the primary goals taken into account in cloud computing. The SLA, which outlines such qualities, can specify this.
PS Number: PSCCV005
Domain Bucket: Cloud Computing
Category: Software
Dataset : NA
To make sure that business intelligence and big data analytics apps can access high-quality data.
Large data repositories are collected, stored, governed, organised, managed, and delivered using various technologies.
Background of the Problem
The rapid increase in demand for computation, increasing amount of data storage needed for running high performance computing enterprises increases the energy and power consumed by large infrastructure. Cloud computing provides a solution to reduce the adverse environmental impacts and saves energy. Our paper draws the attention on the various methods enforced on the cloud environment to make it more energy efficient. One of the main objectives considered in cloud computing is to provide reliable QoS. This can be defined in the SLA which describes such characteristics. The different ways to minimize energy and power in the cloud computing services are also being discussed in this paper. Our work surveys the various models which will pave the road map for an energy efficient cloud.
Objective
To ensure a high level of data quality and accessibility for business intelligence and big data analytics applications Background. Big data management is a broad concept that encompasses the policies, procedures and technology used for the collection, storage, governance, organization, administration and delivery of large repositories of data. It can include data cleansing, migration, integration and preparation for use in reporting and analytics.Big data management is closely related to the idea of data lifecycle management (DLM).
Summary
It is an extremely large volume of data and datasets that come in diverse forms and from multiple sources. Many organizations have recognized the advantages of collecting as much data as possible. But it’s not enough just to collect and store big data—you also have to put it to use. It helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. Businesses that use big data with advanced analytics gain value in many ways, such as: Reducing cost.