Middlebox State Transfer for Cloud Scaling
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Solution Overview
Problem
Scaling middlebox functions in cloud-based computing systems is challenging as it requires maintaining performance standards while minimizing operating costs without adversely affecting accuracy, which is difficult to achieve with existing methods.
Innovation Solution
A system that dynamically transfers network traffic flows and associated state information between middlebox instances, allowing for on-demand scaling without significant loss of accuracy, by buffering and processing packets to ensure continuity and order preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If middlebox functions are scaled by waiting for current flows to abate, then accuracy is maintained, but scaling speed is reduced
Solution Approach 1:
The system performs preliminary actions by capturing and storing state information about network flows before middlebox instances are terminated. This state information includes flow identification, packet counts, and other relevant metrics. When scaling occurs, this pre-captured state is transferred to new middlebox instances, eliminating the need to wait for flows to naturally abate and maintaining accuracy without sacrificing scaling speed.
2Speed
If middlebox instances are rapidly replaced, then scaling speed is improved, but state rebuilding errors increase
Solution Approach 1:
The system creates accurate copies of state information from existing middlebox instances to new middlebox instances during scaling operations. Rather than allowing new instances to rebuild state from scratch (which causes errors and delays), the system copies relevant state data including flow states, counters, and configuration information, ensuring rapid scaling without state rebuilding errors.
3Measurement precision
If state information is transferred between middlebox instances, then scaling accuracy is maintained, but system complexity increases
Solution Approach 1:
The system implements a universal state transfer mechanism that works across different middlebox instance types and scaling scenarios. The state capture and transfer framework is designed to be multi-functional, handling various flow types, middlebox configurations, and scaling patterns through a single unified approach, thereby maintaining accuracy without proportionally increasing system complexity.
4Reliability
If packet buffering is implemented during state transfer, then packet loss is reduced, but processing time increases
Solution Approach 1:
The system implements beforehand cushioning by buffering packets during the state transfer process between middlebox instances. This cushioning mechanism temporarily holds packets that arrive during scaling operations, preventing packet loss while the state is being transferred. The buffer is managed to minimize delays, releasing packets once state transfer is complete, thus maintaining reliability while limiting the time impact.
Data Source
AI summary
An enterprise computer system efficiently adjusts the number of middleboxes associated with the the enterprise, for example, with changes in demand, by transferring not only flows of instructions but also middlebox states associated with those flows. Loss-less transfer preventing the loss of packets and its state, and order-preserving transfer preserving packet ordering may be provided by a two-step transfer process in which packets are buffered during the transfer and are marked to be processed by a receiving middlebox before processing by that middlebox of ongoing packets for the given flow.