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Chapter 7 Scheduling Copyright © 2008
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.2Operating Systems, by Dhananjay Dhamdhere2 Introduction Scheduling Terminology and Concepts Nonpreemptive Scheduling Policies Preemptive Scheduling Policies Scheduling in Practice Real-Time Scheduling Case Studies Performance Analysis of Scheduling Policies
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.3Operating Systems, by Dhananjay Dhamdhere3 Scheduling Terminology and Concepts Scheduling is the activity of selecting the next request to be serviced by a server –In an OS, a request is the execution of a job or a process, and the server is the CPU
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.4Operating Systems, by Dhananjay Dhamdhere4 Scheduling Terminology and Concepts (continued)
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.6Operating Systems, by Dhananjay Dhamdhere6 Fundamental Techniques of Scheduling Schedulers use three fundamental techniques: –Priority-based scheduling Provides high throughput of the system –Reordering of requests Implicit in preemption –Enhances user service and/or throughput –Variation of time slice Smaller values of time slice provide better response times, but lower CPU efficiency Use larger time slice for CPU-bound processes
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.7Operating Systems, by Dhananjay Dhamdhere7 The Role of Priority Priority: tie-breaking rule employed by scheduler when many requests await attention of server –May be static or dynamic Some process reorderings could be obtained through priorities –E.g., Short processes serviced before long ones –Some reorderings would need complex priority functions What if processes have the same priority? –Use round-robin scheduling May lead to starvation of low-priority requests –Solution: aging of requests
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.8Operating Systems, by Dhananjay Dhamdhere8 Nonpreemptive Scheduling Policies A server always services a scheduled request to completion Attractive because of its simplicity Some nonpreemptive scheduling policies: –First-come, first-served (FCFS) scheduling –Shortest request next (SRN) scheduling –Highest response ratio next (HRN) scheduling
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.9Operating Systems, by Dhananjay Dhamdhere9 FCFS Scheduling
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.10Operating Systems, by Dhananjay Dhamdhere10 Shortest Request Next (SRN) Scheduling May cause starvation of long processes
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.11Operating Systems, by Dhananjay Dhamdhere11 Highest Response Ratio Next (HRN) Use of response ratio counters starvation
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.12Operating Systems, by Dhananjay Dhamdhere12 Preemptive Scheduling Policies In preemptive scheduling, server can switch to next request before completing current one –Preempted request is put back into pending list –Its servicing is resumed when it is scheduled again A request may be scheduled many times before it is completed –Larger scheduling overhead than with nonpreemptive scheduling Used in multiprogramming and time-sharing OSs
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.13Operating Systems, by Dhananjay Dhamdhere13 Round-Robin Scheduling with Time- Slicing (RR) In this example, δ = 1
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.14Operating Systems, by Dhananjay Dhamdhere14 Example: Variation of Response Time in RR Scheduling At small values of δ, rt for a request may be higher for smaller values of δ
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.15Operating Systems, by Dhananjay Dhamdhere15 Least Completed Next (LCN) Issues: -Short processes will finish ahead of long processes -Starves long processes of CPU attention -Neglects existing processes if new processes keep arriving in the system
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.16Operating Systems, by Dhananjay Dhamdhere16 Shortest Time to Go (STG) Since it is analogous to the SRN policy, long processes might face starvation.
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.17Operating Systems, by Dhananjay Dhamdhere17 Scheduling in Practice To provide a suitable combination of system performance and user service, OS has to adapt its operation to the nature and number of user requests and availability of resources –A single scheduler using a classical scheduling policy cannot address all these issues effectively Modern OSs employ several schedulers –Up to three schedulers Some of the schedulers may use a combination of different scheduling policies
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.18Operating Systems, by Dhananjay Dhamdhere18 Long-, Medium-, and Short-Term Schedulers These schedulers perform the following functions: –Long-term: Decides when to admit an arrived process for scheduling, depending on: Nature (whether CPU-bound or I/O-bound) Availability of resources –Kernel data structures, swapping space –Medium-term: Decides when to swap out a process from memory and when to load it back, so that a sufficient number of ready processes are in memory –Short-term: Decides which ready process to service next on the CPU and for how long Also called the process scheduler, or scheduler
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.19Operating Systems, by Dhananjay Dhamdhere19
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.20Operating Systems, by Dhananjay Dhamdhere20 Example: Long, Medium-, and Short- Term Scheduling in Time-Sharing
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.21Operating Systems, by Dhananjay Dhamdhere21 Scheduling Data Structures and Mechanisms Interrupt servicing routine invokes context save Dispatcher loads two PCB fields—PSW and GPRs— into CPU to resume operation of process Scheduler executes idle loop if no ready processes
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.22Operating Systems, by Dhananjay Dhamdhere22 Priority-Based Scheduling Overhead depends on number of distinct priorities, not on the number of ready processes Can lead to starvation of low-priority processes –Aging can be used to overcome this problem Can lead to priority inversion –Addressed by using the priority inheritance protocol
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.23Operating Systems, by Dhananjay Dhamdhere23 Round-Robin Scheduling with Time- Slicing Can be implemented through a single list of PCBs of ready processes –List is organized as a queue Scheduler removes first PCB from queue and schedules process described by it –If time slice elapses, PCB is put at the end of queue –If process starts I/O operation, its PCB is added at end of queue when its I/O operation completes PCB of a ready process moves toward the head of the queue until the process is scheduled
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.24Operating Systems, by Dhananjay Dhamdhere24 Multilevel Scheduling A priority and a time slice is associated with each ready queue –RR scheduling with time slicing is performed within it –High priority queue has a small time slice Good response times for processes –Low priority queue has a large time slice Low process switching overhead A process at the head of a queue is scheduled only if the queues for all higher priority levels are empty Scheduling is preemptive Priorities are static
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.25Operating Systems, by Dhananjay Dhamdhere25 Multilevel Adaptive Scheduling Also called multilevel feedback scheduling Scheduler varies priority of process so it receives a time slice consistent with its CPU requirement Scheduler determines “correct” priority level for a process by observing its recent CPU and I/O usage –Moves the process to this level Example: CTSS, a time-sharing OS for the IBM 7094 in the 1960s –Eight-level priority structure
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.26Operating Systems, by Dhananjay Dhamdhere26 Fair Share Scheduling Fair share: fraction of CPU time to be devoted to a group of processes from same user or application Ensures an equitable use of the CPU by processes belonging to different users or different applications Lottery scheduling is a technique for sharing a resource in a probabilistically fair manner –Tickets are issued to applications (or users) on the basis of their fair share of CPU time –Actual share of the resources allocated to the process depends on contention for the resource
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.27Operating Systems, by Dhananjay Dhamdhere27 Kernel Preemptibility Helps ensure effectiveness of a scheduler –With a noninterruptible kernel, event handlers have mutually exclusive access to kernel data structures without having to use data access synchronization If handlers have large running times, noninterruptibility causes large kernel latency May even cause a situation analogous to priority inversion –Preemptible kernel solves these problems A high-priority process that is activated by an interrupt would start executing sooner
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.28Operating Systems, by Dhananjay Dhamdhere28 Scheduling Heuristics Scheduling heuristics reduce overhead and improve user service –Use of a time quantum After exhausting quantum, process is not considered for scheduling unless granted another quantum –Done only after active processes have exhausted their quanta –Variation of process priority Priority could be varied to achieve various goals –Boosted while process is executing a system call –Vary to more accurately characterize the nature of a process
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.29Operating Systems, by Dhananjay Dhamdhere29 Power Management Idle loop used when no ready processes exist –Wastes power –Bad for power-starved systems E.g., embedded systems Solution: use special modes in CPU –Sleep mode: CPU does not execute instructions but accepts interrupts Some computers provide several sleep modes –“Light” or “heavy” OSs like Unix and Windows have generalized power management to include all devices
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.30Operating Systems, by Dhananjay Dhamdhere30 Real-Time Scheduling Real-time scheduling must handle two special scheduling constraints while trying to meet the deadlines of applications –First, processes within real-time applications are interacting processes Deadline of an application should be translated into appropriate deadlines for the processes –Second, processes may be periodic Different instances of a process may arrive at fixed intervals and all of them have to meet their deadlines
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.31Operating Systems, by Dhananjay Dhamdhere31 Process Precedences and Feasible Schedules Dependences between processes (e.g., P i → P j ) are considered while determining deadlines and scheduling Response equirements are guaranteed to be met (hard real-time systems) or are met probabilistically (soft real- time systems), depending on type of RT system RT scheduling focuses on implementing a feasible schedule for an application, if one exists A process precedence graph (PPG) is a directed graph G ≡ (N,E) such that P i N represents a process, and an edge (P i,P j ) E implies P i → P j. Thus, a path P i,...,P k in PPG implies P i P k. A process P k is a descendant of P i if P i P k.
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.32Operating Systems, by Dhananjay Dhamdhere32 Process Precedences and Feasible Schedules (continued) Another dynamic scheduling policy: optimistic scheduling – Admits all processes; may miss some deadlines
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.33Operating Systems, by Dhananjay Dhamdhere33 Deadline Scheduling Two kinds of deadlines can be specified: –Starting deadline: latest instant of time by which operation of the process must begin –Completion deadline: time by which operation of the process must complete We consider only completion deadlines in the following Deadline estimation is done by considering process precedences and working backward from the response requirement of the application D i = D application −∑ k Є descendant(i) x k
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.34Operating Systems, by Dhananjay Dhamdhere34 Example: Determining Process Deadlines Total of service times of processes is 25 seconds If the application has to produce a response in 25 seconds, the deadlines of the processes would be:
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.35Operating Systems, by Dhananjay Dhamdhere35 Deadline Scheduling (continued) Deadline determination is actually more complex –Must incorporate several other constraints as well –E.g., overlap of I/O operations with CPU processing Earliest Deadline First (EDF) Scheduling always selects the process with the earliest deadline If pos(P i ) is position of P i in sequence of scheduling decisions, deadline overrun does not occur if –Condition holds when a feasible schedule exists Advantages: Simplicity and nonpreemptive nature Good policy for static scheduling
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.36Operating Systems, by Dhananjay Dhamdhere36 Deadline Scheduling (continued) EDF policy for the deadlines of Figure 7.13: P 4 : 20 indicates that P 4 has the deadline 20 P 2,P 3 and P 5,P 6 have identical deadlines –Three other schedules are possible –None of them would incur deadline overruns
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.37Operating Systems, by Dhananjay Dhamdhere37 Example: Problems of EDF Scheduling PPG of Figure 7.13 with the edge (P 5,P 6 ) removed –Two independent applications: P 1 –P 4 and P 6, and P 5 –If all processes are to complete by 19 seconds Feasible schedule does not exist –Deadlines of the processes: –EDF scheduling may schedule the processes as follows: P 1,P 2,P 3,P 4,P 5,P 6, or P 1,P 2,P 3,P 4,P 6,P 5 Hence number of processes that miss their deadlines is unpredictable
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.38Operating Systems, by Dhananjay Dhamdhere38 Feasibility of schedule for Periodic Processes Fraction of CPU time used by P i = x i / T i In the following example, fractions of CPU time used add up to 0.93 –If CPU overhead of OS operation is negligible, it is feasible to service these three processes In general, set of periodic processes P 1,...,P n that do not perform I/O can be serviced by a hard real-time system that has a negligible overhead if:
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.39Operating Systems, by Dhananjay Dhamdhere39 Rate Monotonic (RM) Scheduling Determines the rate at which process has to repeat –Rate of P i = 1 / T i Assigns the rate itself as the priority of the process –A process with a smaller period has a higher priority Employs a priority-based scheduling Can complete its operation early
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.40Operating Systems, by Dhananjay Dhamdhere40 Rate Monotonic Scheduling (continued) Rate monotonic scheduling is not guaranteed to find a feasible schedule in all situations –For example, if P 3 had a period of 27 seconds If application has a large number of processes, may not be able to achieve more than 69 percent CPU utilization if it is to meet deadlines of processes The deadline-driven scheduling algorithm dynamically assigns process priorities based on their current deadlines –Can achieve 100 percent CPU utilization –Practical performance is lower because of the overhead of dynamic priority assignment
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.41Operating Systems, by Dhananjay Dhamdhere41 Case Studies Scheduling in Unix Scheduling in Solaris Scheduling in Linux Scheduling in Windows
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.42Operating Systems, by Dhananjay Dhamdhere42 Scheduling in Unix Pure time-sharing operating system –In Unix 4.3 BSD, priorities are in the range 0 to 127 Processes in user mode have priorities between 50 and 127 Processes in kernel mode have priorities between 0 and 49 Uses a multilevel adaptive scheduling policy Process priority = base priority for user processes + f (CPU time used recently) + nice value For fair share –Add f (CPU time used by processes in group)
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.43Operating Systems, by Dhananjay Dhamdhere43 Example: Process Scheduling in Unix
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.44Operating Systems, by Dhananjay Dhamdhere44 Example: Fair Share Scheduling in Unix
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.45Operating Systems, by Dhananjay Dhamdhere45 Scheduling in Solaris Solaris supports four classes of processes –Time-sharing and interactive processes have priorities between 0 and 59 Scheduling governed by a dispatch table –For each entry, indicates how priority should change with nature of process and to avoid starvation –System processes have priorities between 60-99 They are not time-sliced –RT processes have priorities between 100 and 159 Scheduled by a RR policy within a priority level –Interrupt servicing threads: priorities 160 - 169 Solaris 9 supports a fair share scheduling class
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.46Operating Systems, by Dhananjay Dhamdhere46 Scheduling in Linux Supports real-time and non-real-time applications –RT processes have static priorities between 0-100 Scheduled FIFO or RR within each priority level –Scheduling of a process is determined by a flag –Non RT processes have dynamic priorities (-20 to 19) Initially, 0 priority Priority can be varied through nice system calls Kernel varies process priority according to its nature Scheduled by using the notion of a time quantum 2.6 kernel uses a scheduler that incurs less overhead and scales better
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.47Operating Systems, by Dhananjay Dhamdhere47 Scheduling in Windows Scheduling is priority-driven and preemptive –Within a priority level, RR policy with time-slicing –Priorities of non-RT threads are dynamically varied, hence also called the variable priority class Favor interactive threads –RT threads are given higher priorities (16-31) –Effective priority depends on: base priority of process, base priority of thread, and a dynamic component Provides a number of low power-consumption system states for responsiveness, e.g., hybernate and standby Vista introduced new state sleep, which combines features of hybernate and standby
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.48Operating Systems, by Dhananjay Dhamdhere48 Performance Analysis of Scheduling Policies The set of requests directed at a scheduling policy is called its workload –First step in performance analysis of a policy is to accurately characterize its typical workload Three approaches could be used for performance analysis of scheduling policies: –Implementation of a scheduling policy in an OS –Simulation –Mathematical modeling
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.49Operating Systems, by Dhananjay Dhamdhere49 Performance Analysis through Implementation The scheduling policy to be evaluated is implemented in a real OS that is used in the target operating environment The OS receives real user requests; services them, using the scheduling policy; and collects data for statistical analysis of the policy’s performance Disruptive approach –Disruption can be avoided using virtual machine software
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.50Operating Systems, by Dhananjay Dhamdhere50 Simulation Simulation achieved by coding scheduling policy and relevant OS functions as a simulator and using a typical workload as its input Analysis may be repeated with many workloads to eliminate the effect of variations across workloads
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.51 Mathematical modeling A mathematical model is a set of expressions for performance characteristics such as arrival times and service times of requests –Queuing theory is employed To provide arrival and service patterns Exponential distributions are used because of their memoryless property –Arrival times: F(t) =1 – e –αt, where α is the mean arrival rate –Service times: S(t) = 1 – e –ωt, where ω is the mean execution rate Mean queue length is given by Little’s formula –L = α x W, where L is the mean queue length and W is the mean wait time for a request Operating Systems, by Dhananjay Dhamdhere51
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.52Operating Systems, by Dhananjay Dhamdhere52 Mathematical Modeling (continued)
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.53Operating Systems, by Dhananjay Dhamdhere53 Summary Scheduler decides process to service and how long Three techniques: –Priority-based, reordering of requests, and variation of time slice Scheduling can be: –Non-preemptive: E.g., SRN, HRN –Preemptive: E.g., RR, LCN, STG OS uses three schedulers: long-term, medium-term, and short-term scheduler
Operating Systems, by Dhananjay Dhamdhere Copyright © 20087.54Operating Systems, by Dhananjay Dhamdhere54 Summary (continued) Different scheduling policies –Time-sharing: Multilevel adaptive scheduling Fair share scheduling –Real-time: Deadline scheduling Rate monotonic scheduling Performance analysis is used to study and tune performance of scheduling policies
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