Name
Abstract | ||
|---|---|---|
Operating a cloud-scale service is a huge challenge. There are millions of users worldwide and millions of requests per seconds. For example, Amazon's Simple Storage Service (S3) in 2013 contained two trillion objects and its logs contained 1.1 million log lines per second, which are approximately 10 PB of log records per year (see [1]). Cloud scale implies thousands of servers and network elements, and hundreds of services from multiple cross-regional data centers. Cloud service operation data is scattered over various types of semi-structured and unstructured logs (e.g., application, error, debug), telemetry and network data, as well as customer service records. It is therefore extremely difficult for the multiple owners and administrators in such systems, coming from different units of the organization, to follow the possible paths and system alternatives in order to detect problems, solve issues and understand the service operation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3078468.3078495 | SYSTOR |
Field | DocType | Citations |
Virtualization,Customer service,Computer science,Visualization,Server,Memory management,Network element,Database,Debugging,Cloud computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dean H. Lorenz | 1 | 340 | 32.77 |
| Eran Raichstein | 2 | 9 | 3.34 |
| Barabash, Katherine | 3 | 1 | 2.71 |
| Hillel Kolodner | 4 | 28 | 3.24 |
| Liran Schour | 5 | 41 | 4.66 |
| Shelly Garion | 6 | 1 | 1.37 |