Decentralized Resource Management Framework for Distributed Datacenters
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Solution Overview
Problem
Current datacenter management systems face scalability issues due to centralized resource controllers that cannot efficiently manage large numbers of servers, leading to limitations in resource allocation and geographic dispersion, as well as independent operation of clusters which hinders efficient scaling with increased resources.
Innovation Solution
A decentralized resource management framework using contact lists generated based on workload types or discovery policies, allowing collaborative resource managers to dynamically allocate resources across a distributed datacenter, enabling seamless pooling of resources beyond single servers or clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized resource controllers are used to manage datacenter resources, then resource allocation decisions can be made sequentially and monitoring can be centralized, but the system cannot scale beyond certain limits due to large computational overhead and increased response times
Solution Approach 1:
The patent divides the centralized resource controller into multiple distributed resource managers, each responsible for a specific subset of servers. This segmentation allows parallel processing of resource allocation requests, reducing computational overhead and enabling the system to scale to larger datacenters without increasing response times.
Solution Approach 2:
The patent introduces a new dimensional approach by organizing resource managers in a distributed manner across multiple dimensions (geographic location, server subsets, etc.) rather than a single centralized point. This allows the system to handle large-scale resource management by distributing the control plane across multiple nodes, reducing the computational burden on any single controller.
2Reliability
If centralized resource controllers are used, then resource monitoring can be centralized, but response times increase and the system cannot scale to larger numbers of servers
Solution Approach 1:
The patent segments the centralized monitoring function into distributed monitoring capabilities across multiple resource managers. Each resource manager monitors its local subset of servers independently, enabling parallel monitoring operations that maintain reliability while reducing overall response times through distributed processing.
3Ease of operation
If clusters are fixed in size for centralized management, then resource controllers can be proximate to servers, but geographic dispersion of servers in a single cluster is prevented and scaling is hindered
Solution Approach 1:
The patent segments the datacenter into multiple autonomous clusters, each with its own resource manager that operates independently. This allows each cluster to maintain tight coupling between controllers and servers for ease of operation, while the overall system achieves geographic dispersion and scalability through the collection of independent clusters.
Solution Approach 2:
The patent merges multiple independent clusters into a unified federated system where resource managers from different clusters can collaborate. This allows the system to maintain the operational simplicity of small fixed clusters while achieving the adaptability and geographic dispersion of a large distributed system through inter-cluster resource sharing.
4Productivity
If clusters operate independently without interaction, then each resource manager can work autonomously, but efficient scaling with increased resources is hindered
Solution Approach 1:
The patent merges independent clusters into a federated system where resource managers maintain autonomy but can interact with other clusters when local resources are insufficient. This allows the system to preserve the productivity benefits of autonomous operation while enabling scaling across the entire datacenter through inter-cluster collaboration and resource sharing.
Data Source
AI summary
A method for allocating resources to applications in a distributed datacenter based on generated contact lists is described. The method includes, receiving, by a first resource manager, a placement request, which identifies resources needed for execution of an application; determining a policy associated with the application; generating a first contact list for the first resource manager based on the determined policy for the application; and searching resources in the distributed datacenter, based on the first contact list, to attempt to meet the identified resources of the placement request.