Virtual Machine Power Management via Grouped Activity Detection
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Solution Overview
Problem
Cloud computing server farms consume excessive energy and generate heat even when idle, due to the need for constant operation to maintain service levels during peak demand, leading to inefficient resource utilization.
Innovation Solution
Implement a power management system that groups virtual machines based on functionality and determines their activity levels, instructing idle groups to enter a low power mode, thereby reducing energy consumption and heat generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If servers run 24 hours a day to maintain service levels during peak demand, then reliability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts server power states based on real-time workload monitoring. Servers transition between active, idle, and low-power states according to demand fluctuations, enabling energy savings during low-utilization periods while maintaining service levels during peak demand
Solution Approach 2:
The power management system automatically monitors workload metrics and makes autonomous decisions about server power states without manual intervention. The system self-adjusts by grouping virtual machines, evaluating collective workload, and transitioning groups between power states based on predefined thresholds
2Reliability
If multiple servers are deployed to support heavy load and reduce outage risk, then reliability is improved, but heat generation increases
Solution Approach 1:
The system dynamically consolidates or distributes virtual machine groups across servers based on workload demands. During low-demand periods, servers are consolidated into low-power states reducing heat generation. During high-demand periods, the system distributes load across more servers to maintain reliability while minimizing the number of active servers needed
3Use of energy by moving object
If servers are kept idle to reduce energy consumption, then energy efficiency is improved, but response time deteriorates
Solution Approach 1:
The system maintains virtual machine groups in a low-power state with preserved state information, enabling rapid reactivation. When workload increases, the system can quickly restore servers to full operational capacity without lengthy boot sequences, as the virtual machine state is already loaded and ready for resumption
Solution Approach 2:
The system maintains the capability for useful action in low-power state by preserving virtual machine state information. This allows near-immediate resumption of computational tasks when demand increases, maintaining service continuity while reducing energy consumption during low-utilization periods
Data Source
AI summary
Methods and system for power management of computing resources supporting one or more virtual machines involves grouping the plurality of virtual machines into a plurality of groups. The grouping can comprise assigning each of the plurality of virtual machines to one or more of the plurality of groups based on virtual machine functionality. For each group, a further determination is made as to whether the level of activity is indicative of an idle state. Upon determining that the level of activity associated with a group is indicative of an idle state, that group of virtual machines is instructed to enter a low power mode.