Survivable Continual Data Streams

连续数据流是由于两个主要因素导致的连续数据流的泛化：(1)不规则的数据突发和(2)需要集成所有类型的数据和元数据，而不是纯粹的时间序列或多媒体。连续数据流自然会在性能可伸缩性属性和系统可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，以降低更新和传播成本。由于连续数据流的不规则到达和集成，这种在可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。该项目将研究在传统复制中为副本提供绝对一致性保证的一种极端方法，以及在另一种极端中为传统代理缓存中的缓存副本提供随机一致性/零保证的方法。在形式上，该方法基于有界不一致性的概念，例如Epsilon可串行化。其主要技术思想是将副本与原始副本之间的距离保持在指定的阈值内(以处理突发)，同时优化性能可伸缩性并整合异类数据流。

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.