Scheduling. Purpose of scheduling. Scheduling. Scheduling. Concurrent & Distributed Systems Purpose of scheduling.

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1 427 Concurrent & Distributed Systems Uwe R. Zimmer - The Australian National University 429 Motivation and definition of terms Purpose of scheduling 2017 Uwe R. Zimmer, The Australian National University page 429 of 759 (chapter 6: up to page 458) 428 References for this chapter [ Ben2006 ] Ben-Ari, M Principles of Concurrent and Distributed Programming second edition, Prentice-Hall 2006 [ Stallings2001 ] Stallings, William Operating Systems Prentice Hall, 2001 [AdaRM2012] Ada Reference Manual - Language and Standard Libraries; ISO/IEC 8652:201x (E) 2017 Uwe R. Zimmer, The Australian National University page 428 of 759 (chapter 6: up to page 458) 430 Motivation and definition of terms Purpose of scheduling Two scenarios for scheduling algorithms: 1. Ordering resource assignments (CPU, network access, ). live, on-line application of scheduling algorithms. 2. Predicting system behaviours under anticipated loads. simulated, off-line application of scheduling algorithms. Predictions are used: at compile : to confirm the feasibility of the system, or to predict resource needs, at run : to permit admittance of new requests or for load-balancing, 2017 Uwe R. Zimmer, The Australian National University page 430 of 759 (chapter 6: up to page 458)

2 431 Motivation and definition of terms Criteria Performance criteria: Predictability criteria: Process / user perspective: minimize the minimize deviation from given Waiting minima / maxima / average / variance value / minima / maxima Response minima / maxima / average / variance value / minima / maxima / deadlines Turnaround minima / maxima / average / variance value / minima / maxima / deadlines System perspective: maximize the Throughput minima / maxima / average Utilization CPU busy 2017 Uwe R. Zimmer, The Australian National University page 431 of 759 (chapter 6: up to page 458) 433 Definition of terms Time scales of scheduling pre-emption or cycle done ready Short-term dispatch executing CPU ready, suspended suspend (swap-out) suspend (swap-out) swap-in blocked, suspended unblock swap-out Medium-term blocked block or synchronize 2017 Uwe R. Zimmer, The Australian National University page 433 of 759 (chapter 6: up to page 458) 432 Definition of terms Time scales of scheduling pre-emption or cycle done ready Short-term dispatch executing CPU blocked block or synchronize 2017 Uwe R. Zimmer, The Australian National University page 432 of 759 (chapter 6: up to page 458) 434 Definition of terms Time scales of scheduling Long-term pre-emption or cycle done admit batch ready creation Short-term dispatch executing CPU terminate. ready, suspended suspend (swap-out) suspend (swap-out) swap-in blocked, suspended unblock swap-out Medium-term blocked block or synchronize 2017 Uwe R. Zimmer, The Australian National University page 434 of 759 (chapter 6: up to page 458)

3 435 Requested resource s Tasks have an average between instantiations of T i and a constant computation of C i 2017 Uwe R. Zimmer, The Australian National University page 435 of 759 (chapter 6: up to page 458) 437 First come, first served () Waiting : 0..11, average: 5.4 Turnaround : 3..12, average: 8.0 In this example: the average waiting s vary between 5.4 and 5.9 the average turnaround s vary between 8.0 and 8.4 Shortest possible maximal turnaround! 2017 Uwe R. Zimmer, The Australian National University page 437 of 759 (chapter 6: up to page 458) 436 First come, first served () Waiting : 0..11, average: 5.9 Turnaround : 3..12, average: 8.4 As tasks apply concurrently for resources, the actual sequence of arrival is non-deterministic. hence even a deterministic scheduling schema like can lead to different outcomes Uwe R. Zimmer, The Australian National University page 436 of 759 (chapter 6: up to page 458) 438 Round Robin () Waiting : 0..5, average: 1.2 Turnaround : 1..20, average: 5.8 Optimized for swift initial responses. Stretches out long tasks. Bound maximal waiting! (depended only on the number of tasks) 2017 Uwe R. Zimmer, The Australian National University page 438 of 759 (chapter 6: up to page 458)

4 439 Feedback with 2 i pre-emption intervals Implement multiple hierarchical ready-queues. Fetch processes from the highest filled ready queue. Dispatch more CPU for lower priorities (2 i units). admit priority 0 priority 1 CPU priority i dispatch 2 0 dispatch 2 1 dispatch 2 i executing Processes on lower ranks may suffer starvation. New and short tasks will be preferred Uwe R. Zimmer, The Australian National University page 439 of 759 (chapter 6: up to page 458) 441 Feedback with 2 i pre-emption intervals - overlapping Waiting : 0..3, average: 0.9 Turnaround : 1..45, average: 7.7 Optimized for swift initial responses. Prefers short tasks and long tasks can suffer starvation. Long tasks are delayed until all queues run empty! 2017 Uwe R. Zimmer, The Australian National University page 441 of 759 (chapter 6: up to page 458) 440 Feedback with 2 i pre-emption intervals - sequential Waiting : 0..5, average: 1.5 Turnaround : 1..21, average: 5.7 Optimized for swift initial responses. Prefers short tasks and long tasks can suffer starvation. Very short initial response s! and good average turnaround s Uwe R. Zimmer, The Australian National University page 440 of 759 (chapter 6: up to page 458) 442 Shortest job first Waiting : 0..11, average: 3.7 Turnaround : 1..14, average: 6.3 Optimized for good average performance with minimal task-switches. Prefers short tasks but all tasks will be handled. Good choice if computation s are known and task switches are expensive! 2017 Uwe R. Zimmer, The Australian National University page 442 of 759 (chapter 6: up to page 458)

5 443 Shortest job first Waiting : 0..10, average: 3.4 Turnaround : 1..14, average: 6.0 Can be sensitive to non-deterministic arrival sequences Uwe R. Zimmer, The Australian National University page 443 of 759 (chapter 6: up to page 458) 445 Shortest Remaining Time First () Waiting : 0..6, average: 0.7 Turnaround : 1..21, average: 4.4 Optimized for good averages. Prefers short tasks and long tasks can suffer starvation.. Better averages than Feedback scheduling but with longer absolute waiting s! 2017 Uwe R. Zimmer, The Australian National University page 445 of 759 (chapter 6: up to page 458) 444 Highest Response Ration C Wi+ Ci i First () Waiting : 0..9, average: 4.1 Turnaround : 2..13, average: 6.6 Blend between Shortest-Job-First and First-Come-First-Served. Prefers short tasks but long tasks gain preference over. More task switches and worse averages than but better upper bounds! 2017 Uwe R. Zimmer, The Australian National University page 444 of 759 (chapter 6: up to page 458) 446 Comparison (in order of appearance) Waiting s Turnaround s 2017 Uwe R. Zimmer, The Australian National University page 446 of 759 (chapter 6: up to page 458)

6 Uwe R. Zimmer, The Australian National University page 449 of 759 (chapter 6: up to page 458) G Providing upper bounds to turnaround s G No tasks are left behind Uwe R. Zimmer, The Australian National University page 450 of 759 (chapter 6: up to page 458) Turnaround s Waiting s G Providing good average performance G High throughput systems 40 Turnaround s Comparison by shortest average turnaround Comparison by shortest maximal turnaround Waiting s page 448 of 759 (chapter 6: up to page 458) Uwe R. Zimmer, The Australian National University page 447 of 759 (chapter 6: up to page 458) 0 Turnaround s Waiting s G Providing short average waiting s G Very swift response in most cases Uwe R. Zimmer, The Australian National University G Providing upper bounds to waiting s G Swift response systems Turnaround s Waiting s Comparison by shortest average waiting Comparison by shortest maximal waiting

7 451 Selection Preemption Comparison overview Waiting Turnaround Preferred jobs Starvation possible? Methods without any knowledge about the processes max ( W i ) no long equal share yes bound FB priority queues yes very short long average & short maximum good average & large maximum short average & long maximum equal no short no short no Methods employing computation C i and elapsed E i min ( C i ) no medium medium short yes W C C + max ( ) no i i i controllable compromise controllable compromise controllable no min ( Ci- Ei) yes very short wide variance short yes 2017 Uwe R. Zimmer, The Australian National University page 451 of 759 (chapter 6: up to page 458) 453 Temporal scopes Common attributes: Minimal & maximal delay after creation Maximal elapsed Maximal execution Absolute deadline Task i max. delay min. delay max. exec. max. elapse deadline created 2017 Uwe R. Zimmer, The Australian National University page 453 of 759 (chapter 6: up to page 458) 452 Towards predictable scheduling Task requirements (Quality of service): Guarantee data flow levels Guarantee reaction s Guarantee deadlines Guarantee delivery s Provide bounds for the variations in results Examples: Streaming media broadcasts, playing HD videos, live mixing audio/video, Reacting to users, Reacting to alarm situations, Delivering a signal to the physical world at the required, 2017 Uwe R. Zimmer, The Australian National University page 452 of 759 (chapter 6: up to page 458) 454 Temporal scopes Common attributes: Minimal & maximal delay after creation Maximal elapsed Maximal execution Absolute deadline Task i max. delay min. delay max. exec. max. elapse deadline created activated 2017 Uwe R. Zimmer, The Australian National University page 454 of 759 (chapter 6: up to page 458) t t

8 455 Temporal scopes Common attributes: Minimal & maximal delay after creation Maximal elapsed Maximal execution Absolute deadline Task i max. delay min. delay elapse max. exec. max. elapse deadline created activated re-activated suspended terminated 2017 Uwe R. Zimmer, The Australian National University page 455 of 759 (chapter 6: up to page 458) 457 Common temporal scope attributes Temporal scopes can be: Periodic controllers, routers, schedulers, streaming processes, Aperiodic periodic on average tasks, i.e. regular but not rigidly d, Sporadic / Transient user requests, alarms, I/O interaction, Deadlines can be: Hard Firm Soft single failure leads to severe malfunction and/or disaster results are meaningless after the deadline only multiple or permanent failures lead to malfunction results are still useful after the deadline Semantics defined by application 2017 Uwe R. Zimmer, The Australian National University page 457 of 759 (chapter 6: up to page 458) 456 Temporal scopes Common attributes: execution Minimal & maximal delay after creation Maximal elapsed Maximal execution Absolute deadline Task i max. delay min. delay elapse max. exec. max. elapse deadline created activated re-activated suspended terminated 2017 Uwe R. Zimmer, The Australian National University page 456 of 759 (chapter 6: up to page 458) 458 Summary Basic performance scheduling Motivation & Terms Levels of knowledge / assumptions about the task set Evaluation of performance and selection of appropriate methods Towards predictable scheduling Motivation & Terms Categories & Examples 2017 Uwe R. Zimmer, The Australian National University page 458 of 759 (chapter 6: up to page 458) t t