The document discusses scheduling techniques to reduce processor energy use in macOS, focusing on the evaluation of various strategies for optimizing CPU power consumption. The research analyzes performance impacts and energy savings from different scheduling strategies, concluding that a composite strategy (referred to as 'big') provides the best energy savings while maintaining workload performance. Although the findings offer valuable insights, the relevance of this research to modern systems is limited.

1. Scheduling techniques for reducing processor
energy use in MacOS
Jacob R. Lorch and Alan Jay Smith
CSL704 – Advanced Operating Systems
25/Jan/2019 – Class Presentation
Anurag Banerjee (2018CSM1007)

2. Introduction
Background
Research Problem
Data
Methodologies(Terms/Strategies/Tools)
Results
Conclusion
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3. Introduction
 What components consume power?
 CPU, fan, physical drives
 Components depend on each other
fan on CPU etc.
 When on battery, high power consumption is undesirable
 Battery life
 Fan noise
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4. Background
 Work set in late 90’s
 Focuses on PowerPC Duo 230 and 280c
 Runs on System 7 OS later rebranded as Mac OS
 It is a single user, single tasking OS
 Single memory space for OS and user processes
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5. Background (contd…)
 “Macintosh lacks multitasking, but, tries to fake it…” – Byte, Sept. 1986
 Uses co-operative multitasking – non-preemptive
 Rapid process switching, when active window changes
 Regular cycling of applications
 Switching controlled by applications not OS
 One process crashes – OS can crash
 MacOS X Mojave is latest, renders most of this research obsolete
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6. Research Problem
 CPU major power consumer in System 7 or Mac OS 7
 Does busy waiting
 Identify and put CPU in low power state
 This task is non-trivial
 Heuristics to identify busy wait – parametric scheduling
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7. Data
 Trace-driven simulation data was collected from 6 Apple Computers, Inc. Engineers
 Workload focused on engineer’s usage pattern
 6 users, less data – can’t generalize results with high confidence
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8. Methodologies
 The optimum scheduling results are calculated theoretically
 All strategies compared against it
 The current scheduling strategy is compared with new strategies proposed by
authors
 Comparison is done for processor energy savings and performance impact
 For evaluation, performance impact of current strategy is taken as baseline
 Scheduling is event driven, in a non-preemptive environment
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9. Terms
 Performance Impact: percent increase in workload runtime
 Greediness Threshold: Count of CPU gain and yield without doing useful work
 Block: when process has no event to process
 Cooperative Multitasking: Processor only yields control when it wants to
 Quantum: period between CPU assignment and yield of control
 Event or Activity:
 user input,
 I/O device read or write,
 any change in the appearance of the cursor,
 any time spent with the cursor as a watch
 Turns on sound chip
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10. Strategies
 Current Strategy(C): inactivity timer based – 2s no activity, 15s no I/O
 Why inactivity timer? high overhead in CPU on/off
 Schedule process even when not ready (OS)
 Process can request CPU even if nothing to do (application)
 Simple Scheduling Technique(I): do not schedule a process, when it requested to
be blocked (i.e., until conditions are fulfilled)
 Greediness Technique(G): heuristic to decide if process making un-necessary
request and block it
 Sleep extension technique(S): constant factor * sleep-times
 Basic Strategy(B): Turn off processor when no process is available to run
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11. Tools Used
 IdleTracer: collects traces of events to simulate different strategies (authors
created)
 ItmSim: simulates current Mac OS power management strategy
 AsmSim: to simulate the proposed strategies
 Process behavior on modified sleep time ignored
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12. Results
(per strategy)
 Optimal: 82% energy saving – 17.67% of useful work in 29.56 hours of trace
 *(ES – energy saving, PI – performance impact)
 C: 28.79% ES with 1.84% PI
 Recovers 35% of idle time
 B: 31.98% ES with 0% PI
 Recovers 39% of idle time
 BI: 47.10% ES with 1.08% PI
 Recovers 57% of idle time
 BIS: 51.72% ES for 1.84% PI, sleep multiplier of 2.25
 BIG: 66.18% ES for 1.84% PI, forced sleep period of 0.52 and greediness threshold
of 61
 Recovers 80% of idle time
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13. Results
(per strategy)
 BIGS: BIG is best BIGS for 1.84% PI
 If sleep multiplier increased, bad results
 changes on increasing PI threshold
 Results have low sensitivity to these parameters
 BIG > BIR > BIS > BI > C for energy saving
 BIR – process requests a sleep time of zero, with probability p we force it to sleep for a period
f instead
 For user 2, BIG > C > B
 Finder – yield with sleep time zero
 Greediness technique helps
 Sleep extension technique useless
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14. 14

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16. Results
(discussions)
 Less data, possibility of type II error (when truth is rejected)
 Useful quanta considered non-useful or other way round
 Composite strategy BIG better than current
 A: Average total Power consumption
 P: Power consumed by components that remain on
 S: Power saving by turning CPU off
 𝐴 = 𝑃 +
1−𝐸𝑆
1+𝑃𝐼
𝑆
 For given workload, current strategy consumed A = 8.27 W
 For given workload, BIG strategy consumed A = 6.89 W
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17. 17 Reciprocal of slope of the curve is an indicator of efficiency

18. Conclusion
 “designers of OS and applications for single-user systems are not generally
concerned with rigorous management of processor time”
 Rather compactness of code and short development cycle
 Not scheduling non-useful quanta contributes to increased energy saving
 Delay in scheduling useful quanta (due to sleep etc.) causes performance impact
 This work is good for academic purposes, quite obsolete for present day
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19. Future Research
 Actually implement it in the OS and observe benefits in real time
 Gather more trace data
 Applicability to other single user OS (like those of Microsoft)
 It may be re-emphasized that this direction of research is no longer relevant
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20. Thank You
 Questions ?
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