Cloud Instance Performance Evaluation and Selection
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Solution Overview
Problem
Public cloud performance is highly unpredictable and varies significantly for users and processing jobs due to underlying hardware differences and resource contention, leading to inconsistent resource allocation for the same price, with no reliable mechanism for users to assess relative advantages across different cloud services.
Innovation Solution
Implementing a method to run multiple instances of virtualized resources on a cloud, evaluate their performance characteristics, and determine which instances to maintain or terminate, allowing users to strategically optimize instance placement and migration based on predicted performance, thereby improving efficiency and cost-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple instances are run on public clouds, then performance heterogeneity can be exploited to improve efficiency, but performance unpredictability and variability remain due to hardware differences and resource contention
Solution Approach 1:
The system performs preliminary performance evaluation of cloud instances before committing to long-term deployments. By running benchmark tests and assessing performance characteristics in advance, the system identifies high-performing instances and makes informed decisions about where to place workloads, thereby exploiting performance heterogeneity while mitigating unpredictability
Solution Approach 2:
The system continuously monitors performance metrics of cloud instances and uses this feedback to dynamically adjust workload placement decisions. By evaluating performance characteristics over time and incorporating this information into future deployment decisions, the system adapts to performance variations and maintains reliable predictions
2Loss of information
If cloud service providers keep performance data confidential, then provider security and competitive advantage are maintained, but user ability to assess and optimize cloud performance is limited
Solution Approach 1:
The system enables users to independently evaluate and assess cloud instance performance without relying on provider-provided data. By implementing autonomous performance measurement and benchmarking capabilities, users can directly measure performance characteristics and make informed decisions about instance selection and workload placement
Solution Approach 2:
The system acts as an intermediary between users and cloud providers by implementing a performance evaluation layer that measures and characterizes cloud instance performance independently. This intermediary assessment mechanism provides users with reliable performance information without requiring providers to disclose confidential operational data
3Productivity
If instances are selectively maintained or terminated based on performance evaluation, then cost-effectiveness improves, but system complexity increases due to continuous monitoring and decision-making requirements
Solution Approach 1:
The system performs preliminary performance evaluation and establishes baseline metrics before deployment. By pre-assessing instance performance and setting performance thresholds in advance, the system simplifies ongoing management decisions and reduces the complexity of continuous monitoring while maintaining cost-effectiveness
Solution Approach 2:
The system implements automated feedback loops that monitor performance metrics and trigger instance maintenance or termination decisions based on predefined criteria. This automation reduces manual intervention complexity while enabling continuous optimization of cost-effectiveness through performance-based instance management
Data Source
AI summary
A method includes the step of running a set of instances on at least one cloud for a first time interval, each of the instances comprising a bundle of virtualized resources. The method also includes the step of evaluating one or more performance characteristics of each of the instances in the set of instances over the first time interval. The method further includes the step of determining a first subset of the set of instances to maintain for a second time interval and a second subset of the set of instances to terminate for the second time interval responsive to the evaluating step. The steps are performed by at least one processing device comprising a processor coupled to a memory.