Dynamic Cloud Cluster Scaling via Virtual Instance Provisioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional clustered computing applications face challenges in scalability and cost management, as they require static hardware allocation, leading to increased costs during peak loads and inefficiencies during decreased loads, with no efficient way to decommission hardware for cost savings.
Innovation Solution
A cloud-based system with a three-tier topology using virtual instances, where nodes are dynamically scaled using a control server and asynchronous messaging for load balancing, application, and database tiers, enabling elastic scalability through manual or rule-based adjustments based on load measurements and performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware is statically allocated to a cluster, then the cluster can maintain stable performance, but the cost increases during peak loads and cannot be reduced during decreased loads
Solution Approach 1:
The patent implements dynamic hardware provisioning where virtual machine instances are automatically added or removed from the cluster based on real-time load conditions. The system transitions from static hardware allocation to dynamic scaling, allowing the cluster to adapt its resource capacity to match actual demand, thereby reducing costs during low-load periods while maintaining performance during peak periods.
Solution Approach 2:
The system changes the operational parameters of the cluster by monitoring load metrics and automatically adjusting the number of active nodes. When load exceeds thresholds, new virtual instances are provisioned; when load decreases, instances are decommissioned. This parameter-based control enables cost optimization without sacrificing reliability.
2Productivity
If new hardware is purchased and brought online to handle increased load, then the cluster capacity increases, but the time required for deployment increases to days or weeks
Solution Approach 1:
The patent employs pre-configured virtual machine images and automated provisioning scripts that are prepared in advance. When scaling is needed, these pre-prepared templates are rapidly instantiated rather than building from scratch, dramatically reducing deployment time from days to minutes while maintaining full cluster capacity.
Solution Approach 2:
Instead of physically purchasing and installing new hardware, the system creates virtual copies of existing node configurations. These virtual instances can be rapidly cloned and deployed across the cloud infrastructure, enabling quick capacity expansion without the time-consuming processes of physical hardware procurement, installation, and configuration.
3Quantity of substance
If hardware is decommissioned to reduce costs during decreased load, then cost savings are realized, but there is no efficient way to do so in traditional clusters
Solution Approach 1:
The system implements automated self-service scaling where the cluster monitors its own load conditions and automatically decommissions excess hardware instances when demand decreases. This eliminates the need for manual intervention in hardware management, making the decommissioning process as easy as configuring initial parameters while realizing cost savings during low-utilization periods.
Solution Approach 2:
The patent incorporates continuous feedback loops that monitor cluster load and automatically trigger decommissioning actions when load falls below defined thresholds. This feedback-driven approach enables efficient hardware reduction by continuously adjusting resource allocation based on actual usage patterns, making cost optimization an automated rather than manual process.
Data Source
AI summary
A system is provided to provision and manage cloud-based clustered application deployment. In one example embodiment, a clustered computing application is detected as being launched on a cluster of nodes. Each node in the cluster is a virtual instance of a machine within a virtualization service. Status of the nodes in the cluster are monitored to detect availability of a server within the virtualization service A notification message addressed to the server is issued in response to detecting the availability of the server. The notification message includes information for connecting to the cluster.