Lazy update propagation for data replication in cloud computing

Aiqiang, Gao; Diao, Luhong

doi:10.1109/icpca.2010.5704107

Cited by 14 publications

(13 citation statements)

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“…Gao et al [28] discussed the consistency between multiple data replicas. Reference [28] proposed the use of lazy update propagation to maintain consistency amongst the replicas in a cloud.…”

Section: Data Replication Approachesmentioning

confidence: 99%

“…Reference [28] proposed the use of lazy update propagation to maintain consistency amongst the replicas in a cloud. In the lazy approach, the update is propagated to all other replicas after the update is committed to the original site.…”

Section: Data Replication Approachesmentioning

confidence: 99%

“…The data replication is a mechanism in which the data or some fragments of data are stored at multiple nodes or servers [28]. If one of the nodes is not accessible then the data can be accessed from a different node [29].…”

mentioning

confidence: 99%

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Performance analysis of data intensive cloud systems based on data management and replication: a survey

Malik

Khan

Ewen

et al. 2015

Distrib Parallel Databases

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As we delve deeper into the 'Digital Age', we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobiledevices, sensors, individual 123 Distrib Parallel Databases archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such 'Data Explosions' has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service 123 Distrib Parallel Databases (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.

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Section: Data Replication Approachesmentioning

confidence: 99%

Section: Data Replication Approachesmentioning

confidence: 99%

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Performance analysis of data intensive cloud systems based on data management and replication: a survey

Malik

Khan

Ewen

et al. 2015

Distrib Parallel Databases

View full text Add to dashboard Cite

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“…The technique takes availability and efficient access into account while predicting files in data centers. Besides replication, Gai and Daio [15] investigate replica consistency issue in cloud computing system. The authors adopt a lazy update scheme to split data access and update that can improve throughput and cut down response time.…”

Section: Related Workmentioning

confidence: 99%

“…Data replication technique is widely used in the cloud computing system to provide high data availability [13], [14], [15], [16]. While research has been done on replica placement, little has focused on the dynamic maintenance of the replicas over the time.…”

Section: Introductionmentioning

confidence: 99%

On the Dynamic Maintenance of Data Replicas based on Access Patterns in A Multi-Cloud Environment

Shorfuzzaman

2017

ijacsa

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Abstract-Cloudcomputing provides services and infrastructures to enable end-users to access, modify and share massive geographically distributed data. There are increasing interests in developing data-intensive (big data) applications in this computing environment that need to access huge datasets. Accessing such data in an efficient way is deterred with factors such as dynamic changes in resource availability, provision of diverse service quality by different cloud providers. Data replication has already been proven to be an effective technique to overcome these challenges. Replication offers reduced response time in data access, higher data availability and improved system load balancing. Once the replicas are created in a multi-cloud environment, it is of utmost importance to continuously support maintenance of these replicas dynamically. This is to ensure that replicas are located in optimal data center locations to minimize replication cost and to meet specific user and system requirements. First, this paper proposes a novel approach to distributed placement of static replicas in appropriate data center locations. Secondly, motivated by the fact that a multi-cloud environment is highly dynamic, the paper presents a dynamic replica maintenance technique that reallocates replicas to new data center locations upon significant performance degradation. The evaluation results demonstrate the effectiveness of the presented dynamic maintenance technique with static placement decisions in a multi-cloud environment.

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Concurrency control for real‐time and mobile transactions: Historical view, challenges, and evolution of practices

Chaudhry

Yousaf

2021

Concurrency and Computation

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Recently, mobile computing has changed the way that spatial data and GIS are processed. Unlike wired and stand-alone GIS, now the trend has been switched from offline to real-time data processing using location aware services, such as GPS technology. The increased usage of location aware services in multiuser real-time environment has made transaction management incredibly significant. If the simultaneous query operations on the same data item are not handled intelligently then this results in data inconsistency issue. Concurrency control protocol is one of the primary aspects that helps in overcoming this issue a in multiuser environment. To the best of our knowledge, the impact of technological advancements on concurrency control has not been thoroughly studied in the literature. In this article, we explored the literature on concurrency control algorithms in depth with respect to real-time applications and the applications with moving objects. We defined a taxonomy of concurrency control solutions and assessed the maturity of these solutions in the light of characteristics of real-time and mobile environment. We compared the most recent developments made in the literature and presented meaningful insights. Challenges are also identified and discussed, which can assist in doing research in this domain in future. K E Y W O R D S concurrency control, mobile, real time 1 INTRODUCTION IoT (Internet of Things) emerged as a promising technology that has revolutionized the way of living. GPS-based devices and sensors are commonly used in majority of the applications. The convergence of multiple domains such as real-time GIS, mobile computing, and IoT has made it necessary to solve the computing problem considering the requirements of each domain. Mobile GIS domain integrates several technologies: GPS, mobile computing, and GIS. In recent times, the data processing environment deals with varying kinds of sensors. The real-time data collected by sensors need to be processed in timely manner to ensure efficient data management. The sensors can be categorized into two types depending on their mobility: fixed and agile/mobile sensors. Fixed sensors are spatially fixed, whereas mobile sensors change their location with respect to time. Mobile sensors collect data on geographical grounds and this data can be referred to as spatiotemporal data. This has given rise to a new research domain of real-time mobile data processing and management. Thus, real-time processing domain emerged with respect to time depending on the nature of sensor technology and evolution of IoT. The future processing systems are expected to meet the requirements of spatiotemporal and real-time data, leading toward a new domain of real-time GIS. Real-time data can be used for both static and dynamic processing. For example, in the first aspect, mobile sensors can process and analyze the real-time spatiotemporal data for dynamic processing. On the other hand, real-time data can be collected and saved in a log for future offline analysis (static processing).

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Lazy update propagation for data replication in cloud computing

Cited by 14 publications

References 13 publications

Performance analysis of data intensive cloud systems based on data management and replication: a survey

Performance analysis of data intensive cloud systems based on data management and replication: a survey

On the Dynamic Maintenance of Data Replicas based on Access Patterns in A Multi-Cloud Environment

Concurrency control for real‐time and mobile transactions: Historical view, challenges, and evolution of practices

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