Map Reduce Framework for Distributed Resource Provisioning
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Solution Overview
Problem
Conventional resource provisioning in distributed computing environments is manual, time-consuming, and lacks parallel execution and standardized communication across multiple computing nodes, making it difficult to manage and provision resources efficiently for distributed applications.
Innovation Solution
A map reduce framework is implemented to facilitate resource provisioning by using a splitter process to select resources, a mapper process to convert abstract addresses to absolute addresses, and a reduce process to consolidate results, enabling parallel execution and standardized communication across multiple computing nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual resource provisioning is used, then resource allocation can be performed, but the process is time-consuming and lacks parallel execution capability
Solution Approach 1:
The provisioning process is segmented into distinct map and reduce phases. The map phase divides resource provisioning tasks across multiple computing nodes, enabling parallel execution. Each node independently provisions resources based on its local data, significantly reducing overall provisioning time compared to sequential manual processes.
Solution Approach 2:
A standardized communication interface acts as an intermediary between distributed computing nodes and resource provisioning systems. This intermediary layer enables consistent parallel communication and coordination across nodes, allowing simultaneous resource provisioning while maintaining system-wide consistency through standardized protocols.
2Productivity
If distributed resource provisioning is implemented, then parallel execution is achieved, but standardized communication across multiple nodes becomes complex
Solution Approach 1:
A universal standardized communication interface is implemented across all computing nodes, enabling the same communication protocol to handle diverse provisioning tasks. This multi-functional interface simplifies parallel communication by providing a consistent method for nodes to interact with resource provisioning systems, reducing the complexity that would otherwise arise from node-specific communication variations.
3Productivity
If manual provisioning processes are used, then resource allocation can be performed, but consistency and organization across distributed nodes is difficult to achieve
Solution Approach 1:
The reduce phase implements a feedback mechanism that collects provisioning results from all map phase executions across distributed nodes. This feedback loop consolidates and validates the results, ensuring consistency and organization of provisioned resources system-wide. The feedback mechanism detects and corrects discrepancies, maintaining stable and consistent resource composition across the distributed environment.
Data Source
AI summary
A provisioning system for a distributed computing environment includes a map reduce framework that includes a splitter process, a mapper process, and a reduce process. The splitter process receives a task associated with a distributed application to be deployed on the distributed computing environment, and selects which of the resources are to be provisioned for executing the distributed application. The mapper process converts at least one abstract address included in the task to an absolute address, and executes the task for provisioning the selected resources, while the reduce process consolidates results of the executed task.