It is hard to orchestrate the smooth cooperation of the individual components, especially during maintenance of the entire \gls{IOT} application.
\Gls{TOP} is a declarative programming paradigm designed to describe multi-tiered interactive systems from a single source.
Such a tierless system prevents the orchestration problems of the tiered approach.
+The type system of the host language checks the \gls{ITASK} and \gls{MTASK} components and their interaction.
However, it is not straightforward to run \gls{TOP} systems on resource-constrained devices such as edge devices.
-The type system of the host language checks the \gls{ITASK} and \gls{MTASK} components as well as their interaction.
The \gls{MTASK} system bridges this gap by providing a \gls{TOP} programming language for edge devices.
It is a full-fledged \gls{TOP} language hosted in a tiny \gls{FP} language.
It integrates seamlessly in \gls{ITASK}, a \gls{TOP} system for interactive web applications.
In \gls{ITASK}, abstractions are available for the gritty details of interactive web applications such as program distribution, web applications, data storage, and user management.
The \gls{MTASK} system abstracts away of all technicalities specific to edge devices such as communication, abstractions for sensors and actuators, interrupts and (multi) task scheduling.
-With \gls{ITASK} and \gls{MTASK}, all layers of an \gls{IOT} system can be programmed from a single declarative specification.
+When \gls{MTASK} is used together with \gls{MTASK}, all layers of the \gls{IOT} application are programmed from a single declarative specification.
Any device equipped with the \gls{MTASK} \gls{RTS} can be used in the system and dynamically receive tasks for execution.
This domain-specific \gls{OS} only is uploaded once, hence saving precious write cycles on the program memory.
\section{Related work}
The novelties of the \gls{MTASK} system can be compared to existing systems in several categories.
It is an interpreted (\cref{sec:related_int}) \gls{TOP} (\cref{sec:related_top}) \gls{DSL} (\cref{sec:related_dsl}) that may seem similar at first glance to \gls{FRP} (\cref{sec:related_frp}), it is implemented in a functional language (\cref{sec:related_fp}) and due to the execution semantics, multitasking is automatically supported (\cref{sec:related_multi}).
-\Cref{sec_t4t:TiredvsTierless} contains an elaborate related work section regarding tierless systems in general.
+\Cref{sec_t4t:TiredvsTierless} contains an elaborate related work section regarding tierless systems.
\subsection{Interpretation}\label{sec:related_int}
There are a myriad of interpreted programming languages available for more powerful edge devices.
Another interpretation solution for the tiniest devices is Firmata, a protocol for remotely controlling the microcontroller using a server as the interpreter host \citep{steiner_firmata:_2009}.
\citet{grebe_haskino:_2016} wrapped this in a remote monad for integration with \gls{HASKELL} that allowed imperative code to be interpreted on the microprocessors.
Later this system was extended to support multithreading as well, stepping away from Firmata as the basis and using their own \gls{RTS} \citep{grebe_threading_2019}.
-It differs from our approach because continuation points need to be defined by hand there is no automatic safe data communication.
+It differs from our approach because it is required to mark continuation points by hand and there is no automatic safe data communication.
\Citet{baccelli_reprogramming_2018} provide a single language \gls{IOT} system based on the RIOT \gls{OS} that allows runtime deployment of code snippets called containers.
Both client and server are written in JavaScript.
However, there is no integration between the client and the server other than that they are programmed from a single source.
-Mat\`e is an example of an early tierless sensor network framework where devices are provided with a virtual machine using TinyOS for dynamic provisioning \citep{levis_mate_2002}.
+Mat\`e is an example of a tierless framework for sensor networks where devices run a virtual machine using TinyOS for dynamic provisioning \citep{levis_mate_2002}.
\subsection{DSLs}\label{sec:related_dsl}
Many \glspl{DSL} provide higher-level programming abstractions for microcontrollers, for example providing strong typing or memory safety.
There are many solutions to overcome this problem in imperative languages.
If the host language is a functional language (e.g.\ the aforementioned scheme variants) multitasking can be achieved without this burden relatively easy using continuation style multiprocessing \citep{wand_continuation-based_1980}.
Writing in this style is complicated and converting an existing program in this continuation passing style results in relatively large programs.
-Furthermore, there is no built-in thread-safe communication possible between the tasks.
-A \gls{TOP} or \gls{FRP} based language is superior to manual threading because the programmer is not required to explicitly define continuation points.
+Moreover, there is no built-in thread-safe communication possible between the tasks.
+A \gls{TOP} or \gls{FRP} language is superior to manual threading because the programmer is not required to explicitly define continuation points.
Regular preemptive multithreading is too memory intensive for smaller microcontrollers and therefore not suitable.
Manual interleaving of imperative code can be automated to certain extents.
-Solutions often require an \gls{RTOS}, have a high memory requirement, do not support local variables, no thread-safe shared memory, no composition or no events as described in \cref{tbl:multithreadingcompare}.
-This table extends a comparison table with various solutions to multitasking to \gls{MTASK} in the relevant categories.
+Solutions often require an \gls{RTOS}, have a high memory requirement, do not support local variables, no thread-safe shared memory, no composition, or no events as described in \cref{tbl:multithreadingcompare}.
+This table extends the comparison table with \gls{MTASK} in the relevant categories.
\begin{table}
\begin{threeparttable}
\Gls{TOP} allows for more complex collaboration patterns than \gls{FRP} \citep{wang_maintaining_2018}.
Consequently, \gls{TOP} is unable to provide strong guarantees on memory usage, something \gls{FRP} is capable of.
For example, arrowised \gls{FRP} can give guarantees on upper memory bounds \citep{nilsson_functional_2002}.
-
The way \gls{FRP}, and for that matter \gls{TOP}, systems are programmed stays close to the design when the domain matches suits the paradigm.
The \gls{IOT} domain seems to suit this style of programming very well in just the device layer but also for entire \gls{IOT} systems.
For example, Potato is an \gls{FRP} language for building entire \gls{IOT} systems using powerful devices such as the Raspberry Pi leveraging the Erlang \gls{VM} \citep{troyer_building_2018}.
It requires client devices to be able to run the Erlang \gls{VM} which makes it unsuitable for low memory environments.
-
The emfrp language compiles a \gls{FRP} specification for a microcontroller to \gls{C} code \citep{sawada_emfrp:_2016}.
The \gls{IO} part, the bodies of some functions, still need to be implemented.
These \gls{IO} functions can then be used as signals and combined as in any \gls{FRP} language.
Due to the compilation to \gls{C} it is possible to run emfrp programs on tiny computers.
However, in contrast to in \gls{MTASK}, the tasks are not interpreted and there is no automated communication with a server.
-
Other examples are CFRP \citep{suzuki_cfrp_2017}, XFRP \citep{shibanai_distributed_2018}, Juniper \citep{helbling_juniper:_2016}, Hailstorm \citep{sarkar_hailstorm_2020}, Haski \citep{valliappan_towards_2020}, arduino-copilot \citep{hess_arduino-copilot_2020}.
\subsection{Task-oriented programming}\label{sec:related_top}
The future and related work for the security of \gls{MTASK} and tierless systems is more thoroughly presented in \cref{ssec_t4t:security}.
\subsection{Advanced edge devices techniques}
-Edge devices produce a lot of data and it is not always effective to send this data to the server for processing.
+Edge devices produce a lot of data.
+It is not always effective to send this data to the server for processing.
Leaving the produced data and computations on the edge device is called edge computing \citep{shi_edge_2016}.
The \gls{MTASK} system exhibits many properties of edge computing because it is possible to run entire workflows on the device.
However, it is interesting to see how far this can be extended.
Examples of such blocks are \gls{I2C} transmissions or calculations that rely on recent sensor data.
In \gls{MTASK}, all work expressed by tasks is already split up in atomic pieces of work, i.e.\ the work is a side effect of rewriting.
Furthermore, creating checkpoints is fairly straightforward as \gls{MTASK} tasks do not rely on any global state---all information required to execute a task is stored in the task tree.
-It is interesting to see what \gls{TOP} abstraction are useful for intermittent computing and what solutions are required to make this work.
+It is interesting to see what \gls{TOP} abstractions are useful to support intermittent computing properly and what solutions are required to make it work.
Mesh networks allow for communication not only to and fro the device and server but also between devices.
The \gls{ITASK} system already contains primitives for distributed operation.
For example, it is possible to run tasks or share data with \glspl{SDS} on different machines.
It is interesting to investigate how this networking technique can be utilised in \gls{MTASK}.
-\Glspl{FPGA} have been becoming cheaper and faster recently, allowing for purely functional code to be translated to \glspl{FPGA} code efficiently \citep{baaij_digital_2015}.
+\Glspl{FPGA} are highly customisable integrated chips consisting of programmable gates.
+Promising research has gone into translating purely functional code to \gls{FPGA} configurations \citep{baaij_digital_2015}.
It would be interesting to see how and whether (parts of) \gls{TOP} programs or the functionality of the \gls{MTASK} \gls{OS} could be translated to \gls{FPGA} specifications.
\subsection{Formal semantics}
It could be fruitful to investigate which workflows cannot be specified with the limited set of combinators available in \gls{MTASK}.
Furthermore, it is unclear whether all derived combinators from \gls{ITASK} can be expressed in terms of \gls{MTASK} combinators.
\Citet{van_der_aalst_workflow_2003} defines a benchmark set of workflow patterns.
-It is interesting to see which patterns can be implemented with \gls{MTASK}, and what additional combinators are needed.
+It is interesting to see which patterns can already be implemented with just \gls{MTASK}, which require a round-trip with the server, and what additional combinators would be needed.
Editors are a crucial part of \gls{TOP}.
In \gls{MTASK}, sensors can be seen as read-only shared editors that are updated by the system.
repositories / phd-thesis.git / blobdiff