Dynamic Server Provisioning for Cloud Job Completion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In cloud computing environments, there is a challenge in efficiently allocating resources and data to meet performance requirements while optimizing costs, as users often lack knowledge of the necessary resources needed to satisfy performance demands and suppliers aim to minimize costs.
Innovation Solution
A method and system that dynamically identify replica servers with data blocks, estimate completion times for job requests, and initiate new servers if existing servers cannot meet expected completion times, relocating data blocks and job requests to ensure performance requirements are met, thereby optimizing resource allocation and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing replica servers are used to execute job requests, then resource utilization is maintained, but performance requirements cannot be met when completion time exceeds expected time
Solution Approach 1:
The system dynamically provisions new servers based on real-time performance monitoring. When replica servers fail to meet expected completion times, the system automatically initiates new servers to handle job requests, transforming a static resource allocation into a dynamic response that adapts to changing performance requirements.
Solution Approach 2:
The system changes the operational parameters by switching from using existing replica servers to provisioning new servers when performance thresholds are not met. This parameter change enables the system to meet performance requirements by altering the server state from reused to freshly provisioned.
2Productivity
If new servers are initiated to meet performance requirements, then job completion time is reduced, but resource allocation costs increase
Solution Approach 1:
The system implements self-service by automatically monitoring performance metrics and autonomously deciding when to provision new servers. This eliminates the need for manual intervention and enables the system to self-optimize resource allocation based on actual performance requirements, reducing unnecessary resource consumption.
Solution Approach 2:
The system uses feedback from performance monitoring to drive resource allocation decisions. By continuously measuring job completion times against expected thresholds, the system receives feedback that triggers selective server provisioning only when performance requirements are not met, optimizing the balance between productivity and resource costs.
3Reliability
If data blocks are relocated to new servers, then performance is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer (the server provisioning and data management system) that handles the complexity of data relocation automatically. This intermediary manages the coordination between job requests, replica servers, and new servers, abstracting the complex data movement operations from the user and simplifying the overall system interaction.
Data Source
AI summary
Technical solutions are described for optimizing operation of a server cluster. An example method includes receiving a job request that executes using a set of data blocks, the job request being associated with an expected completion time. The cluster server is used to identify a set of replica servers, wherein each server from the set of replica servers contains the set of data blocks. In response to each server from the set of replica servers estimating a completion time for the job request that is more than the expected completion time, a new server is initiated, the set of data blocks is relocated from a first server from the set of replica servers to the new server, and the job request is allocated to the new server.