Multi-tenant

Feature Introduction

Modern enterprises' data clusters are developing towards centralization and cloudification. Enterprise-class big data clusters must meet the following requirements:

• Carry data of different types and formats and run jobs and applications of different types (analysis, query, and stream processing).
• Isolate data of a user from that of another user who has demanding requirements on data security, such as a bank or government institute.

The preceding requirements bring the following challenges to the big data cluster:

• Proper allocation and scheduling of resources to ensure stable operating of applications and jobs
• Strict access control to ensure data and service security

Multi-tenant isolates the resources of a big data cluster into resource sets. Users can lease desired resource sets to run applications and jobs and store data. In a big data cluster, multiple resource sets can be deployed to meet diverse requirements of multiple users.

The MRS big data cluster provides a complete enterprise-class big data multi-tenant solution. Multi-tenant is a collection of multiple resources (each resource set is a tenant) in an MRS big data cluster. It can allocate and schedule resources, including computing and storage resources.

Advantages

• Proper resource configuration and isolation

  The resources of a tenant are isolated from those of another tenant. The resource use of a tenant does not affect other tenants. This mechanism ensures that each tenant can configure resources based on service requirements, improving resource utilization.

• Resource consumption measurement and statistics

  Tenants are system resource applicants and consumers. System resources are planned and allocated based on tenants. Resource consumption by tenants can be measured and recorded.

• Ensured data security and access security

  In multi-tenant scenarios, the data of each tenant is stored separately to ensure data security. The access to tenants' resources is controlled to ensure access security.

Enhanced Schedulers

Schedulers are divided into the open source Capacity scheduler and Huawei proprietary Superior scheduler.

To meet enterprise requirements and tackle challenges facing the Yarn community in scheduling, Huawei develops the Superior scheduler. In addition to inheriting the advantages of the Capacity scheduler and Fair scheduler, this scheduler is enhanced in the following aspects:

• Enhanced resource sharing policy

  The Superior scheduler supports queue hierarchy. It integrates the functions of open source schedulers and shares resources based on configurable policies. In terms of instances, MRS cluster administrators can use the Superior scheduler to configure an absolute value or percentage policy for queue resources. The resource sharing policy of the Superior scheduler enhances the label scheduling policy of Yarn as a resource pool feature. The nodes in the Yarn cluster can be grouped based on the capacity or service type to ensure that queues can more efficiently utilize resources.

• Tenant-based resource reservation policy

  Resources required by tenants must be ensured for running critical tasks. The Superior scheduler builds a mechanism to support the resource reservation policy. By doing so, reserved resources can be allocated to the tasks run by the tenant queues in a timely manner to ensure proper task execution.

• Fair sharing among tenants and resource pool users

  The Superior scheduler allows shared resources to be configured for users in a queue. Each tenant may have users with different weights. Heavily weighted users may require more shared resources.

• Ensured scheduling performance in a big cluster

  The Superior scheduler receives heartbeats from each NodeManager and saves resource information in memory, which enables the scheduler to control cluster resource usage globally. The Superior scheduler uses the push scheduling model, which makes the scheduling more precise and efficient and remarkably improves cluster resource utilization. Additionally, the Superior scheduler delivers excellent performance when the interval between NodeManager heartbeats is long and prevents heartbeat storms in big clusters.

• Priority policy

  If the minimum resource requirement of a service cannot be met after the service obtains all available resources, a preemption occurs. The preemption function is disabled by default.