Cluster Tuner Using Simulated Workload for Resource Allocation
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Solution Overview
Problem
Existing systems for managing production clusters face challenges in efficiently allocating computing resources across nodes, leading to decreased overall performance due to the inability to account for the impact of changes in resource allocation on multiple jobs executing within the cluster.
Innovation Solution
A system comprising a workload simulator and a cluster tuner that uses a scaled-down test cluster to simulate the workload of a production cluster, allowing for the determination of optimal resource allocation across nodes, thereby improving cluster performance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional tools are used to tune resource allocation for a single job, then the performance of that specific job is improved, but the overall cluster performance decreases due to inability to account for impacts on other jobs
Solution Approach 1:
The patent creates a simulated workload that copies the resource consumption patterns and job types of the production cluster. This simulated workload is executed on the production cluster itself to evaluate the impact of resource allocation changes on overall cluster performance before applying tuning decisions, thus resolving the contradiction between improving individual job performance and maintaining overall cluster productivity.
2Productivity
If more nodes are added to the production cluster to handle increased workload, then processing capacity is improved, but operational costs and resource wastage increase
Solution Approach 1:
The patent performs preliminary evaluation of resource allocation changes using simulated workload before implementing them on the production cluster. This allows the system to determine optimal resource allocation that maximizes processing capacity utilization without adding unnecessary nodes, thereby improving productivity while avoiding the operational costs and resource wastage associated with over-provisioning.
3Productivity
If resource allocation is optimized for one job type, then that job type's efficiency is improved, but other job types experience performance degradation
Solution Approach 1:
The patent implements a feedback mechanism where the simulated workload execution provides information about the impact of resource allocation changes on different job types. This feedback is used to adjust and optimize resource allocation to achieve balanced performance across multiple job types, resolving the contradiction between optimizing for one job type and maintaining adaptability across diverse workloads.
Data Source
AI summary
A production cluster executes a workload, such that jobs associated with the executed workload are allocated, according to a first configuration. A cluster monitor extracts production cluster information from the production cluster, monitors configuration information during execution of the workload, and transmits each to a cluster tuner. The cluster tuner receives the information and determines a first recommended configuration for the production cluster. The cluster tuner causes the test cluster to execute a simulated workload according to the first recommended configuration. In response to determining that the first recommended configuration results in a decrease in resource consumption, the cluster tuner causes the production cluster to operate according to the first recommended configuration.