CSC 4320/6320 Operating Systems Lecture 5 CPU Scheduling

1. Saurav Karmakar CSC 4320/6320Operating SystemsLecture 5 CPU Scheduling

2. Chapter 5: CPU Scheduling • Basic Concepts • Scheduling Criteria • Scheduling Algorithms • Thread Scheduling • Multiple-Processor Scheduling • Operating Systems Examples • Algorithm Evaluation

3. Objectives • To introduce CPU scheduling, which is the basis for multiprogrammed operating systems • To describe various CPU-scheduling algorithms • To discuss evaluation criteria for selecting a CPU-scheduling algorithm for a particular system

4. Basic Concepts • Uniprocessor system one process may run at a time • Objective of multiprogramming some process running at all times  to maximize CPU utilization • When one process has to wait, the operating system takes the CPU away from that process and gives the CPU to another process • Almost all computer resources are scheduled before use

5. CPU-I/O Burst Cycle • Success of CPU scheduling follows : • Process execution  CPU–I/O Burst Cycle • Consists of a cycle of CPU execution and I/O wait • Basically While a process waits for I/O, CPU sits idle if no multiprogramming • Instead the OS can give CPU to another process • CPU burst distribution • Distribution for frequency vs duration of CPU bursts. • Exponential or hyperexpoinential in nature

6. Alternating Sequence of CPU And I/O Bursts

7. Histogram of CPU-burst Times

8. CPU Scheduler • Selects from among the processes in memory that are ready to execute, and allocates the CPU to one of them : Short-term Scheduler • The ready queue is not necessarily a FIFO queue • CPU scheduling decisions may take place when a process: 1. Switches from running to waiting state 2. Switches from running to ready state 3. Switches from waiting to ready • Terminates • Scheduling under 1 and 4 leaves us no choice • But option 2 and 3 does.

9. CPU Scheduler • Nonpreemptive : once the CPU has been allocated to a process, the process keeps the CPU until it releases the CPU either • Scheduling under 1 and 4 is Nonpreemptive/Cooperative • All other scheduling is Preemptive • Preemptive Scheduling incurs some costs : • Access to shared data • Effects on the design of operating system kernel • Effects of interrupts

11. Dispatcher • Dispatcher module gives control of the CPU to the process selected by the short-term scheduler; this involves: • Switching context • Switching to user mode • Jumping to the proper location in the user program to restart that program • Dispatch latency – time it takes for the dispatcher to stop one process and start another running

14. P1 P2 P3 0 24 27 30 First-Come, First-Served (FCFS) Scheduling ProcessBurst Time P1 24 P2 3 P3 3 • Suppose that the processes arrive in the order: P1 , P2 , P3 The Gantt Chart for the schedule is: • Waiting time for P1 = 0; P2 = 24; P3 = 27 • Average waiting time: (0 + 24 + 27)/3 = 17

15. P2 P3 P1 0 3 6 30 FCFS Scheduling (Cont) Suppose that the processes arrive in the order P2 , P3 , P1 • The Gantt chart for the schedule is: • Waiting time for P1 = 6;P2 = 0; P3 = 3 • Average waiting time: (6 + 0 + 3)/3 = 3 • Much better than previous case • So Average waiting time vary substantially if the burst time for processes vary – Generally quite long. • This is non-preemptive in nature  troublesome for time sharing system

17. FCFS Algorithm • Process Arrival Service • Time Time • 1 0 8 • 2 1 4 • 3 2 9 • 4 3 5 CPU SCHEDULING FCFS P1 P2 P3 P4 0 8 12 21 26 Average wait = ( (8-0) + (12-1) + (21-2) + (26-3) )/4 = 61/4 = 15.25 Residence Time at the CPU 5: CPU-Scheduling

18. Shortest-Job-First (SJF) Scheduling • Associate with each process the length of its next CPU burst. • Using these lengths to schedule the process with the shortest time • If two processes have the same length next CPU burst, FCFS scheduling is used to break the tie. • Scheduling depends on the length of the next CPU burst(lower the better) • SJF is optimal – gives minimum average waiting time for a given set of processes • Difficulty knowing the length of the next CPU request

19. Shortest-Job-First (SJF) Scheduling

20. P3 P2 P4 P1 3 9 16 24 0 Example of SJF ProcessBurst Time P1 6 P2 8 P3 7 P4 3 • SJF scheduling chart • Average waiting time = (3 + 16 + 9 + 0) / 4 = 7 • By moving a short process before a long one, the waiting time of the short process decreases more than it increases the waiting time of the long process. Consequently, the average waiting time decreases. • SJF scheduling is used frequently in long-term scheduling.

22. Determining Length of Next CPU Burst • How to implement it at short-term scheduler ? • Can only estimate the length • Can be done by using the length of previous CPU bursts, using exponential averaging

23. Examples of Exponential Averaging •  =0 • n+1 = n • Recent history does not count •  =1 • n+1 =  tn • Only the actual last CPU burst counts • If we expand the formula, we get: n+1 =  tn+(1 - ) tn-1+ … +(1 -  )j tn-j+ … +(1 -  )n +1 0 • Since both  and (1 - ) are less than or equal to 1, each successive term has less weight than its predecessor

24. Prediction of the Length of the Next CPU Burst

26. Preemptive SJF Algorithm Shortest Remaining Time First Algorithm • Process Arrival Service • Time Time • 1 0 8 • 2 1 4 • 3 2 9 • 4 3 5 Preemptive Shortest Job First P1 P2 P4 P1 P3 0 1 5 10 26 17 Average wait = ( (10-1) + (1-1) + (17-2) + (5-3) )/4 = 26/4 = 6.5 5: CPU-Scheduling

28. Priority Scheduling • SJF is a special case of the general priority schedule algorithm • Priority inverse of the next CPU Burst • A priority number (integer) is associated with each process • The CPU is allocated to the process with the highest priority (smallest integer  highest priority) • Preemptive • Nonpreemptive • Problem  Starvation– low priority processes may never execute • Solution Aging – as time progresses increase the priority of the process

29. Priority Scheduling • Average Waiting Time = (0+1+6+16+18)/5 = 8.2

30. What about fairness ? • What about it? • Strict fixed-priority scheduling between queues is unfair (run highest, then next, etc): • long running jobs may never get CPU • In Multics, shut down machine, found 10-year-old job • Must give long-running jobs a fraction of the CPU even when there are shorter jobs to run • Tradeoff: fairness gained by hurting avg response time! • How to implement fairness? • Could give each queue some fraction of the CPU • What if one long-running job and 100 short-running ones? • Like express lanes in a supermarket—sometimes express lanes get so long, get better service by going into one of the other lines

31. Fair Share Scheduling • Problems with priority-based systems – Priorities are absolute: no guarantees when multiple jobs with same priority – No encapsulation and modularity • Behavior of a system module is unpredictable: a function of absolute priorities assigned to tasks in other modules • Solution: Fair-share scheduling Each job has a share: some measure of its relative importance denotes user’s share of system resources as a fraction of the total usage of those resources  e.g., if user A’s share is twice that of user B » then, in the long term, A will receive twice as many resources as B • Traditional implementations – keep track of per-process CPU utilization (a running average) – reprioritize processes to ensure that everyone is getting their share – are slow!

32. Lottery Scheduling • Yet another alternative here. • Give each job some number of lottery tickets • On each time slice, randomly pick a winning ticket • On average, CPU time is proportional to number of tickets given to each job • How to assign tickets? • To approximate SRTF, short running jobs get more, long running jobs get fewer • To avoid starvation, every job gets at least one ticket (everyone makes progress) • Advantage over strict priority scheduling: • behaves gracefully as load changes • Adding or deleting a job affects all jobs proportionally, independent of how many tickets each job possesses

33. Lottery Scheduling Example • Assume short jobs get 10 tickets, long jobs get 1 ticket

34. Round Robin (RR) • Designed specially for time sharing system. • Each process gets a small unit of CPU time (time quantum), usually 10-100 milliseconds. After this time has elapsed, the process is preempted and added to the end of the ready queue. • The ready queue generally is circular queue. • Avg wait time is often long • If there are nprocesses in the ready queue and the time quantum is q, then each process gets 1/n of the CPU time in chunks of at most q time units at once. No process waits more than (n-1)q time units. • Performance • q large  FIFO • q small  q must be large with respect to context switch, otherwise overhead is too high ; processor sharing

35. P1 P2 P3 P1 P1 P1 P1 P1 0 10 14 18 22 26 30 4 7 Example of RR with Time Quantum = 4 ProcessBurst Time P1 24 P2 3 P3 3 • The Gantt chart is: • Avg Waiting Time = ((10-4)+4+7)/3 =5.66 • Typically, higher average turnaround than SJF, but no better response

36. Time Quantum and Context Switch Time

37. Turnaround Time Varies With The Time Quantum

38. Multilevel Queue • Ready queue is partitioned into separate queues:foreground (interactive)background (batch) • Each queue has its own scheduling algorithm • foreground – RR • background – FCFS • Scheduling must be done between the queues • Fixed priority scheduling; (i.e., serve all from foreground then from background). Possibility of starvation. • Time slice – each queue gets a certain amount of CPU time which it can schedule amongst its processes; i.e., 80% to foreground in RR 20% to background in FCFS

39. Multilevel Feedback Queue • A process can move between the various queues; aging can be implemented this way • Multilevel-feedback-queue scheduler defined by the following parameters: • number of queues • scheduling algorithms for each queue • method used to determine when to upgrade a process • method used to determine when to demote a process • method used to determine which queue a process will enter when that process needs service

40. Example of Multilevel Feedback Queue • Three queues: • Q0 – RR with time quantum 8 milliseconds • Q1 – RR time quantum 16 milliseconds • Q2 – FCFS • Scheduling • A new job enters queue Q0which is servedFCFS. When it gains CPU, job receives 8 milliseconds. If it does not finish in 8 milliseconds, job is moved to queue Q1. • At Q1 job is again served FCFS and receives 16 additional milliseconds. If it still does not complete, it is preempted and moved to queue Q2.

41. Thread Scheduling : Contention Scope • The contention scope of a user thread defines how it is mapped to a kernel thread. • System contention scope/global contention scope user thread is a user thread that is directly mapped to one kernel thread. • All user threads in a 1:1 thread model have system contention scope. • Process contention scope/local contention scope user thread is a user thread that shares a kernel thread with other (process contention scope) user threads in the process. • All user threads in a M:1 thread model have process contention scope.

42. Contention Scope • In an M:N thread model, user threads can have either system or process contention scope – Mixed Scope • The concurrency levelis a property of M:N threads libraries. • It defines the number of VPs used to run the process contention scope user threads. • This number cannot exceed the number of process contention scope user threads, and is usually dynamically set by the threads library.

43. Pthread Scheduling • Pthread API allows specifying either PCS or SCS during thread creation • PTHREAD_SCOPE_ PROCESS schedules threads using PCS scheduling • PTHREAD_SCOPE_SYSTEM schedules threads using SCS scheduling. • Two functions for setting and getting : • pthread_attr_setscope(pthread_attr_t *attr, int scope) • pthread_attr_getscope(pthread_attr_t *attr, int scope)

44. #include <pthread.h> #include <stdio.h> #define NUM THREADS 5 int main(int argc, char *argv[]) { int i; pthread_t tid[NUM THREADS]; pthread _attr_t attr; /* get the default attributes */ pthread_attr_init(&attr); /* set the scheduling algorithm to PROCESS or SYSTEM */ pthread_attr _setscope(&attr, PTHREAD_SCOPE_SYSTEM); /* set the scheduling policy - FIFO, RR, or OTHER */ pthread_attr_setschedpolicy(&attr, SCHED_OTHER); /* create the threads */ for (i = 0; i < NUM THREADS; i++) pthread_create(&tid[i],&attr,runner,NULL); /* now join on each thread */ for (i = 0; i < NUM THREADS; i++) pthread_join(tid[i], NULL); } /* Each thread will begin control in this function */ void *runner(void *param){ printf("I am a thread\n"); pthread_exit(0); }

45. Multiple-Processor Scheduling • CPU scheduling more complex when multiple CPUs are available • Here we consider homogeneous processors within a multiprocessor • Asymmetric multiprocessing – only one processor accesses the system data structures, alleviating the need for data sharing • Symmetric multiprocessing (SMP) – each processor is self-scheduling, all processes in common ready queue, or each has its own private queue of ready processes • Processor affinity • Process has affinity for processor on which it is currently running • For avoiding to invalidate and repopulate caches due to migration • Soft Affinity (Ex : Solaris) • Hard Affinity (Ex : Linux, Solaris)

46. NUMA and CPU Scheduling

47. Multiple-Processor Scheduling : Load Balancing • Distributing the load • Necessary for system, where each processor has it’s own private queue. • Approaches : • Push Migration • Pull Migration • Generally implemented together • It often counteracts the benefit of processor affinity.

48. Multicore Processors • Recent trend to place multiple processor cores on same physical chip • Faster and consume less power • Multiple threads per core also growing • Takes advantage of memory stall to make progress on another thread while memory retrieve happens

49. Virtualization • It creates fake impression of the CPU(resources) for the guest operating systems running in virtual machines. • E.g Running a time sharing system or real time OS in virtual machine • Thus it can undo the good scheduling efforts.

50. Algorithm Evaluation • Need to make our criteria more specific by adding constraints • Approaches : • Analytic Evaluation • Deterministic modeling – takes a particular predetermined workload and defines the performance of each algorithm for that workload • It requires exact input and outcome is bound to the defined cases.