1. CS 4284 Systems Capstone Godmar Back Resource Allocation & Scheduling

2. Resource Allocation and Scheduling

3. CS 4284 Spring 20133 Resource Allocation & Scheduling Resource management is primary OS function Involves resource allocation & scheduling –Who gets to use what resource and for how long Example resources: –CPU time –Disk bandwidth –Network bandwidth –RAM –Disk space Processes are the principals that use resources –often on behalf of users

4. CS 4284 Spring 20134 Preemptible vs Nonpreemptible Resources Nonpreemptible resources: –Once allocated, can’t easily ask for them back – must wait until process returns them (or exits) Examples: Locks, Disk Space, Control of terminal Preemptible resources: –Can be taken away (“preempted”) and returned without the process noticing it Examples: CPU, Memory

5. CS 4284 Spring 20135 Physical vs Virtual Memory Classification of a resource as preemptible depends on price one is willing to pay to preempt it –Can theoretically preempt most resources via copying & indirection Virtual Memory: mechanism to make physical memory preemptible –Take away by swapping to disk, return by reading from disk (possibly swapping out others) Not always tolerable –resident portions of kernel –Pintos kernel stack pages

6. CS 4284 Spring 20136 Space Sharing vs Time Sharing Space Sharing: Allocation (“how much?”) –Use if resource can be split (multiple CPUs, memory, etc.) –Use if resource is non-preemptible Time Sharing: Scheduling (“how long?”) –Use if resource can’t be split –Use if resource is easily preemptible

7. CS 4284 Spring 20137 CPU vs. Other Resources CPU is not the only resource that needs to be scheduled Overall system performance depends on efficient use of all resources –Resource can be in use (busy) or be unused (idle) Duty cycle: portion of time busy –Consider I/O device: busy after receiving I/O request – if CPU scheduler delays process that will issue I/O request, I/O device is underutilized Ideal: want to keep all devices busy

8. CS 4284 Spring 20138 Per-process perspective Process alternates between CPU bursts & I/O bursts I/OCPU I/O Bound Process CPU Bound Process

9. CS 4284 Spring 20139 Global perspective If these were executed on the same CPU: I/OCPU I/O Bound Process CPU Bound Process Waiting

10. CPU Scheduling Part I

11. CS 4284 Spring 201311 CPU Scheduling Terminology A job (sometimes called a task, or a job instance) –Activity that’s scheduled: process or part of a process Arrival time: time when job arrives Start time: time when job actually starts Finish time: time when job is done Completion time (aka Turn-around time) –Finish time – Arrival time Response time –Time when user sees response – Arrival time Execution time (aka cost): time a job needs to execute CPUI/OCPU burstwaiting Arrival TimeStart TimeFinish Time Completion TimeResponse Time

12. CS 4284 Spring 201312 CPU Scheduling Terminology (2) Waiting time = time when job was ready- to-run –didn’t run because CPU scheduler picked another job Blocked time = time when job was blocked –while I/O device is in use Completion time –Execution time + Waiting time + Blocked time

13. CS 4284 Spring 201313 Static vs Dynamic Scheduling Static –All jobs, their arrival & execution times are known in advance, create a schedule, execute it Used in statically configured systems, such as embedded real-time systems Dynamic or Online Scheduling –Jobs are not known in advance, scheduler must make online decision whenever jobs arrives or leaves Execution time may or may not be known Behavior can be modeled by making assumptions about nature of arrival process

14. CS 4284 Spring 201314 Scheduling Algorithms vs Scheduler Implementations Scheduling algorithms’ properties are (usually) analyzed under static assumptions first; then adapted for dynamic scenarios Algorithms often consider only an abstract notion of (CPU) “jobs”, but a dynamic scheduler must map that to processes with alternating - and repeating - CPU and IO bursts –Often applies static algorithm to current ready queue Algorithms often assume length of job/CPU burst is known, but real scheduler must estimate expected execution cost (or make assumptions)

15. CS 4284 Spring 201315 Preemptive vs Nonpreemptive Scheduling Q.: when is scheduler asked to pick a thread from ready queue? Nonpreemptive: –Only when RUNNING  BLOCKED transition –Or RUNNING  EXIT –Or voluntary yield: RUNNING  READY Preemptive –Also when BLOCKED  READY transition –Also on timer (forced call to yield upon intr exit) RUNNING READY BLOCKED Process must wait for event Event arrived Scheduler picks process Process preempted

16. CS 4284 Spring 201316 CPU Scheduling Goals Minimize latency –Can mean (avg) completion time –Can mean (avg) response time Maximize throughput –Throughput: number of finished jobs per time-unit –Implies minimizing overhead (for context-switching, for scheduling algorithm itself) –Requires efficient use of non-CPU resources Fairness –Minimize variance in waiting time/completion time

17. CS 4284 Spring 201317 Scheduling Constraints Reaching those goals is difficult, because –Goals are conflicting: Latency vs. throughput Fairness vs. low overhead –Scheduler must operate with incomplete knowledge Execution time may not be known I/O device use may not be known –Scheduler must make decision fast Approximate best solution from huge solution space

18. CS 4284 Spring 201318 First Come First Serve Schedule processes in the order in which they arrive –Run until completion (or until they block) Simple! Example: 0202227 Q.: what is the average completion time? 27

19. CS 4284 Spring 201319 FCFS (cont’d) Disadvantage: completion time depends on arrival order –Unfair to short jobs Possible Convoy Effect: –1 CPU bound (long CPU bursts, infrequent I/O bursts), multiple I/O bound jobs (frequent I/O bursts, short CPU bursts). –CPU bound process monopolizes CPU: I/O devices are idle –New I/O requests by I/O bound jobs are only issued when CPU bound job blocks – CPU bound job “leads” convoy of I/O bound processes FCFS not usually used for CPU scheduling, but often used for other resources (network device)

20. CS 4284 Spring 201320 Round-Robin Run process for a timeslice (quantum), then move on to next process, repeat Decreases avg completion if jobs are of different lengths No more unfairness to short jobs! 027 Q.: what is the average completion time? 58

21. CS 4284 Spring 201321 Round Robin (2) What if there are no “short” jobs? 021714 Q.: what is the average completion time? What would it be under FCFS?

22. CS 4284 Spring 201322 Round Robin – Cost of Time Slicing Context switching incurs a cost –Direct cost (execute scheduler & context switch) + indirect cost (cache & TLB misses) Long time slices  lower overhead, but approaches FCFS if processes finish before timeslice expires Short time slices  lots of context switches, high overhead Typical cost: context switch < 10µs Time slice typically around 100ms Note: time slice length != interval between timer interrupts where periodic timers are used

23. CS 4284 Spring 201323 Shortest Process Next (SPN) Idea: remove unfairness towards short processes by always picking the shortest job If done nonpreemptively also known as: –Shortest Job First (SJF), Shortest Time to Completion First (STCF) If done preemptively known as: –Shortest Remaining Time (SRT), Shortest Remaining Time to Completion First (SRTCF)

24. CS 4284 Spring 201324 SPN (cont’d) Provably optimal with respect to avg waiting time: –Moving shorter job up reduces its waiting time more than it delays waiting time of longer job that follows Advantage: Good I/O utilization Disadvantage: –Can starve long jobs 02727 Big Q: How do we know the length of a job?

25. CS 4284 Spring 201325 Practical SPN Usually don’t know (remaining) execution time –Exception: profiled code in real-time system; or worst- case execution time analysis (WCET) Idea: determine future from past: –Assume next CPU burst will be as long as previous CPU burst –Or: weigh history using (potentially exponential) average: more recent burst lengths more predictive than past CPU bursts Note: for some resources, we know or can compute length of next “job”: –Example: disk scheduling (shortest-seek time first)

26. CS 4284 Spring 201326 Multi-Level Feedback Queue Scheduling Kleinrock 1969 Want: –preference for short jobs (tends to lead to good I/O utilization) –longer timeslices for CPU bound jobs (reduces context-switching overhead) Problem: –Don’t know type of each process – algorithm needs to figure out Use multiple queues –queue determines priority –usually combined with static priorities (nice values) –many variations of this idea exist

27. CS 4284 Spring 201327 MLFQS MIN MAX Higher Priority 1 2 4 3 Longer Timeslices Process that use up their time slice move down Processes start in highest queue Higher priority queues are served before lower-priority ones - within highest-priority queue, round-robin Processes that starve move up Only ready processes are in this queue - blocked processes leave queue and reenter same queue on unblock

28. CS 4284 Spring 201328 Basic Scheduling: Summary FCFS: simple –unfair to short jobs & poor I/O performance (convoy effect) RR: helps short jobs –loses when jobs are equal length SPN: optimal average waiting time –which, if ignoring blocking time, leads to optimal average completion time –unfair to long jobs –requires knowing (or guessing) the future MLFQS: approximates SPN without knowing execution time –Can still be unfair to long jobs

29. CPU Scheduling Part II

30. CS 4284 Spring 201330 Case Study: 2.6 Linux Scheduler (pre 2.6.23) Variant of MLFQS 140 priorities –0-99 “realtime” –100-140 nonrealtime Dynamic priority computed from static priority (nice) plus “interactivity bonus” 0 100 120 140 “Realtime” processes scheduled based on static priority SCHED_FIFO SCHED_RR Processes scheduled based on dynamic priority SCHED_OTHER nice=0 nice=19 nice=-20

31. CS 4284 Spring 201331 Linux Scheduler (2) Instead of recomputation loop, recompute priority at end of each timeslice –dyn_prio = nice + interactivity bonus (-5…5) Interactivity bonus depends on sleep_avg –measures time a process was blocked 2 priority arrays (“active” & “expired”) in each runqueue (Linux calls ready queues “runqueue”)

32. CS 4284 Spring 201332 Linux Scheduler (3) struct prio_array { unsigned int nr_active; unsigned long bitmap[BITMAP_SIZE]; struct list_head queue[MAX_PRIO]; }; typedef struct prio_array prio_array_t; /* find the highest-priority ready thread */ idx = sched_find_first_bit(array->bitmap); queue = array->queue + idx; next = list_entry(queue->next, task_t, run_list); struct prio_array { unsigned int nr_active; unsigned long bitmap[BITMAP_SIZE]; struct list_head queue[MAX_PRIO]; }; typedef struct prio_array prio_array_t; /* find the highest-priority ready thread */ idx = sched_find_first_bit(array->bitmap); queue = array->queue + idx; next = list_entry(queue->next, task_t, run_list); /* Per CPU runqueue */ struct runqueue { prio_array_t *active; prio_array_t *expired; prio_array_t arrays[2]; … } /* Per CPU runqueue */ struct runqueue { prio_array_t *active; prio_array_t *expired; prio_array_t arrays[2]; … } Finds highest-priority ready thread quickly Switching active & expired arrays at end of epoch is simple pointer swap (“O(1)” claim)

33. CS 4284 Spring 201333 Linux Timeslice Computation Linux scales static priority to timeslice –Nice [ -20 … 0 … 19 ] maps to [800ms … 100 ms … 5ms] Various tweaks: –“interactive processes” are reinserted into active array even after timeslice expires Unless processes in expired array are starving –processes with long timeslices are round- robin’d with other of equal priority at sub- timeslice granularity

34. CS 4284 Spring 201334 Proportional Share Scheduling Aka “Fair-Share” Scheduling None of algorithms discussed so far provide a direct way of assigning CPU shares –E.g., give 30% of CPU to process A, 70% to process B Proportional Share algorithms do by assigning “tickets” or “shares” to processes –Process get to use resource in proportion of their shares to total number of shares Lottery Scheduling, Weighted Fair Queuing/Stride Scheduling [Waldspurger 1995]Waldspurger 1995

35. Lottery Scheduling Idea: number tickets between 1…N –every process gets p i tickets according to importance –process 1 gets tickets [1… p 1 -1] –process 2 gets tickets [p 1 … p 1+ p 2 -1] and so on. Scheduling decision: –Hold a lottery and draw ticket, holder gets to run for next time slice Nondeterministic algorithm Q.: how to implement priority donation? CS 4284 Spring 201335

36. Weighted Fair Queuing Uses ‘per process’ virtual time Increments process’s virtual time by a “stride” after each quantum, which is defined as (process_share) -1 Choose process with lowest virtual finishing time –‘virtual finishing time’ is virtual time + stride Also known as stride scheduling Linux now implements a variant of WFQ/Stride Scheduling as its “CFS” completely fair scheduler CS 4284 Spring 201336

37. WFQ Example (A=3, B=2, C=1) Ready Queue is sorted by Virtual Finish Time (Virtual Time at end of quantum if a process were scheduled) TimeTask ATask BTask CReady Queue Who Runs One scheduling epoch. A ran 3 out of 6 quanta, B 2 out of 6, C 1 out of 6. This process will repeat, yielding proportional fairness. 01/31/21A (1/3) B (1/2) C (1)A 12/31/21B (1/2) A (2/3) C (1)B 22/311A (2/3) C(1) B(1)A 3111C(1) B(1) A(1) C 4112B(1) A(1) C(2)B 513/22A(1) B(3/2) C(2)A 64/33/22A (4/3) B(3/2) C(2) 37CS 4284 Spring 2013

38. WFQ (cont’d) WFQ requires a sorted ready queue –Linux now uses R/B tree –Higher complexity than O(1) linked lists, but appears manageable for real-world ready queue sizes Unblocked processes that reenter the ready queue are assigned a virtual time reflecting the value that their virtual time counter would have if they’d received CPU time proportionally Accommodating I/O bound processes still requires fudging –In strict WFQ, only way to improve latency is to set number of shares high – but this is disastrous if process is not truly I/O bound –Linux uses “sleeper fairness,” to identify when to boost virtual time; similar to the sleep average in old scheduler CS 4284 Spring 201338

39. CS 4284 Spring 201339 Linux SMP Load Balancing Runqueue is per CPU Periodically, lengths of runqueues on different CPU is compared –Processes are migrated to balance load Aside: Migrating requires locks on both runqueues static void double_rq_lock( runqueue_t *rq1, runqueue_t *rq2) { if (rq1 == rq2) { spin_lock(&rq1->lock); } else { if (rq1 < rq2) { spin_lock(&rq1->lock); spin_lock(&rq2->lock); } else { spin_lock(&rq2->lock); spin_lock(&rq1->lock); } static void double_rq_lock( runqueue_t *rq1, runqueue_t *rq2) { if (rq1 == rq2) { spin_lock(&rq1->lock); } else { if (rq1 < rq2) { spin_lock(&rq1->lock); spin_lock(&rq2->lock); } else { spin_lock(&rq2->lock); spin_lock(&rq1->lock); }

40. CS 4284 Spring 201340 Real-time Scheduling Real-time systems must observe not only execution time, but a deadline as well –Jobs must finish by deadline –But turn-around time is usually less important Common scenario are recurring jobs –E.g., need 3 ms every 10 ms (here, 10ms is the recurrence period T, 3 ms is the cost C) Possible strategies –RMA (Rate Monotonic) Map periods to priorities, fixed, static –EDF (Earliest Deadline First) Always run what’s due next, dynamic

41. EDF – Example TaskTC A41 B84 C123 5010152025 Assume deadline equals period (T). A B C Hyper-period CS 4284 Spring 201341

42. 42 EDF – Example TaskTC A41 B84 C123 5010152025 Assume deadline equals period (T). A B C CS 4284 Spring 2013

43. EDF – Example TaskTC A41 B84 C123 5010152025 Assume deadline equals period (T). A B C CS 4284 Spring 201343

44. EDF – Example TaskTC A41 B84 C123 5010152025 Assume deadline equals period (T). A B C Lexical order tie breaker (C > B > A) CS 4284 Spring 201344

45. EDF – Example TaskTC A41 B84 C123 5010152025 Assume deadline equals period (T). A B C CS 4284 Spring 201345

46. EDF – Example TaskTC A41 B84 C123 5010152025 Assume deadline equals period (T). A B C CS 4284 Spring 201346

47. EDF – Example TaskTC A41 B84 C123 5010152025 Assume deadline equals period (T). A B C CS 4284 Spring 201347

48. EDF – Example TaskTC A41 B84 C123 5010152025 Assume deadline equals period (T). A B C CS 4284 Spring 201348

49. EDF – Example TaskTC A41 B84 C123 5010152025 Assume deadline equals period (T). A B C Pattern repeats CS 4284 Spring 201349

50. EDF Properties Feasibility test: U = 100% in example Bound theoretical Sufficient and necessary Optimal CS 4284 Spring 201350

51. CS 4284 Spring 201351 Scheduling Summary OS must schedule all resources in a system –CPU, Disk, Network, etc. CPU Scheduling affects indirectly scheduling of other devices Goals for general purpose schedulers: –Minimizing latency (avg. completion or waiting time) –Maximing throughput –Provide fairness In Practice: some theory, lots of tweaking