Reliable Scheduling Again – just a single hit onto a very large area

DAIMIHenrik Bærbak Christensen2 Literature Christensen and Eriksen 2003: –An Architectural Style for Closed-loop Process- Control –Center for Pervasive Computing Tech Report Van der Linden 2007 –Dependability Experience in Philips

DAIMIHenrik Bærbak Christensen3 Center for Object Technology Late 1990’s I was involved in the Center for Object Technology Purpose: –The overall goal of Centre for Object Technology (COT) is to carry out research, development and technology transfer in the area of object-oriented software construction. The activities in COT are based on actual needs from the participating industrial partners.

DAIMIHenrik Bærbak Christensen4 One Case Danfoss was very happy about the outcome and continued into a new project together with us. Purpose –Develop a new embedded motor control system Basically a closed-loop control system: –Sample process variable, calculate response, enact –Architectural Drivers Performance: –Extremely high sampling rate to provide ultra high motor control Modifiability –Plug-n-play configuration of distributed system

DAIMIHenrik Bærbak Christensen5 Deployment view, Archecture 1 Digital Input Card CC A/D Card Discrete Input Sampling Input Sampling Store Event Store Reference Handler Feedback Handler Command Handler Reference (setpoint): The freq. the motor must operate at. Feedback (input): Measured freq.

DAIMIHenrik Bærbak Christensen6 Problem Problem: –Handling of state information in feedback closed-loop process-control distributed embedded systems (inhale!) –Hard deadlines for state information delivery –Very short control intervals –Need for secure synchronisation of input variables –Minimal memory footprint –Focus on maintainability of solution

DAIMIHenrik Bærbak Christensen7 First Idea The project was called OO-Dist = Object- oriented Distributed (something). Proposal: Distributed Objects and RMI/CORBA like semantics.

DAIMIHenrik Bærbak Christensen8 Deployment view, Archecture 1 Digital Input Card CC A/D Card Discrete Input Sampling Input Sampling Store Event Store Reference Handler Feedback Handler Command Handler Commandhandler:run() { SamplingStore ss1 = lookup(“s1”); double v = ss1.fetchValue(); …

DAIMIHenrik Bærbak Christensen9 Scheduling … is problematic. Timing is everything! –Must know time of sampling precisely! Otherwise the control calculations become wrong –Must finish motor control tasks within a N millisecond cycle Otherwise we do not control the motor precisely CC Reference Handler Feedback Handler Command Handler

DAIMIHenrik Bærbak Christensen10 Global clock Time is divided into control intervals The global clock ensure that all nodes agree on start time of these periods Node 1 Node 2 Node 3 Node 4 control interval i control interval i+1

DAIMIHenrik Bærbak Christensen11 A scenario P-Node 1 P-Node 2 C-Node Input signal - early computation Store - early computation Consumer – fixed time computation All stores are consistent PC-Node

DAIMIHenrik Bærbak Christensen12 Challenges Concurrency –In each device there are concurrent tasks, and how to ensure consistency of read/written state variables? Timing –How to guaranty that all input/feedback variables are read, stable (all nodes agree), and available in time for the control algorithm to process them –How to guaranty that there is enough time within each control interval to finish the control algorithm

DAIMIHenrik Bærbak Christensen13 A Proposal Blackboard architecture, using CAN bus Digital Input Card CC A/D Card Discrete Input Sampling Input Sampling Store Event Store Reference Handler Feedback Handler Command Handler BlackBoard

DAIMIHenrik Bærbak Christensen14 A Proposal All communication between processes/objects is only via reading or writing state variables in the distributed store –i.e. no ‘method calls’ between (remote) objects Strict sequence of each task’s run method –A.step(); B.step(); C.step(); [start over again once the next control interval starts]

DAIMIHenrik Bærbak Christensen15 Scenario A feedback value is read (using one node) and used by the motor control (on another node).

DAIMIHenrik Bærbak Christensen16 What do we gain? No synchronization problems –CAN bus ensures distributed store consistency across all nodes –No concurrent access due to strict sequencing of each process’ execution Strict timing analysis possible –Read/write variable is constant time, thus time budget of each task can be made isolated Compare if all components could call methods in all other components!!! Require global analysis –Task sequencing under strict control

DAIMIHenrik Bærbak Christensen17 What do we gain? Performance –The main architectural driver –Simple analysis can guaranty the deadlines are met Modifiability –Distributed system, adding new devices is simple –Configurable set of tasks –Simple component model, easy to understand and reason about Testability –Easy to test components on a PC rather than on embedded device

Relations Philips Experience

DAIMIHenrik Bærbak Christensen19 Virtual Processors van der Linden describe virtual processors: –Processes are assigned a category (own, MMI, …) –Specific time is guarantied for each category Round robin scheduling: –Round-robin (RR) is one of the simplest scheduling algorithms for processes in an operating system, which assigns time slices to each process in equal portions and in circular order, handling all processes without priority.scheduling algorithmsprocessesoperating systemtime slicespriority –If time slice not used, handed over to other categories as outlined

DAIMIHenrik Bærbak Christensen20 What do they gain? No starvation/competition between processes –Each category has its own “virtual processor” Even in overload situations, core processing is guarantied –As e.g. background tasks cannot steal processing power from OWN tasks –Operators/users can always interact with device

DAIMIHenrik Bærbak Christensen21 What they both require! That you can write your own process scheduler! Most OS view this as such an intimate detail that you cannot alter it –Instead you express yourself in complex concepts like priorities etc., but these are not easy to translate into hard-real time constraints

DAIMIHenrik Bærbak Christensen22 Summary If dependability rely in hard-real time processing –Like controlling the motor of a crane holding 100 tons of cargo above the head of workers… Then the scheduling algorithms and the architecture / component model becomes vital for dependability and performance Traditional OS scheduling models not always the obvious choice.