Thus the grace period allows sessions to be maintained across fail-overs that exceed the normal lease timeout. This is a joint post with him. Both Red Hat and Oracle have developed clustering software for Linux. Shark is a research data analysis system built on a novel coarse-grained distributed shared-memory abstraction. People are OK with their analytic jobs return results hours later. Categories : Distributed computing architecture. If a master election occurs quickly, clients can contact the new master before their local approximate lease timers expire.
Service Level Objectives
The cache is maintained by a lease, and kept consistent by invalidations sent by the master, which keeps a list of what each client may be caching. I have to confess that there are several things I could not understand in this paper. There is a reason the client is not sending the request toward the end of the lease and i rather sending it early on. Indicators An SLI is a service level indicator —a carefully defined quantitative measure of some aspect of the level of service that is provided. A filesystem directory with millions of small…. Many indicator metrics are most naturally gathered on the server side, using a monitoring system such as Borgmon see Practical Alerting from Time-Series Data or Prometheus, or with periodic log analysis—for instance, HTTP responses as a fraction of all requests.
The Chubby lock service for loosely-coupled distributed systems – Google Research
How can autonomous, mutually-distrusting parties cooperate safely and effectively? Storage systems often emphasize latency , availability , and durability. While changes of the lock ownership are typically infrequent, clients tend to periodically poll the lock, creating a lot of read traffic. While understanding the merits and limits of a system is essential, adopting values without reflection may lock you into supporting a system that requires heroic efforts to meet its targets, and that cannot be improved without significant redesign.
Description: Some metrics are seemingly straightforward, like the number of requests per second served, but even this apparently straightforward measurement implicitly aggregates data over the measurement window. This is from page 13 of the paper: "Originally, we did not appreciate the critical need to cache the absence of files, nor to reuse open file handles. Companies care about cheap computing. So much for the theory—now for the experience. Can we access the data on demand?