Large-scale cluster management at Google with Borg Large-scale cluster management at Google with Borg
Paper summary Borg is Google's cluster manager. Users submit jobs, a collection of tasks, to Borg which are then run in a single cell, many of which live inside a single cluster. Borg jobs are either high priority latency-sensitive production jobs (e.g. user facing products and core infrastructure) or low priority non-production batch jobs. Jobs have typical properties like name and owner and can also express constraints (e.g. only run on certain architectures). Tasks also have properties and state their resource demands. Borg jobs are specified in BCL and are bundled as statically linked executables. Jobs are labeled with a priority and must operate within quota limits. Resources are bundled into allocs in which multiple tasks can run. Borg also manages a naming service, and exports a UI called Sigma to developers. Cells are managed by five-way replicated Borgmasters. A Borgmaster communicates with Borglets running on each machine via RPC, manages the Paxos replicated state of system, and exports information to Sigma. There is also a high fidelity borgmaster simulator known as the Fauxmaster which can used for debugging. One subcomponent of the Borgmaster handles scheduling. Submitted jobs are placed in a queue and scheduled by priority and round-robin within a priority. Each job undergoes feasibility checking where Borg checks that there are enough resources to run the job and then scoring where Borg determines the best place to run the job. Worst fit scheduling spreads jobs across many machines allowing for spikes in resource usage. Best fit crams jobs as closely as possible which is bad for bursty loads. Borg uses a scheduler which attempts to limit "stranded resources": resources on a machine which cannot be used because other resources on the same machine are depleted. Tasks that are preempted are placed back on the queue. Borg also tries to place jobs where their packages are already loaded, but offers no other form of locality. Borglets run on each machine and are responsible for starting and stopping tasks, managing logs, and reporting to the Borgmaster. The Borgmaster periodically polls the Borglets (as opposed to Borglets pushing to the Borgmaster) to avoid any need for flow control or recovery storms. The Borgmaster performs a couple of tricks to achieve high scalability. - The scheduler operates on slightly stale state, a form of "optimistic scheduling". - The Borgmaster caches job scores. - The Borgmaster performs feasibility checking and scoring for all equivalent jobs at once. - Complete scoring is hard, so the Borgmaster uses randomization. The Borgmaster puts the onus of fault tolerance on applications, expecting them to handle occasional failures. Still, the Borgmaster also performs a set of nice tricks for availability. - It reschedules evicted tasks. - It spreads tasks across failure domains. - It limits the number of tasks in a job that can be taken down due to maintenance. - Avoids past machine/task pairings that lead to failure. To measure cluster utilization, Google uses a cell compaction metric: the smallest a cell can be to run a given workload. Better utilization leads directly to savings in money, so Borg is very focused on improving utilization. For example, it allows non-production jobs to reclaim unused resources from production jobs. Borg uses containers for isolation. It also makes sure to throttle or kill jobs appropriately to ensure performance isolation.
doi.acm.org
sci-hub.cc
scholar.google.com
Large-scale cluster management at Google with Borg
Verma, Abhishek and Pedrosa, Luis and Korupolu, Madhukar and Oppenheimer, David and Tune, Eric and Wilkes, John
ACM EuroSys - 2015 via Local Bibsonomy
Keywords: dblp


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