Survivable Continual Data Streams

连续数据流是连续数据流的泛化，主要有两个因素：(1)不规则的数据爆发；(2)需要集成各种数据和元数据，而不是纯粹的时间序列或多媒体。连续数据流自然需要在性能可伸缩性属性和系统生存性属性之间进行权衡。一方面，生存性需要增加冗余，因为节点和网络的不稳定不可避免地会导致系统的某些部分不可用。另一方面，可伸缩性要求减少数据冗余，以减少更新和传播成本。由于连续数据流的不规则到达和集成，这种生存性和可伸缩性之间的内在权衡是一个主要的研究挑战。该项目将研究跨越两个极端的方法，一个极端是传统复制中副本的绝对一致性保证，另一个极端是传统代理缓存中缓存副本的偶然一致性/零保证。从形式上讲，这种方法是基于有界不一致的概念，例如Epsilon serializable。主要的技术思想是在优化性能可伸缩性和集成异构数据流的同时，将副本和原始副本之间的距离保持在指定的阈值内（以处理突发）。

Continual data streams are a generalization of continuous data streams due to two major factors: (1) irregular data bursts and (2) the need to integrate all kinds of data and metadata, instead of pure time series or multimedia. Continual data streams naturally have a trade-off between performance scalability properties and system survivability properties. On the one hand, survivability requires increased redundancy since node and network instability inevitably renders parts of the system unavailable. On the other hand, scalability requires a decrease in data redundancy, to reduce update and propagation costs. This inherent trade-off between survivability and scalability is a major research challenge due to the irregular arrival and integration of continual data streams. The project will investigate the approaches that span the spectrum between absolute consistency guarantees for replicas in traditional replication on one extreme and by-chance consistency/zero guarantees for cached copies in traditional proxy caches on the other extreme. Formally, this approach is based on the notion of bounded inconsistency such as Epsilon Serializability. The main technical idea is to keep the distance between a replica and the original to within the specified threshold (to handle bursts) while optimizing the performance scalability and integrating heterogeneous data streams.