Infrastructure as a service (IaaS): A comparative performance analysis of open-source cloud platforms

Shahzadi, Sonia; Iqbal, Muddesar; Qayyum, Zia Ul; Dagiuklas, Tasos

doi:10.1109/camad.2017.8031522

Cited by 26 publications

(11 citation statements)

References 23 publications

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“…• Time consumption for encryption/decryption files (T): is the total time the system takes to encrypt/decrypt all blocks of the selected file using the chosen algorithm from the start of the encryption/decryption process to the end [48,49]. It could be calculated by Equation (3).…”

Section: Performance Evaluation Measuresmentioning

confidence: 99%

“…In general, cloud computing offers three main categories of service models; Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). IaaS provides the infrastructure as virtual computing machines (e.g., CPU, memory, storage, and network) accessible by cloud users [3]. PaaS is another kind of cloud computing service that offers a platform for users to create, run, and manage applications without going into detail about their infrastructure construction [4].…”

Section: Introductionmentioning

confidence: 99%

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A New Hybrid Automated Security Framework to Cloud Storage System

2021

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The need for cloud storage grows day after day due to its reliable and scalable nature. The storage and maintenance of user data at a remote location are severe issues due to the difficulty of ensuring data privacy and confidentiality. Some security issues within current cloud systems are managed by a cloud third party (CTP), who may turn into an untrustworthy insider part. This paper presents an automated Encryption/Decryption System for Cloud Data Storage (AEDS) based on hybrid cryptography algorithms to improve data security and ensure confidentiality without interference from CTP. Three encryption approaches are implemented to achieve high performance and efficiency: Automated Sequential Cryptography (ASC), Automated Random Cryptography (ARC), and Improved Automated Random Cryptography (IARC) for data blocks. In the IARC approach, we have presented a novel encryption strategy by converting the static S-box in the AES algorithm to a dynamic S-box. Furthermore, the algorithms RSA and Twofish are used to encrypt the generated keys to enhance privacy issues. We have evaluated our approaches with other existing symmetrical key algorithms such as DES, 3DES, and RC2. Although the two proposed ARC and ASC approaches are more complicated, they take less time than DES, DES3, and RC2 in processing the data and obtaining better performance in data throughput and confidentiality. ARC outperformed all of the other algorithms in the comparison. The ARC’s encrypting process has saved time compared with other algorithms, where its encryption time has been recorded as 22.58 s for a 500 MB file size, while the DES, 3DES, and RC2 have completed the encryption process in 44.43, 135.65, and 66.91 s, respectively, for the same file size. Nevertheless, when the file sizes increased to 2.2 GB, the ASC proved its efficiency in completing the encryption process in less time.

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Section: Performance Evaluation Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Hybrid Automated Security Framework to Cloud Storage System

2021

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“…e cloud service model is mainly divided into three types, namely, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) [25][26][27].…”

Section: Cloud Computingmentioning

confidence: 99%

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Molo

Badejo

Adetiba

et al. 2021

Applied Computational Intelligence and Soft Computing

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Cloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. This technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of Things (IoT), several challenges have emerged with these new technologies. Therefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosystem.

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“…There are three services in cloud computing [3][4] [5]. They are infrastructure as a service (IAAS), platform as a service (PAAS) and software as a service (SAAS).…”

Section: Fig1 Architecture Of Multi Cloud Environmentmentioning

confidence: 99%

Process Optimization of Big-Data Cloud Centre Using Nature Inspired Firefly Algorithm and K-Means Clustering

T*¹,

Samath²

2019

IJITEE

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During the last decade, the growth of big data is immeasurable in information technology. Big data has the potential to take all the decisions necessary for a company or business. But it has many challenges as well. As its size and volume are immeasurably ample it is a very challenging task to store, process and mines it. At the same time as a boon to it cloud computing has a large capacity to store this big data and provides tremendous processing power. It is a challenging task to process large amount of data frequently in the big-data cloud center through the thousands of interconnected servers. Due to the day by day growth of the big-data, big-data cloud center is forced to improve its Quality of Service (QoS) metrics like throughput, latency and response time. Hence, to develop an optimal data processing optimization method is a current research problem that has to be solved. The major intention of this paper is to develop an application that provides maximum throughput, minimum latency and reduce the response time. Toward this, we have developed an optimization technique using nature-inspired firefly optimization algorithm and k-means clustering (FA-KMeans). The developed optimization method has been evaluated with state of art algorithms. Its experimental result elucidates that our proposed method provides good throughput, reduces latency and response time.

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Infrastructure as a service (IaaS): A comparative performance analysis of open-source cloud platforms

Cited by 26 publications

References 23 publications

A New Hybrid Automated Security Framework to Cloud Storage System

A New Hybrid Automated Security Framework to Cloud Storage System

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Process Optimization of Big-Data Cloud Centre Using Nature Inspired Firefly Algorithm and K-Means Clustering

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