Streaming Graph Topology Dynamic Operator Instance Allocation
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Solution Overview
Problem
Streaming applications face challenges in dynamically managing system resources to meet service level agreements (SLAs) without over-allocating or under-allocating resources, leading to potential performance violations.
Innovation Solution
Monitoring system resource usage and performance metrics to identify when hosting is near to violating SLAs, and dynamically initializing additional instances of parallelizable operators to direct workloads, while terminating underutilized instances to ensure optimal resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional instances of parallelizable operators are initialized to meet SLA criteria, then service level compliance is improved, but system resource consumption increases
Solution Approach 1:
The patent implements dynamic initialization and termination of operator instances based on real-time monitoring of performance metrics and SLA compliance status. The system transitions from static resource allocation to dynamic adjustment, where operator instances are created or terminated according to current workload demands and SLA requirements, optimizing the balance between service level compliance and resource consumption.
Solution Approach 2:
The system continuously monitors performance metrics and uses this feedback to determine whether to initialize or terminate operator instances. The monitoring mechanism provides real-time information about system resource usage and SLA compliance status, enabling the system to adjust operator instance allocation based on actual performance data rather than predetermined fixed allocation.
2Use of energy by moving object
If operator instances are terminated to reduce resource consumption, then system resource utilization is improved, but SLA compliance may be compromised
Solution Approach 1:
The system uses continuous monitoring of performance metrics as feedback to determine safe termination points for operator instances. By analyzing real-time performance data, the system can identify when reducing operator instances will not compromise SLA compliance, enabling resource optimization without sacrificing service level guarantees.
Solution Approach 2:
The system dynamically adjusts operator instance allocation based on current workload patterns and performance trends. Rather than maintaining fixed allocation, the system adapts the number of active operator instances to match actual demand, terminating underutilized instances while ensuring SLA compliance is maintained through continuous performance monitoring.
3Reliability
If real-time monitoring and dynamic adjustment are implemented, then SLA compliance is improved, but device complexity increases
Solution Approach 1:
The system segments the streaming application into parallelizable operators that can be independently initialized and terminated. This segmentation allows the complex monitoring and adjustment task to be broken down into manageable units, where each operator instance can be managed independently, reducing the overall system complexity despite the dynamic adjustment capabilities.
Data Source
AI summary
System resource usage by a streaming application processing workloads can be monitored, the streaming application comprising at least one parallelizable operator, at least a first instance of the parallelizable operator being initialized to execute. Based on the monitoring, at least one performance metric for the streaming application in processing the workloads can be determined. Based on the at least one performance metric, whether hosting of the streaming application is, or is near to, violating at least one criteria can be determined. If so, at least one additional instance of at least one parallelizable operator of the streaming application that is currently executing can be initialized. Responsive to initializing the at least one additional instance of at least one of the parallelizable operators, a portion of the workloads can be directed to the at least one additional instance of at least one of the parallelizable operators.