1. 27 Mar, 2023 3 commits
  2. 22 Mar, 2023 6 commits
    • Kirill Smelkov's avatar
      amari.xlog: Sync, reverse reading, timestamps for eNB < 2022-12-01 · 67466ae5
      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
      67466ae5
    • Kirill Smelkov's avatar
      amari.xlog: attach,sync += information about on-service time · 0c772eb4
      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.
      0c772eb4
    • Kirill Smelkov's avatar
      amari.xlog: Teach Reader to read xlog in reverse order from end to start · dbecc158
      Kirill Smelkov authored
      This functionality is useful to look at tail of a log without reading it
      in full from the start.
      dbecc158
    • Kirill Smelkov's avatar
      amari.xlog: Reader: tests: Verify yielded .pos · 515c1573
      Kirill Smelkov authored
      .pos verification should be there from xlog.Reader start in 0633d26f
      (amari.xlog += Reader)
      515c1573
    • Kirill Smelkov's avatar
      amari.xlog: Require sync to be present at least every 1000 records · b412d488
      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.
      b412d488
    • Kirill Smelkov's avatar
      amari.xlog: Unify start with sync · 9d9d20f3
      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.
      9d9d20f3
  3. 21 Mar, 2023 2 commits
    • Kirill Smelkov's avatar
      amari.xlog: Emit config_get after every sync(attached) instead of only after every attach · 67ece601
      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.
      67ece601
    • Kirill Smelkov's avatar
      amari.xlog: Emit sync events periodically · 964f954a
      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.
      964f954a
  4. 17 Mar, 2023 2 commits
  5. 09 Mar, 2023 3 commits
    • Kirill Smelkov's avatar
      Draft support for E-UTRAN IP Throughput KPI · 2a016d48
      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
      2a016d48
    • Kirill Smelkov's avatar
      *: Cosmetics + minor · 43aac33e
      Kirill Smelkov authored
      Noticed while developing support for E-UTRAN IP Throughtput.
      43aac33e
    • Kirill Smelkov's avatar
      demo/kpidemo.*: Add support for E-UTRAN IP Throughput KPI + demonstrate it in the notebook · 4b2c8c21
      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.
      4b2c8c21
  6. 08 Mar, 2023 15 commits
    • Kirill Smelkov's avatar
      demo/kpidemo.*: Refactor commonly used bits into helper routines · 51785980
      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.
      51785980
    • Kirill Smelkov's avatar
      t/udpflood: Test program to simulate transmission bursts · 1dc74b0c
      Kirill Smelkov authored
      It is useful to verify E-UTRAN IP Throughput KPI implementation, as
      that KPI is defined in terms of burst samples.
      1dc74b0c
    • Kirill Smelkov's avatar
      kpi: Calc: Add support for E-UTRAN IP Throughput KPI · 2824f50d
      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.
      2824f50d
    • Kirill Smelkov's avatar
      amari.kpi: Teach LogMeasure to handle x.drb_stats messages · 499a7c1b
      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.
      499a7c1b
    • Kirill Smelkov's avatar
      amari.kpi: Rework LogMeasure to prepare Measurement incrementally · fd7870f4
      Kirill Smelkov authored
      We added LogMeasure in 71087f67 (amari.kpi: New package with driver for
      Amarisoft LTE stack to retrieve KPI-related measurements from logs) and
      its original logic is to read `stats` messages and to create Measurement
      that covers [Sx, Sx+1) only after seeing Sx+1.
      
      However in the next patch we will need to also take into account other
      smaller messages besides stats, and for that messages we need
      being-prepared Measurement to already exist to be able to amend it with
      partial data we see. So we need to rework the process to create
      Measurement that will cover [Sx, Sx+1) right after seeing Sx without
      waiting for Sx+1 to come in.
      
      This patch does that.
      
      Along the way it unifies how events and stats are handled. Previously
      events and stats were handled via different objects and the code had many
      scattered places that tried to handle cases like event-event,
      event-stats, stats-event and stats-stats. And for all those cases the
      intent was that we still want to emit corresponding Measurement for all
      of them, even if maybe if all NA data besides timestamps. Thus it does
      not make sense to split events and stats into different flows - as we can
      handle all combinations by considering just one flow of "stats or
      events". This simplifies logic and removes several sporadic branches
      of code to emit M(ø) around events. It also discovers several places
      where we were not emitting such M(ø) even though the intent was to do
      so. All this is fixed now with updated tests.
      fd7870f4
    • Kirill Smelkov's avatar
      amari.{drb,xlog}: Provide aggregated DRB statistics in the form of synthetic x.drb_stats message · 5bf7dc1c
      Kirill Smelkov authored
      This patch provides next building block for E-UTRAN IP Throughput KPI
      and continues d102ffaa (drb: Start of the package). Quoting that patch
      
          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	<-- NOTE
          new `x.drb_stats` message, and teach `xamari xlog` to save that messages to		<-- NOTE
          `enb.xlog` together with `stats`.							<-- NOTE
      
          Then further adjust `amari.kpi.LogMeasure` and generic
          `kpi.Measurement` and `kpi.Calc` to handle DRB-related data.
      
      So here we implement the noted step:
      
      - add drv._x_stats_srv server that polls eNB at 100Hz rate, uses Sampler
        to extract bursts and aggregates information about those bursts.
      
      - teach xlog to organize servers for synthetic messages and communicate
        with them, and register drv._x_stats_srv as such server to handle
        generation of x.drb_stats message.
      5bf7dc1c
    • Kirill Smelkov's avatar
      amari.drb += _IncStats · 78f26e3a
      Kirill Smelkov authored
      An utility class to compute avg/std incrementally.
      
      Thanks to https://www.johndcook.com/blog/standard_deviation/ for the
      recipe of how to do it.
      78f26e3a
    • Kirill Smelkov's avatar
      amari.drb: Start of the package · d102ffaa
      Kirill Smelkov authored
      This package will be used to implement E-UTRAN IP Throughput KPI.
      
      In hereby patch we add `drb.Sampler` that extracts samples of
      transmission bursts from `ue_get[stats]` observations.
      
      Let's go through what E-UTRAN IP Throughput KPI is and how it motivates
      functionality provided by this patch.
      
      Overview of E-UTRAN IP Throughput computation
      ---------------------------------------------
      
      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.
      
      ----------------------------------------
      
      In this patch we provide first building block - `Sampler` that extracts bursts
      of data transmissions from stream of `ue_get[stats]` observations.
      
      Even though main idea behind `Sampler` 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.
      
         We bring `δ(tx_bytes)` and `#tx_tb` in sync ourselves via _BitSync.
      
         (*) see e.g. Figure 8.1 in "An introduction to LTE, 2nd ed."
      
      2. when we see multiple transmissions related to UE on different QCIs, we
         cannot directly use corresponding 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.
      d102ffaa
    • Kirill Smelkov's avatar
      amari.xlog: Move main logger to a thread · 79d10eb9
      Kirill Smelkov authored
      We will soon need to run 2 threads:
      
      - one with the main logger, and
      - another one to serve requests for synthetic x.drb_stats queries
      
      Both main and the second thread will be run via sync.WorkGroup to cancel
      each other in case of failure somewhere. So since WorkGroup.wait(),
      similarly to all pygolang operations, is not interrupted by signals(*),
      we need to wire ctx to be passed through all operations and manage to
      cancel that context on SIGINT/SIGTERM.
      
      This patch:
      
      1. adjusts xlog to wire ctx through all call chains and moves ._xlog1()
         to be run in the thread.
      2. adjusts amari.Conn to take ctx as argument on all operations and
         react reasonably on that ctx cancel. We need to do it here because
         xlog uses Conn internally.
      3. adjusts xamari main driver to setup root context that is canceled on
         SIGINT/SIGTERM similarly e.g. to how nxdtest does it in
         nexedi/nxdtest@b0cf277d .
      
      (*) see nexedi/pygolang@e18adbab for details.
      79d10eb9
    • Kirill Smelkov's avatar
      amari.xlog: Rework "service detach" to be detected and done via defer instead of try/except · c967c8b5
      Kirill Smelkov authored
      We will soon add more levels of trying to this part of the code and
      linear defers are easier to follow compared to many levels of try/except
      nesting.
      c967c8b5
    • Kirill Smelkov's avatar
      amari: Conn: Provide a way to retrieve websocket URI to where a Conn is connected · 749e1659
      Kirill Smelkov authored
      We will soon need this to know at runtime the address of eNB service
      attached by Conn to establish another connection attached to the same eNB.
      749e1659
    • Kirill Smelkov's avatar
      kpi: Add support for QCI to Measurements · ffffb933
      Kirill Smelkov authored
      Previously for Measurement fields with .QCI or .CAUSE suffix we had only
      the .sum value and no per-QCI nor per-CAUSE values. In other words
      support for QCI and CAUSE was stub. In this patch we add support for
      QCI: every field X.QCI is now automatically expanded into X[256] array
      and X.sum . For convenience we also provide X.<qci> aliases that alias
      X[qci]. For example field DRB.IPVolDl.9 aliases 9'th element of
      DRB.IPVolDl array.
      
      We will need QCI support for E-UTRAN IP Throughput KPI which is required
      to provide resulting values for every QCI individually.
      
      CAUSE support remains stub for now.
      ffffb933
    • Kirill Smelkov's avatar
      kpi: Fix Σqci and Σcause when invoked on NA data · 6eccb446
      Kirill Smelkov authored
      There was a thinko that led to returning 0 instead of NA when there are
      not .QCI or .CAUSE fields except .sum . Without added fix, e.g.
      
      	Σqci(Measurement(), 'ERAB.EstabInitAttNbr.QCI')
      
      was returning 0 instead of NA.
      
      -> Fix it.
      6eccb446
    • Kirill Smelkov's avatar
      kpi: Fix naming of Measurement fields related to IP Throughput · 762153ab
      Kirill Smelkov authored
          DRB.IPThpVol   ->  DRB.IPVol	(no "Thp" inside)
          DRB.IPThpTime  ->  DRB.IPTime	(no "Thp" inside
      
      TS 32.450 and TS 32.425 defines those names as in corrected variants -
      please see corresponding references in the code for details.
      
      It was my thinko in dc1d5481 (kpi: Start of the package) to use "Thp" in
      the names.
      762153ab
    • Kirill Smelkov's avatar
      kpi: Fix NA(dtype) to return object with the same dtype · c30ee6db
      Kirill Smelkov authored
      We were not caring about that and so previously e.g. NA(np.int16) was
      giving int instead of np.int16 .
      
      Fix it.
      c30ee6db
  7. 05 Jan, 2023 2 commits
    • Kirill Smelkov's avatar
      setup: Make check-manifest happy · e1a5ceea
      Kirill Smelkov authored
      Before this patch it was complaining:
      
          (xlte3.venv) kirr@deca:~/src/wendelin/xlte$ check-manifest
          lists of files in version control and sdist do not match!
          missing from sdist:
            demo/kpidemo.ipynb
            demo/kpidemo.py
            xlte.py
          suggested MANIFEST.in rules:
            include *.py
            recursive-include demo *.ipynb
            recursive-include demo *.py
      
      Noticed during !2 review.
      e1a5ceea
    • Xavier Thompson's avatar
      setup.py: Include CHANGELOG.rst in sdist · 6bd55e52
      Xavier Thompson authored
      Make `python setup.py sdist` include CHANGELOG.rst in the tar.gz.
      Without it, installing from the released tar.gz will fail because
      setup.py expects to read CHANGELOG.rst to generate the long description.
      
      /reviewed-by @kirr
      /reviewed-on !2
      6bd55e52
  8. 03 Jan, 2023 2 commits
  9. 12 Dec, 2022 5 commits
    • Kirill Smelkov's avatar
      demo/kpidemo.ipynb: New JupyterLab notebook that demonstrates how to compute KPIs with xlte · b3370cd8
      Kirill Smelkov authored
      Add JupyterLab notebook that shows how to build KPI-computing pipeline
      and to compute the KPIs. The notebook comes with extensive comments
      describing every step. Please see those comments for details.
      b3370cd8
    • Kirill Smelkov's avatar
      demo/kpidemo.py: New program that demonstrates how to compute KPIs with xlte · 7934cb0e
      Kirill Smelkov authored
      Add demo program that shows how to build KPI-computing pipeline and to
      compute the KPIs. It can be used e.g. as follows:
      
          $ ./demo/kpidemo.py 60 https://lab.nexedi.com/kirr/misc/raw/162307b9/lte/20221211-overload.xlog
      
      The program comes with extensive comments describing every step.
      Please see those comments for details.
      
      The next patch will also add analogous JupyterLab notebook.
      7934cb0e
    • Kirill Smelkov's avatar
      kpi += Calc · f3f3ec89
      Kirill Smelkov authored
      kpi.Calc is calculator to compute KPIs. It can be instantiated on
      MeasurementLog and time interval over which to perform computations.
      It currently implements calculations for only one "E-RAB Accessibility KPI".
      
      Please see added docstrings and tests for details.
      
      The next patch will also add demo program that uses all kpi.Calc and
      other parts of KPI-computation pipeline to build and visualize E-RAB
      Accessibility from real data.
      f3f3ec89
    • Kirill Smelkov's avatar
      amari.kpi: New package with driver for Amarisoft LTE stack to retrieve... · 71087f67
      Kirill Smelkov authored
      amari.kpi: New package with driver for Amarisoft LTE stack to retrieve KPI-related measurements from logs
      
      amari.kpi provides LogMeasure that takes enb.xlog (TODO and enb.log) as
      input, and produces kpi.Measurements on output.
      
          enb.xlog     ─────────
          ─────────>  │   Log   │
                      │         │ ────> []kpi.Measurement
          ─────────>  │ Measure │
          enb.log      ─────────
      
      We read log data organizing periods around stats queries, and for now we
      build Measurement from stats' counters. To do so we take δ(stats_prev, stat)
      and process it mapping Amarisoft counters to 3GPP ones specified by
      kpi.Measurement.
      
      We emit measurement X after reading stats X+2 - i.e. we emit measurement
      for a period after reading data covering _next_ period. It is organized
      this way to account for init/fini correction:
      
                   fini adjust
                  -------------
                 '             '
           Sx    v     Sx+1    '   Sx+2
        ────|───────────|───────────|────
             Measurement Measurement
                  X          X+1
      
      This approach has following limitations:
      
      - for most of the counters there is no direct mapping in between
        Amarisoft and 3GPP. For example we currently use s1_erab_setup_request for
        ERAB.EstabAddAtt.sum, but this mapping is not strictly correct and will
        break if corresponding S1 E-RAB SETUP REQUEST message contains multiple
        ERABs. The code has corresponding FIXME marks where such approximations
        are used.
      
      - it is not possible to implement init/fini correction precisely. From
        aggregated statistics we only get total amount for a fini value for a
        period - without knowing which part of it corresponds to init events
        from previous period, and which part to init events from current one.
        With that it is only possible to make a reasonable guess and try to
        preserve statistical properties, but not more. See m_initfini in the
        code for details.
      
      - it is possible to handle eNB with single cell only. This limitation
        comes from the fact that in Amarisoft LTE stack S1-related counters
        come as "globals" ones, while e.g. RRC-related counters are "per-cell".
        It is thus not possible to see how much S1 connection establishments
        are associated with one particular cell if there are several of them.
      
      TODO also parse enb.log to fix those issues.
      71087f67
    • Kirill Smelkov's avatar
      amari.xlog += Reader · 0633d26f
      Kirill Smelkov authored
      xlog.Reader could be used to parse and read back data previously saved
      by xlog. In the next patch we will use it in Amarisoft driver for KPI
      measurements.
      0633d26f