Probability-Based Cloud Storage Providers Selection Algorithms with Maximum Availability

Chang, Chia‐Wei; Liu, Pangfeng; Wu, Jan‐Jan

doi:10.1109/icpp.2012.51

Cited by 38 publications

(25 citation statements)

References 12 publications

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“…On the contrary, we focus on maximizing object availability for a given budget in a set of data centers. Our work has a similar objective function with the problem studied by Chang et al [4], but the main difference is an object is split to chunks and replicated across cloud providers in our work, while it is fully replicated in their model. The main motivation for our strategy is agile reacting to any changes in cloud providers [5].…”

Section: Related Workmentioning

confidence: 81%

“…According to the above description, we determine a set of data centers with two parameters, Failure Probability (FP) and Cost Per Object (CPO) for our simulation as shown in Table II. Since we have the failure probability of Amazon S3, Rackspace and Google's storage infrastructure, we use them as baseline, and add 6 data centers with the assumption that as availability of service is increased, the storage cost of object is raised [4]. It is also assumed that the cost and the failure probability reported in Table II remains constant during the simulation.…”

Section: A Simulation Settingmentioning

confidence: 99%

“…In the first experiment, we relax the constraint (4) and assume that all objects have the same priority from the user's perspective.…”

Section: B Algorithm 1: Minimum Cost Fixed Expected Availabilitymentioning

confidence: 99%

“…Availability of services is defined as the ratio of the total time that the storage services of a cloud provider is accessible during a given interval (e.g., one year) to the length of the interval. The metric which we use for availability is number of nines [4]. For example, if the availability of the system is 99.9% then we refer it as three nines.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Brokering Algorithms for Optimizing the Availability and Cost of Cloud Storage Services

Mansouri

Toosi

Buyya

2013

2013 IEEE 5th International Conference on Cloud Computing Technology and Science

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Abstract-In recent years, cloud storage providers have gained popularity for personal and organizational data, and provided highly reliable, scalable and flexible resources to cloud users. Although cloud providers bring advantages to their users, most cloud providers suffer outages from time-to-time. Therefore, relying on a single cloud storage services threatens service availability of cloud users. We believe that using multi-cloud broker is a plausible solution to remove single point of failure and to achieve very high availability. Since highly reliable cloud storage services impose enormous cost to the user, and also as the size of data objects in the cloud storage reaches magnitude of exabyte, optimal selection among a set of cloud storage providers is a crucial decision for users. To solve this problem, we propose an algorithm that determines the minimum replication cost of objects such that the expected availability for users is guaranteed. We also propose an algorithm to optimally select data centers for striped objects such that the expected availability under a given budget is maximized. Simulation experiments are conducted to evaluate our algorithms, using failure probability and storage cost taken from real cloud storage providers.

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Section: Related Workmentioning

confidence: 81%

Section: A Simulation Settingmentioning

confidence: 99%

“…In the first experiment, we relax the constraint (4) and assume that all objects have the same priority from the user's perspective.…”

Section: B Algorithm 1: Minimum Cost Fixed Expected Availabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Brokering Algorithms for Optimizing the Availability and Cost of Cloud Storage Services

Mansouri

Toosi

Buyya

2013

2013 IEEE 5th International Conference on Cloud Computing Technology and Science

View full text Add to dashboard Cite

show abstract

“…This lead to increasing effort in investigating and developing native cloud systems by leveraging such cloud services in a multi-cloud environment [15,1,14]. In our work, we are interested in the development of cloud-native software-defined elastic systems (SESs), designed, developed and constructed directly in cloud, from functionality collectively provided by cloud services.…”

Section: Introductionmentioning

confidence: 99%

QUELLE – A Framework for Accelerating the Development of Elastic Systems

Moldovan

Copil

Truong

et al. 2014

Advanced Information Systems Engineering

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An elastic recommender process for cloud service recommendation scalability

Zhou

Wang

et al. 2022

Concurrency and Computation

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Cloud computing services are ubiquitous in society and cloud recommender systems play a crucial role in intelligently selecting services for cloud users. Currently, recommendations are static with low scalability. Only one recommendation list is generated at a time and the recommender strategy in the recommendation cycle is not adjustable. This paper presents a new elastic recommender process (ERP) for cloud users. A Markov model is used to characterize the dynamic relationship between different user states. The ERP generates an elastic recommendation that can be used to dynamically adjust the recommender strategy to meet the user's needs based on their browsing records in the current service cycle without the recommender system's involvement. Experimental results show that the ERP improves the effectiveness of the recommender thus increasing the accuracy and diversity of its recommendations.

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Probability-Based Cloud Storage Providers Selection Algorithms with Maximum Availability

Cited by 38 publications

References 12 publications

Brokering Algorithms for Optimizing the Availability and Cost of Cloud Storage Services

Brokering Algorithms for Optimizing the Availability and Cost of Cloud Storage Services

QUELLE – A Framework for Accelerating the Development of Elastic Systems

An elastic recommender process for cloud service recommendation scalability

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