Cloud Control System for Dynamic Resource Provisioning
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Solution Overview
Problem
Cloud data centers face challenges in dynamically managing hardware resources to handle varying workload demands from multiple users, leading to inefficiencies and potential disruptions due to uneven resource distribution and temporary spikes in demand.
Innovation Solution
A cloud control system that monitors and manages hardware resources by sending status messages among servers, using decision-making logic to provision additional resources to virtual machines experiencing high demand, and shifting workloads from shared to dedicated resources as needed, based on economic and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware resources are shared among multiple users in a cloud data center, then resource utilization efficiency is improved, but performance disruptions occur when temporary spikes in demand exceed available resources
Solution Approach 1:
The patent segments hardware resources into shared resources and dedicated resources. Shared resources are allocated dynamically based on demand, while dedicated resources are reserved for specific users or workloads to guarantee minimum performance levels. This segmentation allows the system to maintain high utilization efficiency through shared resources while preventing performance disruptions through dedicated resource allocations.
Solution Approach 2:
The patent implements preliminary resource allocation by reserving dedicated hardware resources in advance for users or workloads that require guaranteed performance. This preliminary action ensures that when demand spikes occur, the system can immediately allocate reserved resources without causing performance disruptions, while still allowing efficient utilization of shared resources during normal conditions.
2Reliability
If additional hardware resources are allocated to handle peak demand, then service continuity is improved, but system complexity increases due to dynamic resource provisioning
Solution Approach 1:
The patent employs feedback mechanisms where the cloud control system continuously monitors hardware resource utilization and workload demand. Based on this feedback, the system dynamically provisions additional resources or reallocates existing resources to match actual demand patterns. This feedback-driven approach maintains service continuity by responding to real-time conditions while managing complexity through automated decision-making based on established policies.
Solution Approach 2:
The patent changes system parameters dynamically by adjusting resource allocation policies, capacity reservation levels, and provisioning thresholds based on historical data and predicted demand patterns. These parameter changes enable the system to maintain service continuity through proactive resource allocation while managing complexity through standardized parameter adjustment mechanisms rather than ad-hoc decisions.
3Reliability
If dedicated hardware resources are allocated to specific users, then performance reliability is improved, but fair share scheduling becomes more difficult to maintain
Solution Approach 1:
The patent applies local quality by providing different levels of resource guarantees to different users or workloads based on their specific needs and priorities. Critical workloads receive dedicated resources with guaranteed performance, while less critical workloads share resources with best-effort service. This local differentiation maintains performance reliability for essential services while preserving fair share scheduling flexibility for other workloads through prioritized resource allocation.
Data Source
AI summary
In some embodiments, a method for managing resources in a data center includes a data center having a plurality of servers in a network. The data center provides a virtual machine for each of a plurality of users, each virtual machine to use a portion of hardware resources of the data center. The hardware resources include storage and processing resources distributed onto each of the plurality of servers. The method further includes sending messages amongst the servers, some of the messages being sent from a server including status information regarding a hardware resource utilization status of that server. The method further includes detecting a request from the virtual machine to handle a workload requiring increased use of the hardware resources, and provisioning the servers to temporarily allocate additional resources to the virtual machine, wherein the provisioning is based on status information provided by one or more of the messages.