Deferrable Virtual Machine Scheduling for Cloud Resource Optimization
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Solution Overview
Problem
Cloud computing systems face challenges in efficiently managing resources due to variable demand, leading to excessive capacity provisioning that increases costs and results in unused resources during low demand periods.
Innovation Solution
Implementing deferrable virtual machines (VMs) that can be scheduled flexibly based on predicted surplus capacity on server nodes, allowing for the utilization of spare resources without disrupting existing VMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud computing systems provision excessive capacity to accommodate peak demand, then service availability during peak periods is improved, but costs and resource waste during low demand periods increase
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time demand conditions. Virtual machines are configured to migrate between active and standby states, and resource provisioning is continuously optimized based on monitored performance metrics and demand patterns, allowing the system to adapt capacity to actual needs rather than maintaining static excessive capacity
Solution Approach 2:
The system changes operational parameters by transitioning virtual machines between different states (active, standby, suspended) and adjusting resource allocation levels. This includes modifying CPU allocation, memory provisioning, and network bandwidth assignment based on demand conditions, enabling the system to maintain reliability while reducing resource waste during low-demand periods
2Reliability
If cloud computing systems provision excessive capacity to accommodate peak demand, then service availability during peak periods is improved, but provider costs increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring virtual machine templates and preparing resource pools in advance. Virtual machines are instantiated in standby states with pre-allocated resources that can be rapidly activated when demand increases, avoiding the need to provision and maintain full capacity continuously while ensuring rapid response to peak demand
Solution Approach 2:
The system enables self-service through automated resource management where virtual machines self-regulate their resource consumption based on demand conditions. The system automatically monitors performance metrics, triggers migration or suspension decisions, and reallocates resources without manual intervention, reducing operational costs while maintaining service availability
3Productivity
If cloud computing systems use prediction models to allocate resources for deferrable VMs, then resource utilization is improved, but complexity of the resource management system increases
Solution Approach 1:
The system achieves universality by implementing a multi-functional resource management platform that combines prediction modeling, automated decision-making, virtual machine orchestration, and real-time monitoring in a single integrated system. This unified approach handles multiple tasks (demand forecasting, resource allocation, VM migration, performance monitoring) through a common architecture, reducing overall system complexity despite the advanced capabilities
Solution Approach 2:
The system introduces intermediary components including prediction models that act as mediators between demand signals and resource allocation decisions. These intermediaries process complex analytics and translate them into actionable provisioning decisions, simplifying the overall system architecture by decoupling the complexity of prediction algorithms from the resource management logic while still achieving high resource utilization
Data Source
AI summary
The present disclosure relates to systems, methods, and computer readable media for predicting surplus capacity on a set of server nodes and determining a quantity of deferrable virtual machines (VMs) that may be scheduled over an upcoming period of time. This determination of VM quantity may be determined while minimizing risks associated with allocation failures on the set of server nodes. This disclosure described systems that facilitate features and functionality related to improving utilization of surplus resource capacity on a plurality of server nodes by implementing VMs having some flexibility in timing of deployment while also avoiding significant risk caused as a result of over-allocated storage and computing resources. In one or more embodiments, the quantity of deferrable VMs is determined and scheduled in accordance with rules of a scheduling policy.