Name
Abstract | ||
|---|---|---|
Todayu0027s web applications and social networks are serving billions of users around the globe. These users generate billions of key lookups and millions of data object updates per second. A single useru0027s social network page load requires hundreds of key lookups. This scale creates many design challenges for the underlying storage systems. First, these systems have to serve user requests with low latency. Any increase in the request latency leads to a decrease in user interest. Second, storage systems have to be highly available. Failures should be handled seamlessly without affecting user requests. Third, users consume an order of magnitude more data than they produce. Therefore, storage systems have to be optimized for read-intensive workloads. To address these challenges, distributed in-memory caching services have been widely deployed on top of persistent storage. In this tutorial, we survey the recent developments in distributed caching services. We present the algorithmic and architectural efforts behind these systems focusing on the challenges in addition to open research questions. |
Year | Venue | Field |
|---|---|---|
2017 | PVLDB | Open research,Data mining,World Wide Web,Social network,Latency (engineering),Computer science,Web application,Latency (engineering),Data objects,Database |
DocType | Volume | Issue |
Journal | 10 | 12 |
Citations | PageRank | References |
1 | 0.36 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor Zakhary | 1 | 23 | 5.72 |
| Amr El Abbadi | 2 | 6767 | 1569.95 |
| Divyakant Agrawal | 3 | 8201 | 1674.75 |