CloudBFT: Elastic Byzantine Fault Tolerance
CloudBFT: Elastic Byzantine Fault Tolerance
复制标题
CloudBFT：弹性拜占庭容错
DOI：
10.1109/prdc.2014.31
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
R. Barbosa
中科院分区：
文献类型：
--
作者：
Rodrigo Nogueira;Filipe Araújo;R. Barbosa
随着行业转向外包计算资源作为减少投资和管理成本，同时提高安全性、可靠性和性能的手段，云计算变得越来越重要。云运营商使用多租户，通过将虚拟机 (VM) 分组为几个物理机 (PM) 来池化计算资源，从而为客户提供弹性。尽管基于云的容错方案会带来通信和同步开销，但云为关键应用程序提供了出色的设施，因为它可以在独立资源中托管不同数量的副本。考虑到这些矛盾的力量，确定云是否可以托管弹性关键服务是一个主要的研究问题。我们从具有关系数据的标准三层系统的角度来应对这一挑战。我们建议使用放置在不同物理机器上的副本组来容忍拜占庭错误，作为避免应用程序面临相关故障的一种方法。为了提高系统的可扩展性，我们划分数据以实现并行访问。使用实际设置，此设置可以实现大大超过分区数量的加速。即使负载变化很大，系统也能将延迟和吞吐量保持在合理的范围内。我们相信，我们观察到的弹性证明了使用关系数据库的云服务器中容忍拜占庭错误的可行性。
Cloud computing is increasingly important, with the industry moving towards outsourcing computational resources as a means to reduce investment and management costs, while improving security, dependability and performance. Cloud operators use multi-tenancy, by grouping virtual machines (VMs) into a few physical machines (PMs), to pool computing resources, thus offering elasticity to clients. Although cloud-based fault tolerance schemes impose communication and synchronization overheads, the cloud offers excellent facilities for critical applications, as it can host varying numbers of replicas in independent resources. Given these contradictory forces, determining whether the cloud can host elastic critical services is a major research question. We address this challenge from the perspective of a standard three-tiered system with relational data. We propose to tolerate Byzantine faults using groups of replicas placed on distinct physical machines, as a means to avoid exposing applications to correlated failures. To improve the scalability of our system, we divide data to enable parallel accesses. Using a realistic setup, this setting can reach speedups largely exceeding the number of partitions. Even for a wide variation of the load, the system preserves latency and throughput within reasonable bounds. We believe that the elasticity we observe demonstrates the feasibility of tolerating Byzantine faults in a cloud-based server using a relational database.