Job Management via Workload Prediction Model
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Solution Overview
Problem
Processing systems face inefficiencies due to latency and resource fluctuations, leading to extra overhead from constantly starting and stopping processing resources, as they cannot accurately predict workloads and allocate resources effectively.
Innovation Solution
A workload model is introduced to determine both current and future workloads based on job descriptions and historical data, allowing for more efficient management of jobs by optimizing the allocation of processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If processing resources are constantly started and stopped to match workload needs, then resource allocation efficiency is improved, but system latency increases and overhead increases
Solution Approach 1:
The patent applies preliminary action by predicting future workloads before they occur and proactively adjusting resource allocation. The workload prediction module forecasts future job arrivals and characteristics, allowing the system to prepare resources in advance rather than reacting after latency has already occurred. This predictive approach enables resource allocation to be optimized before workload changes manifest, reducing the time loss associated with reactive resource management.
2Productivity
If processing resources are constantly started and stopped to match workload needs, then resource allocation efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces a workload prediction module as an intermediary between the job queue and resource allocation system. This prediction module acts as a mediator that analyzes job characteristics, predicts future workloads, and provides guidance to the resource allocation mechanism. By inserting this intermediary layer, the system transforms complex, reactive resource management into a more manageable predictive process, reducing the overall complexity of coordinating resource start-ups and shut-downs while maintaining allocation efficiency.
3Speed
If processing resources are kept available to avoid latency, then response time is improved, but resource waste increases
Solution Approach 1:
The patent applies dynamics by making resource allocation adaptive and flexible based on predicted workload conditions. Rather than maintaining static resource availability or using rigid threshold-based allocation, the system dynamically adjusts resource allocation according to predicted future workloads. The resource allocation module continuously adapts its decisions based on predictions from the workload prediction module, allowing resources to be allocated precisely when needed without premature or excessive provisioning, thus avoiding energy waste while maintaining processing speed.
4Productivity
If workload prediction is implemented to optimize resource allocation, then resource allocation efficiency is improved, but measurement precision requirements increase
Solution Approach 1:
The patent applies partial action by implementing a workload prediction system that focuses on predicting only the most critical aspects of future workloads rather than attempting to predict every detail with perfect precision. The prediction module identifies key job characteristics and patterns that have the greatest impact on resource allocation decisions, making predictions for these specific parameters while accepting that not all workload attributes need to be predicted with equal accuracy. This selective prediction approach achieves sufficient resource allocation efficiency without requiring impossible measurement precision across all workload dimensions.
Data Source
AI summary
The present disclosure relates to a method, device and computer program product for managing jobs in a processing system. The processing system comprises multiple client devices. In the method, based on a group of jobs from the multiple client devices, a current workload of the group of jobs is determined. A group of job descriptions associated with the group of jobs is determined based on configuration information of various jobs in the group of jobs. A future workload associated with the group of jobs is determined based on associations, comprised in a workload model, between job descriptions and future workloads associated with the job descriptions. The group of jobs in the processing system are managed based on the current workload and the future workload. With the foregoing example implementation, jobs in the processing system may be managed more effectively, and latency in processing jobs may be reduced. Further, there is provided a device and computer program product for managing jobs in a processing system.