Auto-scalable and fault-tolerant load balancing mechanism for cloud computing based on the proof-of-work election

Feng, Xiaoqin; Ma, Jianfeng; Liu, Shaobin; Miao, Yinbin; Liu, Ximeng

doi:10.1007/s11432-020-2939-3

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Crash and overload of the system can occur when the server requires multiple inputs. Prakash et al [79] introduced an Opportunistic load-balancing algorithm which easily handles incomplete requests in chronological sequence to the current host. The requests are structured steadily as it does not consider the ongoing execution of the host.…”

Section: A Taxanomy Of Lb Algorithmsmentioning

confidence: 99%

Load Balancing in Cloud Environment: A State-of-the-Art Review

Lohumi,

Gangodkar,

Srivastava

et al. 2023

IEEE Access

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IT services and resources on-demand through Internetwork are offered by Cloud Computing (CC), including the pay-for-you-go aspect. A lot is offered by the CC paradigm, such as Infrastructure related services, computing services, storage, and environments for deployment are also provided. The objective of this study is to survey one of the significant challenges in cloud computing, which is a multi-variant, multi-constraint issue termed Load unbalancing, resulting in the demising of the scalability, efficiency, and performance of the system. Equilibrium in the server workload distribution is still strived for by cloud service providers. The unbalancing issue is resolved by load balancing solutions in two ways: overloading and underloading. An extensive structural literature analysis of Load balancing and its constituent domains with the inclusion of various parameters, such as scalability, make-span, and throughput, are depicted in this research paper to enhance the QoS. A detailed and organized taxonomy of all the Load balancing algorithms based on nature system state, techniques, functionality, and types is also presented. The major focus of the survey is around the Static, dynamic, hybrid, and nature-inspired Load-Balancing algorithms.

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Section: A Taxanomy Of Lb Algorithmsmentioning

confidence: 99%

Load Balancing in Cloud Environment: A State-of-the-Art Review

Lohumi,

Gangodkar,

Srivastava

et al. 2023

IEEE Access

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“…With the development of big data and 5G, numerous data have been generated according to the requirements of users [105,106]. The service schedule, which is a technique that improves resource utilization by scheduling the execution of service, renders the data storage and analysis convenient [107,108]. However, potential faults, e.g., missing data, may occur in service scheduling.…”

Section: Goals On Service Fault-tolerant Schedulingmentioning

confidence: 99%

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Luo¹,

Li²,

Yang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this area. In particular, we divide intelligent identification over power big data into data acquisition and storage processes, anomaly detection and fault discrimination processes, and fault tracing for dispatching operations during communication. A detailed survey of the solutions to the challenges in intelligent identification over power big data is then presented. Moreover, opportunities and future directions are outlined.

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“…where cosin ij⟶x (d ij , d xv ) is the fusion clustering feature set of public library's massive resources under the condition of network environment and cultural construction. Note that the well-known sigmoid function is used to normalize the feature mapping formula of public library's massive resources under the condition of network environment and cultural construction using the following relationship [21]:…”

Section: Self-adaptive Fusion Clustering Processing Of Massive Resour...mentioning

confidence: 99%

Cloud Computing-Based Online Sharing Method of Mass Resources in Public Libraries

Jun

2022

Mobile Information Systems

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In order to realize the online sharing of massive resources and capitals in public libraries under the circumstances of network environment and cultural construction, it is desirable to invent new sharing policies. In this paper, we put forward a method of online sharing of massive resources in public libraries which is established on cloud computing. Cloud computing technique is implemented to collect the statistical information characteristics of public library’s massive resources under the condition of network environment and culture construction, and an adaptive fusion clustering processing model of public library’s massive resources under the condition of network environment and culture construction is constructed. Moreover, the correlation statistical feature detection approach is used to accomplish fuzzy scheduling and feature mining of public library’s massive resources under the condition of network environment and culture construction. The information storage center management database of public library’s massive resources under the condition of network environment and culture construction is established. The fuzzy C-means clustering approach is implemented in the database to cluster the massive resources of public library under the condition of network environment and culture construction. Combined with cloud computing technology, the sharing and classified management scheduling of massive resources of public library under the condition of network environment and culture construction are comprehended. The simulation outcomes confirm that the adaptive performance of public library’s massive resource sharing management under the condition of network environment and culture construction is better, and the adaptive scheduling performance of resources is better. The data clustering of public library’s massive resources under the condition of network environment and culture construction is stronger, which improves the online sharing level of public library’s massive resources under the condition of network environment and culture construction.

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Auto-scalable and fault-tolerant load balancing mechanism for cloud computing based on the proof-of-work election

Cited by 5 publications

References 26 publications

Load Balancing in Cloud Environment: A State-of-the-Art Review

Load Balancing in Cloud Environment: A State-of-the-Art Review

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Cloud Computing-Based Online Sharing Method of Mass Resources in Public Libraries

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