Adaptive Data Replication in Real-Time Reliable Edge Computing for Internet of Things

Wang, Chao; Gill, Christopher; Lu, Chenyang

doi:10.1109/iotdi49375.2020.00019

Cited by 13 publications

(8 citation statements)

References 9 publications

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“…The outcomes demonstrated that relative to a simple replication algorithm, a random algorithm for replication could reach a bandwidth that is stronger in terms of savings. Studies of data replication techniques and a real-time and fault-tolerant edge computing architecture for IoT applications have been published by Wang et al (2020). They also discussed similarities between the implementation of data replication tasks and edge computing tasks.…”

Section: Distributed Data Replicationmentioning

confidence: 99%

Data replication techniques in the Internet of Things: a systematic literature review

Sun

Wang

et al. 2021

LHT

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PurposeIn data grids, replication has been regarded as a crucial optimization strategy. Computing tasks are performed on IoT gateways at the cloud edges to obtain a prompt response. So, investigating the data replication mechanisms in the IoT is necessary. Henceforth, a systematic survey of data replication strategies in IoT techniques is presented in this paper, and some suggestions are offered for the upcoming works. In two key classifications, various parameters dependent on the analysis of the prevalent approaches are considered. The pros and cons associated with chosen strategies have been explored, and the essential problems of them have been presented to boost the future of more effective data replication strategies. We have also discovered gaps in papers and provided solutions for them.Design/methodology/approachProgress in Information Technology (IT) growth has brought the Internet of Things (IoT) into life to take a vital role in our everyday lifestyles. Big IoT-generated data brings tremendous data processing challenges. One of the most challenging problems is data replication to improve fault-tolerance, reliability, and accessibility. In this way, if the primary data source fails, a replica can be swapped in immediately. There is a significant influence on the IoT created by data replication techniques, but no extensive and systematic research exists in this area. There is still no systematic and full way to address the relevant methods and evaluate them. Hence, in the present investigation, a literature review is indicated on the IoT-based data replication from papers published until 2021. Based on the given guidelines, chosen papers are reviewed. After establishing exclusion and inclusion criteria, an independent systematic search in Google Scholar, ACM, Scopus, Eric, Science Direct, Springer link, Emerald, Global ProQuest, and IEEE for relevant studies has been performed, and 21(6 paper analyzed in section 1 and 15 paper analyzed in section 3) papers have been analyzed.FindingsThe results showed that data replication mechanisms in the IoT algorithms outperform other algorithms regarding impressive network utilization, job implementation time, hit ratio, total replication number, and the portion of utilized storage in percentage. Although a few ideas have been suggested that fix different facets of IoT data management, we predict that there is still space for development and more study. Thus, in order to design innovative and more effective methods for future IoT-based structures, we explored open research directions in the domain of efficient data processing.Research limitations/implicationsThe present investigation encountered some drawbacks. First of all, only certain papers published in English were included. It is evident that some papers exist on data replication processes in the IoT written in other languages, but they were not included in our research. Next, the current report has only analyzed the mined based on data replication processes and IoT keyword discovery. The methods for data replication in the IoT would not be printed with keywords specified. In this review, the papers presented in national conferences and journals are neglected. In order to achieve the highest ability, this analysis contains papers from major global academic journals.Practical implicationsTo appreciate the significance and accuracy of the data often produced by different entities, the article illustrates that data provenance is essential. The results contribute to providing strong suggestions for future IoT studies. To be able to view the data, administrators have to modify novel abilities. The current analysis will deal with the speed of publications and suggest the findings of research and experience as a future path for IoT data replication decision-makers.Social implicationsIn general, the rise in the knowledge degree of scientists, academics, and managers will enhance administrators' positive and consciously behavioral actions in handling IoT environments. We anticipate that the consequences of the present report could lead investigators to produce more efficient data replication methods in IoT regarding the data type and data volume.Originality/valueThis report provides a detailed literature review on data replication strategies relying on IoT. The lack of such papers increases the importance of this paper. Utilizing the responses to the study queries, data replication's primary purpose, current problems, study concepts, and processes in IoT are summarized exclusively. This approach will allow investigators to establish a more reliable IoT technique for data replication in the future. To the best of our understanding, our research is the first to provide a thorough overview and evaluation of the current solutions by categorizing them into static/dynamic replication and distributed replication subcategories. By outlining possible future study paths, we conclude the article.

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Section: Distributed Data Replicationmentioning

confidence: 99%

Data replication techniques in the Internet of Things: a systematic literature review

Sun

Wang

et al. 2021

LHT

View full text Add to dashboard Cite

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“…An IoT gateway may replicate data to a backup gateway to assure data delivery by utilizing the primary backup method. If the primary IoT gateway fails, the backup edge computing activities may process the waiting data, and the embedded sensing devices can re-transmit data to the backup gateway [36].…”

Section: What Is the Necessity Of Data Replication And Placement Techniques In Iot And Edge Computing Platforms?mentioning

confidence: 99%

“…Several IoT apps rely on fast and accurate data processing from embedded sensor devices. Computing tasks are conducted on IoT gateways to obtain a quick response, and the gateways provide data replication to backup gateways for fault tolerance [36]. Edge computing and data replication set the stage for fast and dependable IoT workflow localization services.…”

Section: Wireless Networkmentioning

confidence: 99%

“…Proposing both simple and random algorithms of replication When compared to a straightforward replication method, a random technique for replicating can reach a bandwidth that is superior in terms of savings Wang et al [36] Adaptive data replication in real-time reliable edge computing for IoT Presenting the adaptive real-time reliable edge computing architecture…”

Section: • Innovation Of the Papermentioning

confidence: 99%

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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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“…To achieve fault tolerance, the authors of [ 21 ] proposed a reliable and fault-tolerant agent-based hierarchical IoT–cloud architecture by replicating data at the edge of the network, which redirects the backup server when the service fails to apply. The system in [ 22 ] adaptively replicates data according to application requirements for data loss and latency. The replication operation is deferred to some extent, and the data loss and latency requirements are satisfied.…”

Section: Related Workmentioning

confidence: 99%

FASDQ: Fault-Tolerant Adaptive Scheduling with Dynamic QoS-Awareness in Edge Containers for Delay-Sensitive Tasks

Wang

Chen

Yao

et al. 2021

Sensors

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As the requirement for real-time data analysis increases, edge computing is being implemented to leverage the resources of edge devices to reduce system response times and decrease the latency. However, due to the resource constraints of edge clouds, edge servers are more prone to failures than other systems. Therefore, guaranteeing the reliability of services in edge clouds is critical. In this paper, we propose a fault-tolerant adaptive scheduling mechanism with dynamic quality of service (QoS) awareness (FASDQ), which extends the primary/backup (PB) model by applying QoS on demand to task copies. The aim of the method is to reduce the latency and achieve reliable service for tasks by changing the execution time of task copies. This paper also proposes a container resource-adaptive adjustment mechanism, which adjusts the timing of resources when the available resources cannot meet the task copy requirements. Finally, this paper reports the results of simulation experiments on the EdgeCloudSim platform to evaluate the difference in performance between FASDQ and other methods. The results show that the mechanism effectively reduces the execution time of task copies and outperforms other methods in terms of reliability and general resource utilization.

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Adaptive Data Replication in Real-Time Reliable Edge Computing for Internet of Things

Cited by 13 publications

References 9 publications

Data replication techniques in the Internet of Things: a systematic literature review

Data replication techniques in the Internet of Things: a systematic literature review

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

FASDQ: Fault-Tolerant Adaptive Scheduling with Dynamic QoS-Awareness in Edge Containers for Delay-Sensitive Tasks

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