CloudBFT: Elastic Byzantine Fault Tolerance

Nogueira, Rodrigo; Araújo, Filipe; Barbosa, Raul

doi:10.1109/prdc.2014.31

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…Mondal et al [35] have evaluated different replication schemes such as cold replication, hot replication, and warm replication, which include the effects of software failure, failure detection, recovery mechanisms, and imperfect coverage on recovery mechanisms. Chai et al [36] and Nogueira et al [37] have proposed the byzantine fault tolerance approach for services using optimistic replication techniques. They have suggested that four replicas kept for a server can guarantee the correctness property.…”

Section: Results Of the Systematic Reviewmentioning

confidence: 99%

Fault analysis of service‐oriented systems: a systematic literature review

Bhandari

Gupta

2018

IET softw.

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Due to the increasing scale and complexity of service-oriented systems (SOSs) understanding fault and its recovery mechanism is a tedious task so there is a strong demand for a summary of the SOSs fault analysis knowledge that can assist further developers and researchers to plan appropriate action and guidelines for SOSs development. The objective of this systematic literature review (SLR) is to summarise the current state-of-the-art of fault analysis in SOSs. The authors have used the predefined SLR method employing a manual search of 11 highly reputed international journals and three conference proceedings. The categorisation and organisation were done according to the research questions regarding approaches, fault types, current issues and challenges and tools of fault analysis on SOSs. After applying selection criteria, this SLR includes 123 papers published between January 2008 and April 2017, 65 papers address SOSs-fault handling approaches, 35 papers report SOS-specific faults and 51 papers address SOS issues, challenges, and testing and 17 papers are of tooling. They have discussed the main approaches, techniques, concepts, contributions and research methods used for fault analysis of SOSs. Through the paper, a comprehensive review work has been presented that provides empirical evidence for establishing future research agendas.

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Section: Results Of the Systematic Reviewmentioning

confidence: 99%

Fault analysis of service‐oriented systems: a systematic literature review

Bhandari

Gupta

2018

IET softw.

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“…The Average percentage relative error (APE), Average absolute percentage relative error (AAPE) and Standard deviation (SD) were also obtained. Equation (18)(19)(20) gives the mathematical definition of these parameters and (Table 5)…”

Section: A Performance Comparison Of Resultsmentioning

confidence: 99%

“…Recent research that focuses on Cloud fault tolerance [32,33,[25][26][27][28][23][24][25][26][27][28][29][30][31][32][33][34] is more broadly covering aspects for standard real time systems [22,25,27,36,38], [16][17][18][19][20]. Very few works have addressed optimised fault tolerance approaches in real time cloud computing environment with focus on achieving high system availability.…”

Section: Related Workmentioning

confidence: 99%

Optimising Fault Tolerance in Real-Time Cloud Computing IaaS Environment

Mohammed¹,

Kiran²,

Awan³

et al. 2016

2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud)

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Fault tolerance is the ability of a system to respond swiftly to an unexpected failure. Failures in a cloud computing environment are normal rather than exceptional, but fault detection and system recovery in a real time cloud system is a crucial issue. To deal with this problem and to minimize the risk of failure, an optimal fault tolerance mechanism was introduced where fault tolerance was achieved using the combination of the Cloud Master, Compute nodes, Cloud load balancer, Selection mechanism and Cloud Fault handler. In this paper, we proposed an optimized fault tolerance approach where a model is designed to tolerate faults based on the reliability of each compute node (virtual machine) and can be replaced if the performance is not optimal. Preliminary test of our algorithm indicates that the rate of increase in pass rate exceeds the decrease in failure rate and it also considers forward and backward recovery using diverse software tools. Our results obtained are demonstrated through experimental validation thereby laying a foundation for a fully fault tolerant IaaS Cloud environment, which suggests a good performance of our model compared to current existing approaches.

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“…Furthermore, to make the system tolerant to Byzantine attacks, we target to decentralized the architecture in order to provide an elastic Byzantine Fault Tolerant approach similar to CloudBFT [73]. For the stream processing scenario a relevant work to compare with shall be [33], as the authors integrate fault tolerance with operators scale-out, but without proposing a real autoscaling solution as they do not scale-in.…”

Section: Fault Tolerancementioning

confidence: 99%

PASCAL: An architecture for proactive auto-scaling of distributed services

Lombardi

Muti

Aniello

et al. 2019

Future Generation Computer Systems

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One of the main characteristics that today makes cloud services so popular is their ability to be elastic, i.e., they can adapt their provisioning to variable workloads, thus increasing resource utilization and reducing operating costs. At the core of any elastic service lies an automatic scaling mechanism that drives provisioning on the basis of a given strategy. In this paper we propose PASCAL, an architecture for Proactive Auto-SCALing of generic distributed services. PASCAL combines a proactive approach, to forecast incoming workloads, with a profiling system, to estimate required provision. Scale-in/out operations are decided according to an application-specific strategy, which aims at provisioning the minimum number of resources needed to sustain the foreseen workload. The main novelties introduced with PASCAL architecture are: (i) a strategy to proactively auto-scale a distributed stream processing system (namely, Apache Storm) with the aim of load balancing operators through an accurate system performance estimation model, and (ii) a strategy to proactively auto-scale a distributed datastore (namely, Apache Cassandra), focussed on how to choose when executing scaling actions on the basis of the time needed for the activation/deactivation of storage nodes so as to have the configuration ready when needed. We provide a prototype implementation of PASCAL for both use cases and, through an experimental evaluation conducted on a private cloud, we validate our approach and demonstrate the effectiveness of the proposed strategies in terms of saved resources and response time.

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CloudBFT: Elastic Byzantine Fault Tolerance

Cited by 11 publications

References 24 publications

Fault analysis of service‐oriented systems: a systematic literature review

Fault analysis of service‐oriented systems: a systematic literature review

Optimising Fault Tolerance in Real-Time Cloud Computing IaaS Environment

PASCAL: An architecture for proactive auto-scaling of distributed services

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