50 Fast Infoset still proves to be faster that SAX 2 to 3 times faster. Fast Infoset with post
compression and EXI with Schema perform approximately the same. Fast Infoset with post compression is only around 2 times slower than SAX, while its compaction
performance is greatly improved.
11.2.1.2.4 Conclusion
Fast Infoset is clearly the best option when it comes to compactiontime ratio. The very important throughput it allows makes it an adapted solution for very fast communication
networks. While offering very acceptable compaction performance in terms of compaction compared to EXI, it clearly demonstrate its lower complexity in terms of
CPU time. EXI with Schema knowledge but no post compression is still in the race, offering decent compression ratio with CPU comparable to Fast Infoset
11.2.1.3 Gzip compaction time analysis
This section quickly tackles the Gzip different level of compaction from 1 to 9 and put it in parallel with the CPU time used, to draw tradeoff conclusions regarding usage of
simple deflate.
Figure 15 - Gzip Compaction Speed Results
As one can see the level 9 costs between 2 and 4 times the cost of level 1. Level 56 is enough for a decent compression and avoids using too much additional CPU for only few
percent of additional compaction.
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11.2.1.4 Memory footprint
To consider every one of the parameters we are looking at, we need to finally verify the resources usage of each compression algorithm, to have a consistent overview of their
strengths and weaknesses. This has been done on family 2 only, for the results are similar for family 3 and may be flawed for family 1 due to the benchmarking environment cf
[OGC 11-097].
Figure 16 - Compression Algorithm Resource Allocation
One can observe that EXI is consuming a lot of memory compared to other algorithms, especially with schema knowledge and deflate post-compression. As the memory used by
the output buffer is taken in consideration, the raw SAX consumes more than gzip and Fast Infoset.
Anyway, EXI with schema and deflate is consuming too much: Around 150-200 MB for family 1,
Around 275-300 MB for family 2, Around 250-500 MB for family 3
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11.2.2 Overall analysis
The results of the different tests operated have allowed clear identification of the strengths and weaknesses of the compression algorithms. In a given environment, with its
limits and its requirements, some compression method will be more adapted, while they will not make sense in other.
The following sections aim to identify the different environment in which we will be operating, together with their limitations and constraints. Based on that, the most
appropriate algorithms will be identified.
11.2.2.1 Strong real-time constraint
This can be the case for specific types of data, like emergency alerts, etc… This automatically rules out EXI with post compression with or without schema. In this case,
there can be different constraints on other parameters:
11.2.2.1.1 “Unlimited” resources available: between DMS and information service
In this case, where we consider that the memory consumption is not an issue, the best option is Fast Infoset, as being by far the fastest algorithm of all even faster than SAX
parser. An interesting choice could also be EXI with Schema knowledge. It has one of the best performances in terms of compaction and operates quite quickly.
11.2.2.1.2 Limited resources available: between Client and DMS
The resources on the aircraft client side can be limited, due to limitations in terms of equipment that can be embedded in an aircraft. This automatically rules out EXI with
schema knowledge.
In this case where there is a strong time constraint and resources are limited, EXI without schema knowledge and without post compression is a fair choice. However, the cost of
air time could call for an efficient solution also in terms of compaction; In that case, Fast Infoset with post compression would be an excellent tradeoff.
11.2.2.2 Strong Compaction constraint
In the case of air to ground and ground to air communications and alike, the most important thing for operators, from a business perspective, is to keep the costs as low as
possible and therefore send as few data as possible, while ensuring that information are still exchanged between parties. Several options are foreseeable depending of other
constraints:
