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-   TO DO
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publication_in_acm_format/fitsAll/comment.cut

-To Robert, for the bagels and explaining CMYK and color spaces.

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publication_in_acm_format/fitsAll/introduction.tex

-
-\section{Introduction}
-1 - Long running processes: Most services deployed on mobile phones or nodes use long 
-running processes. These are processes that have a fairly long or even interminable duration,
- and can mobilise several nodes for their complete execution. Also, they do not require direct 
- user interaction. They consist of several tasks deployed on different mobile nodes.
-  The tasks of long-running systems are cpu intensive and do not have a rigid time 
-  constraint like those of responsive applications; the node operating system can then
-   slow them down or delay their execution to increase the energy efficiency As an example,
-    sensing systems consist of ....... . Another example and that of Federated Learning is 
-    ....... There are also long running systems consisting of a single mobile node. The main 
-    task of this type of system can be the monitoring of the mobile or the installation of 
-    updates... 
-
-2 - Energy efficiency problem: These long running systems are energy intensive.
- One of the reasons for this is that the mobile node hardware was not originally designed
-  to run long running tasks [1].... 
-
-3 - Global solutions: To overcome this energy efficiency problem, 
-solutions proposed in the context of Federated learning, for example, propose algorithms 
-for scheduling long-running tasks on nodes. These algorithms aim to choose on which nodes 
-the tasks that make up the long-running process should be executed, its workload and possibly the duration of time allocated to it. 
- - But these global scheduling algorithms only optimise efficiency at the global level. They are therefore limited because once on the node, the long-running process tasks are inadequately handled by the operating system. Indeed, the operating system that is not adapted for these types of tasks does not execute them efficiently, hence the need for local solutions.... 
-
- 4 - Local solutions: Local solutions have been proposed to optimise the energy efficiency of cpu intensive tasks and to do this they influence their execution. They do this by modifying system parameters such as the frequency of the cores or their temperature. Others, like Meng Zhu [2], act directly on the system scheduler, executing these tasks only under certain conditions of the current socket of the task in question (to preserve the idle state...). 
-- The Android case: And even Android, the most used operating system in the world, does not have a scheduler adapted to long running tasks. To handle the tasks that come closest, application developers have to run them under conditions such as "connected to wifi or charging". But these conditions are not suitable because long running tasks can be offloaded, but not depending on asynchronous and unpredictable user phone usage conditions, but on as much as possible depending on the long running global system prossess. Knowing that the global system process include users from all over the Word that do not have sychronous and almost predictable usage habits.
-
-
-5 - Unfounded decision making: Although the proposed solutions present convincing results, 
-they are based on architectural principles that do not always hold in all contexts. 
-For example, Meng Zhu assumes that reducing the frequency of the core during the 
-execution of a long running task reduces energy consumption. But this assertion does not
- always hold true depending on whether you are on a big core or a little core ....[Add proove]. 
- Moreover, from one phone to another, the behaviour of the cpus can change: while increasing 
- the number of tasks on a single socket also increases the energy efficiency of the whole 
- phone on a google pixel 4 to 5g, doing the same action on a samsung galaxy S 8 does not have 
- a significant change on the energy efficiency [Graph already made].
-  We therefore assert that "one size does not fit all".   
-
-6 - Our framework: In view of all these previous limitations, we propose Fits'All, 
-a local scheduling system, adapted to each range of phone. For a given range of phones,
- Fits' all generates a very light task scheduling automata that can be easily integrated 
- into the scheduler and the operating system governors. 
-  [I plan to think in the next few days about the algorithm that does it.]
-
-
-7 - Main idea: Fits'all is based on a principle already used by the local solutions
- mentioned above. According to these solutions, before a scheduler takes the decision to
-  soberly schedule a task on core i, it must know the state of another core, for example 
-  core j. If the state of core j is suitable for core i it schedules the thread on core i, 
-  if not it does not schedule it. The task graph scheduling allows to know if two cores will
-   be in suitable states or not. It thus helps to know the interaction between the two cores. 
-   And to know this interaction, Fits'All uses the interpretation power of the kernel ridge
-    model for this purpose through the exploration of marginal effects [3] [4]. 
-  We therefore make the following contributions. 
-
-  \begin{itemize}
-    \item   A new method for interpreting the marginal effects of the kernel ridge model.
-     This new method allows to produce a scheduiling task graph that can be directly
-      integrated into the scheduler and the governor of the operating systems. 
-
-    \item  A framework named FitsAll, implementing this method for the google pixel 4a 5g 
-    and Samsung galaxy S8 phones [not yet finished as the task graph scheduling has not yet 
-    been produced].
-
-    \item A method for evaluating the task graph produced by FitsAll called Task Graph Score. 
-    The Task Graph Score is based on the comparison between the suggestions of the 
-    task graph and those produced by a test sample. This test sample is taken from
-     the actual experiments carried out.  
-    \item   An evaluation of the Task Graph Scheduling produced by FitsAll on a set of more than 1000 configurations experimentally tested on the above mentioned phones. The results are of the order of 0.9 on average for the R2 score and [To compute]% for the newly created Task Graph Score.  
-    
- 
-    \end{itemize}
-    
-
-
-
-
-
-

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-%% consistent spelling of the heading.
-\begin{acks}
-To Robert, for the bagels and explaining CMYK and color spaces.
-\end{acks}
-
-%%
-%% The next two lines define the bibliography style to be used, and
-%% the bibliography file.
-\bibliographystyle{ACM-Reference-Format}
-\bibliography{sample-base}
-
-
-%%
-%% If your work has an appendix, this is the place to put it.
-\appendix
-
-\section{Research Methods}
-
-\subsection{Part One}
-
-Lorem ipsum dolor sit amet, consectetur adipiscing elit. Morbi
-malesuada, quam in pulvinar varius, metus nunc fermentum urna, id
-sollicitudin purus odio sit amet enim. Aliquam ullamcorper eu ipsum
-vel mollis. Curabitur quis dictum nisl. Phasellus vel semper risus, et
-lacinia dolor. Integer ultricies commodo sem nec semper.
-
-\subsection{Part Two}
-
-Etiam commodo feugiat nisl pulvinar pellentesque. Etiam auctor sodales
-ligula, non varius nibh pulvinar semper. Suspendisse nec lectus non
-ipsum convallis congue hendrerit vitae sapien. Donec at laoreet
-eros. Vivamus non purus placerat, scelerisque diam eu, cursus
-ante. Etiam aliquam tortor auctor efficitur mattis.
-
-\section{Online Resources}
-
-Nam id fermentum dui. Suspendisse sagittis tortor a nulla mollis, in
-pulvinar ex pretium. Sed interdum orci quis metus euismod, et sagittis
-enim maximus. Vestibulum gravida massa ut felis suscipit
-congue. Quisque mattis elit a risus ultrices commodo venenatis eget
-dui. Etiam sagittis eleifend elementum.
-
-Nam interdum magna at lectus dignissim, ac dignissim lorem
-rhoncus. Maecenas eu arcu ac neque placerat aliquam. Nunc pulvinar
-massa et mattis lacinia.
-
-\end{document}
-\endinput
-%%
-%% End of file `sample-sigplan.tex'.