Evaluating alternative DaaS solutions in private and public OpenStack Clouds

Celesti, Antonio; Mulfari, Davide; Fazio, Maria; Puliafito, Antonio; Villari, Massimo

doi:10.1002/spe.2498

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The design of networks in a cloud infrastructure configuration is usually divided into public, management, and guest networks [3]. A guest network is capable of data communication between virtual instances which are running on one host, multiple hosts, or across the different subnets [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of container-based networking solutions for high-performance computing cloud

Lim¹,

Woo²,

Li³

2020

IJECE

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Recently, cloud service providers have been gradually changing from virtual machine-based cloud infrastructures to container-based cloud-native infrastructures that consider performance and workload-management issues. Several data network performance issues for virtual instances have arisen, and various networking solutions have been newly developed or utilized. In this paper, we propose a solution suitable for a high-performance computing (HPC) cloud through a performance comparison analysis of container-based networking solutions. We constructed a supercomputer-based test-bed cluster to evaluate the serviceability by executing HPC jobs.

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Section: Introductionmentioning

confidence: 99%

Performance analysis of container-based networking solutions for high-performance computing cloud

Lim¹,

Woo²,

Li³

2020

IJECE

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“…As a consequence, completely new kinds of useful information about resource usage characteristics can be derived. To test the solution in a realistic cloud environment, we decided to use the OpenStack real-life cloud framework, which is among the most popular solutions, for building and managing private and public clouds [11,12].…”

Section: Introductionmentioning

confidence: 99%

VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing

et al. 2019

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In this paper we propose a novel reservation plan adaptation system based on machine learning. In the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. If necessary, the plan may allocate new resources upon reservation where a sufficient amount of resources is available. Our solution allows the updating of a reservation plan initially prepared by an administra

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“…Thus, it is better to allow the users to control their own privacy data. The general solution [ 15 , 16 ] is that the devices should have two context-based operating modes: public and personal. Then, the user can choose the personal model and define the secure policy when they require privacy protection.…”

Section: Introductionmentioning

confidence: 99%

Centralized Duplicate Removal Video Storage System with Privacy Preservation in IoT

Yan

Wang

et al. 2018

Sensors

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In recent years, the Internet of Things (IoT) has found wide application and attracted much attention. Since most of the end-terminals in IoT have limited capabilities for storage and computing, it has become a trend to outsource the data from local to cloud computing. To further reduce the communication bandwidth and storage space, data deduplication has been widely adopted to eliminate the redundant data. However, since data collected in IoT are sensitive and closely related to users’ personal information, the privacy protection of users’ information becomes a challenge. As the channels, like the wireless channels between the terminals and the cloud servers in IoT, are public and the cloud servers are not fully trusted, data have to be encrypted before being uploaded to the cloud. However, encryption makes the performance of deduplication by the cloud server difficult because the ciphertext will be different even if the underlying plaintext is identical. In this paper, we build a centralized privacy-preserving duplicate removal storage system, which supports both file-level and block-level deduplication. In order to avoid the leakage of statistical information of data, Intel Software Guard Extensions (SGX) technology is utilized to protect the deduplication process on the cloud server. The results of the experimental analysis demonstrate that the new scheme can significantly improve the deduplication efficiency and enhance the security. It is envisioned that the duplicated removal system with privacy preservation will be of great use in the centralized storage environment of IoT.

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Evaluating alternative DaaS solutions in private and public OpenStack Clouds

Cited by 6 publications

References 12 publications

Performance analysis of container-based networking solutions for high-performance computing cloud

Performance analysis of container-based networking solutions for high-performance computing cloud

VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing

Centralized Duplicate Removal Video Storage System with Privacy Preservation in IoT

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