Virtual Machine Fleet Provisioning Service
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Solution Overview
Problem
The complexity of managing and provisioning virtual machines in large-scale data centers is increased by the numerous types and sizes of virtual machines offered, making it cumbersome for customers to choose appropriate resources that meet their specific needs for tasks such as batch processing or web applications, requiring a more streamlined approach to specify hardware requirements and job objectives.
Innovation Solution
A provisioning service that allows customers to specify basic hardware requirements and job objectives, such as CPU, memory, deadline, and budget, which then determines a suitable fleet of virtual machine instances, including their types, availability zones, and pricing models, to satisfy customer needs without requiring detailed knowledge of available instance types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If service providers offer a large assortment of virtual machine types and sizes optimized for different use cases, then customer needs and application requirements can be satisfied, but the complexity of choosing appropriate resources increases and the provisioning process becomes cumbersome
Solution Approach 1:
The patent introduces a provisioning service as an intermediary between customers and the complex array of virtual machine types. This service automatically determines suitable instance types based on customer-specified requirements (CPU, memory, storage, networking, deadline, budget) without requiring customers to understand the detailed specifications of each instance type. The provisioning service translates high-level requirements into specific instance selections, thereby resolving the contradiction between offering diverse instance types and maintaining ease of operation.
2Manufacturing precision
If customers specify detailed hardware requirements and instance types, then resource allocation can be precise, but the provisioning process becomes more complex and time-consuming
Solution Approach 1:
The provisioning service enables self-service by automatically determining appropriate instance types and configurations based on customer-specified requirements. Customers provide high-level parameters (CPU, memory, storage, networking, deadline, budget) and the system autonomously selects suitable instance types without requiring customers to manually configure detailed hardware specifications or navigate complex instance type options. This maintains precision in resource allocation while reducing provisioning complexity.
3Reliability
If customers need to understand various instance types and pricing models to make informed decisions, then resource selection can be optimized, but the time and expertise required increase significantly
Solution Approach 1:
The provisioning service acts as an expert intermediary that encapsulates knowledge about instance types, pricing models, and optimal resource configurations. Instead of requiring customers to invest significant time and expertise in understanding these complexities, the service automatically applies this knowledge to determine suitable instances based on customer requirements. This maintains reliable resource selection while dramatically reducing the time and expertise burden on customers.
Solution Approach 2:
The system incorporates feedback mechanisms where the provisioning service evaluates customer requirements against available instances, pricing models, and constraints (deadline, budget) to automatically determine optimal configurations. This feedback loop enables the system to make informed decisions about instance selection and pricing without requiring customers to manually analyze multiple factors, thereby maintaining selection quality while reducing decision-making time.
Data Source
AI summary
Systems and methods permit customers of a service provider network to specify various constraints on a desired fleet of virtual machine instances without having to specify the hardware types of instances to be included in the fleet. Instead, the customer can specify per-instance hardware constraints (number of CPUs, amount of memory, etc.) and job constraints (e.g., deadline, budget, application type, etc.). A provisioning service accesses an internal database containing instance cost data, instance availability data, and mappings between application type and fleet configurations to propose a fleet of instances that complies with the customer-specified per-instance hardware and job constraints, thereby freeing the customer from having to be conversant in the particular instances offered by the service provider.