Virtual Environment Performance Data Collection via Intermediary Mediator
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Solution Overview
Problem
Existing systems lack efficient methods for deploying and managing software services across both virtualized and non-virtualized environments, leading to suboptimal resource allocation and monitoring in enterprise application server setups.
Innovation Solution
A system and method that utilize a data consumer to manage data collectors on virtualized and non-virtualized machines, employing an operating contract with metric descriptors and a push manager to register and process performance data, along with a Controller and Agents for centralized governance and resource management, ensuring compliance with Service Level Agreements (SLAs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hypervisor-based virtualization is used to consolidate IT deployments onto reduced number of servers, then resource utilization and infrastructure efficiency are improved, but application performance monitoring and management complexity increase
Solution Approach 1:
The patent introduces an intermediary system comprising data consumers, data collectors, and push managers that act as a mediator between virtualized applications and monitoring infrastructure. This intermediary layer simplifies management by providing standardized interfaces and automated data collection mechanisms, reducing the complexity burden despite increased virtualization consolidation.
Solution Approach 2:
The monitoring system is segmented into distinct functional components: data consumers that define monitoring requirements, data collectors that reside on virtualized machines and extract performance data, and push managers that handle data transmission. This segmentation allows each component to be independently managed and optimized, reducing overall system management complexity.
2Adaptability or versatility
If application virtualization is implemented to package and deploy enterprise applications in virtual data centers, then operational flexibility and responsiveness are improved, but performance data collection and monitoring become more difficult
Solution Approach 1:
The data collectors are designed to autonomously extract performance data from virtualized applications using operating contracts. The system enables self-service monitoring where the virtualized environment itself provides the necessary performance data through standardized interfaces, eliminating the need for complex external monitoring tools and simplifying data collection despite increased operational flexibility.
3Loss of energy
If multiple virtual machines are hosted on powerful multi-core processors, then infrastructure cost reduction and space optimization are achieved, but performance monitoring overhead increases
Solution Approach 1:
The push manager establishes continuous periodic intervals for performance data collection and transmission. By maintaining continuous monitoring operations rather than periodic batch processing, the system efficiently utilizes available computational resources and reduces overall monitoring overhead, ensuring that performance data is collected and transmitted without unnecessary delays or repeated processing cycles.
Data Source
AI summary
A system and method can manage software services in virtualized and non-virtualized environments. A plurality of data collection components in a computing environment can include a plurality of virtual machines running on the one or more microprocessors, wherein the plurality of virtual machines are adapted to be deployed with services and applications. Each data collection component operates to use one or more data structures to communicate with a data consumer, wherein each data structure describes metrics to be collected by said data collection component and is capable of containing collected metric values.