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Title: Saving Energy and Enhancing Quality of Service in Battery-Operated Medical and Medication Systems


1
Saving Energy and Enhancing Quality of Service in
Battery-Operated Medical and Medication Systems
  • Albert M. K. Cheng
  • Real-Time Systems Laboratory
  • Department of Computer Science
  • University of Houston, TX 77204, USA
  • Supported in part by NSF under Award No. 0720856.

2
Medical and Medication Devices
  • Medical and medication devices (MMDs) are
    increasingly controlled by computer systems with
    hardware and software components.
  • These devices are real-time systems with safety
    and timing requirements many are
    battery-operated.
  • They range from hard-real-time, embedded, and
    reactive systems such as pacemakers and
    vital-sign monitors to soft-real-time diagnosis
    instruments and medication dispensers.

3
Project Objectives
  • Apply (m,k)-firm constraints as a guide to
    increase the Quality-of-Service (QoS) and
    stability of medical devices.
  • Use a ventilator system as a motivating example
    to illustrate our approaches for improving its
    performance which translates into speedier
    recover for the patient.
  • One goal is to dynamically vary the ventilator
    assist rate by adapting it to the respiratory
    capability of the patient.
  • Manage and reduce power consumption in
    battery-operated medical systems using modified
    scheduling techniques.
  • Target battery-operated systems with Multiple
    Feasible Interval (MFI) jobs.
  • Consider systems with rechargeable batteries.

4
Ventilator System
  • A device for mechanically moving breathable air
    into and out of the lungs of a patient with
    respiratory difficulties, such as the inability
    to breathe on his/her own or breathing
    insufficiently caused by one or more ailments.
  • Attached to the patient via an endotracheal tube
    connected through the mouth to the trachea
    (larynx intubation) or a tracheotomy canula (a
    more comfortable and practical approach requiring
    minor surgery if connected for more than two
    weeks).
  • The ventilator pneumatically compresses the air
    reservoir several times per minute to deliver
    normal room air or a mixture of air and oxygen to
    the patient.

5
High-Level Diagram of a Ventilator System
6
Ventilator Monitors
  • Patient-related parameters such as air flow,
    pressure, and volume from sensors attached to the
    patient and to the connection between the
    ventilator and the patient as described above.
  • Ventilator functions such as power failure, air
    leakage, pneumatic system, and mechanical
    problems.
  • Backup power supply typically, system is
    connected to the power grid, but may need to be
    battery-operated in the field or during emergency
    black-out.
  • Oxygen tanks

7
Operation of Ventilator
  • A clinician (doctor, respiratory therapist, or
    nurse) can set the ventilator to provide full
    support where the patient does not initiate
    breaths,
  • Or to provide partial support in which case the
    patients inspiratory efforts trigger some or all
    the breaths delivered by the ventilator.

8
Ventilator Operation
  • In the assist-control mode of ventilation, the
    clinician manually sets a respiratory rate and
    volume of gas (or inspiratory pressure) for the
    patient.
  • The normal breaths per minute (BPM) for a healthy
    person is 12.
  • The ventilator rate is usually set at between 4
    and 12 breaths per minute (BPM).
  • Therefore, it is desirable to maintain a combined
    BPM of 12 from the ventilator-assist A and the
    patients natural breathing B, that is,
  • A B 12 BPM

9
State of the Practice
  • The clinician manually adjust the assist BPM and
    other output parameters on a periodic basis
    (usually once or twice a day).
  • Once this assist rate is set, the ventilator
    attempts to adapt its pressure and flow
    characteristics of the air delivered to the
    patient while keeping this preset assist BPM.
  • However, the ventilator does not dynamically
    adjust the assist BPM even if the patients
    breathing capability becomes stronger as a result
    of medication and other factors.
  • There may be an overuse of the ventilator assist.
    The patient has to wait until the clinician
    revisits in the next period in order for the
    assist rate to be adjusted.

10
Project Objectives
  • Make the ventilators assist rate dynamically
    adaptive to the breathing performance of the
    patient in real-time while maintaining a combined
    BPM of 12.
  • If the breathing capability of the patient is
    improving (even slightly), this adaptive
    ventilator performance would potentially shorten
    the assistive period, allowing the patient to
    have a speedier recovery.
  • Note that the patients breathing capability
    should not be measured by transient performance
    but by stable performance over an interval of
    time.

11
(m,k)-firm Scheduling
  • In the (m,k)-constrained model, m out of any k
    consecutive jobs deadlines must be met.
  • For instance, (1,1)-constrained represents the
    hard real-time requirement and (2,3)-constrained
    designates at most one deadline can be missed in
    any three consecutive job releases.
  • Our recent work (Lin and Cheng, RTCSA 2006) makes
    use of the surplus computing time once the
    original pre-defined (m,k) constraints have been
    satisfied.
  • We aim to complete additional important mandatory
    jobs (thus yielding finer granularity) rather
    than to run optional jobs.
  • To accomplish it, each task Ti associates with a
    class of finite levels of QoS (e.g., (m, k) OR
    (m1, k) OR OR (k, k)) and each level in the
    class has a specific reward called the
    Granularity of Quality of Service reward
    (GQoS-reward).

12
Modeling with (m,k)-firm Constraints
  • Can be readily modeled by the (m,k)-firm model
    with k being the length of this interval
    expressed as the number of total breathes by the
    patient and ventilator assist, and m being the
    number of breathes by the ventilator.
  • Here, m can vary from 0 (no ventilator assist) to
    k (full ventilator assist). As the patient
    improves his/her breathing capability, m becomes
    smaller, and thus the ventilator assist becomes
    lower.
  • Therefore, we have a sequence of (m,k)-firm
    constraints where m is a varying parameter over
    time.

13
Modeling the Adaptive Assist Behavior
  • Dynamically varying assist rate can be modeled by
    varying the parameter m in the (m,k) constraint,
    treating k as the window or interval length. In
    this model, m 0 means no ventilator assist, and
    m k means full ventilator support.
  • Furthermore, we are studying whether patterns can
    be used to provide better control of the air flow
    and pressure settings in real-time.

14
Partitioning Strategies for Sequence of Jobs
  • Currently, there are two main partitioning
    strategies for mandatory and optional jobs in a
    sequence.
  • The first is the deeply-red pattern proposed by
    Koren et al a job Tij, i.e., the jth instance of
    task Ti , is determined to be mandatory if 1 j
    mod ki mi (for mi lt ki. If mi ki, Ti is a hard
    real time task. If mi 0, all jobs of Ti are
    optional) otherwise, it is optional.
  • The second strategy is the evenly-distributed-mand
    atory pattern proposed by Ramanathan et al.
    According to this pattern, Tij is mandatory if
    and only if j (mi gt 0. If mi 0, all jobs of
    Ti are optional similarly) otherwise, it is
    optional.
  • We use the evenly-distributed-mandatory pattern
    in our work since it has been reported that it
    exhibits a relative good schedulability and
    stability.

15
Improving Fault Tolerance
  • In most ventilator systems, a backup processor is
    ready to run all critical tasks in the event of
    the failure of the primary processor.
  • If the backup processor has lower performance
    (for instance, a slower CPU speed) than the
    primary processor, it still guarantees the
    satisfaction of the deadlines of all critical
    tasks such as the ventilator assists but it may
    not be able to meet the deadlines of non-critical
    tasks, such as those for refreshing the digital
    displays of the systems parameters and settings.
  • Employing (m,k)-firm scheduling would allow us to
    ensure a higher QoS derived from the non-critical
    tasks and to achieve greater performance
    stability.

16
Scheduling Real-Time Tasks on Battery-Operated
Embedded Systems
  • Introduction
  • Real-time or embedded system.
  • Low power design for embedded systems.
  • Power-Aware Scheduling of Multiple Feasible
    Interval Jobs
  • Static method a Simulated Annealing (SA)
    approach.
  • Dynamic method an on-line greedy heuristic.
  • A leakage-aware method.

17
An Embedded System or Real-time System
  • Real-time system
  • Produces correct results in a timely manner.
  • Embedded system
  • computer hardware and software embedded as part
    of complete device to perform one or a few
    dedicated functions
  • often with real-time requirements.
  • Examples
  • MMDs, PDAs, Cell phones, GPS, etc.

18
Low Power Design for Real-Time Systems
  • Low power (energy) consumption is a key design
    for embedded systems
  • Batterys life during operation.
  • Reliability.
  • Size of the system.
  • Power-aware real-time scheduling
  • Minimize the energy consumption
  • Problem I Power-aware scheduling for multiple
    feasible interval jobs.
  • Achieving some goal while satisfying the
    real-time and/or energy constraints.
  • Problem II Real-time Task Assignment on
    Rechargeable Multiprocessor System.

19
Power-Aware Scheduling for Multiple Feasible
Interval Jobs
  • What is a Multiple Feasible Interval Job?
  • Dynamic Voltage Scaling (DVS or DFS)
  • A Motivational Example
  • Techniques to minimize the CPUs/System-wides
    energy consumption
  • Static
  • Dynamic
  • Experimental results.

20
Multiple Feasible Interval Job
  • Regular Real-time task/job
  • It has only one feasible interval for execution
    ready time, deadline, and a computation time
    demand.
  • Multiple Feasible Interval Job
  • It has more than one feasible interval.
  • A job can be executed in any of its feasible
    intervals.
  • A feasible interval is defined as I (S, E. The
    set of the feasible intervals of Ji is denoted by
    ? Ii, 1, Ii, 2, Ii, 3,, Ii, m.

21
Dynamic Voltage Scaling (DVS) Technique for
Real-Time Task
  • CPUs energy/power consumption is a convex
    function of the CPUs speed, e.g. P CV2f -gt P
    s3.
  • Slowing down CPUs speed reduces the energy usage
    for CPU.
  • Saving energy consumption V.S. Meeting deadline.
  • Reducing the CPUs speed as much as possible
    while meeting every tasks deadline.
  • A minimum constant speed is always an optimal
    solution (if possible).
  • If more than one speed are needed, a smooth
    selection is better.
  • For regular single instance real-time jobs with
    only one feasible interval, Yao designed an
    algorithm for computing the optimal solution.

22
A Motivational Example (EDF)
Job Feasible Intervals Comp. Time
J1 (1, 9 2
J2 (2, 7, (8, 13 2
J3 (1, 4, (5, 8, (9, 13 2
23
Formulation of the Problem
  • Problem Energy-MFIJ
  • Given a set of MFI jobs, finding a speed schedule
    on a single processor such that 1) every job in
    the set is schedulable and 2) the energy
    consumption by the jobs is minimized.
  • Determining whether a set of multiple feasible
    interval jobs is schedulable or not is NP-Hard.
  • Even though for a schedulable set of MFI jobs,
    the problem is still NP-Hard.

24
Determining the speed statically introduction
of the Simulated Annealing (SA) algorithm
  • Simulated Annealing Algorithm
  • simulates the process of annealing in metallurgy
    used to temper or harden metals and glass.
  • Repeat
  • An initial feasible solution is chosen as the
    starting point p, and then the cost or energy at
    this point is evaluated.
  • A neighbor, next, is chosen randomly as an
    alternative solution and the cost/energy of this
    neighbor is evaluated.
  • If the cost/energy of the neighbor is higher, the
    acceptance probability for accepting the neighbor
    solution is exponentially decreasing with the
    cost/energy difference and is slowly lowered with
    time. If the neighbor solution has lower cost,
    accept.

25
Determining the speed statically for Energy
MFIJ problem
  • An initial feasible schedule is given, and the
    total energy consumption by the schedule is
    calculated.
  • The neighbor solution in the algorithm is
    obtained by randomly selecting a job to
    re-determine its executing interval.
  • If the reselected interval is feasible, we
    calculate the energy of the schedule based on the
    new executing interval. The modification is
    accepted only if the new feasible schedule has
    less energy consumption or with a probability
    which decreases exponentially with the energy
    difference and is lowered in each iteration.

26
Algorithm Simulated Annealing Static
27
Exploiting on-line slack
  • The Worst Case Execution Time is too
    conservative, and typically the real-time jobs
    take a small fraction of their WCET.
  • A significant amount of energy can be saved by
    exploiting the dynamic slacks generated at
    run-time.
  • For MFI jobs, on-line slack can be used by
    fetching a job ahead for execution by
    re-selecting its executing interval.

28
An Example.
Job Feasible Intervals Comp. Time
J1 (1, 9 2
J2 (2, 7, (8, 13 2
J3 (1, 4, (5, 8, (9, 13 2
29
Two Rules to use on-line slacks
  • Rule 1 using the slack by upcoming jobs first.
  • using the slack as early as possible.
  • Rule 2 , fetching a job ahead for execution only
    when it is guaranteed to save energy.
  • It needs exponential time to find the optimal
    allocation.

30
A heuristic for Rule 2
31
An illustrative example for dynamic fetching
Job Feasible Intervals Comp. Time
J1 (1, 9 2
J2 (2, 7, (8, 13 2
J3 (1, 4, (5, 8, (9, 13 2
J4 (4.5, 8.5, (9, 14 3
32
Considering power consumption for leakage current
  • As VLSI technology marches towards deep submicron
    and nanoscale circuits operating at multi-GHz
    frequencies, the rapidly elevated leakage power
    dissipation will soon become comparable to, if
    not exceeding, the dynamic power consumption
  • Pleak I leak V
  • P Pdyn Pleak
  • A critical speed s s where P(s) P(s)s
  • Shut down the CPU when it is idle.
  • Shut-down overhead.

33
Techniques for reducing the overall energy usage
  • Only executing jobs at s or higher.
  • Shut-down CPU if an idle interval larger than the
    minimum idle interval exists.
  • A job can be pushed back by re-selecting its
    later feasible interval as its executing interval
  • only when it can execute at a speed not higher.
  • only when it will not cause any job missing the
    deadline.

34
Algorithm Leakage-aware
35
Results for SA algorithm
36
Results for Dynamic Fetching
37
Results for Leakage-Aware Algorithm
38
Real-time Task Assignment in Rechargeable
Multiprocessor Systems
  • Scheduling of frame-based real-time tasks in
    partitioning schemes for multiprocessor systems
    powered by rechargeable batteries.
  • In frame-based real-time systems, a set of tasks
    must execute in a frame, and the whole frame is
    repeated. This system model is widely used in
    real-time communication, real-time imaging and a
    lot of other real-time/embedded systems,
    including medical systems.
  • The problem for uniprocessor system has been
    studied in Allavena and Mosse 2001, in which an
    algorithm of complexity O(N) was proposed for
    determining the feasibility of a task set.
  • However, doing so in a rechargeable
    multiprocessor system is NP-Hard Lin and Cheng
    2008.
  • We propose heuristic and approximation
    algorithms. Simulation results have shown that
    our algorithms exhibit very good behavior.

Figure Algorithm for rechargeable single
processor Allavena and Mosse 2001
39
Real-time Task Assignment in Heterogeneous
Distributed Systems with Rechargeable Batteries
  • Our techniques to solve the problem are based on
    four heuristics, namely Minimum Schedule Length
    (MSL), Min-min Schedule Length (MmSL), Genetic
    Algorithm (GA), and Ant Colony Optimization
    (ACO).
  • While the modifications of the MSL, MmSL and GA
    approaches from their original implementation are
    somewhat straight-forward, we design a novel
    structure using ACO.
  • Performance comparisons of these four techniques
    are performed and the results are discussed in
    Lin and Cheng 2009.

40
RealEnergy a New Framework and Tool to Evaluate
Power-Aware Real-Time Scheduling Algorithms
Intel XScale/PXA255 Module
41
Example of the Measured Current using RealEnergy
42
Actual Energy Consumption Using DVS as meaured by
RealEnergy
43
Concluding Remarks
  • Achieve higher QoS in real-time/embedded systems
  • Reduce power consumption
  • Ensure stable power supply
  • Evaluate systems with actual implementations and
    measurements
  • Deliver actual benefit to society

44
References
  • J. Lin and A. M. K. Cheng, Maximizing Guaranteed
    QoS in (m,k)-firm Real-time Systems, Proc. 12th
    IEEE International Conference on Embedded and
    Real-Time Computing Systems and Applications
    (RTCSA), Sydney, Australia, Aug. 2006.
  • J. Lin, Y. H. Chen, and A. M. K. Cheng, "On-Line
    Burst Header Scheduling in Optical Burst
    Switching Networks,'' Proc. 22nd IEEE
    International Conference on Advanced Information
    Networking and Applications (AINA), Okinawa,
    Japan, 2008.
  • J. Lin and A. M. K. Cheng, Real-time Task
    Assignment in Recharegable Multiprocessor
    Systems, Proc. 14th IEEE International
    Conference on Embedded and Real-Time Computing
    Systems and Applications (RTCSA), Kaohsiung,
    Taiwan, Aug. 2006.
  • J. Lin and A. M. K. Cheng, Real-time Task
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  • J. Lin and A. M. K. Cheng, Real-time Task
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  • A. M. K. Cheng, Cyber-Physical Medical and
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45
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