程序代写案例-COSC 1114-Assignment 1



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COSC 1114 Operating Systems Principles
Semester 2, 2022
Assignment 1

Assessment
Type


Individual assignment.
See Canvas for submission details
Marks awarded for meeting requirements as closely as possible. Clarifications/updates may be
made via announcements/relevant discussion forums.
Submission
Due Date
Start of week 7.
Marks 40 (40% of semester)

1. Research Question
We can use Load Balancing as a technique of ensuring that all processes/threads in a
particular [problem are treated in such a fashion that they all finish at approximately the same
time. We frame the problem in the form of a map/reduce problem, and then use Static Load
Balancing (S-LBAN) to adjust the process load for optimal net performance.

The question is how accurate can we do this? Is it worth the effort? Clearly Amazon’s Elastic
Computing (EC2) tells us that this is so on a large scale, which typically includes a lot of data
movement and networking. But what about in a real-time system, with no significant
networking – the data arriving in a batch before the program begins, and having repeatable
statistical properties?

In other words, we measure, adjust, and deliver optimally.

The central task of this assignment is to produce an evidence-based report answering the
above questions.

Methodology
We propose to answer the RQ by creating a series of tasks. First some simple ones in order to
establish a baseline of result expectations, then to rewrite the tasks using the map/reduce
software pattern to produce a set of concurrent threads of different complexity. We can then
adjust the scheduling in order to answer the research question.

2. Overview of Methodology
In this assignment, you must write C/C++ code to implement a map / reduce problem. Map() is a process or function that
maps the input problem to the available resources which may mean subdividing the problem into components and solving
each of those component problems using prioritized job scheduling.
Then reduce() gathers the component solutions and combines them to form the global solution.
You will be provided with “dirty data” and the assignment is then divided into a number of tasks, each of which purports to
solve the problem a different way. In each case, there will be a performance measuring phase, and for the later methods, an
adjustment phase based on the performance metrics obtained.
Throughout, you will be measuring and properly documenting performance (and hopefully improving it)
In the last task, where streaming data is used, it will be important to reduce idle time by ensuring that all subtasks created by
map() finish at the same time, so that reduce() is not kept waiting.



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3. Learning Outcomes
This assessment relates to the following course learning outcomes:
• Summarise the full range of considerations in the design of file systems, summarise techniques
for achieving synchronisation in an operation system,
• Compare and contrast the common algorithms used for both pre-emptive and non-pre-emptive
scheduling of tasks in operating systems, such as priority, performance comparison, and fair-
share schemes. Contrast kernel and user mode in an operating system
• Evaluate and report appropriate design choices when solving real-world problems
• Analyse the key trade-offs between multiple approaches to operating system design.
4. Assessment details
General
Your solution to the problem you choose must not use busy waiting, which is where a program sits in a tight loop until some
condition is met as this waste’s CPU time. Instead, you must have each thread sleep until a condition is met (use a condition
variable), wake up and do some processing and then perhaps signal other threads that the job has been done. Please ensure
sufficient output so that it is clear when your program performs any actions such as adding to an array, locking a lock, etc.
You should ensure in your design of the program that you only share between threads the minimum state (variables) possible
as the more information that is shared the more likely there is to be a conflict (deadlocks, race conditions, etc). Please see the
courseware material for more detail on these problems. If your algorithm requires randomness, you should ensure you seed
the random number generator correctly.
To ease marking of your assignment, you must call your executables "taskN", where N is the task number as described below.
All materials necessary for the markers to build your tasks must be included in the submitted ZIP file. The markers will use
the CS servers (titan, saturn, jupiter) for the marking, and the code is expected to run there.
Makefile

Your solution must be written in C / c++ and you must supply a Makefile that builds your solution incrementally and then
links them together. If you are feeling a big rusty about make files or have not used them before, we recommend going
through the following tutorial:
https://www.tutorialspoint.com/makefile/index.htm

Compiler Settings

Please note that as a minimum you must use the ‘-Wall -g’ flags on gcc or equivalent on other compilers to generate
warnings. You may use any supported c++ compiler on the server - if you wish to use a standard above c++ on the server
with g++ you will need to use the scl command, e.g.:
scl enable devtoolset-9 bash
This should get you gcc version 9.3.1 (2020) instead of gcc 4.8.5 (2015). Use “gcc –version” to confirm.

Graceful Exit

In order to account for the possibility of thread starvation, you will need to gracefully exit your simulation at the end of
around 10 seconds. We would normally expect even the slowest task – task1() – to not take longer than 10 seconds to
execute.

Use the following method to do this: once you have launched all the required threads from main, call the sleep function, to
specify sleep for ten seconds. Once the sleep function finishes, change the value of a global variable to indicate that all the
threads should exit, which they should be checking regularly.

A less graceful exit might be to call exit() after 10 seconds, but that risks leaving zombies behind with unfinished business –
potentially leading to data loss.



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Valgrind

The solution you submit will ideally be as bug-free as possible including free of memory bugs. Your markers will mark your
submission on the titan/jupiter/saturn servers provided by the university and we will use the tool "valgrind" to test that your
program is memory correct (no invalid access to memory and all memory freed) and as such you should check your program
on titan before submission with this tool. This is for debugging and memory testing only. When gathering performance data,
you should do this on a dedicated machine or VM, since on titan, being a shared resource, the timings will obviously not be
repeatable.

The following command:
valgrind --track-origins=yes --leak-check=full --show-leak-kinds=all ./simulation
Will run your program with valgrind. Please note that in this case the name of the executable produced by compilation of
your program is 'executable'.

Allowed Concurrency Functions

For the concurrency part of the assignment, you must limit yourself to the following functions:

• pthread_create
• pthread_join
• pthread_detach
• pthread_mutex_init
• pthread_mutex_destroy
• pthread_mutex_trylock
• pthread_mutex_lock
• pthread_cond_wait
• pthread_cond_signal
• pthread_cond_init
• you may also need the pthread_mutexattr_* functions
• you may also use the scheduling priority function.

This list is not exhaustive and so if you are unsure, please ask on the discussion board about the function you wish to use.

Please note that in practice beyond this course, you would use higher-level c++ functions that call these functions, but part
of what you are learning here is the underlying calls that are made as part of managing concurrency. That is, part of what
you are learning is to apply the theory that you learn in the workshops to practical problems.

The Source Data

To start off, you may use a words list conventionally stored in ‘/usr/share/dict/linux.words’ as a clean data file. You may
need to install this in your distro. Titan does not have it.

The actual data to be used is at https://www.keithv.com/software/wlist/.

You will find a number of ZIP files containing text files containing (ideally) 1 word per line which you read into an array for
subsequent processing. In fact, the data is ‘dirty’ and you will need to clean It up first. In your performance measurement
using Amdahl’s Law, this is the serial part.

Performance Measurement and Reporting

You will need to devise a way of measuring improvement using primarily execution time, but also resource usage. Consider
some of the metrics discussed in class.

You then need to describe this using graphs and other ways to describe how what you did improved performance, and why.
See reporting details below.



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5. The Tasks in detail.

The fundamental task is to receive a set of words, clean it up, then use map/reduce ultimately to sort it. Words of length
3-15 characters are to be sorted on the third character onwards. Words of other lengths are to be ignored (filtered out).
The tasks below achieve this in different ways.

Below you will find a selection of five tasks called task1 … task5 that must all be done in sequence. In each case, the
simulations should not exceed about 10 seconds and terminate cleanly - no crashes, no deadlocks, no race conditions. This
should be enough time to gather event statistics. You may need to replicate the data in order to make it run long enough. If
so, then please describe what you did.

You should also print a line of text for each action in your program such as adding an element to an array. All normal
messages should be printed to stdout (use cout) and error messages should be printed to stderr (use cerr). This will allow
your marker to capture normal output and error output separately if they wish to. In bash, a command like:
./task2 (args) 1>outfile 2>errfile
would do the job (where args are whatever arguments deemed necessary).

Be sure to prefix each process/thread with its identity, where needed. I would also timestamp the data reported. An
example of this is the additional output given when the -v or -vv or -vvv options on ssh are used.

Performance Measurement

Use Linux-supplied tools and functions and your judgement for performance measurement (for example: ‘perf’, ‘profile’,
‘ftime’, ‘getrlimit’, ‘gccpg’, ‘perf stat -d’, or others) . In the report you must justify the tool you are using and what it actually
measures.

Map() / Reduce()

For each task, you will use a different mapN() to separate the words list into separate lists,, each to be executed according
to the task. A corresponding reduceN() function will gather the distributed output produced by map() to a final outcome,
again different for each task. The N above refers to the task number.

In all tasks , the same file should be produced as output for a given input. In that sense, all tasks are equivalent – the only
difference being HOW they do their job.

You should call the function mapN() and reduceN() where N is the task number, to make this clear to the markers.


Task 1: Task 1 – Manually Capturing, Examining, and Filtering the data
• Use the coreutils programs (“grep, sed, sort, etc..”) to discover ways of cleaning the data. The files may contain
punctuation and other symbols. Also remove duplicates. Consider and document some simple filtering rules, In
order to come up with a clean data file. Finally, since sorting an already sorted file is not a good test, use ‘shuf’ to
shuffle the data into random order.
• Distill what you created into a shell file called Task1.sh
• Now replicate this filtering and shuffling in a C/C++ function called TaskFilter(), and compile this separately as you
will link to it in the subsequent tasks. Make a main() that simply calls this function of the file supplied as an
argument on the command line as
Task1filter DirtyFile Cleanfile
Where the resultant clean file should be identical to the file produced by the coreutils and shell script tools above.

• Report is to include:
a. The combination of coreutils tools used to generate the equivalent of Task1filter.
b. The source of Task1filter
c. The number of words of length 3 to 15 letters in the data set you end up with. Include this data set in your
submission. This forms the basis for the load balancing statistics you may use later. Ignore words of
length 1,2, or more than 15 letters
d. the performance data for this task




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Task 2: Process based Solution
Read the data from the original file. Use Task1filter on the original data to produce a clean version . Then do the following.

• Use map2() to separate the words list into separate lists, one with words of length 3, the next of length 4, to 15.
Ignore words of length 1,2 or more than 15.
• Map2() then uses fork() to call “sort” and have it sort each file on the third letter of each word using appropriate
arguments, saving each result in its own file.

• Use reduce() to open each of the 13 files reading one line at a time for each file, and writing the lowest sort order
word from each file, and then reading the next word from that file. In other words, a 13 → 1 merge sort.

Task 3: Threads using shared memory without conflicts
Here, we use FIFO files to transfer the data from map3() to reduce3(). Specifically

• The main program calls the filter function to clean the data. The function will return a global string array of valid
words. then main() creates both the map3() and reduce3() threads.
• The map3() function creates 13 index arrays and threads, one for each word length, indexing into the main string
array. So if the global array contained [ “air”, ”airbag”, ”and” ] an index array had [1,3,…] then global[index[0]] is
‘air’, global[index[1]] = ‘and’.
• Global[] is only ever read by map before the threads, and only thread3 reads index3[]. Since no word can be two
lengths at the same time, there will never be a conflict between threads .
• Having created the mapping, map3() now creates 13 threads. Each thread uses the C function qsort() to perform
the same sort as before – on the third letter onwards – and when done, it creates 13 FIFO files opened for write
and each thread will then output words to their corresponding file.
• In this case, the reduce3() function will wait, and then read those same 13 files, one line at a time in sort order.
Note that qsort() sorts the indexes, not the array itself.
• This should produce the same file as Task2 (and Task 1 – after sorting.)
• At the end, each thread opens a FIFO file for writing, and dumps the global strings in sorted index order.
• reduce3() should keep a count of words for each thread.
• Meanwhile, reduce3() needs only to perform the reduction merge step. It waits for each map3() thread to signal
its completion. When all are done, reduce3() will open all 13 FIFO files for read, and perform the merge step as in
Task2,
• Report: Compare performance. Consider where the time was saved or lost.

Task 4 Optimizing thread performance
It should be self-evident that some word lengths are more frequent than others, so some threads will take longer to
complete than others.

For this task
• When, map4() – same as map3() - creates the thread, it additionally assigns them a specific priority based on the
performance ratios in Task3, so that all threads finish at roughly the same time. Think how you would do that?
(Hint: ‘mice’)
• Does reduce4() need to change from reduce3*( ?
• To get reasonably reliable performance data, you may need to pad out the input data with more duplicates.
• Can you tweak the thread performance so that they indeed finish at about the same time?
• Now compare the tweaked priorities with what you believe they should be. Do some research to discover how the
priority levels are related to speed.
• Describe all that you did in the report and what conclusions you came to.

Report: Document the following.
• Where are the threads spending most of their time?
• Can you come up with a big O representation? Justify your reasons.
• For the larger word lengths the string compare will also take longer. Is that significant?
• By checking the performance of the Task 3 threads, how close was the ratio of thread performance with the big O
expectation? For example if thread was O(n2), then for larger n, a 3000-word thread should run 4 times as long as a
1500-word thread. So we have execution time and word count as two metrics for comparison.



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Task 5 Converting to Input Stream
Instead of the data coming from a fixed file, it comes from a streaming input source. There are several implications in this.
• Can you sort streaming data? Try oy with “cat - | sort”. When do you get to see the sort output? Why?
• Now you cannot preselect historical ratios. Instead you must be adaptive. You have word count and cumulative
execution time for each thread. What can you do with it?
• Let us suppose you block the data, and sort each block. How does that change things.?
• Report on all the above and any other relevant considerations.


For this final task
• Create a ‘stream server’ ,
a. This reads the whole original (dirty) file into a string array.
b. Then the server outputs a random entry to a FIFO file that is connected to the map5() input stream at
some constant rate.
c. In this case, since the rate is slow, map5() will need to know when the FIFO is non-empty. You cannot use
a busy-wait loop to find out.
• Report:
a. Give a detailed description of how you would change Task4 in order to make it streamable, but without
sorting (so no blocking needed) So here map5() does not change, but the threads do.
b. Describe what CPU scheduling algorithm could be used if there was no sort but there was a precedence
rule that said that shortest words pass first in reduce5().
• Implement the above task rescheduling.
Task 6 Abstract Mapping (2 Bonus marks)
• This is where the power of the map/reduce methodology becomes evident.
• Code changes
a. Build tasks 3,4,5 in such a manner that the selection / mapping and reduction methods can be change with
little change to the rest of the code.
b. Instead of word lengths, map3a(), map4a(), and map5a(),are to divide its work into 26 threads, each
starting with a different letter of the alphabet. There will be a corresponding change in reduce3a(),
reduce4a(), andrreduce5a(),.
c. If you have coded the previous tasks appropriately, this change should be relatively trivial.
• Report
a. Note how the difference in work division has affected the overall performance.
b. In particular, note how the reduce() parts simplify. Explain why this is so.

6. Report and GitHub

You are to use GitHub as your project space. Make it a private project, and include your tutorial tutor as a collaborator.

In addition to the program solution, you are required to write a DOCX/PDF report describing the following.
1. Identify yourself using student ID, Name, and GitHub project name/URL
2. Describe any issues and limitations of your implementation.
3. Describe all the outcomes mentioned in the tasks above.


7. Submission

Submit a ZIP file to Canvas containing
1. Makefile
2. A complete set of source files needed to build your solution
3. A Readme file explaining what needs to be done other than just calling 'make' to run your program.
4. A copy of the report file produced.

A Report in DOCX/PDF format as per previous section is to be separately submitted in Canvas.


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Students are advised and expected to make 3 submissions of the ZIP file, in week 5,6,7, with 7 being the
final submission. Only the final submission will be used for marking, but the others are needed to
demonstrate progress.


Assessment declaration:
When you submit work electronically, you agree to the assessment declaration:
https://www.rmit.edu.au/students/student-essentials/assessment-and-
exams/assessment/assessment-declaration

8. Academic integrity and plagiarism (standard warning)
It is your responsibility to ensure that all files you submit are your own work. We will check your submission against other submissions using automated
software to check for plagiarism. You must agree with the RMIT Assessment declaration available here:
Academic integrity is about honest presentation of your academic work. It means acknowledging the work of others while developing your own insights,
knowledge and ideas. You should take extreme care that you have:
• Acknowledged words, data, diagrams, models, frameworks and/or ideas of others you have quoted (i.e. directly copied), summarized,
paraphrased, discussed or mentioned in your assessment through the appropriate referencing methods,
• Provided a reference list of the publication details so your reader can locate the source if necessary. This includes material taken from Internet
sites.
If you do not acknowledge the sources of your material, you may be accused of plagiarism because you have passed off the work and ideas of another person
without appropriate referencing, as if they were your own.
RMIT University treats plagiarism as a very serious offence constituting misconduct. Plagiarism covers a variety of inappropriate behaviors, including:
• Failure to properly document a source
• Copyright material from the internet or databases
• Collusion between students
For further information on our policies and procedures, please refer to https://www.rmit.edu.au/students/student-essentials/rights-and-
responsibilities/academic-integrity



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9. Marking Guidelines
A marking guide is described below. A more detail marking rubric nay be attached to the
assignment section in Canvas, and it will be the final guide. In all cases, in giving you less than
full marks, your marker will detail the problems in the provided implementation.

Report and Data Management (10 marks)
Performance data preparation (2 marks)
Correct methods of data collection (2 marks)
Appropriateness of data presentation (2 marks)
Coherence of argument (for example t (2 marks)
Formatting (2 marks)

Code Details (Maximum 10 marks)
Makefile and version control (2 marks)
You must submit a Makefile to compile your assignment. It must build each
source file into a compiled object file and then link them all together. Project
must be on GitHub and there must be evidence of version control.
Compilation (2 marks)
Your program must compile without errors or warnings (compiler and option -Wall must be used).
Programming Style (2 marks)
You must follow good programming style - avoid use of “magic numbers”,
name your constants, variables and functions meaningfully, etc.
Appropriate use of coreutils tools (2 marks)
Synchronization Primitives (2 marks)
use correct functions from the pthread library to implement threads, locking and sleeping.
Use the first letter method of work subdivision (bonus marks) (up to 2 marks)
Build in functionality into tasks 3,4,5 to enable alphabetic work division instead of word length.
Note that total marks for this section not to exceed 10 marks.

Completion of Code (5x4 = up to 20 marks)
Excellent: the solution you have provided exceeds assignment requirements. (4 marks, or)
Very Good. The solution you have provided is correct as per assignment requirements (3 marks or)
Good. the solution is good but there are some minor issues with your implementation. (2 marks or)
Fair. Not so good: you gave it a go but it's a long way from the required implementation. (1 mark or)
No marks: no implementation provided. (0 marks)
Late Submission Policy: 10% of the available marks will be deducted for every day late. Please note that this
includes days on the weekend.

Note that the published rubric for this assignment will apply, even if there are small differences with the
marking described herein.


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