Load Balancer Dynamic Resource Allocation
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Solution Overview
Problem
In network-based computing environments, existing load balancers face challenges in dynamically adjusting computing resources to match varying workloads, leading to inefficiencies such as over-allocation or under-allocation of resources, which can impact request processing times and increase costs.
Innovation Solution
A system that monitors and analyzes historical resource utilization to dynamically adjust the allocation of computing resources to a load balancer by selecting the appropriate type and quantity of resources based on statistical measures like percentiles and volatility, ensuring timely processing of requests while minimizing waste and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computing resources are statically allocated to a load balancer, then the system structure is simple and easy to manage, but the resources cannot adapt to varying workload demands, leading to over-allocation or under-allocation
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring resource utilization metrics (CPU utilization, memory utilization, network I/O) and adjusting the number of computing resources allocated to the load balancer based on current workload conditions. This transforms the static resource allocation into a dynamic system that adapts to changing demands, resolving the contradiction between adaptability and system complexity.
Solution Approach 2:
The system employs feedback mechanisms by monitoring resource utilization metrics and using this information to adjust resource allocation. The monitoring component tracks resource usage, and this feedback is used by the resource allocation component to dynamically adjust the number of computing resources, creating a closed-loop control system that balances adaptability with manageable complexity.
2Productivity
If additional computing resources are allocated to handle peak workloads, then request processing speed is improved, but resource waste increases during low-utilization periods
Solution Approach 1:
The system dynamically adjusts the number of computing resources allocated to the load balancer based on real-time monitoring of resource utilization metrics. During peak workloads, additional resources are allocated to maintain request processing speed, while during low-utilization periods, resources are deallocated to minimize waste, thus resolving the contradiction between productivity and energy loss.
Solution Approach 2:
The system changes the parameter of resource allocation (number of computing resources) based on monitored utilization metrics. By adjusting this parameter dynamically according to actual workload conditions, the system optimizes both request processing speed and resource efficiency, preventing both resource waste and performance degradation.
3Loss of energy
If computing resources are reduced to minimize costs, then resource efficiency improves, but request processing delays occur during high-utilization periods
Solution Approach 1:
The monitoring component continuously tracks resource utilization metrics and provides feedback to the resource allocation component. This feedback mechanism ensures that resources are reduced only when utilization is low, maintaining service reliability during high-utilization periods while improving resource efficiency during low-demand periods, thus resolving the contradiction between energy loss and reliability.
Solution Approach 2:
The system performs preliminary monitoring and analysis of resource utilization patterns to predict when resource reduction will not impact service reliability. By taking preliminary actions to monitor and analyze utilization metrics before making allocation decisions, the system ensures that resource reduction does not compromise reliability while still improving overall resource efficiency.
4Productivity
If the number of computing resources is increased, then the load balancer can handle higher traffic volumes, but the cost of operation increases
Solution Approach 1:
The system dynamically adjusts the number of computing resources based on actual traffic volume and resource utilization metrics. This dynamic approach allows the load balancer to handle higher traffic volumes when necessary while minimizing operational costs during lower traffic periods, resolving the contradiction between productivity and quantity of substance (operational cost).
Solution Approach 2:
The system changes the allocation parameter (number of computing resources) based on monitored traffic handling requirements. By adjusting this parameter dynamically, the system optimizes the balance between traffic handling capacity and operational cost, ensuring adequate capacity when needed while minimizing costs during normal operation.
Data Source
AI summary
A web services system includes a plurality of computing devices configurable to implement a plurality of compute instances of a provider network. One or more of the computing devices is configured to implement a network device and a resource manager. The network device is configured to receive traffic directed to an application. The resource manager is configured to determine an amount of computing resources employed by the network device to process a first workload presented to the network device over a predetermined precedent time interval, to determine, based on the amount of computing resources employed by the network device, an amount of computing resources needed by the network device to process a second workload; and to allocate the amount of computing resources to the network device.