Workload Summarization for Server Congestion Avoidance
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Solution Overview
Problem
Existing autoscaling solutions in computing environments, such as Kubernetes, fail to effectively manage workload congestion due to unpredictable resource requirements, leading to overcommitment, pod evictions, and resource contention, especially in edge environments with limited resources.
Innovation Solution
Implement a workload summarization method that uses a machine learning model to predict resource usage based on real-time analysis of workload requests, allowing proactive congestion avoidance by holding off or adjusting workload execution to prevent resource overallocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing autoscaling solutions are used to manage workload in cloud environments, then resource allocation can be automated, but resource overcommitment and congestion occur due to unpredictable workload requirements
Solution Approach 1:
The system performs preliminary analysis of workload requests by generating summaries that predict resource requirements before the workload is executed. This advance prediction allows the system to prepare appropriate resource allocation and avoid overcommitment, resolving the contradiction between automated scaling and reliable resource allocation.
Solution Approach 2:
The system implements a feedback mechanism where workload summaries and predicted resource requirements are continuously monitored and used to adjust resource allocation decisions. This closed-loop control enables the system to adapt to unpredictable workload patterns while maintaining accurate resource allocation, addressing the reliability issue in automated autoscaling.
2Reliability
If more resources are allocated to handle unpredictable workload peaks, then service availability is improved, but resource waste increases during low-utilization periods
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time workload characteristics and predictions. Instead of static over-provisioning, the resource allocation changes flexibly with workload demands, ensuring service availability during peaks while minimizing resource waste during low-utilization periods.
Solution Approach 2:
The system changes resource allocation parameters based on workload summary analysis. By predicting resource requirements and adjusting allocation parameters dynamically, the system maintains service availability when needed while avoiding permanent over-provisioning that would cause resource waste during low-demand periods.
3Speed
If workload requests are processed immediately without prediction, then response time is reduced, but congestion and pod evictions occur due to resource contention
Solution Approach 1:
The system performs preliminary workload analysis and resource requirement prediction before processing requests. This advance preparation enables the system to queue and schedule workloads appropriately, maintaining fast processing speeds while preventing congestion and pod evictions by ensuring resources are ready before workload arrival.
Solution Approach 2:
The system takes preliminary anti-action by predicting resource requirements and proactively managing workload queuing to prevent congestion before it occurs. This preventive approach maintains processing speed by avoiding the delays that would result from reactive congestion management and pod evictions.
4Productivity
If resource allocation is optimized for edge environments with limited resources, then resource efficiency is improved, but the system becomes more vulnerable to congestion under unpredictable workloads
Solution Approach 1:
The system performs preliminary analysis of workload requests to predict resource requirements before execution. This advance prediction enables edge environments with limited resources to allocate resources efficiently while maintaining congestion resistance, as the system knows in advance what resources will be needed and can prepare accordingly.
Solution Approach 2:
The system implements continuous monitoring and feedback mechanisms that use workload summaries to adjust resource allocation in real-time. This feedback loop enables edge environments to maintain both resource efficiency and congestion resistance by adapting to unpredictable workloads while operating within limited resource constraints.
Data Source
AI summary
Methods, systems and computer program products for workload summarization and congestion avoidance are provided. Aspects include generating a workload summary associated with the workload request responsive to receiving a workload request. Aspects also include detecting potential congestion associated with the workload request by predicting resource usage based on the workload summary and a knowledge base model. Aspects further include performing an action to avoid the potential congestion, the action comprising holding off the workload request for at least a predetermined duration.