Batch Job Optimization Module for SLA Compliance
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Solution Overview
Problem
Batch processing systems face challenges in optimizing processing time while adhering to Service Level Agreements (SLAs), often resulting in SLA violations due to workload spikes and inadequate computational capacity.
Innovation Solution
A system and method that includes a data collection, management, processing, generation, computing, optimization, and reconfiguration module to analyze job dependencies, identify potential SLA violations, and apply optimal levers such as postponing jobs, reducing workload, or relaxing dependencies to minimize processing time and ensure SLA compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If batch processing is used to handle high volume repetitive tasks, then cost is reduced and productivity is improved, but processing time cannot be optimized in real-time leading to SLA violations
Solution Approach 1:
The system performs preliminary actions by predicting potential SLA violations before they occur and pre-computing optimal job schedules and resource allocations. The optimization module analyzes job dependencies, run-times, and SLA definitions in advance to determine the best processing sequence, ensuring time optimization is built into the batch processing framework rather than applied reactively.
Solution Approach 2:
The system introduces dynamics to static batch processing by enabling real-time optimization of job schedules based on current workload conditions. The optimization module dynamically adjusts processing sequences, resource allocations, and job timing based on predicted SLA compliance requirements, transforming rigid batch processing into an adaptive system that can respond to changing conditions while maintaining efficiency.
2Reliability
If computational capacity is increased to handle workload spikes, then SLA compliance is improved, but system cost and complexity increase
Solution Approach 1:
The system implements feedback mechanisms where the optimization module continuously monitors SLA compliance status and adjusts processing decisions accordingly. By analyzing actual job execution data and comparing it against SLA definitions, the system learns from past performance to make more accurate predictions about future SLA compliance, enabling proactive adjustments without requiring excessive computational capacity.
Solution Approach 2:
The system optimizes SLA compliance by changing processing parameters such as job sequence, resource allocation, and timing rather than increasing computational capacity. The optimization module manipulates parameters like job start times, execution sequences, and resource distribution to achieve SLA compliance with existing infrastructure, avoiding the need for costly capacity expansions.
3Manufacturing precision
If job dependencies are strictly enforced, then processing accuracy is maintained, but processing time increases due to sequential execution requirements
Solution Approach 1:
The system segments job dependencies into critical and non-critical paths, allowing flexible scheduling of independent job segments while maintaining accuracy for dependent tasks. The optimization module identifies which job dependencies can be relaxed or reordered without compromising accuracy, enabling parallel processing of independent segments and reducing overall processing time while preserving execution integrity.
Solution Approach 2:
The system applies partial relaxation of job dependencies where appropriate, allowing certain non-critical jobs to execute in parallel or out of strict sequence without affecting overall accuracy. By selectively enforcing or relaxing dependencies based on impact analysis, the system achieves time optimization while maintaining sufficient processing accuracy for critical operations.
Data Source
AI summary
A method and system is disclosed herein for optimizing batch processing time required for executing one or more batch jobs received in real time, while adhering to service level agreements (SLAs) compliance in one batch job arrangement of an information technology service management (ITSM). A batch job system is characterized by the set of jobs and dependencies between jobs. Each job is in turn characterized by run-time, from-time and SLA definitions. SLAs can be of two kinds Start-time and End-time. Start-time SLA requires that the job execution starts before the specified time while End-time SLA necessitates that the job finishes its execution before the specified time. To optimize processing time required for executing one or more batch jobs the disclosure identifies SLA violations and solves them to produce a set of actionable levers.