A replicas placement approach of component services for service-based cloud application

Wu, Jiaxuan; Bin, Zhang; Yang, Lei; Wang, Peng

doi:10.1007/s10586-016-0552-2

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…Mohamed et al [74] presented a review of service-based replication, their challenges, their techniques, their types, and their algorithms in different distributed setups (serviceoriented architecture (SOA), cloud, and mobile). Additionally, they also examined and explained the participation of replication in promoting various QoS attributes, such as availability, reliability, scalability, performance, and security [74] Some of the latest research in the field of service-oriented replication for cloud computing are addressed in Reference [72] for replica provisioning policy, Reference [75] for dependency aware dynamic replication, Reference [76] for replicas placement, and Reference [77] for consistency-based replication.…”

Section: Related Surveysmentioning

confidence: 99%

Quantitative Analysis and Performance Evaluation of Target-Oriented Replication Strategies in Cloud Computing

Waseem¹,

Din

Alshamrani

et al. 2021

Electronics

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Data replications effectively replicate the same data to various multiple locations to accomplish the objective of zero loss of information in case of failures without any downtown. Dynamic data replication strategies (providing run time location of replicas) in clouds should optimize the key performance indicator parameters, like response time, reliability, availability, scalability, cost, availability, performance, etc. To fulfill these objectives, various state-of-the-art dynamic data replication strategies has been proposed, based on several criteria and reported in the literature along with advantages and disadvantages. This paper provides a quantitative analysis and performance evaluation of target-oriented replication strategies based on target objectives. In this paper, we will try to find out which target objective is most addressed, which are average addressed, and which are least addressed in target-oriented replication strategies. The paper also includes a detailed discussion about the challenges, issues, and future research directions. This comprehensive analysis and performance evaluation based-work will open a new door for researchers in the field of cloud computing and will be helpful for further development of cloud-based dynamic data replication strategies to develop a technique that will address all attributes (Target Objectives) effectively in one replication strategy.

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Section: Related Surveysmentioning

confidence: 99%

Quantitative Analysis and Performance Evaluation of Target-Oriented Replication Strategies in Cloud Computing

Waseem¹,

Din

Alshamrani

et al. 2021

Electronics

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“…The proposed algorithm aimed to mitigate the void network congestion and guarantee appropriate accessibility of data. In [6], an adaptive sorting method was proposed to consider the communicative performance between different computational nodes in a cloud. In [7], a heuristic algorithm was proposed to transfer network administration to the cloud.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Allocating Duplicate Copies for IoT Data in Cloud Computing based on Harmony Search Algorithm

Jahandideh

Mirzaei

2021

IETE Journal of Research

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The Internet of things (IoT) generates a plethora of data nowadays, and cloud computing has been introduced as an efficient solution to IoT data management. A cloud resource administrator usually adopts the replication strategy to guarantee the reliability of IoT data. This mechanism can significantly reduce data access time, and evidently, more replicas of data increase the data storage cost. Furthermore, the process of selecting mini clouds for replica allocation and sorting replicas in mini clouds is considered an NP-hard problem. Therefore, this paper proposes an approach based on the harmony search (HS) algorithm to allocate replicas to the IoT data in the cloud computing environment in order to mitigate the data access cost. The HS algorithm was employed in the proposed approach to determine the best location for data replication in the cloud computing environment. According to the implementation results, the proposed approach outperformed the other methods and managed to significantly decrease data access time and delay as well as energy consumption.

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“…The proposed method was not ideal and requires further modification from static to dynamic in the future. It should be scalable and have more service data replication components [29].…”

Section: S Long Et Al Suggested a Multi-objective Optimizedmentioning

confidence: 99%

An Artificial Bee Colony Algorithm for Data Replication Optimization in Cloud Environments

et al. 2020

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Cloud computing is a modern technology for dealing with large-scale data. The Cloud has been used to process the selection and placement of replications on a large scale. Most previous studies concerning replication used mathematical models, and few studies focused on artificial intelligence (AI). The Artificial Bee Colony (ABC) is a member of the family of swarm intelligence based algorithms. It simulates bee direction to the final route and has been proven to be effective for optimization. In this paper, we present the different costs and shortest route sides in the Cloud with regard to replication and its placement between data centers (DCs) through Multi-Objective Optimization (MOO) and evaluate the cost distance by using the knapsack problem. ABC has been used to solve shortest route and lower cost problems to identify the best selection for replication placement, according to the distance or shortest routes and lower costs that the knapsack approach has used to solve these problems. Multi-objective optimization with the artificial bee colony (MOABC) algorithm can be used to achieve highest efficiency and lowest costs in the proposed system. MOABC can find an optimal solution for the best placement of data replicas according to the minimum distance and the number of data transmissions, affording low cost with the knapsack approach and availability of data replication.Low cost and fast access are characteristics that guide the shortest route in the CloudSim implementation as well. The experimental results show that the proposed MOABC is more efficient and effective for the best placement of replications than compared algorithms. INDEX TERMS Cloud computing, multi-objective optimization, artificial bee colony, replication, cloudsim, and knapsack problem.

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A replicas placement approach of component services for service-based cloud application

Cited by 10 publications

References 30 publications

Quantitative Analysis and Performance Evaluation of Target-Oriented Replication Strategies in Cloud Computing

Quantitative Analysis and Performance Evaluation of Target-Oriented Replication Strategies in Cloud Computing

Allocating Duplicate Copies for IoT Data in Cloud Computing based on Harmony Search Algorithm

An Artificial Bee Colony Algorithm for Data Replication Optimization in Cloud Environments

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