Virtual Machine Scheduling Service for Cluster Host Allocation
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Solution Overview
Problem
Existing virtualized computer systems face challenges in optimizing virtual machine allocation to cluster hosts, leading to inefficiencies in load balancing and resource allocation, as they struggle to dynamically adjust to changing workloads and constraints.
Innovation Solution
A virtual machine scheduling service that queries the virtualization manager for cluster configuration, state, and user request parameters to produce an ordered list of operations, such as migration or start operations, to achieve a quasi-optimal configuration score by applying scheduling policy rules, using methods like simulating annealing and heuristic algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual machines are dynamically allocated to cluster hosts, then hardware utilization and resource allocation improve, but system complexity and computational overhead increase
Solution Approach 1:
The patent introduces a virtual machine scheduling service as an intermediary component that sits between the virtualization manager and the cluster hosts. This service receives configuration information, state information, and user request parameters, then applies scheduling policy rules to generate an ordered list of operations. By centralizing the scheduling logic in this intermediary service, the system achieves optimized hardware utilization through dynamic VM allocation while containing complexity in a dedicated management layer rather than distributing it across all system components.
2Productivity
If scheduling policy rules are applied to achieve quasi-optimal configuration, then resource allocation efficiency improves, but computational time and processing overhead increase
Solution Approach 1:
The patent implements preliminary action by pre-defining scheduling policy rules that encode optimization criteria and constraints. These rules are established in advance and include criteria such as load balancing thresholds, resource availability conditions, and migration priorities. When the scheduling service needs to make allocation decisions, it applies these pre-established rules to the current system state, avoiding the need to perform complex optimization calculations from scratch each time. This approach improves resource allocation efficiency while reducing computational time by leveraging pre-computed scheduling logic.
3Adaptability or versatility
If the system dynamically adjusts to changing workloads, then adaptability improves, but system stability and predictability decrease
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring cluster configuration information, state information, and performance metrics from virtualization managers and cluster hosts. The virtual machine scheduling service receives this feedback data and uses it to adjust scheduling decisions dynamically. The system evaluates current workload conditions, resource availability, and system state against the scheduling policy rules, then makes adaptive adjustments to VM allocation. This feedback loop enables the system to adapt to changing workloads while maintaining stability through controlled, rule-based adjustments rather than uncontrolled changes.
Data Source
AI summary
Systems and methods for optimizing a virtual machine cluster. An example method may comprise receiving, by a processing device, an information characterizing a virtual machine cluster, the information comprising at least one of: values of one or more cluster configuration parameters, values of one or more cluster state parameters, or values of one or more user request parameters; and producing, in view of the received information, an ordered list of cluster configuration operations to be performed on virtual machines of the virtual machine cluster, the cluster configuration operations designed to yield a resulting configuration of the virtual machine cluster, wherein the resulting configuration is characterized by a quasi-optimal configuration score among configuration scores of two or more candidate configurations, the configuration score determined by applying one or more virtual machine scheduling policy rules to parameters of a candidate configuration.