scholarly journals Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaolong Xu ◽  
Xuan Zhao ◽  
Feng Ruan ◽  
Jie Zhang ◽  
Wei Tian ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Placement ◽  
Cloud Services ◽  
Hybrid Cloud ◽  
Public Cloud ◽  
Private Cloud ◽  
The Public ◽  
Placement Method ◽  
The Cost

Nowadays, a large number of groups choose to deploy their applications to cloud platforms, especially for the big data era. Currently, the hybrid cloud is one of the most popular computing paradigms for holding the privacy-aware applications driven by the requirements of privacy protection and cost saving. However, it is still a challenge to realize data placement considering both the energy consumption in private cloud and the cost for renting the public cloud services. In view of this challenge, a cost and energy aware data placement method, named CEDP, for privacy-aware applications over big data in hybrid cloud is proposed. Technically, formalized analysis of cost, access time, and energy consumption is conducted in the hybrid cloud environment. Then a corresponding data placement method is designed to accomplish the cost saving for renting the public cloud services and energy savings for task execution within the private cloud platforms. Experimental evaluations validate the efficiency and effectiveness of our proposed method.

scholarly journals A Rule-based Approach for Effective Resource Provisioning in Hybrid Cloud Environment

2012 ◽  
pp. 128-133
Author(s):  
Rajkamal Kaur Grewal ◽  
Pushpendra Kumar Pateriya
Keyword(s):  
Resource Provisioning ◽  
Hybrid Cloud ◽  
Cloud Environment ◽  
Public Cloud ◽  
Private Cloud ◽  
Resource Manager ◽  
Rule Based ◽  
Heterogeneous Clouds ◽  
Rule Based Approach ◽  
The Cost

Resource provisioning is important issue in cloud computing and in the environment of heterogeneous clouds. The private cloud with confidentiality data configure according to users need. But the scalability of the private cloud limited. If the resources private clouds are busy in fulfilling other requests then new request cannot be fulfilled. The new requests are kept in waiting queue to process later. It take lot of delay to fulfill these requests and costly. In this paper Rule Based Resource Manager proposed for the Hybrid environment, which increase the scalability of private cloud on-demand and reduce the cost. Also set the time for public cloud and private cloud to fulfill the request and provide the services in time. The Evaluated the performance of Resource Manager on the basis of resource utilization and cost in hybrid cloud environment.

scholarly journals An optimization approach to capacity evaluation and investment decision of hybrid cloud: a corporate customer’s perspective

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
In Lee
Keyword(s):  
Cloud Computing ◽  
Investment Decision ◽  
Optimal Investment ◽  
Optimization Approach ◽  
Hybrid Cloud ◽  
Public Cloud ◽  
Private Cloud ◽  
Penalty Cost ◽  
Require Time ◽  
The Public

Abstract While the rapid growth of cloud computing is driven by the surge of big data, the Internet of Things, and social media applications, an evaluation and investment decision for cloud computing has been challenging for corporate managers due to a lack of proper decision models. This paper attempts to identify critical variables for making a cloud capacity decision from a corporate customer’s perspective and develops a base mathematical model to aid in a hybrid cloud investment decision under probabilistic computing demands. The identification of the critical variables provides a means by which a corporate customer can effectively evaluate various cloud capacity investment opportunities. Critical variables included in this model are an actual computing demand, the amount of private cloud capacity purchased, the purchase cost of the private cloud capacity, the price of the public cloud, and the default downtime loss/penalty cost. Extending the base model developed, this paper also takes into consideration the interoperability cost incurred in cloud bursting to the public cloud and derives the optimal investment. The interoperable cloud systems require time and investment by the users and/or cloud providers and there exists a diminishing return on the investment. Hence, the relationship between the interoperable cloud investment and return on investment is also investigated.

scholarly journals A Cost Estimation Model for Cloud Services and Applying to PC Laboratory Platforms

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 76
Author(s):  
KyungWoon Cho ◽  
Hyokyung Bahn
Keyword(s):  
Cost Estimation ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
Public Cloud ◽  
Estimation Model ◽  
The Public ◽  
Laboratory Services ◽  
Cost Estimation Model ◽  
The Cost

IaaS (Infrastructure as a Service) is a well-known computing service, which provides infrastructures over the cloud without owning real hardware resources. This is attractive as resources can be scaled up and down instantly according to the user’s computing demands. Customers of such services would like to adjust the utilization policy promptly by considering the charge of the service, but an instantaneous response is not possible as it takes several hours or even a couple of days for cloud service providers to inform the billing information. In this article, we present an instant cost estimation model for estimating the cost of public cloud resources. Specifically, our model estimates the cost of IaaS by monitoring the usage of resources on behalf of virtual machine instances. As this is performed by generating a user-side metering daemon, it is very precise and thus similar to the resource usage evaluated by the cloud service provider. To validate our model, we run PC laboratory services for 50 students in two classes by making use of a public cloud during a semester. Experimental results show that the accuracy of our model is over 99.3% in comparison with the actual charge of the public cloud.

scholarly journals Desain Dan Implementasi Hybrid Cloud Computing Sebagai Infrastruktur Untuk Analisis Big Data Menggunakan Analytic Hierarchy Process(AHP)

Techno Com ◽  
2018 ◽  
Vol 17 (4) ◽  
pp. 404-414
Author(s):  
Toga Aldila Cinderatama ◽  
Yoppy Yunhasnawa ◽  
Rinanza Zulmy Alhamri
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Analytic Hierarchy Process ◽  
Fuzzy Ahp ◽  
Cloud Provider ◽  
Hybrid Cloud ◽  
Public Cloud ◽  
Private Cloud ◽  
Analytic Hierarchy ◽  
Hierarchy Process

Dalam implementasi big data biasanya membutuhkan sumber daya yang cukup besar untuk dapat melakukan analisis terhadap data-data yang jumlahnya sangat besar tersebut, hal ini biasanya menjadi kendala dikarenakan keterbatasan sumber daya yang dimiliki. Komputasi awan (cloud computing) yang salah satunya mempunyai sifat elasticity di dalamnya, menawarkan solusi keterbatasan sumber daya ini. Sumber daya yang terbatas misalkan dalam hal processor, RAM atau storage, dapat digabungkan dengan sumber daya yang dimiliki public cloud provider yang tersedia di market. Sehingga penggabungan 2 sumber daya ini, private cloud dan public cloud, diharapkan menjadi solusi untuk dapat mengimplementasikan analisis big data yang dapat diterapkan untuk analisis berbagai macam bidang. Secara umum penelitian ini bertujuan untuk mengimplementasikan hybrid cloud yang menggabungkan sumber daya dari private cloud dengan sumber daya dari public cloud sebagai infrastruktur untuk analisis big data. Secara khusus tujuan penelitian ini adalah merumuskan sebuah metode minimalisasi cost dalam pemilihan public cloud dengan pendekatan sistem pendukung keputusan menggunakan Fuzzy AHP pada pemilihan public cloud. Langkah pertama yang dilakukan dalam penelitian ini adalah pengumpulan data cost penggunaan resource dari public cloud. Selanjutnya dilakukan analisis kebutuhan sumber daya yang diperlukan untuk melalukan analisis big data dengan studi kasus topik tertentu. Selanjutnya tahap analisis terhadap pemilihan public cloud yang tepat untuk digunakan sumber dayanya dengan pertimbangan minimalisasi cost. Langkah terakhir adalah implementasi hybrid cloud dan melakukan analisis dan evaluasi terhadap metode yang diusulkan.

scholarly journals Secured Storage of Big Data in Cloud

2019 ◽  
Vol 8 (2S3) ◽  
pp. 6-10
Keyword(s):  
Big Data ◽  
Large Volume ◽  
Sensitive Information ◽  
Hybrid Cloud ◽  
Public Cloud ◽  
Private Cloud ◽  
Sensitive Data ◽  
Unauthorized Access ◽  
Public Data ◽  
Hardware Failures

Big Data refers to large volume of data and necessitates the usage of cloud for storage and processing. Cloud tenants data is not only stored in the cloud, but it is also shared among multiple users. The data stored in cloud must be well protected as it is prone to malicious attacks and hardware failures. Also, user’s data on cloud contain sensitive information that must be protected and highly restricted from unauthorized access. Cloud deployment models such as public cloud, private cloud, and hybrid cloud can be used for storing data of cloud tenants. This paper proposes a secured storage approach for protecting data in cloud by partitioning big dataset into blocks containing user’s sensitive data, insensitive data, and public data. Sensitive data is moved to private cloud and is well protected using proxy re encryption. Insensitive data is stored in public cloud and some data blocks are randomly encrypted. Also, the storage index information of insensitive data blocks on cloud is encrypted and shared among authorized users. Public data is also moved to public cloud and to protect it the storage path information is only encrypted and shared. The proposed approach shows better results with reduced computation overhead and improved security.

A Big Data Application for Energy Consumption Management of Commercial Buildings

2021 ◽  
Vol 2 (1) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Streams ◽  
Design And Implementation ◽  
Available Energy ◽  
Cost Of Energy ◽  
Data Application ◽  
Consumption Data ◽  
Big Data Application ◽  
The Cost

The demand for energy is increasing rapidly and, after a few years, it may surpass the available energy, which may lead the energy providers to increase the cost of energy consumption to compensate the cost for the production. This paper provides design and implementation details of a prototype big data application developed to help large buildings to automatically manage their energy consumption by setting energy consumption targets, collecting periodic energy consumption data, storing the data streams, displaying the energy consumption graphically in real-time, analyzing the consumption patterns, and generating energy consumption graphs and reports. The application is connected to Mongo NoSQL backend database to handle the large and continuously changing data. This big data energy consumption management system is expected to help the users in managing energy consumption by analyzing the patterns to see if it is within or above the desired consumption targets and displaying the data graphically.

Data Placement and Query for Cloud Computing Based on MyHeawood Network

2014 ◽  
Vol 543-547 ◽  
pp. 3100-3104
Author(s):  
Xin Huang ◽  
Yu Xing Peng ◽  
Peng Fei You
Keyword(s):  
Cloud Computing ◽  
Interconnection Network ◽  
Cost Effective ◽  
Data Placement ◽  
Cloud Services ◽  
Massive Data ◽  
Massive Datasets ◽  
The Family ◽  
The Cost ◽  
Hashing Algorithm

The massive data in Data centers network will be frequently accessed massive datasets for cloud services, which will lead to some new requirements and becomes an important issue for interconnection topology and data management in cloud computing. According to the cost-effective, the paper proposes a new interconnection network MyHeawood for cloud computing. MyHeawood is constructed by small switches and servers with dual-port NIC according to recursive method. The data placement strategy in MyHeawood is a hashing algorithm based on the family of hash functions. MyHeawood uses three replicas strategy base on master copy, which is allocated in different sub layer to improve the reliability of data.

Big Data in the Public Cloud

2014 ◽  
Vol 1 (2) ◽  
pp. 13-15 ◽  
Author(s):  
Eli Collins
Keyword(s):  
Big Data ◽  
Public Cloud ◽  
The Public
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