Hierarchical Agent Service Placement in Cloud Resource Pools
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Solution Overview
Problem
Current cloud service placement solutions face challenges in efficiently and accurately placing multi-component services in large, dynamic, and distributed environments due to heterogeneity and complexity, often resulting in combinatorial explosions of possible solutions and reliance on centralized resource management, which limits scalability and accuracy.
Innovation Solution
A decentralized service placement system using a hierarchy of agents that generates and consolidates solution encodings for resource allocation, allowing for rapid and accurate determination of feasible placements that satisfy resource requirements and constraints without a global view of resources, utilizing local information for parallel decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized resource management is used to ensure accurate service placement, then placement accuracy is improved, but scalability deteriorates due to the massive search space and computational complexity
Solution Approach 1:
The patent divides the centralized resource management system into a hierarchical structure with a root agent coordinating multiple child agents, each responsible for specific datacenters or resource pools. This segmentation allows the placement problem to be broken down into smaller sub-problems that can be solved independently and in parallel, maintaining placement accuracy while improving scalability to handle large distributed cloud environments with hundreds of thousands of servers.
Solution Approach 2:
The patent introduces a hierarchical dimension to the resource management architecture, adding a layer of abstraction between the service placement request and the physical resources. The root agent maintains a simplified view of resource availability across all datacenters, while child agents manage detailed resource states locally. This dimensional change allows the system to scale by distributing computational load across multiple hierarchical levels rather than requiring a single centralized controller to process all placement decisions.
2Measurement precision
If a global view of resources is maintained to ensure accurate multi-component service placement, then placement accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the resource view into hierarchical levels: the root agent maintains a global view aggregated from child agents, while each child agent maintains a local view of its assigned datacenter resources. This segmentation allows accurate multi-component service placement by combining the global coordination capability of the root agent with the detailed local knowledge of child agents, reducing the complexity burden on any single component.
Solution Approach 2:
The child agents act as intermediaries between the root agent and the physical resources. They translate detailed local resource states into simplified availability information for the root agent, and translate placement decisions from the root agent into specific resource allocations. This intermediary layer reduces system complexity by filtering and abstracting information at each hierarchical level, preventing the root agent from being overwhelmed by the full complexity of individual resource states.
3Ease of operation
If service components are placed one-by-one to simplify scheduling, then ease of operation is improved, but placement accuracy deteriorates due to inability to consider service as a whole
Solution Approach 1:
The patent segments the service placement process into two phases: (1) The root agent performs coordinated placement considering all service components together by evaluating solution encodings that represent complete service placements. (2) Child agents execute the coordinated placement decisions by allocating specific resources to individual components. This segmentation maintains placement accuracy through global coordination while preserving operational simplicity through localized execution.
Solution Approach 2:
The root agent performs preliminary coordination by evaluating complete service placement solutions before actual resource allocation. It generates and evaluates solution encodings that represent entire service placements, ensuring all components are considered together. Only after this preliminary coordinated decision is made does the system proceed to individual component allocation by child agents, thereby maintaining both accuracy and operational simplicity.
4Productivity
If local resource information is used for parallel decision-making, then scalability is improved, but placement accuracy deteriorates due to lack of global resource visibility
Solution Approach 1:
The patent segments the resource information into hierarchical levels: child agents maintain detailed local resource information for their assigned datacenters, while the root agent maintains an aggregated global view. This segmentation enables parallel decision-making at the child agent level (improving scalability) while maintaining placement accuracy through the root agent's coordinated evaluation of complete service placements using information from all child agents.
Solution Approach 2:
The root agent merges resource availability information from multiple child agents to form a comprehensive global view. Child agents contribute their local resource states to the root agent, which combines this information to evaluate complete service placements across the distributed cloud. This merging process maintains placement accuracy by considering all available resources while preserving scalability through distributed information gathering.
Data Source
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AI summary
Exemplary methods for distributed multi-component service placement in a resource pool include utilizing a hierarchy of agents associated with computing resources of a cloud architecture. A root agent in the hierarchy can receive service requests specifying resource requirements and optionally location or affinity constraints, transform these into service request descriptions, and pass the service request descriptions down through the hierarchy to arrive at leaf nodes. The leaf nodes can each, perhaps in parallel, generate solution encodings indicating possible placements of some or all of the components of the service request that one or more computing devices associated with each agent can locally provide while still satisfying the resource requirements. The generated solution encodings can be passed back up and be consolidated as they flow through the hierarchy, allowing the root agent to quickly and accurately determine whether the service request may be fulfilled, and optionally place the service.