Scalable and Cost-Efficient Algorithms for Reliable and Distributed Cloud Storage

Hadji, Makhlouf

doi:10.1007/978-3-319-29582-4_2

Cited by 6 publications

(10 citation statements)

References 15 publications

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“…In [15], Hadji proposes a commodity flow solution to minimize the cost of storing data and latency to access data centers. However, the network and operation costs are ignored when users access their data.…”

Section: Monetary Cost Optimization Based On Qos Metricsmentioning

confidence: 99%

“…But the proposed algorithm to solve the minimization problem is a simple heuristic solution and cannot obtain the global optimal one. The studies in [15] and [17] only minimize the monetary cost at a certain point in the time slot. Mansouri et al [18] propose the optimal offline algorithm to minimize the residential and migration costs in a time slot where the exact and known future workload is assumed.…”

Section: Monetary Cost Optimization Based On Qos Metricsmentioning

confidence: 99%

“…It also prevents vendor lock-in through splitting a data object into multiple CSPs, but neglects how to determine the replica count. In [15], Hadji discusses the rational the number of chunks to be used to split the original data according to data center failure probabilities, number of replicas of each chunk, and expected data availability.…”

Section: Data Availability Optimization Based On Qos Metricsmentioning

confidence: 99%

“…end if 13: end for 14: for i = 0 to n do corresponding position of the array representing the individual is modified to 1 (lines [14][15][16]. In this optimization problem, the optimal data placement solution includes not only a list of CSPs but also erasure coding parameters.…”

mentioning

confidence: 99%

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Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

Wang

Zhao

Liu

et al. 2020

cai

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With the advent of big data age, data volume has been changed from trillionbyte to petabyte with incredible speed. Owing to the fact that cloud storage offers the vision of a virtually infinite pool of storage resources, data can be stored and accessed with high scalability and availability. But a single cloud-based data storage has risks like vendor lock-in, privacy leakage, and unavailability. Multicloud storage can mitigate these risks with geographically located cloud storage providers. In this storage scheme, one important challenge is how to place a user's data cost-effectively with high availability. In this paper, an architecture for multicloud storage is presented. Next, a multi-objective optimization problem is defined to minimize total cost and maximize data availability simultaneously, which can be solved by an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions called the Pareto-optimal set. Then, a method is proposed which is based on the entropy method to determine the most suitable solution for users who cannot choose one from the Pareto-optimal set directly. Finally, the performance of the proposed algorithm is validated by extensive experiments based on real-world multiple cloud storage scenarios.

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Section: Monetary Cost Optimization Based On Qos Metricsmentioning

confidence: 99%

Section: Monetary Cost Optimization Based On Qos Metricsmentioning

confidence: 99%

Section: Data Availability Optimization Based On Qos Metricsmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

Wang

Zhao

Liu

et al. 2020

cai

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“…In this propose utility-based replication strategies on clouds server. In this process address the data replication for availability in the face of unreliable works, this is different from this optimization work [8,9].…”

Section: Related Workmentioning

confidence: 99%

Popularity (Hit Rate) Based Replica Creation for Enhancing the Availability in Cloud Storage

Selvi¹,

Anbuselvi²

2018

IJIES

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Abstract:In cloud computing, the replication management system has been well adopted in cloud storage applications. To provide the availability and reliability, the replication system replicates the files and can be stored in different server. The system led some complicated issues such as high memory consumption, incurred high storage cost and to access the file is more complicated issues in recent cloud storage applications. In the existing technique, File Accessing Frequency based Ranking (FAFR) Algorithm and Dynamically Reduced Replica for Rarely Accessed files (DRRRA) algorithm work jointly and identify the rarely accessed files and retain the replica in two server other replicated files are deleted. To provide access to more request with 2 or 3-replica is a complicated issue. Thus, this paper proposes a Dynamic replica Creation for Availability enhanced Storage (DRCAES) algorithm which jointly work with FAFR algorithm to predict most frequently accessed files and automatically replicated to other server based on server memory. The aim of this proposed approach is to enhance the availability, thereby reducing the request-response delay time. Thus the proposed approach optimizes the number of replicas, occupied space, and cost.

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Cost-Effective and Latency-Minimized Data Placement Strategy for Spatial Crowdsourcing in Multi-Cloud Environment

Wang

Chen

Zhou

et al. 2023

IEEE Trans. Cloud Comput.

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Scalable and Cost-Efficient Algorithms for Reliable and Distributed Cloud Storage

Cited by 6 publications

References 15 publications

Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

Popularity (Hit Rate) Based Replica Creation for Enhancing the Availability in Cloud Storage

Cost-Effective and Latency-Minimized Data Placement Strategy for Spatial Crowdsourcing in Multi-Cloud Environment

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