Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers

Mansouri, Yaser; Toosi, Adel Nadjaran; Buyya, Rajkumar

doi:10.1109/tcc.2017.2659728

Cited by 85 publications

(34 citation statements)

References 30 publications

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“…However, the algorithm required a high cost of operation. Prabavathy et al, [3] have presented "Improving Read Throughput of Deduplicate Cloud Storage using Frequent Pattern-Based Prefetching Technique" to recognizes the frequent access patterns on history of usage. It is also predicting the "combine of fingerprints" which is most probably be accessed in the immediate future on the cache values.…”

Section: Related Workmentioning

confidence: 99%

A Cloud Storage Monitoring System using Deduplication and File Access Pattern

A*¹,

SudalaiMuthu²

2019

IJRTE

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Cloud computing is important on current demanding business requirements and it has been emerged as unavoidable technology. The usage of memory storage for Cloud Computing IaaS Service is expanding exponentially every year. The cloud storages are used by the cloud user due to less storage cost compare with other storage methods. The replication of files provides high availability, reliability. It helps in attractive the data availability which reduces the overall access time of the files, but at the same time it occupies more storage space and yielded high storage cost. The cloud user holds storage space twice than which is needed. It is a dire need of a system to find unwanted files in the cloud and the frequency of file access in order to optimize the storage space in cloud. This paper proposed a system, Cloud Storage Monitoring (CSM) system, which is monitoring the IaaS storage usage and analyzes the file access patterns by various parameters to identify the frequency of access, size, future access prediction, replication of files in the cloud storage. This proposed system generates a ranking for each file which is also predicts future access pattern. The CSM system generates a dashboard to the user to reorganize or delete or archive the files to eliminate duplicate files in the cloud storage which can increase the availability of the files. The CSM system is experimented in the Cloud Sim environment, the results showed that the availability have been increased and the storage space is reduced as 10.91% lower than the normal system.

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Section: Related Workmentioning

confidence: 99%

A Cloud Storage Monitoring System using Deduplication and File Access Pattern

A*¹,

SudalaiMuthu²

2019

IJRTE

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“…Sometimes it may provide poor user flexibility and energy efficiency. To keep up with the rapid evolution in information infrastructure a more flexible way of controlling cloud systems is proposed to satisfy the user and energy cost in [96]. The bidding strategies [100] based on three performance metrics such as cost, waiting time, and interruption rate is another cost based method.…”

Section: Minimize Costmentioning

confidence: 99%

Review of the quality of service scheduling mechanisms in cloud

RemeshBabu¹,

Samuel²

2018

IJET

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Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.

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“…e only shortcoming of this model is the practical difficulties in implementing it in real-world applications. Mansouri et al [35] presented a cost optimization model for ensuring dynamic replication and subsequent migration in cloud. is model utilized an optimal offline algorithm of dynamic and linear programming to analyse the workload of the system.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective Glowworm Swarm Optimization-Based Dynamic Replication Algorithm for Real-Time Distributed Databases

Thalij

Hakkoymaz

2018

Scientific Programming

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Distributed systems offer resources to be accessed geographically for large-scale data requests of different users. In many cases, replication of the vital data files and storing their replica in multiple locations accessible to the requesting clients is vital in improving the data availability, reliability, security, and reduction of the execution time. It is important that real-time distributed databases maintain the consistency constraints and also guarantee the time constraints required by the client requests. However, when the size of the distributed system increases, the user access time also tends to increase, which in turn increases the vitality of the replica placement. Thus, the primary issues that emerge are deciding upon an optimal replication number and identifying perfect locations to store the replicated data. These open challenges have been considered in this study, which turns to develop a dynamic data replication algorithm for real-time distributed databases using a multiobjective glowworm swarm optimization (MGSO) strategy. The proposed algorithm adapts the random patterns of the read-write requests and employs a dynamic window mechanism for replication. It also models the replica number and placement problem as a multiobjective optimization problem and utilizes MGSO for resolving it. The cost models are presented to ensure the time constraint satisfaction in servicing user requests. The performance of the MGSO dynamic data replication algorithm has been studied using competitive analysis, and the results show the efficiency of the proposed algorithm for the distributed databases.

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Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers

Cited by 85 publications

References 30 publications

A Cloud Storage Monitoring System using Deduplication and File Access Pattern

A Cloud Storage Monitoring System using Deduplication and File Access Pattern

Review of the quality of service scheduling mechanisms in cloud

Multiobjective Glowworm Swarm Optimization-Based Dynamic Replication Algorithm for Real-Time Distributed Databases

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