Streaming Query Resiliency Cost Modeling
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Solution Overview
Problem
Current technologies lack reliable tools to quantify and select the most cost-effective resiliency strategies for streaming queries in cloud computing environments, leading to suboptimal choices based on ease of implementation rather than specific scenario needs.
Innovation Solution
A method to model baseline costs of streaming query deployments and additional costs of implementing resiliency strategies, allowing for the selection of cost-effective and SLA-compliant strategies by calculating recovery NIC bandwidth reservations and total reserved NIC bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resiliency strategies are implemented to protect against failure and data loss, then reliability is improved, but cost increases
Solution Approach 1:
The patent applies parameter changes by varying resiliency strategy parameters (checkpointing frequency, replication factor, recovery time objectives) to find the optimal balance between reliability and cost. The system models different parameter configurations and their associated costs to identify the most cost-effective strategy that meets SLA requirements.
Solution Approach 2:
The system dynamically selects resiliency strategies based on workload characteristics, failure rates, and cost constraints. Rather than using a static approach, the system adapts the resiliency configuration to match current operational conditions, optimizing the trade-off between reliability and cost in real-time.
2Reliability
If stronger resiliency strategies are used to reduce recovery latency, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the resiliency strategy into distinct components (checkpointing mechanisms, replication strategies, recovery procedures) that can be independently configured and optimized. This modular approach reduces overall system complexity by allowing each component to be managed separately based on specific requirements.
Solution Approach 2:
The system performs preliminary actions by pre-configuring resiliency parameters and modeling recovery scenarios before actual failures occur. This advance planning reduces the complexity of handling failures in real-time, as the recovery strategy is already determined based on pre-computed models.
3Reliability
If resiliency strategies are customized for specific scenarios, then reliability is improved, but ease of operation decreases
Solution Approach 1:
The system provides self-service by automatically modeling and selecting appropriate resiliency strategies based on input parameters describing the workload and SLA requirements. The automated modeling process eliminates the need for manual strategy selection, making the system easy to operate while still providing scenario-specific optimization.
Solution Approach 2:
The system uses feedback from workload characteristics and SLA compliance metrics to automatically adjust and select the most appropriate resiliency strategy. This closed-loop approach ensures that the system adapts to specific scenarios while maintaining ease of operation through automated decision-making.
Data Source
AI summary
Costs associated with deploying a streaming query according to one or more resiliency strategies given a particular service level agreement (SLA) specification are modeled to enable selection and/or recommendation of a particular resiliency strategy. A baseline cost model represents costs associated with deploying the streaming query non-resiliently. For each of any number of resiliency strategies, a resiliency model represents additional costs associated with deploying the streaming query according to a particular resiliency strategy.