Transparent Network Content Compression via Dictionary References
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Solution Overview
Problem
Centralized data centers improve administration costs but lead to performance issues and increased networking costs due to slower WAN traffic, while distributing servers closer to clients increases complexity and operational costs.
Innovation Solution
Implementing content compression technology that allows senders to construct compressed packets with references to information maintained at the receiver, using dictionaries and hash values to recreate original content, and employing RDC* and speculative compression mechanisms to reduce data transmission redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If servers are centralized in consolidated data centers, then administration costs are reduced, but networking costs increase and performance deteriorates
Solution Approach 1:
The patent segments network traffic into compressed packets with references to original content, allowing selective transmission of only essential data portions. This segmentation enables centralized data centers to maintain administrative simplicity while reducing network bandwidth consumption and improving performance for distributed clients.
2Productivity
If servers are distributed closer to clients, then networking performance improves, but complexity and operational costs increase
Solution Approach 1:
The patent creates compressed copies of original packets that reference stored content rather than transmitting complete data. This copying mechanism allows performance improvement through local reference resolution while maintaining centralized content storage, thereby avoiding the complexity of distributed server infrastructure.
3Ease of operation
If content is transmitted without compression, then transmission simplicity is maintained, but bandwidth usage increases and networking costs rise
Solution Approach 1:
The patent changes the parameter representation of network data by transforming original packets into compressed packets with references. This parameter transformation reduces the quantity of transmitted data while maintaining the ability to reconstruct original content, thereby reducing bandwidth usage without significantly complicating the transmission process.
Data Source
AI summary
Described is transparently compressing content for network transmission, including end-to-end compression. An end host or middlebox device sender sends compressed packets to an end host or middlebox device receiver, which decompresses the packets to recover the original packet. The sender constructs compressed packets including references to information maintained at the receiver, which the receiver uses to access the information to recreate actual original packet content. The receiver may include a dictionary corresponding to the sender, e.g., synchronized with the sender's dictionary. Alternatively, in speculative compression, the sender does not maintain a dictionary, and instead sends a fingerprint (hash value) by which the receiver looks up corresponding content in its dictionary; if not found, the receiver requests actual content. Scheduling to maintain fairness and smoothing bursts to coexist with TCP congestion control are also described, as are techniques for routing compressed data over networked end hosts and/or compression-enabled middlebox devices.