COMP30023语言编程讲解、Java，CS，Python程序辅导辅导留学生Prolog|辅导留学生 Statistics统计、回归、迭代

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COMP30023 Project 1
Process* Scheduling
Out date: March 15, 2021
Due date: No later than 14:59 March 30, 2021 AEDT
Weight: 15% of the final mark
Background
In this project you will familiarise yourself with process scheduling in a multi-processor environment.
You will write a simulator that allocates CPU (or processor) to the running processes.
Many details on how scheduling works have been omitted to make the project fit the timeline. Please
note that you do not need multiple processes or threads (e.g., no need for fork or pthread) to implement
this project. We strongly advise you to start working on the project as soon as possible.
The program will be invoked via the command line. It will be given a description of arriving processes
including their arrival time, execution time in seconds, their id, and whether they are parallelisable or
not. You do not have to worry about how processes get created or executed in this simulation. The first
process always arrives at time 0, which is when the simulation begins.
1 Single processor scheduler
Processes will be allocated CPU time according to shortest-time-remaining algorithm, a preemptive
scheduling algorithm. When a new process arrives, it is scheduled if there is no other process running.
If, when process j arrives, there is a process i running, then i is postponed and added to a queue if and
only if j has a shorter execution time than the remaining time of i. Process i is resumed where it left
off, if it is the process with the shortest remaining time left among all other processes in the queue (i.e.,
including those that may have arrived after j). Process ids are used to break ties, giving preference to
processes with smaller ids. Since there is only one CPU, you can ignore the information on whether a
process is parallelisable or not.
The simulation should terminate once all processes have run to completion.
2 Two processor scheduler
In this section you are asked to extend your scheduler to work with 2 processors: processors numbered 0
and 1. This will simulate (1) a situation where processes can run truly in parallel and (2) a process that
can run faster with more computational resources (e.g., by creating child/sub processes).
If the process that arrives is not parallelisable, it is assigned to the queue of the CPU with the smallest
amount of remaining execution time for all processes and subprocesses (defined below) allocated to it,
using CPU id to break ties, giving preference to CPU 0. After that the simulation for this process
behaves same as in Section 1. In this project, once a process is assigned to a CPU it cannot be moved
to another CPU. (Think of why it may not be a good idea to migrate processes between CPUs.)
If arriving process with id i and execution time x is parallelisable, two subprocesses are created, i.0
and i.1, each with execution time dx/2e + 1. The extra time simulates the cost of synchronisation. For
example, a parallelisable process that runs on a single CPU in 6 seconds, can finish within 4 seconds if
both CPUs are idle when it arrives.
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Once subprocesses are created, subprocess i.0 is added to the queue of CPU 0 and i.1 is added to CPU 1.
After that they are treated as regular processes when scheduling them on each CPU. A parallelisable
process is considered finished when both of its subprocesses have finished.
3 N processor scheduler
In this task we generalise the 2 processor setting to N ≥ 3 processors. Similar to above, a nonparallelisable
process is assigned to a CPU that has the shortest remaining time to complete all processes
and subprocesses assigned to it so far.
A parallelisable process is split into k ≤ N subproceses, where k is the largest value for which x/k ≥ 1.
Each subprocess is then assigned execution time of dx/ke + 1. Subprocesses follow a similar naming
convention as above: a process with id i is split into subprocesses with id’s i.0, i.1, . . ., i.k
0
, where
k
0 = k − 1. Subprocesses are then assigned to k processors with shortest time left to complete such
that subprocess i.0 is assigned to the CPU that can finish its processes the fastest, i.1 is assigned to the
second fastest processor and so on. Use CPU ids to break ties, giving preference to smaller processor
numbers.
Example: consider a scenario with 4 CPUs, each with one process left to complete; processes on CPU
0,1,2,3 have remaining time of 30,20,20,10, respectively. Then for a process i with execution time of 2,
k is set to 2. Its subprocess i.0 and i.1 will be assigned to CPU 3 and 1 respectively.
Once a process or subprocess is assigned to a CPU it cannot migrate to another CPU. A parallelisable
process is considered finished when all of its subprocesses have finished.
4 Challenge: Improve the performance
You will be asked to measure the overall time of your simulation (Makespan). The challenge task is to
come up with an algorithm that has a shorter makespan on a set of tests (see details in Section 7). For
this task the choice of k when splitting a parallelisable process is left to you. You are also allowed to
“look into the future” and see what processes will be arriving and use this information if you choose to.
(Think of whether it is possible and how one would obtain such information in real life.) You will be
required to explain why your algorithm is more efficient in a short report.
5 Program Specification
Your program must be called allocate and take the following command line arguments. The arguments
can be passed in any order but you can assume that they will be passed exactly once.
-f filename will specify the name of the file describing the processes.
-p processors where processors is one of {1,2,N}, N ≤ 1024.
-c an optional parameter, when provided, invokes your own scheduler from Section 4.
The filename contains the processes to be executed and has the following format. Each line of the file
corresponds to a process. The first line refers to the first process that needs to be executed, and the last
line refers to the last process to be executed. Each line consists of a space-separated tuple (time-arrived,
process-id, execution-time, parallelisable). You can assume that the file will be sorted by time-arrived
which is an integer in [0, 2
32) indicating seconds; all process-ids will be distinct integers in the domain
of [0, 2
32) and the first process will always have time-arrived set to 0; execution-time will be an integer
in [1, 2
32) indicating seconds; parallelisable is either n or p. If it is p, then the corresponding process is
parallelisable; if it is n, it is not. You can ignore n/p when -p is 1. More than one process can arrive at
the same time.
Example: ./allocate -f processes.txt -p 1.
The allocation program is required to simulate execution of processes in the file processes.txt on a
single CPU.
Given processes.txt with the following information:
0 4 30 n
2
3 2 40 n
5 1 20 n
20 3 30 n
The program should simulate execution of 4 processes where process 4 arrives at time 0, needs 30 seconds
running time to finish; process 2 arrives at time 3 and needs 40 seconds of time to complete.
Each line (including the last) will be terminated with a LF (ASCII 0x0a) control character.
You can read the whole file before starting the simulation or read one line at a time. We will not give
malformed input (e.g., negative number of processors after -p or more than 4 columns in the process
description file).
6 Expected Output
In order for us to verify that your code meets the above specification, it should print to standard output
(stderr will be ignored) information regarding the states of the system and statistics of its performance.
All times are to be printed in seconds.
6.1 Execution transcript
For the following events the code should print out a line in the following format:
• When a (sub)process starts and every time it resumes its execution:

,RUNNING,pid=,remaining_time=,cpu=\n
where:
‘current-time’ refers to the time at which CPU is given to a process;
‘process-id’ refers to the id of the process that is about to be run;
‘T’ refers to the remaining execution time for this process;
‘cpu-id’ refers to the processor where the process is scheduled on. It can be 0, 1, 2 . . . , N −1 when
-p N for N ≥ 1;
Sample output could be:
20,RUNNING,pid=15,remaining_time=10,cpu=0
• Every time a process finishes:

,FINISHED,pid=,proc_remaining=\n
where:
– ‘’ is as above for the RUNNING event;
– ‘process-id’ refers to the id of the process that has just been completed;
– ‘num-proc-left’ refers to the number of processes that are waiting to be executed over all
processors (i.e., those that have already arrived but not yet completed, including those that
have unfinished subprocesses).
If there is more than one event to be printed at the same time: print FINISHED before RUNNING and print
events for smaller CPU ids first.
Example: Consider the last remaining process which has 10 seconds left to completion. Then the following
lines may be printed:
20,RUNNING,pid=15,remaining_time=10,cpu=1
30,FINISHED,pid=15,proc_remaining=0
6.2 Performance statistics
When the simulation is completed, 3 lines with the following performance statistics about your simulation
performance should be printed:
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• Turnaround time: average time (in seconds, rounded up to an integer) between the time when the
process completed and when it arrived;
• Time overhead: maximum and average time overhead when running a process, both rounded to the
first two decimal points, where overhead is defined as the turnaround time of the process divided
by its total execution time (i.e., the one specified in the process description file).
• Makespan: the time in seconds when your simulation ended.
Example: For the invocation with arguments -p 1 and the processes file as described above, the simulation
would print
Turnaround time 62
Time overhead 2.93 1.9
Makespan 120
7 Marking Criteria
The marks are broken down as follows:
Task # and description Marks
1. Single processor (Section 1) 2
2. 2 processors, non-parallelisable processes only 2
3. 2 processors, parallelisable and non-parallelisable processes 2
4. N processors, non-parallelisable processes only 2
5. N processors, parallelisable and non-parallelisable processes 2
6. Performance statistics computation (Section 6.2) 1
7. Challenge task (Section 4) 2
8. Quality of software practices 1
9. Build quality 1
Tasks 1-6. We will run all baseline algorithms against our test cases. You will be given access to half
of these test cases and their expected outputs. Half of your mark for the first 6 tasks will be based on
these known test cases. Hence, we strongly recommend you to test your code against them. The first 5
tasks will be marked based on the execution transcript (Section 6.1) and task 6 based on performance
statistics (Section 6.2).
Task 7. The challenge task, as described in Section 4, will be evaluated based on a set of 9 test cases
provided to you and your report. We will use these test cases to compare the Makespan of your scheduling
algorithm (with -c option) with the default one. A scheduling algorithm that improves Makespan (i.e.,
results in a shorter Makespan) over the default algorithm on 2-4 of the 9 test cases will be awarded 0.5
marks, 1 mark for improving on 5-7 test cases and 1.5 marks for improving on 8-9 test cases. We may
use a verification algorithm on your transcript to verify that your scheduler is sound (e.g., that every
process is given CPU time as per its execution time requirements and does not start before its arrival
time). You will not be provided with expected output of the default scheduler for test cases in this task.
Please submit a report explaining your scheduling algorithm and why it performs better than the default
one. Reports longer than 50 words will automatically get 0 marks. Hence, ensure that you get 50 or less
words when you run wc -w report.txt. The report should be in ASCII format. The report will get .5
of a mark if your algorithm improves a makespan on at least one of the 9 test cases.
Task 8. Quality of software practices Factors considered include quality of code, based on the
choice of variable names, comments, indentation, abstraction, modularity, and proper use of version
control, based on the regularity of commit and push events, their content and associated commit
messages (e.g., repositories with a single commit and push, non-informative commit messages will lose
0.5 mark).
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Task 9. Build quality Running make clean && make -B && ./allocate
should execute the submission. If this fails for any reason, you will be told the reason, and be allowed
to resubmit (with the usual late penalty). Compiling using “-Wall” should yield no warnings.
Do not commit allocate or any other executable file (see Practical 2). A 0.5 mark penalty will be applied
if your final commit contains any such files. Executable scripts (with .sh extension) are excepted.
The automated test script expects allocate to exit/return with status code of 0 (i.e. it successfully
runs and terminates).
We will not give partial marks or allow code edits for either known or hidden cases without applying
late penalty (calculated from the deadline).
8 Submission
All code must be written in C (e.g., it should not be a C-wrapper over non C-code) and cannot use any
external libraries. Your code will likely rely on data structures for managing processes and memory. You
will be expected to write your own versions of these data structures. You may use standard libraries
(e.g. to print, read files, sort, parse command line arguments1
etc.). Your code must compile and run
on the provided VMs and produce deterministic output.
The repository must contain a Makefile which produces an executable named “allocate”, along with all
source files required to compile the executable. Place the Makefile at the root of your repository, and
ensure that running make places the executable there too. Make sure that all source code is committed
and pushed. Executable files (that is, all files with the executable bit which are in your repository)
will be removed before marking. Hence, ensure that none of your source files have the executable bit.
For your own protection, it is advisable to commit your code to git at least once per day. Be sure to
push after you commit. You can push debugging branches if your code at the end of a day is in an
unfinished state, but make sure that your final submission is on the master branch. The git history
may be considered for matters such as special consideration, extensions and potential plagiarism. Your
commit messages should be a short-hand chronicle of your implementation progress and will be used for
evaluation in the Quality of Software Practices criterion.
You must submit the full 40-digit SHA1 hash of your chosen commit to the Project 1 Assignment
on LMS. You must also push your submission to the repository named comp30023-2021-project-1
in the subgroup with your username of the group comp30023-2021-projects on gitlab.eng.unimelb.
edu.au. You will be allowed to update your chosen commit. However, only the last commit hash
submitted to LMS before the deadline will be marked without late penalty.
You should ensure that the commit which you submitted is accessible from a fresh clone of your repository.
For example (below ... are added for aesthetic purposes to break the line):
git clone https://gitlab.eng.unimelb.edu.au/comp30023-2021-projects// ...
... comp30023-2021-project-1
cd comp30023-2021-project-1
git checkout
Late submissions will incur a deduction of 2 mark per day (or part thereof).
Extension policy: If you believe you have a valid reason to require an extension, please fill in the form
accessible on Project 1 Assignment on LMS. Extensions will not be considered otherwise. Requests for
extensions are not automatic and are considered on a case by case basis.
9 Testing
You have access to several test cases and their expected outputs. However, these test cases are not
exhaustive and will not cover all edge cases. Hence, you are also encouraged to write more tests to
further test your own implementation.
1https://www.gnu.org/software/libc/manual/html_node/Getopt.html
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Testing Locally: You can clone the sample test cases to test locally, from:
comp30023-2021-projects/project-1.
Continuous Integration Testing: To provide you with feedback on your progress before the deadline,
we have set up a Continuous Integration (CI) pipeline on Gitlab. Though you are strongly encouraged
to use this service, the usage of CI is not assessed, i.e., we do not require CI tasks to complete for a
submission to be considered for marking.
Note that the test cases which are available on Gitlab are the same as the set described above.
The requisite .gitlab-ci.yml file has been provisioned and placed in your repository, but is also available
from the project-1 repository linked above. Please clone, commit and push to trigger.
10 Collaboration and Plagiarism
You may discuss this project abstractly with your classmates but what gets typed into your program
must be individual work, not copied from anyone else. Do not share your code and do not ask others
to give you their programs. Do not post your code on subject’s discussion board Piazza. The best way
to help your friends in this regard is to say a very firm “no” if they ask to see your program, pointing
out that your “no”, and their acceptance of that decision, are the only way to preserve your friendship.
See https://academicintegrity.unimelb.edu.au for more information.
Note also that solicitation of solutions via posts to online forums, whether or not there is payment
involved, is also Academic Misconduct. You should not post your code to any public location (e.g.,
github) while the assignment is active or prior to the release of the assignment marks.
If you use any code not written by you, you must attribute that code to the source you got it from (e.g.,
a book or Stack Exchange).
Plagiarism policy: You are reminded that all submitted project work in this subject is to be your
own individual work. Automated similarity checking software may be used to compare submissions. It
is University policy that cheating by students in any form is not permitted, and that work submitted for
assessment purposes must be the independent work of the student concerned.
Using git is an important step in the verification of authorship.
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COMP30023语言编程讲解、Java，CS，Python程序辅导 辅导留学生Prolog|辅导留学生 Statistics统计、回归、迭代

COMP30023语言编程讲解、Java，CS，Python程序辅导辅导留学生Prolog|辅导留学生 Statistics统计、回归、迭代