Head Node Dynamic Cloud Resource Allocation
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Solution Overview
Problem
Computer clusters face inefficiencies in managing job request execution and resource allocation when leveraging cloud computing services, particularly in determining execution status and dynamically controlling resource usage, leading to costly and inefficient expansion of on-premise capacity to meet evolving client needs.
Innovation Solution
A head node within the computer cluster automatically manages job request execution by directing resource allocations from a cloud computing provider, converting job requests into resource allocation requests, and monitoring performance and resource utilization, enabling dynamic resource leasing and accurate billing verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud computing resources are leased in anticipation of job request demands, then job request execution monitoring is possible, but computing resource leasing cannot be performed dynamically and central control over leased resources is frustrated
Solution Approach 1:
The patent implements dynamic resource leasing by allowing the computer cluster to request cloud computing resources on-demand based on actual job request loads, rather than pre-leasing fixed capacities. The head node continuously monitors job queues and dynamically provisions or releases cloud resources to match current computational demands, enabling both monitoring capability and dynamic adaptability.
Solution Approach 2:
The head node serves as an intermediary between the computer cluster and cloud computing provider. It receives job requests from compute nodes, converts them into resource allocation requests, and manages the leased cloud resources. This intermediary role enables centralized control over dynamically leased resources while maintaining execution monitoring capabilities.
2Productivity
If cloud computing resources are leased dynamically based on actual demands, then cost-effective resource utilization is achieved, but central control over leased resources becomes difficult
Solution Approach 1:
The head node acts as a central intermediary that manages dynamic resource leasing. It receives computational workload information from compute nodes, formulates appropriate resource allocation requests to the cloud provider, and maintains control over the leased resources. This centralized intermediary enables both cost-effective dynamic utilization and manageable control complexity.
Solution Approach 2:
The system implements feedback mechanisms where the head node monitors job request loads from compute nodes and uses this information to dynamically adjust cloud resource allocations. This feedback loop enables cost-effective resource utilization by matching leased capacity to actual demand while maintaining centralized control through automated decision-making based on real-time workload conditions.
Data Source
AI summary
The subject disclosure is directed towards automatically managing job request execution for a computer cluster using cloud computing resource allocations. When client computers to the computer cluster submit job requests to a head node, a set of job requests is selected based on a policy. The head node converts the set of job requests into a set of resource allocation requests based on job specification data. After communicating the resource allocation requests to a cloud computing provider, the head node is granted access and control over one or more worker nodes as a response. The worker nodes proceed to execute the set of job requests and update the head node with status information once the execution finishes.