“Big” Data Management in Cloud Computing Environment

2018 ◽  
pp. 707-716
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Management ◽  
Computing Environment ◽  
Cloud Computing Environment

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

Big Data Optimization for Customer Discounts in Cloud Computing Environment

2017 ◽  
pp. 195-224
Author(s):  
Raghvendra Kumar ◽  
Prasant Kumar Pattnaik ◽  
Priyanka Pandey
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Efficient Algorithms ◽  
Proof Of Concept ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Running Time ◽  
Simulation Process ◽  
Current Implementation ◽  
Simulation Systems

Large companies have different methods of doing this, one of which is to run sales simulations. Such simulation systems often need to perform complex calculations over large amounts of data, which in turn requires efficient models and algorithms. This chapter intends to evaluate whether it is possible to optimize and extend an existing sales system called PCT, which is currently suffering from unacceptably high running times in its simulation process. This is done through analysis of the current implementation, followed by optimization of its models and development of efficient algorithms. The performances of these optimized and extended models are compared to the existing one in order to evaluate their improvement. The conclusion of this chapter is that the simulation process in PCT can indeed be optimized and extended. The optimized models serve as a proof of concept, which shows that results identical to the original system's can be calculated within < 1% of the original running time for the largest customers.

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