2018 14th European Dependable Computing Conference (EDCC)最新文献_第3页

Accurate Robustness Assessment of HDL Models Through Iterative Statistical Fault Injection 基于迭代统计故障注入的HDL模型鲁棒性准确评估

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/EDCC.2018.00013

I. Tuzov, D. Andrés, J. Ruiz

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引用次数: 12

Runtime Verification of Ethereum Smart Contracts 以太坊智能合约的运行时验证

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/EDCC.2018.00036

J. Ellul, Gordon J. Pace

引用次数: 58

A Study of the Interplay Between Safety and Security Using Model-Implemented Fault Injection 基于模型实现的故障注入的安全性与安全性相互作用研究

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/EDCC.2018.00018

B. Sangchoolie, P. Folkesson, J. Vinter

引用次数: 13

Assessing Containerized REST Services Performance in the Presence of Operator Faults 在操作员故障情况下评估容器化REST服务的性能

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/EDCC.2018.00025

Mariana Cunha, N. Laranjeiro

引用次数: 1

An Exploratory Study on Zeroconf Monitoring of Microservices Systems 微服务系统零配置监控的探索性研究

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/EDCC.2018.00028

M. Cinque, Raffaele Della Corte, Raffaele Iorio, A. Pecchia

引用次数: 2

Message from Program Chair 项目主席致辞

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/edcc.2018.00006

引用次数: 0

Formal Verification for Feature-Based Composition of Workflows 基于特征的工作流组合的形式化验证

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/EDCC.2018.00039

Stephan Adelsberger, Bashar Igried, Markus Moser, V. Savenkov, A. Setzer

引用次数: 1

A Proactive Q-Learning Approach for Autoscaling Heterogeneous Cloud Servers 异构云服务器自动扩展的主动q -学习方法

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/EDCC.2018.00038

Federico Lombardi

{"title":"A Proactive Q-Learning Approach for Autoscaling Heterogeneous Cloud Servers","authors":"Federico Lombardi","doi":"10.1109/EDCC.2018.00038","DOIUrl":"https://doi.org/10.1109/EDCC.2018.00038","url":null,"abstract":"Cloud providers offer different physical or virtual machine (VM) types that have different computational power and cost. Choosing the right configuration in a such heterogeneous environment able to sustain a workload while minimising costs is a challenging key aspect. Furthermore, turning-on/off a VM does not come for free, but introduce a reconfiguration overhead that might bring additional costs (e.g. time for moving to the new state and wasted resources for reconfiguration process). In this paper, we aim to find at run time a configuration s.t. (i) is able to sustain an input workload, (ii) does not over-provide resources, and that (iii) is as close as possible to the current one, to minimise the number of involved VMs in the reconfiguration, and thus, minimise the reconfiguration overhead. We propose here a Q-Learning approach to automatically learn the best policy to move from a configuration to another according to a predicted workload. We defined two reward functions which respectively look for (i) a configuration which perfectly fits the requested workload and (ii) a configuration which arrives close to the requested workload, to minimise the reconfiguration overhead. We compared the results with the two reward functions in term of average number of VMs involved in a reconfiguration and we show as with the first reward function we need to change in average 2.3 VM/reconfiguration while with the second reward function we can reduce such number up to 1 VM per reconfiguration with some over-provisioning.","PeriodicalId":129399,"journal":{"name":"2018 14th European Dependable Computing Conference (EDCC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122509679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Towards Safety-Awareness and Dynamic Safety Management 迈向安全意识与动态安全管理

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/EDCC.2018.00027

M. Trapp, D. Schneider, Gereon Weiss

引用次数: 17

Detection of Integrity Attacks to Smart Grids using Process Mining and Time-Evolving Graphs 基于过程挖掘和时间演化图的智能电网完整性攻击检测

2018 14th European Dependable Computing Conference (EDCC) Pub Date : 2018-09-01 DOI: 10.1109/EDCC.2018.00032

Simona Bernardi, Raquel Trillo Lado, J. Merseguer

引用次数: 3