Workload Placement in Heterogeneous Clouds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud computing environments face challenges in efficiently utilizing resources due to heterogeneous server landscapes and varying workload profiles, leading to suboptimal resource allocation and increased operational costs, especially in private cloud settings where applications are onboarded online and reconfiguration capabilities differ.
Innovation Solution
The implementation of a hybrid architecture with a Global Placement Manager and Local Placement Manager that determines workload resource usage patterns, variability requirements, and reconfiguration needs, allowing for the placement of workloads on servers with complementary resource usage profiles, thereby optimizing resource utilization and minimizing reconfiguration costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workloads are placed on servers without considering resource usage patterns, then placement is simple and fast, but resource utilization is suboptimal and operational costs increase
Solution Approach 1:
The placement system is divided into two hierarchical levels: Global Placement Managers that operate at the data center level to select optimal server clusters, and Local Placement Managers that operate at the cluster level to select specific target servers. This segmentation allows each level to make decisions based on appropriate scope and information, improving resource utilization without overwhelming complexity at any single point.
Solution Approach 2:
The system performs preliminary analysis of workload resource usage patterns and server resource usage patterns before making placement decisions. By pre-characterizing workloads and servers based on their resource usage profiles, the system enables informed placement decisions that optimize resource utilization while maintaining manageable complexity through structured data collection and analysis.
2Productivity
If workloads are placed on servers with complementary resource usage patterns, then resource utilization improves, but placement decision time increases
Solution Approach 1:
The system pre-analyzes and characterizes both workload resource usage patterns and server resource usage patterns before placement decisions are needed. This preliminary characterization creates a foundation of knowledge that enables faster matching decisions when actual placement is required, reducing the time penalty for making optimized placement decisions.
Solution Approach 2:
The Global and Local Placement Managers act as intermediaries that translate complex resource usage pattern analysis into concrete placement decisions. They mediate between the detailed characteristics of workloads and servers, applying the complementary pattern matching logic to bridge the gap between analysis and action, thereby reducing overall decision time.
3Loss of energy
If applications are distributed across servers based on complementary patterns, then resource wastage is reduced, but system complexity increases
Solution Approach 1:
The system segments the complex task of reducing resource wastage into two manageable components: Global Placement Managers handle cluster-level resource optimization, while Local Placement Managers handle server-level resource optimization. This segmentation reduces system complexity by distributing the optimization logic across multiple specialized components rather than requiring a single complex system.
Solution Approach 2:
The system performs preliminary characterization of resource usage patterns for both workloads and servers, creating a knowledge base that guides distribution decisions. This advance preparation enables the system to make intelligent distribution decisions that reduce resource wastage without requiring complex real-time analysis, thereby managing system complexity effectively.
4Adaptability or versatility
If online placement is implemented in heterogeneous environments, then adaptability improves, but reconfiguration costs increase
Solution Approach 1:
The system recognizes that different server clusters have heterogeneous reconfiguration capabilities and characteristics. By allowing each Global Placement Manager to make decisions tailored to its local cluster's characteristics, the system adapts to local conditions and minimizes unnecessary reconfigurations, thereby reducing reconfiguration costs while maintaining high adaptability across diverse environments.
Solution Approach 2:
The system pre-characterizes server clusters based on their reconfiguration capabilities and workload patterns before online placement occurs. This preliminary knowledge enables the Global Placement Managers to make informed decisions about where to place workloads, anticipating reconfiguration needs and minimizing costly reconfigurations while maintaining adaptability to heterogeneous environments.
Data Source
AI summary
Systems determine workload resource usage patterns of a computerized workload, using a computerized device. Such systems use the computerized device to place the computerized workload with a computer server cluster within a private cloud computing environment. Also, systems herein place the computerized workload on a selected computer server within the computer server cluster that has a resource usage pattern complementary to the workload resource usage profile, also using the computerized device. The complementary resource usage pattern peaks at different times from the workload resource usage patterns.