Scheduling techniques for reducing processor energy use in MacOS

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)

Introduction
Background
Research Problem
Data
Methodologies(Terms/Strategies/Tools)
Results
Conclusion
2

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
3

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
4

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
5

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
6

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
7

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
8

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
9

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
10

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
11

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
 BI: 47.10% ES with 1.08% PI
 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
12

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
13

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
16

17 Reciprocal of slope of the curve is an indicator of efficiency

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
18

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
19

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