Transit Appliance Selection for Cloud Data Traffic Optimization
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Solution Overview
Problem
Existing data transfer methods across networks, particularly in cloud-based environments, face inefficiencies due to the need for symmetric encoding and decoding of data packets and the complexity of reconfiguring physical network components, leading to increased response times and packet loss.
Innovation Solution
A computer-implemented method for selecting an optimal transit appliance based on network performance metrics, such as latency and round trip time, by measuring and advertising derived performance metrics among network appliances, allowing for efficient data traffic routing to software services hosted in cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data packets are encoded on the transmitting end before transmission through the network, then data transfer optimization is achieved, but symmetric decoding on the receiving end is required which increases complexity
Solution Approach 1:
The patent extracts the decoding function from the receiving end and consolidates it at the transit appliance. The transit appliance performs both decoding of incoming packets and encoding of outgoing packets, eliminating the need for symmetric decoding capabilities at the receiving end. This resolves the contradiction by maintaining data transfer optimization while reducing device complexity at the receiving end.
Solution Approach 2:
The transit appliance acts as an intermediary between the transmitting end and the receiving end. It receives encoded packets, decodes them, processes the data, re-encodes it, and forwards it to the destination. This intermediary approach allows optimization of data transfer while centralizing the complex encoding/decoding operations at the transit appliance rather than requiring symmetric capabilities at both ends.
2Productivity
If physical switches and routers are reconfigured to route data packets through optimization appliances, then data transfer optimization is improved, but coordination complexity among organizations and departments increases
Solution Approach 1:
The transit appliance autonomously performs optimization functions without requiring manual reconfiguration of physical network devices. The appliance self-manages the routing optimization by intercepting packets at the network level and processing them through its optimization algorithms, eliminating the need for complex coordination among organizations and departments to reconfigure physical switches and routers.
Solution Approach 2:
The patent introduces a new dimension of operation by implementing optimization at the application/software layer rather than at the physical network layer. Instead of reconfiguring physical switches and routers, the transit appliance operates as a virtual machine or software-based system that can dynamically optimize data transfer without touching the physical network infrastructure, thereby avoiding coordination complexity.
3Adaptability or versatility
If software services are hosted in cloud environments with equipment in IaaS locations, then service accessibility is improved, but geographic distance increases response time and packet loss
Solution Approach 1:
The transit appliance performs preliminary actions by pre-processing and optimizing data packets before they traverse the geographic distance to cloud data centers. It prepares packets for efficient transmission, applies compression or encoding optimizations in advance, and routes them through optimal paths, thereby reducing the impact of geographic distance on response time while maintaining cloud service accessibility.
Data Source
AI summary
Disclosed is a system and method for optimization of data transfer to a software service. In exemplary embodiments, a computer-implemented method for determining a transit appliance for data traffic to a software service through one or more interconnected networks comprising a plurality of network appliances, comprises determining performance metrics for each of the plurality of network appliances to at least one IP address associated with the software service, and selecting a transit appliance for data transfer to the IP address, the selected transit appliance based at least in part on the performance metrics.