In the realm of flow measurement, the objective is often to maximize the granularity and accuracy of flow statistics while adapting to resource constraints. The discussion at hand presents a comparative analysis between an original flow aggregation methodology and a newly proposed method, aiming to highlight the nuanced differences and potential advantages brought forth by the latter.
The original method [1] hinges on a Gradual Flow Key Reduction process that operates within a designated part of the main buffer, specifically earmarked for flows targeted for aggregation. This method unfolds iteratively, with each iteration employing a reduced set of flow keys for aggregation, based on a predetermined Flow Key Precedence. The iterations persevere until either the total count of flow records in the main buffer descends below a stipulated limit or there ceases to be more flow keys to pare down from, upon which the aggregated flows are relocated to their associated buffers.
In an ideal scenario, characterized by light or moderate traffic dynamics, the aggregated flows predominantly move to Buffer B1, where the aggregation process utilizes four flow keys. Such a scenario, typically satisfies the desired limit with just a single round of aggregation whenever reduction is needed, thereafter relocating the aggregated flows to this buffer.
However, under extreme circumstances engendered by heavy traffic dynamics and a profusion of new flows, the method's intrinsic nature propels it to continue the iterations until the limit in the main buffer is gratified or no more flow keys remain for reduction. This could lead to a situation where flows aggregated using, say, four elements in one iteration might also be included in the next iteration for further aggregation using three elements. Across various aggregation rounds, flows could potentially be aggregated into flow records differing by merely a single element, such as the protocol, leading to considerable information degradation.
Moreover, a notable shortcoming of this method is its lack of handling for buffers that aggregate flows using a higher number of flow key elements. Assuming an instance where all flows migrate solely to Buffer B1 due to the method's intrinsic nature, setting aside the effects of flow expiration and export in this case, a low-capacity scenario could see these flows occupying the entire space, which is not adequately managed within this method.
The proposed method elucidates a structured, stepwise flow aggregation process, employing a multi-buffer structure much like the original method. Each buffer (B0 to B4) has a dedicated purpose for a specific level of flow key aggregation. For instance, Buffer B1 harbors flows aggregated over four elements, Buffer B2 over three, and so forth, representing a progressive decrease in granularity from B0 to B4. However, a distinctive feature of the proposed method is a more structured and stepwise flow aggregation that allows for a more controlled and less abrupt data reduction process compared to the Original Method.
As flows transition from the Main Flow Cache (B0) towards Buffer B4, they undergo a systematic reduction in flow key elements. Each buffer is a receptacle for flows aggregated at a particular level of granularity, ushering a more regimented aggregation process. Contrary to the original method, here, the flow records can only transition for aggregation over one flow key element from a preceding buffer to the next, eliminating the possibility of a direct leap from the main buffer to the last buffer, which could be the case in the original method. By segregating the aggregation process across multiple buffers, this method provides a finer level of control over the granularity of flow data, potentially preserving more information.
Moreover, a nuanced threshold mechanism is introduced within the proposed method, assigning a specific threshold to each buffer. These thresholds, which could be uniform or varied across buffers, serve as a gatekeeper determining which flows are retained and which are earmarked for aggregation. The method further refines this threshold mechanism by making it adaptive to the resource utilization, thus orchestrating a balanced trade-off between information retention and resource management.
A salient feature of the proposed method is the provision for moving overflowing flows down to the next buffer when a buffer aggregating flows with a higher flow key element count is filled. This cascade of flows ensures that even under a deluge of network traffic, the aggregation process remains orderly. Moreover, given the finite range of possible values for the last element, the last buffer (B4) is capable of aggregating all flows that could theoretically occur in the network, thus encapsulating the entire spectrum of network flows within a structured, manageable framework.
This orchestrated approach of the proposed method, from the stepwise reduction of flow key elements to the adaptive threshold mechanism, not only mitigates the risk of abrupt data reduction but also ensures a more resource-aware, organized, and potentially more information-rich flow aggregation process compared to the original method.
The proposed method, with its stepwise flow aggregation and multi-buffer structure, potentially offers a more organized and resource-adaptive approach to flow aggregation. Unlike the original method, where flows are subject to abrupt aggregation rounds directly from the main buffer, the proposed method ensures a gradual reduction in flow key elements, potentially preserving more information and providing a structured pathway for flow aggregation amidst varying resource constraints.
[1] Hitoshi Irino, Masaru Katayama, and Shinichiro Chaki, "Study of adaptive aggregation on IPFIX," in the Proceedings of the 2008 7th Asia-Pacific Symposium on Information and Telecommunication Technologies, Bandos Island, Maldives, 2008, pp. 86-91. doi: 10.1109/APSITT.2008.4653545.