Effective replica management for improving reliability and availability in edge-cloud computing environment

Li, Chunlin; Song, Mingyang; Zhang, Min; Luo, Yingwei

doi:10.1016/j.jpdc.2020.04.012

Cited by 58 publications

(20 citation statements)

References 23 publications

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“…The suggested data replication techniques and architecture can provide the required levels of data loss tolerance while reducing network bandwidth use and preserving latency performance Li et al [77] Examining effective replica management for improving reliability and availability in an edge-cloud computing environment…”

Section: • Innovation Of the Papermentioning

confidence: 99%

Replica selection and placement techniques on the IoT and edge computing: a deep study

Shao

Huang

2021

Wireless Netw

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Internet of Things (IoT) has lately been presented as a new technological transformation in which things are connected via the Internet. Several sensors and devices create data and send vital signals constantly over sophisticated networks that allow machine-to-machine interactions and monitor and manage key smart-world infrastructures. Since huge amounts of data are generated, reducing the data access costs is a critical issue. Edge computing has been developed as a novel paradigm for solving IoT demands to reduce the rise in resource congestion. One of the most significant data management challenges in the IoT is selecting suitable replication things that minimize reaction time and cost. Therefore, our goal is to examine replica selection and placement techniques in IoT and edge computing. The findings revealed that the edge computing environment might significantly enhance system performance regarding access response time, prediction accuracy, effective network, and increased data availability. Furthermore, the results illustrate that data provenance is necessary to raise the accuracy of the data by. Also, the results showed that the most important challenge in data replication and placement techniques in IoT and edge computing was the availability of data and access response time.

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Section: • Innovation Of the Papermentioning

confidence: 99%

Replica selection and placement techniques on the IoT and edge computing: a deep study

Shao

Huang

2021

Wireless Netw

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“…It also uses the intelligent water drop (IWD) algorithm to select the best replica for user requests. In [13], a Markov-based replication method at edge-cloud computing was proposed. A gray Markov chain prediction model calculates the required number of replicas for each block of files.…”

Section: Related Workmentioning

confidence: 99%

“…Replication methods create multiple copies of files and place them near the data requester [6]. Data replication has been widely applied in database management systems (DBMS) [7], distributed database [8], mobile system [9], Data Grid [7], Cloud [10,11], peer-to-peer (p2p) [12], and Fog [13].…”

Section: Introductionmentioning

confidence: 99%

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

Beigrezaei

Haghighat

Mirtaheri

2021

Mathematical Problems in Engineering

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The efficiency of data-intensive applications in distributed environments such as Cloud, Fog, and Grid is directly related to data access delay. Delays caused by queue workload and delays caused by failure can decrease data access efficiency. Data replication is a critical technique in reducing access latency. In this paper, a fuzzy-based replication algorithm is proposed, which avoids the mentioned imposed delays by considering a comprehensive set of significant parameters to improve performance. The proposed algorithm selects the appropriate replica using a hierarchical method, taking into account the transmission cost, queue delay, and failure probability. The algorithm determines the best place for replication using a fuzzy inference system considering the queue workload, number of accesses in the future, last access time, and communication capacity. It uses the Simple Exponential Smoothing method to predict future file popularity. The OptorSim simulator evaluates the proposed algorithm in different access patterns. The results show that the algorithm improves performance in terms of the number of replications, the percentage of storage filled, and the mean job execution time. The proposed algorithm has the highest efficiency in random access patterns, especially random Zipf access patterns. It also has good performance when the number of jobs and file size are increased.

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“…Bhamare et al in [18], discussed ML models to improve data security. The concept of distributed computing was discussed because it expands critical traction and virtualized server farms becoming well-known as a practical framework and an answer to enormous business applications [19]. The researchers used an assistance model to solve security threats and protection challenges in distributed computing [20].…”

Section: Related Workmentioning

confidence: 99%

A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

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Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.

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Effective replica management for improving reliability and availability in edge-cloud computing environment

Cited by 58 publications

References 23 publications

Replica selection and placement techniques on the IoT and edge computing: a deep study

Replica selection and placement techniques on the IoT and edge computing: a deep study

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

A Review of Machine Learning Algorithms for Cloud Computing Security

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