Cloud Process Placement via Dynamic Resource Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud service providers face challenges in achieving predictable performance for cloud-hosted applications while optimizing resource utilization, due to imprecise resource demand estimates and the limitations of predefined Virtual Machine sizes, leading to suboptimal resource allocation and potential service level agreement violations, especially in diverse IoT or edge cloud scenarios.
Innovation Solution
A method for process placement in a distributed datacenter that involves receiving placement requests, determining load and demand estimations based on process execution profiles, and dynamically allocating resources, with performance scores updated based on execution success, allowing for intelligent resource management and optimization of placement policies without manual configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If predefined fixed-sized Virtual Machines are used for resource allocation, then resource allocation is simplified, but resource utilization becomes suboptimal due to imprecise matching with application requirements
Solution Approach 1:
The patent segments the monolithic Virtual Machine into smaller, finer-grained containers that can be independently allocated and managed. This allows the system to divide resources into smaller units that can be more precisely matched to application requirements, improving resource utilization while maintaining allocation simplicity through automated management.
Solution Approach 2:
The patent implements dynamic resource allocation where container sizes and resource assignments are not fixed but can be adjusted based on actual application performance and resource demand. This dynamic approach allows the system to optimize resource utilization continuously while maintaining simple allocation through automated policies.
2Reliability
If larger Virtual Machines are allocated to ensure sufficient resources, then service level agreement compliance is improved, but overall resource utilization decreases due to over-commitment
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor application performance and resource consumption. This feedback allows the system to adjust container resource allocations dynamically, ensuring service level agreements are met while optimizing resource utilization by allocating only the necessary resources rather than over-provisioning.
Solution Approach 2:
The patent changes the parameters of resource allocation by transitioning from fixed, coarse-grained Virtual Machine specifications to dynamic, fine-grained container allocations. This allows resource parameters to be adjusted continuously based on actual demand, maintaining reliability while improving utilization.
3Adaptability or versatility
If smaller deployment units like containers are used, then deployment flexibility and resource utilization are improved, but resource defragmentation and stranding occur making large-scale program placement difficult
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically manages container placement, consolidation, and migration without manual intervention. This self-service approach handles the complexity of resource defragmentation and consolidation automatically, maintaining deployment flexibility while managing the complexity of fine-grained resource allocation.
Solution Approach 2:
The patent introduces an intermediary layer (container runtime and management system) that mediates between the fine-grained container deployment units and the underlying physical resources. This intermediary handles the complexity of resource allocation, defragmentation, and consolidation, allowing flexible deployment while managing resource efficiency.
4Ease of operation
If centralized resource placement policies are implemented, then global manageability is improved, but sub-optimal decisions occur in specific domains due to policy generalization
Solution Approach 1:
The patent implements local quality by allowing placement policies to be customized for specific domains, workloads, or infrastructure characteristics while maintaining overall global manageability. Different regions or clusters can have optimized policies tailored to their specific requirements, improving placement decision optimality while the central management system coordinates overall resource allocation.
Data Source
AI summary
A method for placement of processes in a distributed datacenter is described. The method includes receiving, by a first placement node, a placement request corresponding to a process; determining a load estimation for resources managed by the first placement node and a demand estimation for the process based on a process execution profile for the process; determining whether the placement request can be fulfilled with the resources managed by the first placement node based on the demand estimation of the process and the load estimation of the resources managed by the first placement node; executing the process with the resources managed by the first placement node in response to determining that the placement request can be fulfilled with the resources managed by the first placement node; generating an intermediate score based on the execution; and updating a performance score of the process execution profile based on the intermediate score.