Container Manager for Multi-Provider Workload Migration
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Solution Overview
Problem
Conventional virtualization systems face challenges in efficiently managing resources, including I/O congestion, inadequate resource allocation, and inflexible scaling, which leads to inefficiencies and performance issues in cloud environments.
Innovation Solution
The introduction of a Container Manager that uses supply chain economics and virtual currency to optimize resource allocation, automate decision-making, and dynamically adjust resource bundles across multiple cloud providers to ensure efficient and flexible resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated servers are allocated for each application to handle peak demands, then application performance and reliability are improved, but resource utilization efficiency deteriorates due to excessive allocation
Solution Approach 1:
The patent merges multiple applications and their workloads onto shared physical infrastructure through virtualization technology. Virtual machines allow multiple applications to coexist on the same hardware resources, consolidating previously dedicated server allocations into a shared pool that serves multiple purposes simultaneously.
Solution Approach 2:
The patent implements dynamic resource allocation where virtual machine resources (CPU, memory, storage) can be adjusted in real-time based on actual workload demands. The system monitors application performance metrics and automatically scales resources up or down, transforming static dedicated allocations into dynamic shared resources that adapt to changing conditions.
2Loss of energy
If virtualization is implemented to share hardware among applications, then resource utilization efficiency is improved, but I/O congestion and performance issues worsen
Solution Approach 1:
The patent segments I/O operations and traffic flows into isolated virtual channels for different virtual machines. Virtual switches and virtual network functions create separate logical pathways that prevent I/O congestion from one application from affecting others, dividing the shared I/O resources into manageable segments with guaranteed performance levels.
Solution Approach 2:
The patent introduces virtualization layer intermediaries (virtual switches, virtual network functions, storage virtualization layers) that mediate between physical I/O resources and virtual machine demands. These intermediaries manage I/O scheduling, prioritize traffic, and buffer operations to smooth out congestion peaks before they reach physical resources.
3Reliability
If resources are over-allocated to handle peak demands, then service availability is improved, but cost and waste worsen during low-utilization periods
Solution Approach 1:
The patent makes physical infrastructure universal by designing it to serve multiple applications and workloads simultaneously through virtualization. The same physical servers, storage, and network resources can be dynamically assigned to different applications based on demand, making the infrastructure multi-functional rather than dedicated to single purposes.
Solution Approach 2:
The patent changes the allocation parameters of physical resources from fixed to variable states. Virtual machine resource allocations can be modified on-the-fly based on monitored performance metrics and demand patterns, allowing the system to maintain service availability during peaks while reducing allocations during low-utilization periods to minimize waste.
4Ease of manufacture
If traditional virtualization is used without economic models, then implementation simplicity is maintained, but resource allocation optimization deteriorates
Solution Approach 1:
The patent implements feedback loops where virtual machine performance metrics, workload demands, and resource utilization data are continuously monitored and fed back to the virtualization management system. This feedback drives automated decision-making for resource allocation, scaling, and migration decisions, optimizing resource distribution based on actual system state rather than static configurations.
Solution Approach 2:
The patent enables virtual machines and workloads to effectively request and receive resources based on their own needs through integrated monitoring and automation. The system self-adjusts resource allocation without manual intervention, with virtual machines automatically scaling their resource requests based on workload demands and the system automatically provisioning appropriate resources from the available pool.
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 performance criteria associated with moving the consumer from the first provider to the second provider.