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Understanding Operating Systems
Fifth Edition
Chapter 12
System Management
Evaluating an Operating System
• To evaluate an OS, you need to understand:
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Its design goals and history
How it communicates with users
How its resources are managed
What tradeoffs were made to achieve goals
• Operating system’s strengths and weaknesses need
to be weighed in relation to:
• Users of the OS
• Hardware on which the OS will run
• Purpose
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Cooperation Among Components
• Performance dependency
– One resource depends on other system resources
• System improvement
– Requires extensive analysis of the needs of the
• System’s resources, requirements, managers, users
• System change results
– Trade one problem for another
• Consider entire system performance
– Not just individual components
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Role of Memory Management
• Consider actual operating environment
– Before memory-related changes
• Tradeoff
– Memory use versus CPU overhead
– As the memory management algorithm complexity
increases, the CPU overhead increases, which could
affect overall system performance
• But for some operating systems, adding more
memory can result in a remarkable improvement in
performance
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Role of Processor Management
– A multiprogramming system may be desirable if you
wish to increase CPU utilization but this
– Requires synchronization
• Memory manager, processor manager, and I/O devices
– Tradeoffs to consider
• Obtain better CPU utilization versus increased
overhead, slower response time, decreased throughput
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Role of Processor Management
(continued)
• Problems
– System could reach saturation point
• If the CPU is fully utilized and still accepting additional
jobs
• Will lead to higher overhead and less time to run
programs
– Under heavy loads
• CPU time required to manage I/O queues could
dramatically increase the time required to run jobs
– With long queues at channels, control units, and I/O
devices, the CPU could be idle waiting for processes
to finish I/O
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Role of Device Management
• I/O device utilization improvement techniques
include:
– buffering, rescheduling I/O requests
Tradeoffs involved:
• Increased CPU overhead
• Additional memory space used
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Role of Device Management
(continued)
• Buffering
– CPU matches slower I/O device speed (and vice
versa)
– Requires memory space (buffers)
– Tradeoff
• Less multiprogramming versus better I/O device use
• Rescheduling requests
– Seeks to optimize I/O request time by reordering I/O
queues
– Overhead is involved, so the CPU and I/O device
speeds must be weighed against the time it would
take to execute the reordering algorithm
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Role of Device Management
(continued)
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Role of Device Management
(continued)
• Example: without reordering
– CPU 1 and disk drive A
• Access track 1, track 9, track 1, track 9
• Arm already located at track 1
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Role of Device Management
(continued)
• Example: after reordering
– Arm performs both accesses on Track 1 before
traveling Track 9 (35 ms)
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Role of Device Management
(continued)
• Reordering requests not always warranted
– Example: CPU 1 and much faster disk drive C
• Without reordering: access time = 5 + 5 + 5 = 15 ms
• With reordering: access time = 5 + 30 = 35 ms
• Reordering algorithm
– Always on or always off
– Requires reconfiguration by system admin to change
– Initial setting
• Determined by evaluating system on average
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Role of File Management
• The way in which files are stored on secondary storage can affect
system performance
• Important considerations:
– File organization
• Example: If a file is stored non-contiguously that has several
sections residing in widely separated cylinders of a disk pack,
sequentially accessing all of its records could be slow
• This requires compaction (defragmentation). This takes CPU time
and makes the files unavailable to users during compaction.
– Volume directory location
• Affects retrieval time
• Different schemes offer different flexibility but the tradeoff for file
flexibility is CPU overhead
• File management is closely related to device on which files are
stored
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Role of File Management (continued)
• File management related to device where files
stored
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Role of Network Management
• Determines message priorities
• Tries to selects most efficient communication paths
over multiple data communication lines
• The network manager allows a network admin to
monitor the use of individual computers and shared
hardware, and ensure software license agreement
compliance
• Simplifies updating data files and programs on
networked computers by coordinating changes
through a server instead of making changes on
individual computers.
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Measuring System Performance
• Total system performance is the efficiency with
which computer system meets goals
• System efficiency
– Not easily measured
– Affected by three components
• User programs, operating system programs, hardware
• System performance
– Very subjective
– Difficult to quantify
– When quantifiable
• Not an absolute measure
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Measurement Tools
• Measures/metrics of system performance:
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Throughput
Capacity
Response time
Turnaround time
Resource utilization
Availability
Reliability
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Measurement Tools (continued)
• Throughput
• Composite measure
– Indicates system productivity as a whole
– Measured under steady-state conditions
– Example: quantities
• Number of jobs processed per day
• Number of online transactions handled per hour
– Measures work volume handled by system unit
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Measurement Tools (continued)
• Throughput bottlenecks
– Capacity
– Maximum throughput level
• Resources saturated
• Processes not passed along
– Main memory over-committed
• Multiprogramming level reaches peak point. Leads to
thrashing (memory manager continuously swaps pages
between main memory and secondary storage and the CPU
does not make much progress in executing jobs because it is
too busy swapping pages).
• Monitored by hardware or software
• Bottleneck detection
– Monitor queues at each resource
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Measurement Tools (continued)
• Response time
– Online interactive user
– Interval required to process user request
• From when user presses key to send message until
system indicates receipt of message
• Turnaround time
– Batch job response time
• Time from job submission until output returned to user
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Measurement Tools (continued)
• Dependencies: Whether in an online or batch
system, this measure (response time or turnaround
time) depends on both the:
– Workload handled by system at time of request
– Type of job or request being submitted
• Include
– Average values and variance
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Measurement Tools (continued)
• Resource utilization
– How much unit contributing to overall operation
– Percentage of time resource actually in use
• Example: CPU busy 60 percent of time?
– Helps analyst determine
• Whether there is balance among system units
• Whether the system is I/O-bound or CPU-bound
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Measurement Tools (continued)
• Availability
– Indicates likelihood a resource will be available for
use when a user needs it
• Influenced by:
– Mean time between failures (MTBF)
• Average time unit is operational it before breaks down
– Mean time to repair (MTTR)
• Average time needed to fix failed unit and put back in
service
MTBF
Availabili ty(A) 
Availability (A) = MTBF  MTTR
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Measurement Tools (continued)
• Reliability
– Measures probability unit will not fail during given time
period
– Function of MTBF
R(t )  e
 (1 MTBF )( t )
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Measurement Tools (continued)
• Performance measures
– Avoid taking in isolation from system workload
• Overall system performance
– Varies with time
– Important to define actual working environment
• Before making generalizations
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Feedback Loops
• Monitor system resource utilization for adjustments
– Prevents processor time spent on overhead
– More time executing jobs
• Feedback loop types
– Negative feedback loop
– Positive feedback loop
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Feedback Loops (continued)
• Negative feedback loop
– Process arrival rate decreased when system too
congested
• Stabilized system
• Queue lengths close to estimated mean values
• Positive feedback loop
– Arrival rate increased when system underutilized
• Paged virtual memory systems use this
• Implementation more difficult (than negative loops)
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Feedback Loops (continued)
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Feedback Loops (continued)
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Monitoring
• Hardware monitors
– More expensive
– Minimum impact on system
• Outside and attached electronically
– Examples: counters, clocks, comparative elements
• Software monitors
– Relatively inexpensive
– Distortion of analysis results
• Software monitor becomes part of system
– Developed for each specific system
– Difficult to move from system to system
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Monitoring (continued)
• Early systems performance measurements
– Monitored CPU speed
• Today’s measurements
– Other hardware units, operating system, compilers,
other system software
• Measurements made in variety of ways
– Real programs: production programs
• Run with different configurations of CPUs, operating
systems, other components
• Results called benchmarks
– Using simulation models
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Monitoring (continued)
• Benchmarks
– Demonstrate specific advantages
• New CPU, operating system, compiler, or piece of
hardware
– Useful when comparing systems experiencing
extensive changes
– Results dependent upon:
• System’s workload
• System’s design and implementation
• Specific requirements of applications loaded on system
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