Cloud Instance Scaling via Time-Series Pattern Generation
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Solution Overview
Problem
Migrating enterprise applications from on-premise to cloud-computing environments often results in significantly higher operational costs due to the need for 24/7 availability, and traditional manual scaling methods are inefficient and prone to errors, leading to resource wastage and insufficient resource allocation.
Innovation Solution
Implementing an automatic determination of elasticity patterns in cloud-computing environments by resampling and aggregating timeseries data to generate a combined pattern for selective scaling of instances and resources, based on historical workload data, to optimize resource usage and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scaling methods are used to adjust cloud resources, then resource allocation can be controlled, but the process is inefficient and prone to errors leading to resource wastage
Solution Approach 1:
The system automatically generates scaling patterns by analyzing historical timeseries data and executes scaling actions without manual intervention. The pattern generation module creates scaling schedules based on workload patterns, and the instance manager automatically implements these patterns, enabling the system to self-adjust resource allocation efficiently and accurately.
Solution Approach 2:
The system continuously monitors historical timeseries data representing system parameters and uses this feedback to generate optimal scaling patterns. The pattern generation module analyzes past performance data and adjusts scaling schedules accordingly, creating a closed-loop system that learns from historical operations to improve future resource allocation.
2Reliability
If cloud resources are allocated continuously to ensure 24/7 availability, then system reliability is maintained, but operational costs increase significantly
Solution Approach 1:
The system dynamically adjusts the number of active instances based on predicted workload patterns rather than maintaining static continuous operation. The pattern generation module creates time-varying scaling schedules that activate or deactivate instances according to historical workload patterns, enabling the system to maintain reliability during peak periods while reducing costs during low-demand periods.
Solution Approach 2:
The system proactively generates scaling patterns based on historical data before actual scaling events occur. The pattern generation module analyzes past timeseries data to predict future workload patterns and pre-establishes scaling schedules, allowing the instance manager to execute optimal scaling actions in advance rather than reacting to real-time conditions.
3Productivity
If instances are scaled up to meet peak workload demands, then service quality is maintained, but resource wastage occurs during low-demand periods
Solution Approach 1:
The system implements dynamic scaling that adjusts instance capacity according to predicted workload patterns. The pattern generation module analyzes historical timeseries data to identify peak and off-peak periods, creating scaling schedules that provision additional instances only when needed based on historical patterns, thereby maintaining service quality during peaks while avoiding resource wastage during low-demand periods.
Solution Approach 2:
The system applies partial scaling actions based on predicted workload patterns rather than over-provisioning for maximum peak demands continuously. The pattern generation module identifies the minimum necessary scaling actions required to meet historical peak demands and schedules these actions only when predicted to be needed, avoiding excessive resource allocation during off-peak periods.
Data Source
AI summary
Methods, systems, and computer-readable storage media for receiving a set of timeseries, each timeseries in the set of timeseries representing a parameter of execution of the system, resampling data of at least one timeseries to provide data of all timeseries in the set of timeseries in a consistent format, generating a pattern for each timeseries to provide a set of patterns based on data of the set of timeseries, combining patterns of the set of patterns to define a pattern, the pattern representing a schedule of instances over a period of time, and executing, by an instance manager, scaling of the system based on the pattern to selectively scale one or more of instances of the system and controllable resources based on scaling factors of the pattern.