Interference-Aware Client Placement in Cloud Systems
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Solution Overview
Problem
Current client placement strategies in cloud computing do not effectively optimize performance and service level agreements (SLAs) due to limitations in considering resource interferences between clients, leading to suboptimal workload distribution and migration decisions.
Innovation Solution
A management server and method that utilize interference scores to create interference affinity-type rules, recommending target hosts for client placement by accounting for workload interferences between clients, thereby optimizing resource management in distributed computer systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current client placement strategies are used, then resource allocation is simplified, but performance and SLA compliance deteriorate due to unaccounted workload interferences
Solution Approach 1:
The system performs preliminary actions by calculating interference scores for all clients before placement decisions are made. These interference scores are pre-computed based on workload characteristics and resource contention patterns, allowing the placement engine to make informed decisions without complex real-time analysis during the placement process itself.
Solution Approach 2:
The invention introduces an intermediary mechanism - the interference score - that mediates between the complexity of workload interference analysis and the simplicity of placement decisions. Instead of directly handling complex interference models, the system uses these scored intermediaries to guide placement, simplifying the decision-making process while maintaining performance awareness.
2Productivity
If clients are placed without considering interference scores, then placement speed is maintained, but resource contention increases leading to suboptimal workload distribution
Solution Approach 1:
Interference scores are calculated in advance as preliminary actions, allowing the placement engine to access pre-computed interference information during placement decisions. This pre-computation approach enables efficient placement without requiring time-consuming real-time interference analysis, thus maintaining speed while improving resource utilization.
Solution Approach 2:
The system changes the parameter used in placement decisions from raw resource metrics to interference scores. By transforming the decision basis to these pre-computed interference parameters, the system achieves faster placement decisions compared to real-time analysis, while simultaneously improving resource allocation efficiency through interference-aware positioning.
3Reliability
If traditional resource control parameters (reservation, limit, share) are used for placement, then implementation is straightforward, but performance optimization deteriorates
Solution Approach 1:
The interference score acts as an intermediary that bridges traditional resource control parameters with performance optimization goals. It translates complex interference relationships into a simplified scoring mechanism that can be integrated with existing placement frameworks, maintaining ease of implementation while enabling performance-aware decisions.
Solution Approach 2:
The interference score mechanism is designed to be universally applicable across different placement scenarios and workload types. It can be integrated with existing resource control parameters (reservation, limit, share) while adding interference awareness, making the system multi-functional and adaptable to various cloud computing environments without requiring complete redesign.
Data Source
AI summary
A management server and method for performing resource management operations in a distributed computer system utilizes interference scores for clients executing different workloads to create an interference affinity-type rule for at least some of the clients contending for a resource based on the interference scores for that resource. The interference affinity-type rule can then be used to recommend a target host computer to place a client.