Cloud Resource Management via Cost-Performance Migration
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Solution Overview
Problem
Current cloud-based resource management systems face challenges in efficiently allocating and migrating computational workloads due to varying resource bundles and pricing structures across different providers, leading to complex decision-making and increased operational costs.
Innovation Solution
A computer-implemented method that uses supply chain economics to optimize resource management by determining the cost of running workloads on different providers, selecting optimal resource templates, and migrating workloads based on cost and performance metrics, ensuring efficient allocation and utilization of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud providers offer various resource bundles with different pricing structures, then service variety and customer choice are improved, but evaluation and comparison complexity increases
Solution Approach 1:
The patent introduces a resource management system that acts as an intermediary between cloud providers and customers. This system automatically evaluates and compares different resource bundles from multiple providers, translating their varying pricing structures and service offerings into comparable metrics. The intermediary handles the complexity of evaluation internally while presenting simplified options to customers, thus resolving the contradiction between service variety and evaluation complexity.
Solution Approach 2:
The system transforms the complex parameters of different resource bundles (varying pricing structures, service combinations, time periods) into standardized comparison parameters. By changing the representation parameters from provider-specific formats to unified evaluation metrics, the system enables straightforward comparison while maintaining the diversity of underlying service offerings.
2Adaptability or versatility
If cloud resources are added and decommissioned on demand, then flexibility and scalability are improved, but operations management challenges increase
Solution Approach 1:
The patent implements a self-service resource management system that automatically handles the addition and decommissioning of cloud resources based on demand. The system monitors resource utilization, automatically provisions new resources when needed, and decommissions underutilized resources without requiring manual operations intervention. This automation maintains flexibility while reducing operations management complexity by eliminating the need for manual decision-making and execution in dynamic resource allocation.
3Reliability
If resource overloading is used to handle peak demands, then service reliability is improved, but cost and efficiency worsen
Solution Approach 1:
The patent implements dynamic resource allocation that adjusts resource provisioning in real-time based on actual demand patterns. Instead of static overloading to ensure peak demand coverage, the system continuously monitors utilization and dynamically scales resources up or down. This dynamic approach maintains service reliability during peaks while avoiding the continuous operational cost of maintaining excessive idle capacity during low-demand periods.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor resource utilization patterns and use this information to optimize resource allocation. By continuously gathering data on actual usage and feeding this information back into the provisioning decisions, the system can right-size resource allocation to match actual demand, thereby maintaining reliability when needed while reducing operational costs during lower utilization periods.
Data Source
AI summary
Systems, methods and apparatus, including computer program products, are disclosed for regulating access of consumers (e.g., applications, containers, or VMs) to resources and services (e.g., storage). In one embodiment, this regulation occurs through the movement of consumers between different providers of a resource or service, such as a cloud service provider. Moving consumers includes, for example, determining the cost of moving the consumer from a first provider to a second provider. According to various embodiments, the cost of moving the consumer is compared to cost and performance criteria associated with moving the consumer from the first provider to the second provider. Cloud-based services may be priced as templates, reserved instances, or a combination.