- 11 Nov, 2024 6 commits
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Kirill Smelkov authored
We will need to know current cell DL/UL bitrate for multicell BitSync in the next patches.
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Kirill Smelkov authored
Continue preparatory steps to support multicell configurations and for that split the class that tracks UE transmission state into global part (_Utx) and per-cell parts (_UCtx). Everywhere else in the code we still assert that the number of cells an UE attached to is 1, so no support for multicell yet - only preparatory non-functional changes currently.
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Kirill Smelkov authored
Similarly to previous patch this is preparatory non-functional change to support multicell configurations. Previously the number of transmitted transport blocks was passed around as separate argument but with multiple cells each cell will have its own information about how many TB were transmitted/received and we will need to maintain those .tx and .retx in per-cell data structure. Start preparing to that by moving .tx and .retx to be tracked in UE transmission state instead of being passed around separately. No support for multicell yet - only preparatory non-functional changes currently.
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Kirill Smelkov authored
Since 2a016d48 (Draft support for E-UTRAN IP Throughput KPI) there was a hardcoded limitation that x.drb_stats generation works with 1-cell configurations only. However we do use multicell eNB configurations and on such configurations `xamari xlog x.drb_stats` was failing on eNB side with raise RuntimeError(("ue #%s belongs to %d cells; "+ "but only single-cell configurations are supported") % (ue_id, len(ju(['cells'])))) Start fixing that. As the first preparatory step to support multiple cells with x.drb_stats reorganize amari/drb.py code a bit: _QCI_Flow works at whole QCI transmission level which can aggregate and cover multiple cells, but xl_use_avg and rank are per cell things and they can be different for different cells. -> Start preparing to handle them in per-cell way by adjusting the code to take those values into account at higher level in computation stack where we still have cell context. No support for multicell yet - only preparatory non-functional changes currently.
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Kirill Smelkov authored
When computing Measurements amari.kpi derives period from stats messages - for example 60s if `xamari xlog` was running with stats/60s. But inside that period there could be multiple x.drb_stats messages - for example if xlog was running with `stats/60s x.drb_stats/10s`. However the code was handling only the last such x.drb_stats instead of accumulating counters from there during the period. As the result, if. e.g. during the period there were 90B/9s and 2B/0.2s x.drb_stats events, only the latter was accounted giving wrong data for transmitted amount and transmission time. -> Fix it.
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Kirill Smelkov authored
Starting from 71087f67 (amari.kpi: New package with driver for Amarisoft LTE stack to retrieve KPI-related measurements from logs) a limitation was hardcoded that only 1-cell configurations are supported. However we do use multicell eNB configurations and computing E-RAB Accessibility KPI was failing on them as e.g.: xlte.amari.kpi.LogError: t1731059787.321: stats describes 2 cells; but only single-cell configurations are supported -> Remove that limitation and handle stats covering multiple cells.
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- 27 May, 2024 2 commits
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Kirill Smelkov authored
Add new --password option and wire it to go to amari.connect(password=...) Support for password-based authentication in amari.connect was just added in the previous patch. We need to extend filtering of logged fields on service attach a bit since now the first service message can be both 'ready' and 'authenticate', and besides e.g. 'message' we don't want to log what was the 'challenge'. /reported-and-tested-by @lu.xu /reviewed-on kirr/xlte!6
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Kirill Smelkov authored
Sometimes Amarisoft services are setup to require custom challenge/response authentication upon connecting to their WebSocket. In 61ad9032 (amari: Add functionality to interoperate with an Amarisoft LTE service via WebSocket) I've put TODO for that but now we start to need it. -> Implement corresponding support for password-based authentication. In the next patch we will teach XLog to use it. See https://tech-academy.amarisoft.com/lteenb.doc#Startup for details of the authentication handshake protocol. /reported-and-tested-by @lu.xu /reviewed-on kirr/xlte!6
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- 29 Dec, 2023 1 commit
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Kirill Smelkov authored
Contrary to earfcn, where band can be automatically deduced from earfcn number because 4G bands never overlap, most functions in nrarfcn accept as input parameters both nr_arfcn and band, because 5G bands can and do overlap. As the result it is possible to invoke e.g. dl2ul with dl_nr_arfcn being outside of downlink spectrum of specified band. However in b8065120 I've made a thinko and handled such situation with simple assert which does not lead to useful error feedback from a user perspective, for example: In [2]: xnrarfcn.dl2ul(10000, 1) --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) Cell In[2], line 1 ----> 1 n.dl2ul(10000, 1) File ~/src/wendelin/xlte/nrarfcn.py:85, in dl2ul(dl_nr_arfcn, band) 83 if dl_lo == 'N/A': 84 raise AssertionError('band%r does not have downlink spectrum' % band) ---> 85 assert dl_lo <= dl_nr_arfcn <= dl_hi 86 ul_lo, ul_hi = nr.get_nrarfcn_range(band, 'ul') 87 if ul_lo == 'N/A': AssertionError: The issue here is that asserts can be used to only verify internal invariants, and that reported error does not provide details about which nrarfcn and band were used in the query. -> Fix this by providing details in the error reported to incorrect module usage, and by consistently raising ValueError for "invalid parameters" cases. The reported error for above example now becomes ValueError: band1: NR-ARFCN=10000 is outside of downlink spectrum
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- 05 Dec, 2023 1 commit
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Kirill Smelkov authored
Do a package for converting DL/UL NR-ARFCN in between each other and to convert DL NR-ARFCN to SSB NR-ARFCN. The API mimics xlte.earfcn added in 6cb9d37f. xlte.nrarfcn complements pypi.org/project/nrarfcn, which we use here under the hood. See package documentation for API details.
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- 25 Oct, 2023 1 commit
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Kirill Smelkov authored
Do a package which provides calculations like EARFCN -> frequency, EARFCN -> band info, and to convert DL/UL EARFCN in between each other. I was hoping to find something ready on the net, but could find only pypi.org/project/nrarfcn for 5G, while for LTE everything I found was of lesser quality and capability. -> So do it myself. See package documentation for API details.
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- 25 Jul, 2023 2 commits
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Kirill Smelkov authored
Before this patch we were supporting only boolean option flags - with, for example, stats[rf] meaning stats query with {"rf": True} arguments. Now we add support for arbitrary types, so that it is possible to specify e.g. integer or string query options, as well as some boolean flag set to false. This should be good for generality. For backward compatibility the old way to implicitly specify "on" flags is continued to be supported.
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Kirill Smelkov authored
If the parsed period was '60s' we were printing it back as '60.0s' on str. Fix it by using %g insted of %s.
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- 27 Apr, 2023 1 commit
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Kirill Smelkov authored
Adjust plain writer to append to log file instead of truncating it on the open. The rotating writers are already ok as they use "a" mode by default.
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- 20 Apr, 2023 1 commit
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Kirill Smelkov authored
- add Calc.cum to aggregate Measurements. - add ΣMeasurement type to represent result of this. It is very similar to Measurement, but every field comes accompanied with information about how much time there was no data for that field. In other words it is not all or nothing for NA in the result. For example a field might be present 90% of the time and NA only 10% of the time. We want to preserver knowledge about that 90% of valid values in the result. And we also want to know how much time there was no data. - amend kpidemo.py and kpidemo.ipynb to demonstrate this.
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- 18 Apr, 2023 3 commits
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Kirill Smelkov authored
XLTE should be ready to be tried to be used for real now.
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Kirill Smelkov authored
That's python versions we care about. Add explicit tests to cover that.
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Kirill Smelkov authored
Xavier reports that str|None is supported only by python ≥ 3.10, while we still should care to support at least 3.9 - e.g. SlapOS uses it by default as well as Debian 11. Let's not delve deep into typing game. If we cannot express things easily we can omit the type completely or express it in comments. /reported-by @xavier_thompson at !5 (comment 182930)
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- 17 Apr, 2023 1 commit
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Kirill Smelkov authored
Rotate output enb.xlog ourselves at sync points so that nothing is lost in the output (hello `logrotate copytruncate`) and so that we can emit pre- and post- logrotate syncs. Reuse logging's RotatingFileHandler and TimedRotatingFileHandler to implement actual rotation, but carefully wrap them in our writer classes so that we emit exactly the output we prepared explicitly without any headers prepended by logging, and that we explicitly control when rotation happens. /proposed-for-review-at !5
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- 28 Mar, 2023 1 commit
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Kirill Smelkov authored
Nxdtest[1] is tox-like tool to run tests under Nexedi testing infrastructure. [1] https://lab.nexedi.com/nexedi/nxdtest
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- 27 Mar, 2023 3 commits
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Kirill Smelkov authored
py3 worked without it ok, but py2 was failing to import xlte.demo without demo/__init__.py present.
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Kirill Smelkov authored
For py3 it does not matter, but on py2 %s prettifies result a bit: kirr@deca:~$ python Python 2.7.18 (default, Jul 14 2021, 08:11:37) >>> str(0.1-0.01) '0.09' >>> repr(0.1-0.01) '0.09000000000000001' kirr@deca:~$ python3 Python 3.9.2 (default, Feb 28 2021, 17:03:44) >>> str(0.1-0.01) '0.09000000000000001' >>> repr(0.1-0.01) '0.09000000000000001' Should make the diff in between master and py2 a bit smaller.
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Kirill Smelkov authored
This will make diff in between master and py2 backport a bit smaller.
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- 22 Mar, 2023 6 commits
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Kirill Smelkov authored
Rework XLog protocol to come with periodic sync events that come from time to time so that xlog stream becomes self-synchronizing. Sync events should be useful for Wendelin to start reading xlog stream from any point, and to verify that the stream is ok by matching its content vs messages schedule coming in the syncs. Teach xlog.Reader to read streams in reverse order from end to start. This should be useful to look at tail of a log without reading it in full from the start. Teach xlog.Reader to reconstruct messages timestamps for xlog streams produced with Amarisoft releases < 2022-12-01. There messages do not have .utc field added in https://support.amarisoft.com/issues/21934 and come with only .time field that represent internal eNB time using clock originating at eNB startup. We combine message.time and δ(utc, enb.time) from sync to build message.timestamp . See individual patches for details and !3 for preliminary discussion. /reviewed-by @xavier_thompson /reviewed-on !4
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Kirill Smelkov authored
We currently emit information about local time in events, and information about on-service time in messages. Events don't have information about on-service time and messages don't carry information about local time. That is mostly ok, since primary xlog setup is to run on the same machine, where eNB runs because on-service .utc correlates with .time in events. However for eNB < 2022-12-01 on-service time includes only .time field without .utc field with .time representing "time passed since when eNB was started". This way for enb.xlog streams generated on older systems it is not possible for xlog.Reader to know the absolute timestamps of read messages. To fix this we amend "attach" and "sync" events to carry both local and on-service times. This way xlog.Reader, after seeing e.g. "sync" with .time and only .srv_time without .srv_utc, should be able to correlate local and on-service clocks and to approximate srv_utc as srv_utc' = srv_time' + (time - srv_time) where time and srv_time correspond to last synchronization, and srv_time' is what xlog.Reader retrieves for a further-read message in question. See !3 for related discussion.
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Kirill Smelkov authored
This functionality is useful to look at tail of a log without reading it in full from the start.
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Kirill Smelkov authored
.pos verification should be there from xlog.Reader start in 0633d26f (amari.xlog += Reader)
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Kirill Smelkov authored
This way xlog.Reader can be sure that if it looked around in such a window and did not find a sync, then something is not good with the stream and it does not need to go beyond that limit looking around. This is a change of the protocol. But it is early days and existing logs - that we use in the demo, are all below 1000 lines limit, so they will continue to be loaded ok. No direct test for actual Loss Of Sync detection - this functionality is draft for now and should be improved later. However for no-LOS cases xlog.Reader is already covered with tests.
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Kirill Smelkov authored
Let's use "sync(reason=start)" instead of dedicated "start" event for uniformity. Periodic syncs are now "sync(reason=periodic)" and after logrotation support there will be also "pre-logrotate" and "post-logrotate" reasons. Emit "sync(reason=stop)" at xlog shutdown for uniformity and to make it more clear from looking at just enb.xlog about what is xlog state at the end. Stop requiring "start" to be present in the header - we will soon rework xlog reader to look around for nearby sync automatically so that reading could be started from any position in the stream.
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- 21 Mar, 2023 2 commits
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Kirill Smelkov authored
We emit config_get after every attach from the beginning of xlog in e0cc8a38 (amari.xlog: Initial draft). The reasoning here is that it is useful by default to know configuration of a service. In the previous patch we added sync events so that xlog stream becomes self-synchronizing. To continue that line it is now useful to have that config_get emitted after every such synchronization point instead of only after attaching to the service. That's what hereby patch does. As a bonus the code is reworked in a way that config_get setup is not hardcoded anymore and config_get periodicity now can be controlled by users via explicitly specifying config_get in the logspec.
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Kirill Smelkov authored
So that xlog stream becomes self-synchronized and could be used even if we start reading it from some intermediate point instead of only from the beginning. We will need this in general - to be able to start reading long log not only from its beginning, and also in particular for Wendelin systems where logs are uploaded by Fluentd in chunks and some chunks could be potentially lost. Sync events are emitted always unconditionally with default sync interval being 10x the longest specified period. We also provide users a way to control sync periodicity via explicitly specifying "meta.sync/period" query in the logspec. See !3 (comment 175796) and further for related discussion. This is change of xlog protocol. But it is early days and the only direct consumer of xlog is amari.kpi which we adjust accordingly. So it should be ok.
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- 17 Mar, 2023 2 commits
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Kirill Smelkov authored
We will soon need to construct logspecs not only by way of parsing.
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Kirill Smelkov authored
In 79d10eb9 that patch wired ctx through xlog callchains and added corresponding handling of cancellation. But I overlooked one place where plain sleep was used. -> Fix it.
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- 09 Mar, 2023 3 commits
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Kirill Smelkov authored
The most interesting patches are - d102ffaa (drb: Start of the package) - 5bf7dc1c (amari.{drb,xlog}: Provide aggregated DRB statistics in the form of synthetic x.drb_stats message) - 499a7c1b (amari.kpi: Teach LogMeasure to handle x.drb_stats messages) - 2824f50d (kpi: Calc: Add support for E-UTRAN IP Throughput KPI) - 4b2c8c21 (demo/kpidemo.*: Add support for E-UTRAN IP Throughput KPI + demonstrate it in the notebook) The other patches introduce or adjust needed infrastructure. A byproduct of particular note is that kpi.Measurement now supports QCI. A demo might be seen in the last part of https://nbviewer.org/urls/lab.nexedi.com/kirr/xlte/raw/43aac33e/demo/kpidemo.ipynb And below we provide the overall overview of the implementation. Overview of E-UTRAN IP Throughput computation --------------------------------------------- Before we begin explaining how IP Throughput is computed, let's first refresh what it is and have a look at what is required to compute it reasonably. This KPI is defined in TS 32.450[1] and aggregates transmission volume and time over bursts of transmissions from an average UE point of view. It should be particularly noted that only the time, during which transmission is going on, should be accounted. For example if an UE receives 10KB over 4ms burst and the rest of the time there is no transmission to it during, say, 1 minute, the downlink IP Throughput for that UE over the minute is 20Mbit/s (= 8·10KB/4ms), not 1.3Kbit/s (= 8·10KB/60s). This KPI basically shows what would be the speed to e.g. download a response for HTTP request issued from a mobile. [1] https://www.etsi.org/deliver/etsi_ts/132400_132499/132450/16.00.00_60/ts_132450v160000p.pdf#page=13 To compute IP Throughput we thus need to know Σ of transmitted amount of bytes, and Σ of the time of all transmission bursts. Σ of the bytes is relatively easy to get. eNB already provides close values in overall `stats` and in per-UE `ue_get[stats]` messages. However there is no anything readily available out-of-the box for Σ of bursts transmission time. Thus we need to measure the time of transmission bursts ourselves somehow. It turns out that with current state of things the only practical way to measure it to some degree is to poll eNB frequently with `ue_get[stats]` and estimate transmission time based on δ of `ue_get` timestamps. Let's see how frequently we need to poll to get to reasonably accuracy of resulting throughput. A common situation for HTTP requests issued via LTE is that response content downloading time takes only few milliseconds. For example I used chromium network profiler to access various sites via internet tethered from my phone and saw that for many requests response content downloading time was e.g. 4ms, 5ms, 3.2ms, etc. The accuracy of measuring transmission time should be thus in the order of millisecond to cover that properly. It makes a real difference for reported throughput, if say a download sample with 10KB took 4ms, or it took e.g. "something under 100ms". In the first case we know that for that sample downlink throughput is 2500KB/s, while in the second case all we know is that downlink throughput is "higher than 100KB/s" - a 25 times difference and not certain. Similarly if we poll at 10ms rate we would get that throughput is "higher than 1000KB/s" - a 2.5 times difference from actual value. The accuracy of 1 millisecond coincides with TTI time and with how downlink/uplink transmissions generally work in LTE. With the above the scheme to compute IP Throughput looks to be as follows: poll eNB at 1000Hz rate for `ue_get[stats]`, process retrieved information into per-UE and per-QCI streams, detect bursts on each UE/QCI pair, and aggregate `tx_bytes` and `tx_time` from every burst. It looks to be straightforward, but 1000Hz polling will likely create non-negligible additional load on the system and disturb eNB itself introducing much jitter and harming its latency requirements. That's probably why eNB actually rate-limits WebSocket requests not to go higher than 100Hz - the frequency 10 times less compared to what we need to get to reasonable accuracy for IP throughput. Fortunately there is additional information that provides a way to improve accuracy of measured `tx_time` even when polled every 10ms at 100Hz rate: that additional information is the number of transmitted transport blocks to/from an UE. If we know that during 10ms frame it was e.g. 4 transport blocks transmitted to the UE, that there were no retransmissions *and* that eNB is not congested, we can reasonably estimate that it was actually a 4ms transmission. And if eNB is congested we can still say that transmission time is somewhere in `[4ms, 10ms]` interval because transmitting each transport block takes 1 TTI. Even if imprecise that still provides some information that could be useful. Also 100Hz polling turns to be acceptable from performance point of view and does not disturb the system much. For example on the callbox machine the process, that issues polls, takes only about 3% of CPU load and only on one core, and the CPU usage of eNB does not practically change and its reported tx/rx latency does not change as well. For sure, there is some disturbance, but it appears to be small. To have a better idea of what rate of polling is possible, I've made an experiment with the poller accessing my own websocket echo server quickly implemented in python. Both the poller and the echo server are not optimized, but without rate-limiting they could go to 8000Hz frequency with reaching 100% CPU usage of one CPU core. That 8000Hz is 80x times more compared to 100Hz frequency actually allowed by eNB. This shows what kind of polling frequency limit the system can handle, if absolutely needed, and that 100Hz turns out to be not so high a frequency. Also the Linux 5.6 kernel, installed on the callbox from Fedora32, is configured with `CONFIG_HZ=1000`, which is likely helping here. Implementation overview ~~~~~~~~~~~~~~~~~~~~~~~ The scheme to compute E-UTRAN IP Throughput is thus as follows: poll eNB at 100Hz frequency for `ue_get[stats]` and retrieve information about per-UE/QCI streams and the number of transport blocks dl/ul-ed to the UE in question during that 10ms frame. Estimate `tx_time` taking into account the number of transmitted transport blocks. And estimate whether eNB is congested or not based on `dl_use_avg`/`ul_use_avg` taken from `stats`. For the latter we also need to poll for `stats` at 100Hz frequency and synchronize `ue_get[stats]` and `stats` requests in time so that they both cover the same time interval of particular frame. Then organize the polling process to provide aggregated statistics in the form of new `x.drb_stats` message, and teach `xamari xlog` to save that messages to `enb.xlog` together with `stats`. Then further adjust `amari.kpi.LogMeasure` and generic `kpi.Measurement` and `kpi.Calc` to handle DRB-related data. That is how it is implemented. The main part, that performs 100Hz polling and flow aggregation, is in amari/drb.py. There `Sampler` extracts bursts of data transmissions from stream of `ue_get[stats]` observations and `x_stats_srv` organizes whole 100Hz sampling process and provides aggregated `x.drb_stats` messages to `amari.xlog`. Even though the main idea is relatively straightforward, several aspects deserves to be noted: 1. information about transmitted bytes and corresponding transmitted transport blocks is emitted by eNB not synchronized in time. The reason here is that, for example, for DL a block is transmitted via PDCCH+PDSCH during one TTI, and then the base station awaits HARQ ACK/NACK. That ACK/NACK comes later via PUCCH or PUSCH. The time window in between original transmission and reception of the ACK/NACK is 4 TTIs for FDD and 4-13 TTIs for TDD(*). And Amarisoft LTEENB updates counters for dl_total_bytes and dl_tx at different times: ue.erab.dl_total_bytes - right after sending data on PDCCH+PDSCH ue.cell.{dl_tx,dl_retx} - after receiving ACK/NACK via PUCCH|PUSCH this way an update to dl_total_bytes might be seen in one frame (= 10·TTI), while corresponding update to dl_tx/dl_retx might be seen in either same, or next, or next-next frame. `Sampler` brings δ(tx_bytes) and #tx_tb in sync itself via `BitSync`. 2. when we see multiple transmissions related to UE on different QCIs, we cannot directly use corresponding global number of transport blocks to estimate transmissions times because we do not know how eNB scheduler placed those transmissions onto resource map. So without additional information we can only estimate corresponding lower and upper bounds. 3. for output stability and to avoid throughput being affected by partial fill of tail TTI of a burst, E-UTRAN IP Throughput is required to be computed without taking into account last TTI of every sample. We don't have that level of details since all we have is total amount of transmitted bytes in a burst and estimation of how long in time the burst is. Thus, once again, we can only provide an estimation so that resulting E-UTRAN IP Throughput uncertainty window cover the right value required by 3GPP standard. A curious reader might be interested to look at tests in `amari/drb_test.py` , and at the whole changes that brought E-UTRAN IP Throughput alive. Limitations ~~~~~~~~~~~ Current implementation has the following limitations: - we account whole PDCP instead of only IP traffic. - the KPI is computed with uncertainty window instead of being precise even when the connection to eNB is alive all the time. The shorter bursts are the more the uncertainty. - the implementation works correctly for FDD, but not for TDD. That's because BitSync currently supports only "next frame" case and support for "next-next frame" case is marked as TODO. - eNB `t` monitor command practically stops working and now only reports ``Warning, remote API ue_get (stats = true) pending...`` instead of reporting useful information. This is due to that contrary to `stats`, for `ue_get` eNB does not maintain per-connection state and uses global singleton counters. - the performance overhead might be more noticeable on machines less powerful compared to callbox. To address the limitations I plan to talk to Amarisoft about eNB improvements so that E-UTRAN IP Throughput could be computed precisely from DRB statistics directly provided by eNB itself. However it is still useful to have current implementation, even with all its limitations, because it already works today with existing eNB versions. Kirill
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Kirill Smelkov authored
Noticed while developing support for E-UTRAN IP Throughtput.
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Kirill Smelkov authored
Show how to compute that KPI, add corresponding plotting routines, and teach kpidemo.py to display both E-RAB Accessibility and E-UTRAN IP Throughput simultaneously in the same window. Add corresponding demonstration into demo notebook with data from throughput experiment showcasing several scenarious and how E-UTRAN IP Throughput implementation handles them.
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- 08 Mar, 2023 4 commits
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Kirill Smelkov authored
- move code to load amari.kpi.LogMeasure -> kpi.MeasurementLog into load_measurements(). We will need to use that when showcasing E-UTRAN IP Throughput KPI to load another enb.xlog dataset. - factor code to iterate over MeasurementLog and invoke kpi.Calc on each period into calc_each_period(). Same reason. - factor plotting code into helper routines located only in kpidemo.py. The notebook version now uses those routines by way of importing. The plotting code is not helping to understand the KPI computation pipeline usage, so it makes sense not to show it out of the box in the demo notebook.
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Kirill Smelkov authored
It is useful to verify E-UTRAN IP Throughput KPI implementation, as that KPI is defined in terms of burst samples.
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Kirill Smelkov authored
This patch provides the final building block for E-UTRAN IP Throughput KPI. It continues d102ffaa (drb: Start of the package) 5bf7dc1c (amari.{drb,xlog}: Provide aggregated DRB statistics in the form of synthetic x.drb_stats message) 499a7c1b (amari.kpi: Teach LogMeasure to handle x.drb_stats messages) Quoting those patches The scheme to compute E-UTRAN IP Throughput is thus as follows: poll eNB at 100Hz frequency for `ue_get[stats]` and retrieve information about per-UE/QCI streams and the number of transport blocks dl/ul-ed to the UE in question during that 10ms frame. Estimate `tx_time` taking into account the number of transmitted transport blocks. And estimate whether eNB is congested or not based on `dl_use_avg`/`ul_use_avg` taken from `stats`. For the latter we also need to poll for `stats` at 100Hz frequency and synchronize `ue_get[stats]` and `stats` requests in time so that they both cover the same time interval of particular frame. Then organize the polling process to provide aggregated statistics in the form of new `x.drb_stats` message, and teach `xamari xlog` to save that messages to `enb.xlog` together with `stats`. Then further adjust `amari.kpi.LogMeasure` and generic `kpi.Measurement` and `kpi.Calc` to handle DRB-related data. <-- NOTE So here we implement that last noted step: We add Calc.eutran_ip_throughput() whose implementation is relatively straightforward as the hard part is done by amari.drb and amari.kpi - in the Calc we basically need to only divide provided DRB.IPVolDl / DRB.IPTimeDl.
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Kirill Smelkov authored
This patch provides next building block for E-UTRAN IP Throughput KPI and continues d102ffaa (drb: Start of the package) 5bf7dc1c (amari.{drb,xlog}: Provide aggregated DRB statistics in the form of synthetic x.drb_stats message) Quoting those patches The scheme to compute E-UTRAN IP Throughput is thus as follows: poll eNB at 100Hz frequency for `ue_get[stats]` and retrieve information about per-UE/QCI streams and the number of transport blocks dl/ul-ed to the UE in question during that 10ms frame. Estimate `tx_time` taking into account the number of transmitted transport blocks. And estimate whether eNB is congested or not based on `dl_use_avg`/`ul_use_avg` taken from `stats`. For the latter we also need to poll for `stats` at 100Hz frequency and synchronize `ue_get[stats]` and `stats` requests in time so that they both cover the same time interval of particular frame. Then organize the polling process to provide aggregated statistics in the form of new `x.drb_stats` message, and teach `xamari xlog` to save that messages to `enb.xlog` together with `stats`. Then further adjust `amari.kpi.LogMeasure` <-- NOTE and generic `kpi.Measurement` and `kpi.Calc` to handle DRB-related data. So here we implement the noted step: We teach LogMeasure to take x.drb_stats messages into account and update IP Throughput related fields in appropriate Measurement from x.drb_stats data. This process is relatively straightforward besides one place: for stable output E-UTRAN IP Throughput is required to be computed without taking into account last TTI of every sample. We don't have that level of details since all we have is total amount of transmitted bytes in a burst and estimation of how long in time the burst is. Thus we can only provide an estimation for the E-UTRAN IP Throughput as follows: DRB.IPVol and DRB.IPTime are collected to compute throughput. thp = ΣB*/ΣT* where B* is tx'ed bytes in the sample without taking last tti into account and T* is time of tx also without taking that sample's tail tti. we only know ΣB (whole amount of tx), ΣT and ΣT* with some error. -> thp can be estimated to be inside the following interval: ΣB ΣB ───── ≤ thp ≤ ───── (1) ΣT_hi ΣT*_lo the upper layer in xlte.kpi will use the following formula for final throughput calculation: DRB.IPVol thp = ────────── (2) DRB.IPTime -> set DRB.IPTime and its error to mean and δ of ΣT_hi and ΣT*_lo so that (2) becomes (1). for this to work we also need to introduce new fields to Measurement that represent error of DRB.IPTime. The hope is that introduction is temporary and should be removed once we rework DRB stats to provide B* and T* directly.
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