cost effective approach for privacy preserving of intermediate data sets in real time clouds using...

7/29/2019 Cost Effective Approach for Privacy Preserving of Intermediate Data Sets in Real Time Clouds Using Windows Azure

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Cost Effective approach for privacy preserving of intermediate Data sets in

Real time clouds using Windows Azure

Abstract

Cloud computing brings significant benefits for service providers and users because of itscharacteristics: e.g., on demand, pay for use, scalable computing. Cloud computing is anemerging distributed computing paradigm that promises to offer cost-effective scalable on

demand services to users, without the need for large up-front infrastructure investments. Cloud

computing brings significant benefits for both service providers and service users. For serviceusers, they pay the computing resources only on demand and without worrying about hardware,

software maintenance or upgrade. For service providers, with VMs, they can shrink or expand

the utilization of physical resources based on workloads requirements. In this paper, wehighlighted envision about security emphasizing for the maintenance of privacy and trust in

accepting the cloud computing. Encrypting ALL data sets in cloud is widely adopted in existing

approaches to address this challenge. Evaluation results demonstrate that the privacy-preserving

cost of intermediate data sets can be significantly reduced with our approach over existing oneswhere all data sets are encrypted.

Keywords: Privacy preserving, Cloud computing, data storage privacy, intermediate data setprivacy.

Introduction:

A new generation of technology is transforming the world of computing. Advances in Internet-

based data storage, processing, and servicescollectively known as cloud computinghaveemerged to complement the traditional model of running software and storing data on personal

devices or on-premises networks. Many familiar software programs, from email and word

processing to spreadsheets, are now available as cloud services. Many of these applications havebeen offered over the Internet for years, so cloud computing might not feel particularly new to

some users.

Still, several aspects of cloud computing differ markedly from previous computing paradigms

and offer distinct benefits. Todays cloud services are highly scalable, which enables customersto pay only for the computing storage and power they need, when they need it. Datacenters in

diverse geographies allow cloud providers to store and back up information in multiple

locations, which enhances reliability and increases processing speed. And significant economiesof scale generated by server farms that can simultaneously support scores of users mean major

cost savings for customers.

These advantages are leading governments, universities, and businesses of all sizes to movemission-critical services such as customer relationship management, enterprise resource

planning, and financial data management into the cloud. At the same time, the unique attributes

of cloud computing are raising important business and policy considerations regarding howindividuals and organizations handle information and interact with their cloud provider.

In the traditional information technology (IT) model, an organization is accountable for all

aspects of its data protection regime, from how it uses sensitive personal information to how it


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stores and protects such data stored on its own computers. Cloud computing changes the

paradigm because information flows offsite to datacenters owned and managed by cloud

providers.

Cloud customers remain ultimately responsible for controlling the use of the data and protectingthe legal rights of individuals whose information they have gathered. But defining the allocation

of responsibilities and obligations for security and privacy between cloud customers and cloudprovidersand creating sufficient transparency about the allocationis a new challenge. It isimportant for customers and their cloud providers to clearly understand their role and be able to

communicate about compliance requirements and controls across the spectrum of cloud

services.

Objective of the project:

To make our application more cost effective as well as efficient.

To preserve the intermediate data sets

To make the application to be more secure to handle data in the real time clouds.

To make our application data to be reliable.

Literature Review:

M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R. Katz, A. Konwinski, G. Lee, D.Patterson, A. Rabkin, I. Stoica, and M. Zaharia, A View of Cloud Computing,Comm. ACM, vol. 53, no. 4, pp. 50-58, 2010.

Cloud computing brings significant benefits for service providers and users because of its

characteristics: e.g., on demand, pay for use, scalable computing. Virtualization management is

a critical task to accomplish effective sharing of physical resources and scalability. Existingresearch focuses on live Virtual Machine (VM) migration as a workload consolidation strategy.

However, the impact of other virtual network configuration strategies, such as optimizing total

number of VMs for a given workload, the number of virtual CPUs (vCPUs) per VM, and thememory size of each VM has been less studied. This paper presents specific performance

patterns on different workloads for various virtual network configuration strategies. For loosely

coupled CPU-intensive workloads, on an 8-CPU machine, with memory size varying from512MB to 4096MB and vCPUs ranging from 1 to 16 per VM; 1, 2, 4, 8 and 16VMs

configurations have similar running time. The prerequisite of this conclusion is that all 8

physical processors are occupied by vCPUs. For tightly coupled CPU-intensive workloads, the

total number of VMs, vCPUs per VM, and memory allocated per VM, become critical for

performance. We obtained the best performance when the ratio of the total number of vCPUs toprocessors is 2. Doubling the memory size on each VM, for example from 1024MB to 2048MB,

gave us at most 15% improvement of performance when the ratio of total vCPUs to physicalprocessors is 2. This research will help private cloud administrators decide how to configure

virtual resources for given workloads to optimize performance. It will also help public cloud

providers know where to place VMs and when to consolidate workloads to be able to turn on/offPhysical Machines (PMs), thereby saving energy and associated cost. Finally it helps cloud


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service users decide what kind of and how many VM instances to allocate for a given workload

and a given budget.

R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, Cloud Computing andEmerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the Fifth

Utility, Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009.

With the significant advances in Information and Communications Technology (ICT) over the

last half century, there is an increasingly perceived vision that computing will one day be the 5th

utility (after water, electricity, gas, and telephony). This computing utility, like all other fourexisting utilities, will provide the basic level of computing service that is considered essential to

meet the everyday needs of the general community. To deliver this vision, a number of

computing paradigms have been proposed, of which the latest one is known as Cloud

computing. Hence, in this paper, we define Cloud computing and provide the architecture forcreating Clouds with market-oriented resource allocation by leveraging technologies such as

Virtual Machines (VMs). We also provide insights on market-based resource management

strategies that encompass both customer-driven service management and computational riskmanagement to sustain Service Level Agreement (SLA)-oriented resource allocation. In

addition, we reveal our early thoughts on interconnecting Clouds for dynamically creating

global Cloud exchanges andmarkets. Then, we present some representative Cloud platforms, especially those developed in

industries along with our current work towards realizing market-oriented resource allocation of

Clouds as realized in Aneka enterprise Cloud technology. Furthermore, we highlight the

difference between High Performance Computing (HPC) workload and Internet-based services

workload. We also describe a meta-negotiation infrastructure to establish global Cloudexchanges and markets, and illustrate a case study of harnessing Storage Clouds for high

performance content delivery. Finally, we conclude with the need for convergence of competingIT paradigms to deliver our 21st century vision.

L. Wang, J. Zhan, W. Shi, and Y. Liang, In Cloud, Can Scientific

Communities Benefit from the Economies of Scale?, IEEE Trans. Parallel

and Distributed Systems, vol. 23, no. 2, pp. 296-303, Feb. 2012.

The basic idea behind cloud computing is that resource providers offer elastic resources to end

users. In this paper, we intend to answer one key question to the success of cloud computing: in

cloud, can small-to-medium scale scientific communities benefit from the economies of scale?Our research contributions are threefold: first, we propose an innovative public cloud usage

model for small-to-medium scale scientific communities to utilize elastic resources on a public

cloud site while maintaining their flexible system controls, i.e., create, activate, suspend,resume, deactivate, and destroy their high-level management entitiesservice management

layers without knowing the details of management. Second, we design and implement an

innovative systemDawningCloud, at the core of which are lightweight service management

layers running on top of a common management service framework. The common management


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service framework of DawningCloud not only facilitates building lightweight service

management layers for heterogeneous workloads, but also makes their management tasks

simple. Third, we evaluate the systems comprehensively using both emulation and real

experiments. We found that for four traces of two typical scientific workloads: High-Throughput Computing (HTC) and Many-Task Computing (MTC), DawningCloud saves the

resource consumption maximally by 59.5 and 72.6 percent for HTC and MTC service providers,respectively, and saves the total resource consumption maximally by 54 percent for the resourceprovider with respect to the previous two public cloud solutions. To this end, we conclude that

small-to-medium scale scientific communities indeed can benefit from the economies of scale of

public clouds with the support of the enabling system.

H. Takabi, J.B.D. Joshi, and G. Ahn, Security and Privacy Challenges in

Cloud Computing Environments, IEEE Security & Privacy, vol. 8, no. 6, pp.

24-31, Nov./Dec. 2010.

The cloud computing paradigm is still evolving, but has recently gained tremendous

momentum. However, security and privacy issues pose as the key roadblock to its fast adoption.

In this article, the authors present security and privacy challenges that are exacerbated by theunique aspects of clouds and show how they're related to various delivery and deployment

models. They discuss various approaches to address these challenges, existing solutions, and

future work needed to provide a trustworthy cloud computing environment.

D. Zissis and D. Lekkas, Addressing Cloud Computing Security Issues,

Future Generation Computer Systems, vol. 28, no. 3, pp. 583-592, 2011.

Cloud computing is known as the newest technologies in IT field which causes some worries forconsumers and its producers due to its novelty. Looking at its literature, we can see the privacy

and security aspects and trust are the main concerns. It creates an important hindrance for using

by users. So we decided to evaluate some factors such as security for the acceptance of cloud

computing. In this paper, we highlighted envision about security emphasizing for the

maintenance of privacy and trust in accepting the cloud computing. As a result, we are proposed

new recommendations for improving security, decreasing risks, increasing trust and maintaining

privacy which they are necessary to adopt cloud computing.


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Architecture

Methodology:

Methodology for project development is Systems Development life cycle(SDLC)

This is a conceptual model used in software development projects. In this method, there is a

possibility of combining two or more project management methodologies for the best outcome.SDLC also heavily emphasizes on the use of documentation and has strict guidelines on it.

Expected Results

In this paper, we have proposed an approach that identifies which part of intermediate data sets

needs to be encrypted while the rest does not, in order to save the privacy preserving cost. A treestructure has been modeled from the generation relationships of intermediate data sets to

analyze privacy propagation among data sets. We have modeled the problem of saving privacy-preserving cost as a constrained optimization problem which is addressed by decomposing the

privacy leakage constraints. A practical heuristic algorithm has been designed accordingly.

Evaluation results on real-world data sets and larger extensive data sets have demonstrated thecost of preserving privacy in cloud can be reduced significantly with our approach over existing

ones where all data sets are encrypted.


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SOFTWARE REQUIREMENTS:

Operating System : Windows

Technology : Microsoft C#.Net and ASP.Net

Web Technologies : Html, JavaScript, CSS

IDE : Visual studio 2010

Web Server : IIS

Cloud Tool : Windows Azure

Database : MS SQL

.Net Version : 4.0

HARDWARE REQUIREMENTS:

Hardware : Pentium

Speed : 1.1 GHz

RAM : 1GB


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Hard Disk : 20 GB

Floppy Drive : 1.44 MB

Key Board : Standard Windows Keyboard

Mouse : Two or Three Button Mouse

Monitor : SVGA

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