Adaptive Resource Allocation via Reinforcement Learning
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Solution Overview
Problem
Traditional methods for configuring virtualized computational resources often result in inefficient resource allocation and disruptions in computational services, due to manual or heuristic-based decision-making.
Innovation Solution
The implementation of Markov Decision Processes (MDPs), specifically using a multi-armed bandit (MAB) decision process and an enhanced Linear Thompson Sampling (LTS) algorithm, to efficiently and adaptively allocate physical computational resources to virtualized resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual or heuristic-based methods are used to allocate physical resources to virtualized resources, then the allocation decisions can be made with simple rules, but the resource allocation efficiency deteriorates and service disruptions occur
Solution Approach 1:
The patent replaces manual or heuristic-based mechanical decision-making with an automated reinforcement learning system. The RL agent continuously learns optimal resource allocation policies by interacting with the computational environment, substituting simple mechanical rules with an adaptive intelligent system that maximizes resource allocation efficiency without requiring complex manual intervention
Solution Approach 2:
The reinforcement learning system performs self-learning and self-optimization to improve resource allocation. The agent autonomously explores the configuration space, learns from observed rewards (resource utilization metrics), and adapts its allocation strategies without external intervention, enabling the system to serve itself in optimizing resource distribution
2Adaptability or versatility
If dynamic re-scaling is performed to compensate for resource distribution inefficiencies, then resource allocation can adapt to demand fluctuations, but temporary disruptions are introduced to computational services
Solution Approach 1:
The reinforcement learning agent performs preliminary exploration of configuration spaces and pre-learns optimal allocation strategies before actual resource allocation is needed. By pre-training on simulated environments and historical data, the system prepares allocation decisions in advance, enabling smooth transitions when demand changes occur without introducing disruptions
Solution Approach 2:
The system implements continuous feedback loops where the RL agent observes the actual performance of resource allocations (through utilization metrics and service performance data) and uses this feedback to refine its policies. This feedback mechanism enables the system to learn from past actions and make progressively better allocation decisions that balance adaptability with service reliability
3Reliability
If more physical processing devices are allocated to under-utilized VMs, then resource allocation can be more conservative, but processing cycles are wasted due to fallow cycles
Solution Approach 1:
The reinforcement learning system dynamically adjusts the number of physical processing devices allocated to each virtual machine based on real-time demand patterns and predicted workload. Rather than using static conservative allocation, the system continuously adapts allocations to match actual needs, preventing both over-provisioning (and associated waste) and under-provisioning (and associated service failures)
Solution Approach 2:
The system changes allocation parameters (number of processing devices, memory allocation, storage capacity) based on learned patterns in workload behavior. By analyzing historical data and predicting future demands, the RL agent adjusts allocation parameters proactively, optimizing the balance between having sufficient resources for reliability and avoiding waste from excessive allocation
4Productivity
If fewer physical processing devices are allocated to heavily utilized VMs, then resource allocation efficiency improves, but the VM cannot service requests within required latency periods
Solution Approach 1:
The reinforcement learning agent performs preliminary analysis of workload patterns and predicts future demand spikes before they occur. By anticipating heavy utilization periods, the system can proactively allocate additional resources to prevent latency issues, rather than reacting after problems occur. This predictive approach maintains both efficiency and performance requirements
Data Source
AI summary
Systems and methods that enable the efficient and adaptive allocation of resources dedicated to a virtualized resource-based computation (e.g., one or more information processing tasks) are provided. In one embodiment, a reward model is generated based on a set of statistical distributions, for example, in response to receiving a request to launch a set of VCRs. Thereafter, an expected reward is predicting for each configuration of a set of configurations based on the reward model and one or more parameters of the corresponding configuration. The expected reward indicates an efficiency in distribution or allocation of physical computation resources to the set of VCRs. A configuration of the set of configurations is selected based on the predicted expected reward for the configuration. The set of VCRs are then configured with the selected configuration.