Cloud Cluster Pooling for Low-Latency Resource Provisioning
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Solution Overview
Problem
Cloud computing systems face significant latency issues in provisioning clusters for programming jobs due to resource provisioning, network configuring, and authentication complexities, leading to long waiting times before clusters are available for user requests, which is exacerbated by uncertain user demand and the need for on-demand access.
Innovation Solution
A proactive resource provisioning system with intelligent pooling, utilizing a live pool of pre-provisioned clusters managed by a cluster manager that dynamically scales based on predicted allocation latency and idle time, employing machine-learning models and linear programming to optimize cluster availability and cost-efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If clusters are provisioned on-demand when users request them, then resource utilization is optimized and operational costs are reduced, but user wait time for cluster availability increases significantly
Solution Approach 1:
The system pre-provisions clusters in advance before users actually need them. Cluster templates are created and maintained in a pool of available resources, so when a user requests cluster execution, the pre-configured cluster can be allocated immediately without going through the full provisioning process, thus reducing user wait time while maintaining cost efficiency through on-demand usage of pre-provisioned templates
Solution Approach 2:
The system dynamically adjusts the pool of pre-provisioned cluster templates based on real-time demand patterns, historical data, and current resource availability. The pool size and composition are continuously optimized to balance between having enough pre-provisioned clusters to minimize user wait time and not over-provisioning to control operational costs, making the resource allocation adaptive and flexible
2Loss of time
If the number of pre-provisioned clusters is increased to reduce user wait time, then cluster availability improves, but operational costs and resource waste increase
Solution Approach 1:
The system continuously monitors user demand patterns, cluster utilization rates, and performance metrics from the pre-provisioned pool. This feedback information is used to dynamically adjust the pool size and composition, ensuring that enough clusters are pre-provisioned to meet demand and minimize user wait time, while avoiding over-provisioning that would increase operational costs and resource waste
Solution Approach 2:
The system changes key parameters such as pool size, cluster configuration templates, and provisioning thresholds based on analyzed demand patterns and performance data. By adjusting these parameters dynamically, the system optimizes the balance between cluster availability (reducing user wait time) and operational efficiency (controlling costs), adapting to changing workloads and usage patterns
3Speed
If cluster provisioning processes are simplified to reduce setup time, then cluster availability speed improves, but system reliability and configuration accuracy deteriorate
Solution Approach 1:
The system creates and maintains cluster templates that capture complete, validated configurations. When a user needs a cluster, the system copies the pre-validated template configuration rather than performing full provisioning from scratch. This copying approach maintains high provisioning speed while ensuring configuration accuracy and reliability, as the templates have been pre-tested and validated for correctness
Solution Approach 2:
Cluster configuration templates are pre-configured, validated, and stored before actual cluster execution is needed. The preliminary creation and validation of these templates ensures that when clusters are rapidly provisioned by copying templates, both speed and reliability are maintained - the speed comes from avoiding re-validation, while the reliability is preserved through the pre-established correct configurations
Data Source
AI summary
The present application relates to a network, apparatus, and method for allocating clusters of computing nodes for programming jobs. A network includes a plurality of datacenters including computing resources configurable to instantiate nodes for executing programming jobs on a cluster. The computing resources at one of the datacenters are configured to: provision a live pool including a number of clusters, each cluster in the live pool including a plurality of nodes imaged with a configuration for executing the programming jobs in parallel on the cluster; receive a request from a user to execute a programming job; allocate a cluster from the live pool to the user for the programming job when the cluster is available; evict the cluster from the live pool; and provision a new cluster within the live pool to meet the number of clusters. The number of clusters may be optimized based on linear programming and machine-learning.