Virtual Machine Placement Optimizing Network Costs
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Solution Overview
Problem
In cloud computing environments, existing technologies face challenges in optimizing the placement of virtual machines and memory across physical hosts to maximize resource utilization while minimizing costs, particularly in complex computing tasks that require efficient allocation of network resources.
Innovation Solution
A decision-making engine is employed to identify suitable hosts for virtual machines and memory by formulating a cost function based on metrics such as network distance, latency, bandwidth, and task completion times, with the option to collocate virtual machines and storage volumes on the same physical hosts, and selecting alternative hosts if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual machines and memory are allocated across multiple physical hosts, then resource utilization is improved, but network costs and task completion times increase
Solution Approach 1:
The patent combines virtual machines and their associated memory onto the same physical host to eliminate network traffic between separate hosts. This merging reduces network costs while maintaining high resource utilization by ensuring that computationally intensive workloads have direct access to their memory resources without network overhead.
Solution Approach 2:
The patent implements local collocation of virtual machines and memory on the same physical host, creating localized resource pools. This local quality approach ensures that each virtual machine has dedicated, low-latency access to its required memory resources, improving task completion times while reducing overall network traffic and associated costs.
2Loss of time
If virtual machines and storage volumes are collocated on the same physical host, then task completion times are reduced, but host selection complexity increases
Solution Approach 1:
The patent performs preliminary collocation of virtual machines and storage volumes on the same physical host during the resource allocation phase. By establishing this collocation in advance, the system eliminates the need for frequent data transfers between hosts during task execution, significantly reducing task completion times while the host selection complexity is managed through automated placement algorithms.
3Productivity
If hosts are selected based on multiple metrics (network distance, latency, bandwidth), then resource allocation efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent introduces a decision-making engine as an intermediary component that manages the complexity of multi-metric host selection. This engine evaluates network distance, latency, bandwidth, and other metrics to determine optimal host placements, thereby improving resource allocation efficiency while isolating the computational complexity within a dedicated management system rather than distributing it across the entire cloud infrastructure.
Data Source
AI summary
Systems and methods are described for allocating resources in a cloud computing environment. The method includes receiving a computing request, the request for use of at least one virtual machine and a portion of memory. In response to the request, a plurality of hosts is identified and a cost function is formulated using at least a portion of those hosts. Based on the cost function, at least one host that is capable of hosting the virtual machine and memory is selected.