EAGER: Elastic Multi-layer Memcached Tiers

Facebook和YouTube拥有包含许多对象的庞大数据库。对象（例如，图片、视频）从这些数据库中读取并呈现给用户。 这些数据库使用更慢、更便宜的I/O设备。 重复的数据库查询也很慢，所以公司将流行的对象缓存在内存中。 由于RAM比I/O快1000倍，因此对流行对象的查询服务速度更快。 一台计算机不能有效地使用太多的RAM，所以使用了一个名为“Memcached“的分布式内存缓存系统。 Memcached创建了一个节点集群，在更快的内存中形成了一个键值数据库。 当负载增加时，需要更多的缓存节点，但很难添加新节点并将缓存的数据重新分配给更多节点。 类似地，当负载减少时，关闭一些节点并将其缓存的数据迁移到更少的节点上会更困难;缩小集群有助于减少它们消耗的大量能源成本。 Memcached节点数量的任何变化都会导致大多数缓存数据被丢弃，从而导致分布式缓存必须从后端数据库缓慢重新预热的冗长时期。这个早期概念的探索性研究赠款（EAGER）项目研究了平滑扩展Memcached集群的技术，而不会出现任何暂时的性能下降。 该项目还在内存节点和后端数据库之间引入了一个基于Flash的中间存储层，以帮助减少对后端数据库的高延迟，并帮助Memcached集群的平滑扩展。

Facebook and YouTube have massive databases containing many objects.Objects (e.g., pictures, videos) are read from these databases and presentedto users. These databases use slower and cheaper I/O devices. Repeateddatabase queries are also slow, so companies cache popular objects involatile memory (RAM). As RAM is about 1000x faster than I/O, queries topopular objects are serviced faster. A single computer cannot efficientlyuse too much RAM, so a distributed memory caching system called "Memcached"is utilized. Memcached creates a cluster of nodes, forming a key-valuedatabase in faster memory.Memcached's simple architecture led to its popularity, but is also itsAchilles' heel. When loads increase, more caching nodes are needed, but itis difficult to add new nodes and redistribute the cached data to morenodes. Similarly, when loads reduce, it is harder to shut down some nodesand migrate their cached data to fewer nodes; scaling down the cluster helpsreduce the large energy costs they consume. Any changes to the number ofMemcached nodes result in throwing out most cached data, leading to lengthyperiods where the distributed caches have to be slowly re-warmed up from thebackend database.This EArly-concept Grants for Exploratory Research (EAGER) project investigates techniques to scale Memcached clusterssmoothly, without any transient performance degradations. The project alsointroduces an intermediate Flash-based storage tier between the memory nodesand the backend database, to help reduce high latencies to the backenddatabase and help the smooth scaling of the Memcached cluster.