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@ -449,10 +449,7 @@ computing slowdown values given the previously computed execution attempt time
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deltas. Finally, the mean of the computed slowdown values is computed resulting
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deltas. Finally, the mean of the computed slowdown values is computed resulting
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in the clear and coincise tables found in figure~\ref{fig:taskslowdown}.
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in the clear and coincise tables found in figure~\ref{fig:taskslowdown}.
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\section{Analysis: Performance Input of Unsuccessful Executions}
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\section{Analysis: Performance Input of Unsuccessful Executions}
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\input{figures/machine_time_waste}
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Our first investigation focuses on replicating the methodologies used in the
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Our first investigation focuses on replicating the methodologies used in the
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2015 DSN Ros\'a et al.\ paper\cite{vino-paper} regarding usage of machine time
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2015 DSN Ros\'a et al.\ paper\cite{vino-paper} regarding usage of machine time
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@ -465,6 +462,7 @@ from the 2019 traces to the ones that were obtained in 2015 to understand the
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workload evolution inside Borg between 2011 and 2019.
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workload evolution inside Borg between 2011 and 2019.
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\subsection{Temporal Impact: Machine Time Waste}
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\subsection{Temporal Impact: Machine Time Waste}
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\input{figures/machine_time_waste}
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This analysis explores how machine time is distributed over task events and
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This analysis explores how machine time is distributed over task events and
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submissions. By partitioning the collection of all terminating tasks by their
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submissions. By partitioning the collection of all terminating tasks by their
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@ -565,35 +563,59 @@ higher machine time spent for unsuccesful executions (as observed in the
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previous analysis) and increase slowdown rate for this class is not particularly
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previous analysis) and increase slowdown rate for this class is not particularly
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surprising.
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surprising.
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\textbf{TBD}
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The amount of non-successful task terminations in the 2019 traces is also rather
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The \% of finishing jobs is relatively low comparing with the 2011
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high when compared to 2011 data, as it can evinced by the low percentage of
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traces.
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\texttt{FINISH}ed tasks across priority tiers.
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Another noteworthy difference is in the mean response times for all and last
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executions: while the mean response is overall shorter in time in the 2019
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traces by an order of magnitude, the new traces show an overall significantly
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higher mean response time than in the 2011 data.
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Across 2019 single clusters (as in figure~\ref{fig:taskslowdown-csts}), the data
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shows a mostly uniform behaviour, other than for some noteworthy mean slowdown
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spikes. Indeed, cluster A has 82.97 mean slowdown in the ``Free'' tier,
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cluster G has 19.06 and 14.57 mean slowdown in the ``BEB'' and ``Production''
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tier respectively, and Cluster D has 12.04 mean slowdown in its ``Free'' tier.
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\subsection{Spatial Impact: Resource Waste}
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\input{figures/spatial_resource_waste}
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\input{figures/spatial_resource_waste}
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In this analyzis we aim to understand how physical resources of machines
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in the Borg cluster are used to complete tasks. In particular, we compare how
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CPU and Memory resource allocation and usage are distributed among tasks based
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on their termination
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type.
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Due to limited computational resources w.r.t.\ the data analysis process, the
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resource usage for clusters E to H in the 2019 traces is missing. However, a
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comparison between 2011 resource usage and the aggregated resource usage of
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clusters A to D in the 2019 traces can be found in
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figure~\ref{fig:spatialresourcewaste-actual}. Additionally, a
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cluster-by-cluster breakdown for the 2019 data can be found in
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figure~\ref{fig:spatialresourcewaste-actual-csts}.
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From these figures it is clear that, compared to the relatively even
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distribution of used resources in the 2011 traces, the distribution of resources
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in the 2019 Borg clusters became strikingly uneven, registering a combined
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86.29\% of
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CPU resource usage and 84.86\% memory usage for \texttt{KILL}ed tasks. Instead,
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all other task termination types have a significantly lower resource usage:
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\texttt{EVICT}ed, \texttt{FAIL}ed and \texttt{FINISH}ed tasks register respectively
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8.53\%, 3.17\% and 2.02\% CPU usage and 9.03\%, 4.45\%, and 1.66\% memory usage.
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This resource distribution can also be found in the data from individual
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clusters in figure~\ref{fig:spatialresourcewaste-actual-csts}, with always more
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than 80\% of resources devoted to \texttt{KILL}ed tasks.
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With more than 98\% of CPU and memory resources used by ultimately
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non-successful tasks, it is clear the spatial resource waste is high in the 2019
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traces.
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\textbf{TBD figure~\ref{fig:spatialresourcewaste-requested}}
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\input{figures/table_iii} % has table III and table IV in it
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\input{figures/table_iii} % has table III and table IV in it
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\input{figures/figure_5}
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\input{figures/figure_5}
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\hypertarget{reserved-and-actual-resource-usage-of-tasks}{%
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\subsection{Reserved and actual resource usage of
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tasks}\label{reserved-and-actual-resource-usage-of-tasks}}
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Refer to figures \ref{fig:spatialresourcewaste-actual} and
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\ref{fig:spatialresourcewaste-requested}.
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\textbf{Observations}:
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\begin{itemize}
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\item
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Most (mesasured and requested) resources are used by killed job, even
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more than in the 2011 traces.
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\item
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Behaviour is rather homogeneous across datacenters, with the exception
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of cluster G where a lot of LOST-terminated tasks acquired 70\% of
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both CPU and RAM
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\end{itemize}
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Refer to figure \ref{fig:tableIII}.
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Refer to figure \ref{fig:tableIII}.
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\textbf{Observations}:
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\textbf{Observations}:
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