Cloud Broker Aggregating Service Factors for Distributed Resource Optimization
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Solution Overview
Problem
Current cloud computing environments face challenges in optimizing service factors such as price and quality of service (QoS) across distributed deployment groups, failing to effectively manage resources to maximize value for both users and providers.
Innovation Solution
A system comprising a cloud broker/aggregator, a cloud offering optimizer, and a user workload optimizer that aggregates and analyzes information on workload, pricing, QoS, resource capacity, and deployment topology to determine a reallocation plan, optimizing service factors by adjusting resource allocation and deployment topology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computing resources are distributed among multiple deployment groups in geographically dispersed locations, then service availability and user access are improved, but managing and optimizing service factors (price, QoS) across these distributed resources becomes complex and difficult
Solution Approach 1:
The patent introduces a cloud broker as an intermediary component that mediates between cloud users and distributed cloud resources. The cloud broker aggregates information from multiple deployment groups, manages service factor optimization centrally, and presents a unified interface to users, thereby simplifying management complexity while maintaining service availability across geographically distributed resources.
Solution Approach 2:
The cloud broker is designed as a universal platform that handles multiple functions including resource aggregation, service factor optimization, pricing management, and QoS optimization across diverse deployment groups. This multi-functional approach consolidates management tasks and reduces overall system complexity.
2Productivity
If cloud resources are statically allocated to deployment groups, then management and allocation are simplified, but service factors such as price and QoS cannot be optimized in response to changing workload demands
Solution Approach 1:
The patent implements dynamic resource allocation where the cloud broker continuously monitors workload demands and service factors across deployment groups, then dynamically adjusts resource allocation and deployment topology. This allows service factors like pricing and QoS to be optimized in real-time based on changing conditions, moving from static to dynamic management.
Solution Approach 2:
The system incorporates feedback mechanisms where the cloud broker receives information about workload demands, service factor performance, and resource utilization from deployment groups, processes this information, and adjusts resource allocation accordingly. This closed-loop feedback enables continuous optimization of service factors while adapting to changing conditions.
3Loss of energy
If detailed information about workload, pricing, QoS, and resource capacity is collected and analyzed, then optimization decisions can be made to maximize value, but the complexity of information processing and analysis increases
Solution Approach 1:
The patent merges information collection and analysis functions into a single cloud broker platform that consolidates data from multiple deployment groups. By combining these functions centrally, the system reduces redundant processing, simplifies information management, and enables comprehensive optimization decisions without proportionally increasing complexity.
Data Source
AI summary
An approach is provided for optimizing service factors for computing resources in a networked computing environment. Specifically, under one approach, a cloud broker/aggregator, a cloud offering optimizer, and a user workload optimizer may be provided. The cloud aggregator may aggregate information/data pertaining to a set of service factors associated with a set of resources distributed among a set of deployment groups (e.g., cloud pods). The cloud offering optimizer may analyze this information and may determine a reallocation plan to optimize values of the service factors associated with the set of resources. The user workload optimizer may then receive the reallocation plan from the cloud offering optimizer and/or application event information, and modify the deployment topology accordingly.