Deduplication-Based Energy Efficient Storage System in Cloud Environment

Currently, more than 9 billion things are connected in the Internet of Things (IoT). This number is expected to exceed 20 billion in the near future, and the number of things is quickly increasing, indicating that numerous data will be generated. It is necessary to build an infrastructure to manage the connected things. Cloud computing has become important in terms of analysis and data storage for IoT. In this paper, we consider a cloud broker, which is an intermediary in the infrastructure that manages the connected things in cloud computing. We study an optimization problem for maximizing the profit of the broker while minimizing the response time of the request and the energy consumption. A multi-objective particle swarm optimization (MOPSO) is proposed to solve the problem. The performance of the proposed MOPSO is compared with that of a genetic algorithm and a random search algorithm. The results show that the MOPSO outperforms a well-known genetic algorithm for multi-objective optimization.

show abstract

“…Several studies proposed a novel model/framework for solving problems in cloud computing [12]- [16]. CSAM-IISG was proposed in [12].…”

Section: Related Workmentioning

confidence: 99%

“…This system can tolerate pressure more than single cloud service under emergency environment. In [16], an online-deduplication mechanism based on energy efficiency storage system was proposed for virtual machines (VM) storage. The authors also designed a deduplication selection algorithm to minimize the storage energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective Optimization in Cloud Brokering Systems for Connected Internet of Things

Kumrai

Ota

Dong

et al. 2017

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…To reduce the cost for streaming big data computing, an optional method is choose some small cloud providers with lower rate per load. However, small cloud providers have not enough capacity to support large scale work loads [7] [8]. Meanwhile, their services are short of support for big data computing.…”

Section: Introductionmentioning

confidence: 99%

Pricing and Repurchasing for Big Data Processing in Multi-Clouds

Dong

Ota

et al. 2016

IEEE Trans. Emerg. Topics Comput.

View full text Add to dashboard Cite

Processing streaming big data becomes critical as new divers Internet of Tings (IoT) applications begin to emerge. Existing cloud pricing strategy is unfriendly for processing streaming big data with varying load. Multiple cloud environment is a potential solution with an efficient pay-on-demand pricing strategy for processing streaming big data. In this paper, we propose a intermediary framework with multiple cloud environment to provide streaming big data computing service with lower cost per load, in which a cloud service intermediary rents the cloud service from multiple cloud providers and provides streaming processing service to the users with multiple service interfaces. In this framework, we also propose a pricing strategy to maximize the revenue of the multiple cloud intermediary. With extensive simulations, our pricing strategy brings higher revenue than other pricing methods.

show abstract

“…Cloud computing is a shared pool with abundant computing resources, which could provide on‐demand services and applications . It has brought greatly economic savings and flexibility, which attracts people to outsource their files including business plans, private photos and emails records to cloud server.…”

Section: Introductionmentioning

confidence: 99%

PIMRS: achieving privacy and integrity-preserving multi-owner ranked-keyword search over encrypted cloud data

Wen

et al. 2016

Security Comm. Networks

View full text Add to dashboard Cite

Because of the flexibility and convenience brought by cloud computing, it has been adopted in many applications. To preserve the privacy of cloud data, data owner often encrypts all sensitive data files, which makes the keyword search application based on plaintext a very challenging task. Therefore, several privacy‐preserving keyword search algorithms have been developed recently, and most of these works support only single‐data‐owner settings. However, there are always more than one data owners in real applications, which are much more complex and challenging than single‐owner scenario. To support multi‐owner keyword search, those prior search algorithms need to be repeated several times, because each data owner intends to encrypt his own files with a unique private‐key separately. It is absolutely not an efficient way. In this paper, we propose a privacy and integrity‐preserving multi‐owner ranked‐keyword search scheme termed PIMRS. In the PIMRS, we exploit an asymmetric scalar‐product encryption function based on the TF × IDF rule to preserve data privacy and to obtain more precise search results. Furthermore, a circular bi‐direction‐linked list based scheme is proposed to preserve the integrity of search results, which also enables the misbehaviors of cloud server to be detected. The security analysis of PIMRS shows its privacy and integrity property, and extensive experiments based on real‐world data set confirm the high efficiency of proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Deduplication-Based Energy Efficient Storage System in Cloud Environment

Cited by 27 publications

References 15 publications

Multiobjective Optimization in Cloud Brokering Systems for Connected Internet of Things

Multiobjective Optimization in Cloud Brokering Systems for Connected Internet of Things

Pricing and Repurchasing for Big Data Processing in Multi-Clouds

PIMRS: achieving privacy and integrity-preserving multi-owner ranked-keyword search over encrypted cloud data

Contact Info

Product

Resources

About