Dynamic Application Cluster Reconfiguration for Server Resource Allocation
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Solution Overview
Problem
Current server farm management techniques face inefficiencies in resource allocation, leading to poor utilization and responsiveness due to coarse granularity in allocating application server instances, resulting in potential SLA violations and wasted capacity, especially when workloads fluctuate.
Innovation Solution
A method and system for on-demand application resource allocation that dynamically adjusts the size and placement of application clusters by starting and stopping application server instances based on workload predictions and resource requirements, ensuring optimal resource utilization while minimizing disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of application server instances is increased to handle peak load, then service level agreement violations are avoided, but resource utilization during normal operating conditions deteriorates
Solution Approach 1:
The patent implements dynamic allocation strategies where application server instances are automatically instantiated or terminated based on contemporaneous workload measurements. The system continuously adjusts the number of active application servers in response to actual demand, transitioning from static overprovisioning to dynamic scaling that matches real-time workload conditions.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor workload characteristics and SLA compliance in real-time. Based on this feedback, the placement manager automatically adjusts application cluster configurations, creating a closed-loop control system that responds to changing conditions and optimizes resource allocation continuously.
2Device complexity
If a single application is assigned to an application cluster, then resource allocation simplicity is maintained, but resource allocation granularity becomes coarse and wasteful
Solution Approach 1:
The patent segments application servers into multiple independent instances that can be individually allocated and managed. Instead of treating an application cluster as a monolithic unit, the system divides it into discrete server instances that can be selectively instantiated, migrated, or terminated based on fine-grained workload requirements.
Solution Approach 2:
The system dynamically changes allocation parameters such as the number of active instances, instance placement locations, and cluster configurations based on measured workload characteristics. This allows fine-grained control over resource allocation by adjusting multiple parameters simultaneously to match actual demand patterns.
3Loss of energy
If more than one application is assigned to an application cluster, then resource utilization efficiency improves, but the number of applications that can be assigned is limited by memory capacity
Solution Approach 1:
The patent introduces the dimension of server instance multiplication, allowing multiple applications to share physical server resources through virtualization. By deploying multiple application server instances per physical server, the system increases the effective capacity for concurrent applications without being limited by physical memory constraints of individual servers.
4Speed
If application server re-allocation is performed frequently to respond to workload changes, then responsiveness to SLA requirements improves, but reconfiguration time and system disruption increase
Solution Approach 1:
The system performs preliminary actions by maintaining a pool of pre-configured application server instances that can be rapidly deployed. Instead of provisioning servers from scratch during reconfiguration events, the system has advance prepared standardized server templates and configurations that can be instantly activated, reducing reconfiguration time.
Data Source
AI summary
A method, system and apparatus for on-demand application resource allocation. In accordance with the method of the invention, an anticipated workload can be compared to a measured capacity for an application cluster in one or more server computing nodes in a server farm. If the measured capacity warrants a re-configuration of the application clusters, a new placement can be computed for application server instances in individual ones of the server computing nodes. Subsequently, the new placement can be applied to the server computing nodes in the server farm. In this regard, the applying step can include starting and stopping selected ones of the application server instances in different ones of the server computing nodes to effectuate the new placement.