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Apache Hadoop YARN – Resource Manager (RM)


31 March 2014

Requirements’ Origin

  1. Scalability
  2. Multi-tenancy
  3. Serviceability
  4. Locality Awareness
  5. High Cluster Utilization
  6. Reliability/Availability
  7. Secure and Auditable Operation
  8. Support for Programming Model Diversity
  9. Flexible Resource Model
  10. Backward compatibility

Resource Manager (RM)

The ResourceManager exposes two public interfaces towards: 1) clients submitting applications, and 2) ApplicationMaster(s) dynamically negotiating access to resources, and one internal interface towards NodeManagers for cluster monitoring and resource access management. In the following, we focus on the public protocol between RM and AM, as this best represents the important frontier between the YARN platform and the various applications/frameworks running on it.

The RM matches a global model of the cluster state against the digest of resource requirements reported by running applications. This makes it possible to tightly enforce global scheduling properties (different schedulers in YARN focus on different global properties, such as capacity or fairness), but it requires the scheduler to obtain an accurate understanding of applications’ resource requirements. Communication messages and scheduler state must be compact and efficient for the RM to scale against application demand and the size of the cluster [R1]. The way resource requests are captured strikes a balance between accuracy in capturing resource needs and compactness. Fortunately, the scheduler only handles an overall resource profile for each application, ignoring local optimizations and internal application flow. YARN completely departs from the static partitioning of resources for maps and reduces; it treats the cluster resources as a (discretized) continuum [R9]— as we will see in Section 4.1 [YARN in the real-word], this delivered significant improvements in cluster utilization.

ApplicationMasters codify their need for resources in terms of one or more ResourceRequests, each of which tracks:

  1. number of containers (e.g., 200 containers),
  2. resources per container ⟨2GB RAM, 1 CPU⟩ [The resource vector is designed to be extensible.],
  3. locality preferences, and
  4. priority of requests within the application

ResourceRequests are designed to allow users to capture the full detail of their needs and/or a roll-up version of it (e.g., one can specify node-level, rack-level, and global locality preferences [R4 ]). This allows more uniform requests to be represented compactly as aggregates. Furthermore, this design would allow us to impose size limits on ResourceRequests, thus leveraging the roll-up nature of ResourceRequests as a lossy compression of the application preferences. This makes communication and storage of such requests efficient for the scheduler, and it allows applications to express their needs clearly [R1,R5,R9]. The roll-up nature of these requests also guides the scheduler in case perfect locality cannot be achieved, towards good alternatives (e.g., rack-local allocation, if the desired node is busy).

This resource model serves current applications well in homogeneous environments, but we expect it to evolve over time as the ecosystem matures and new requirements emerge. Recent and developing extensions include: explicit tracking of gang-scheduling needs, and soft/hard constraints to express arbitrary co-location or disjoint placement. While the RM uses locality as a weight for placing containers, network bandwidth is not explicitly modeled and reserved as in Oktopus [5] or Orchestra [9].

The scheduler tracks, updates, and satisfies these requests with available resources, as advertised on NM heartbeats. In response to AM requests, the RM generates containers together with tokens that grant access to resources. The RM forwards the exit status of finished containers, as reported by the NMs, to the responsible AMs. AMs are also notified when a new NM joins the cluster so that they can start requesting resources on the new nodes.

A recent extension of the protocol allows the RM to symmetrically request resources back from an application. This typically happens when cluster resources become scarce and the scheduler decides to revoke some of the resources that were given to an application to maintain scheduling invariants. We use structures similar to ResourceRequests to capture the locality preferences (which could be strict or negotiable). AMs have some flexibility when fulfilling such ’preemption’ requests, e.g., by yielding containers that are less crucial for its work (for e.g. tasks that made only little progress till now), by checkpointing the state of a task, or by migrating the computation to other running containers. Overall, this allows applications to preserve work, in contrast to platforms that forcefully kill containers to satisfy resource constraints. If the application is noncollaborative, the RM can, after waiting for a certain amount of time, obtain the needed resources by instructing the NMs to forcibly terminate containers.

Given the prenominate requirements from section 2, it is important to point out what the ResourceManager is not responsible for. As discussed, it is not responsible for coordinating application execution or task fault-tolerance, but neither is is charged with 1) providing status or metrics for running applications (now part of the ApplicationMaster), nor 2) serving framework specific reports of completed jobs (now delegated to a per-framework daemon). YARN does provide generic information about completed apps, containers etc. via a common daemon called Application History Server. This is consistent with the view that the ResourceManager should only handle live resource scheduling, and helps central components in YARN scale beyond the Hadoop 1.0 JobTracker.

Major Research Topic Keywords

Dynamic Negotiation

Discretized Continuum

Explicit Tracking of Gang-Scheduling


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