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CS 4284 Systems Capstone

CS 4284 Systems Capstone. Godmar Back. Resource Allocation & Scheduling. Resource Allocation and Scheduling. Resource Allocation & Scheduling. Resource management is primary OS function Involves resource allocation & scheduling Who gets to use what resource and for how long

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CS 4284 Systems Capstone

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

  2. Resource Allocation and Scheduling

  3. 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 CS 4284 Spring 2013

  4. 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 CS 4284 Spring 2013

  5. 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 CS 4284 Spring 2013

  6. 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 CS 4284 Spring 2013

  7. 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 CS 4284 Spring 2013

  8. Per-process perspective • Process alternates between CPU bursts & I/O bursts I/O Bound Process CPU Bound Process CPU I/O CS 4284 Spring 2013

  9. Global perspective • If these were executed on the same CPU: I/O Bound Process CPU Bound Process Waiting CPU I/O CS 4284 Spring 2013

  10. CPU Scheduling Part I

  11. 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 Arrival Time Start Time Finish Time waiting CPU burst I/O waiting CPU Response Time Completion Time CS 4284 Spring 2013

  12. 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 CS 4284 Spring 2013

  13. 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 CS 4284 Spring 2013

  14. 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) CS 4284 Spring 2013

  15. RUNNING Scheduler picks process Process must wait for event Process preempted BLOCKED READY Event arrived 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) CS 4284 Spring 2013

  16. 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 CS 4284 Spring 2013

  17. 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 CS 4284 Spring 2013

  18. 2 7 First Come First Serve • Schedule processes in the order in which they arrive • Run until completion (or until they block) • Simple! • Example: Q.: what is the average completion time? 0 20 22 27 CS 4284 Spring 2013

  19. 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) CS 4284 Spring 2013

  20. 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! Q.: what is the average completion time? 0 5 8 27 CS 4284 Spring 2013

  21. 7 14 Round Robin (2) • What if there are no “short” jobs? 0 21 Q.: what is the average completion time? What would it be under FCFS? CS 4284 Spring 2013

  22. 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 CS 4284 Spring 2013

  23. 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) CS 4284 Spring 2013

  24. SPN (cont’d) • Provablyoptimalwith respectto 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 0 2 7 27 Big Q: How do we know the length of a job? CS 4284 Spring 2013

  25. 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) CS 4284 Spring 2013

  26. 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 CS 4284 Spring 2013

  27. MAX 4 3 Longer Timeslices Higher Priority 2 1 MIN MLFQS Processes start in highest queue Process that use up their time slice move down Processes that starve move up Higher priority queues are served before lower-priority ones - within highest-priority queue, round-robin Only ready processes are in this queue - blocked processes leave queue and reenter same queue on unblock CS 4284 Spring 2013

  28. 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 CS 4284 Spring 2013

  29. CPU Scheduling Part II

  30. Case Study: 2.6 Linux Scheduler (pre 2.6.23) nice=19 140 • Variant of MLFQS • 140 priorities • 0-99 “realtime” • 100-140 nonrealtime • Dynamic priority computed from static priority (nice) plus “interactivity bonus” Processes scheduled based on dynamic priority SCHED_OTHER nice=0 120 nice=-20 100 “Realtime” processes scheduled based on static priority SCHED_FIFO SCHED_RR 0 CS 4284 Spring 2013

  31. 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”) CS 4284 Spring 2013

  32. 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); /* 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) CS 4284 Spring 2013

  33. 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 CS 4284 Spring 2013

  34. 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] CS 4284 Spring 2013

  35. Lottery Scheduling • Idea: number tickets between 1…N • every process gets pi tickets according to importance • process 1 gets tickets [1… p1-1] • process 2 gets tickets [p1… p1+p2-1] 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 2013

  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 2013

  37. WFQ Example (A=3, B=2, C=1) CS 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 2013

  39. Linux SMP Load Balancing 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); } } } • 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 CS 4284 Spring 2013

  40. 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 CS 4284 Spring 2013

  41. EDF – Example Assume deadline equals period (T). A B C 0 5 10 15 20 25 Hyper-period CS 4284 Spring 2013

  42. EDF – Example Assume deadline equals period (T). A B C 0 5 10 15 20 25 CS 4284 Spring 2013

  43. EDF – Example Assume deadline equals period (T). A B C 0 5 10 15 20 25 CS 4284 Spring 2013

  44. EDF – Example Assume deadline equals period (T). Lexical order tie breaker (C > B > A) A B C 0 5 10 15 20 25 CS 4284 Spring 2013

  45. EDF – Example Assume deadline equals period (T). A B C 0 5 10 15 20 25 CS 4284 Spring 2013

  46. EDF – Example Assume deadline equals period (T). A B C 0 5 10 15 20 25 CS 4284 Spring 2013

  47. EDF – Example Assume deadline equals period (T). A B C 0 5 10 15 20 25 CS 4284 Spring 2013

  48. EDF – Example Assume deadline equals period (T). A B C 0 5 10 15 20 25 CS 4284 Spring 2013

  49. EDF – Example Assume deadline equals period (T). A B C 0 5 10 15 20 25 Pattern repeats CS 4284 Spring 2013

  50. EDF Properties • Feasibility test: • U = 100% in example • Bound theoretical • Sufficient and necessary • Optimal CS 4284 Spring 2013

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