Dynamic Server Farm Provisioning for Load Balancing
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Solution Overview
Problem
Conventional server farms face challenges in managing sudden increases in data traffic, leading to server overload and delays, as they rely on manual or suboptimal methods for adding and removing standby servers, resulting in poor resource utilization and increased costs.
Innovation Solution
Implementing a dynamic provisioning system that automatically adds or removes servers from a server farm based on monitored load thresholds, using a server load balancer and resource manager to provision additional servers from a private or public cloud, enabling on-demand scaling without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or suboptimal methods are used for adding and removing standby servers, then server overload can be prevented, but resource utilization becomes poor and costs increase
Solution Approach 1:
The system automatically monitors server load and provisions additional servers from the cloud without human intervention. The load balancer detects when existing servers are approaching capacity thresholds and autonomously triggers server provisioning, allowing the system to self-regulate resource allocation based on actual demand patterns
Solution Approach 2:
The server farm transitions from a static configuration with fixed standby servers to a dynamic system that continuously adjusts server capacity. Servers are added or removed based on real-time load monitoring, enabling the infrastructure to adapt flexibly to varying traffic demands and optimize resource utilization
2Speed
If standby servers are maintained to handle sudden traffic increases, then response time can be maintained, but operational costs increase
Solution Approach 1:
The system pre-configures cloud-based server resources that can be rapidly deployed when needed. While these resources exist in the cloud, they remain inactive and cost-free until triggered by actual load conditions, eliminating the need to maintain permanently idle standby servers while preserving rapid response capability
Solution Approach 2:
The system changes the operational state of servers dynamically - transitioning from inactive cloud resources to active computing resources based on monitored load parameters. This allows the system to maintain response time performance by having pre-configurable resources available while avoiding the continuous energy consumption of maintaining always-on standby servers
3Productivity
If more servers are added to handle increased traffic, then productivity improves, but device complexity increases
Solution Approach 1:
The load balancer serves as an intermediary that abstracts the complexity of managing multiple servers. It automatically distributes incoming traffic across available servers, handles server provisioning coordination, and manages load balancing algorithms, thereby enabling productivity scaling without proportionally increasing operational complexity
Data Source
AI summary
Techniques are provided for automatically adding/removing servers to/from a server farm in response to monitored load demands. A load among a plurality of servers, resulting from traffic associated with an application hosted on the plurality of servers is balanced. The load on the plurality of servers is monitored and it is determined that the load on the plurality of servers exceeds a predetermined load threshold. In response to such a determination, one or more additional servers are automatically provisioned for use in hosting the application. The load is then balanced between the plurality of servers and the one or more additional servers.