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Department of Computer Science
COMP212 – 2021 – CA Assignment 1
Coordination and Leader Election
Simulating and Evaluating Distributed Protocols in Java
Assessment Information
Assignment Number 1 (of 2)
Weighting 15%
Assignment Circulated 15th February 2021
Deadline 15th March 2021, 17:00 UK Time (UTC)
Submission Mode Electronic via CANVAS
Learning outcomes assessed (1) An appreciation of the main principles underlying
distributed systems: processes, communication, naming,
synchronisation, consistency, fault tolerance, and
security. (3) Knowledge and understanding of the essential
facts, concepts, principles and theories relating
to Computer Science in general, and Distributed Computing
in particular. (4) A sound knowledge of the criteria
and mechanisms whereby traditional and distributed
systems can be critically evaluated and analysed to determine
the extent to which they meet the criteria de-
fined for their current and future development.
Purpose of assessment This assignment assesses the understanding of coordination
and leader election in distributed systems and implementing,
simulating, and evaluating distributed protocols
by using the Java programming language.
Marking criteria Marks for each question are indicated under the corresponding
question.
Submission necessary in order No
to satisfy Module requirements?
Late Submission Penalty Standard UoL Policy.
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1 Overall marking scheme
The coursework for COMP212 consists of two assignments contributing altogether 30% of
the final mark. The contribution of the individual assignments is as follows:
Assignment 1 15%
Assignment 2 15%
TOTAL 30%
2 Objectives
This assignment requires you to implement in Java two distributed algorithms for leader
election in a ring network and then to experimentally validate their correctness and evaluate
their performance.
3 Description of coursework
Throughout this coursework, the network on which our algorithms are to be executed is a
bidirectional ring, as depicted in Figure 1.
Figure 1: A bidirectional ring network on n processors.
In our setting, all processors execute the same algorithm, do not know the number n of
processors in the system in advance, but they do know the structure of the network and
are equipped with unique ids. The ids are not necessarily consecutive and for simplicity
you can assume that they are chosen from {1, 2, . . . , αn}, where α ≥ 1 is a small constant
(e.g., for α = 3, the n processors will be every time assigned unique ids from {1, 2, . . . , 3n −
1, 3n}). Additionally, every processor can distinguish its clockwise from its counterclockwise
neighbour, so that, for example, it can choose to send to only one of them or to send a
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different message to each of them. Processors execute in synchronous rounds, as in every
example we have discussed so far in class.
3.1 Implementing the LCR Algorithm—30% of the assignment
mark
As a first step, you are required to implement the LCR algorithm for leader election in a
ring. The pseudocode of the non-terminating version of LCR can be found in the lecture
notes and is also given here for convenience (Algorithm 1).
Algorithm 1 LCR (non-terminating version)
Code for processor ui
, i ∈ {1, 2, . . . , n}:
Initially:
ui knows its own unique id stored in myIDi
sendIDi
:= myIDi
statusi
:=“unknown”
1: if round = 1 then
2: send hsendIDii to clockwise neighbour
3: else// round > 1
4: upon receiving hinIDi from counterclockwise neighbour
5: if inID > myIDi then
6: sendIDi
:= inID
7: send hsendIDii to clockwise neighbour
8: else if inID = myIDi then
9: statusi
:=“leader”
10: else if inID < myIDi then
11: do nothing
12: end if
13: end if
You are required to implement a terminating version of the LCR algorithm in
which all processors eventually terminate and know the id of the elected leader.
3.2 Implementing the HS Algorithm—30% of the assignment
mark
Next, you are required to implement another algorithm for leader election on a ring, known
as the HS algorithm. As LCR, HS also elects the processor with the maximum id. The
main difference is that HS instead of trying to send ids all the way around in one direction
(which is what LCR does), it has every processor trying to send its id in both directions some
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distance away (e.g., k) and then has the ids turn around and come back to the originating
processor. As long as a processor succeeds, it does so repeatedly (in“phases”) to successively
greater distances (doubling the distance to be travelled each time, e.g., 2k). See Figure 2 for
an illustration.
Figure 2: Trajectories of successive“phases”originating at processor u4 (imagine the rest of
the processors doing something similar in parallel, but not depicted here). The id transmitted
by u4 aims to travel some distance out in both directions and then return back. If it succeeds,
then u4 doubles the aimed distance and repeats.
Informally, each processor ui“operates in phases”l = 0, 1, . . . (where each phase l consists
of one or more rounds). In each phase l, processor ui sends out a“token”(i.e., a message)
containing its id idi
in both directions. These are intended to travel distance 2l
(that is,
as in Figure 2, distance 20 = 1 for l = 0, distance 21 = 2 for l = 1, distance 22 = 4 for
l = 2, and so on) and then return to their origin. If both tokens manage to return back then
ui goes to the next phase, otherwise it stops to produce its own tokens (and only performs
from that point on the rest of the algorithm’s operations). A token is discarded if it ever
meets a processor with greater id while travelling outwards (away from its origin). While
travelling inwards (back to its origin), a token is forwarded by all processors without any
check. The termination criterion is as follows: If a token travelling outwards meets its own
origin ui (meaning that this token managed to perform a complete turn of the whole ring
while travelling outwards), then ui elects itself as the leader. Observe that in order for tokens
to know how far they should travel each time and in which direction, this information has to
be included inside the transmitted messages (that is, apart from the id being transmitted,
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the messages should also contain this auxiliary information).
The pseudocode of the non-terminating version of HS is given in Algorithm 2. As with
LCR, you are required to implement a terminating version of the HS algorithm in
which all processors eventually terminate and know the id of the elected leader.
3.3 Experimental Evaluation, Comparison & Report—40% of the
assignment mark
After implementing the terminating LCR and HS algorithms, the next step is to conduct
an experimental evaluation of their correctness and performance.
Correctness. Execute each algorithm in rings of varying size (e.g., n = 3, 4, . . . , 1000, . . .;
actually, up to a point where simulation does take too much time to complete) and starting
from various different id assignments for each given ring size. For instance, you could
execute them on both specifically constructed id assignments (e.g., ids ascending clockwise
or counterclockwise) and random id assignments. In each execution, your simulator
should check that eventually precisely one leader is elected. Of course, this will not be a
replacement of a formal proof that the algorithms are correct as you won’t be able to test
them on all possible combinations of ring sizes and id assignments, but at least it will be a
first indication that they may do as intended.
Performance. Execute, as above, each algorithm in rings of varying size and starting from
various different id assignments for each given ring size. For each execution, your simulator
should record the number of rounds and the total number of messages transmitted
until termination.
- Execute both algorithms in rings of varying size for the case in which ids are always
clockwise ordered. - Execute both algorithms in rings of varying size for the case in which ids are always
counterclockwise ordered. - Execute both algorithms in rings of varying size and various random id assignments
for each given ring size. Note here that both algorithms should be simulated (e.g.,
one after the other) on every given choice of ring size and id assignment, so that a
comparison of their performance makes sense.
In Summary: For both correctness validation and performance evaluation a suggestion
is to simulate both algorithms (for all types of id assignments mentioned above) in rings
containing up to at least 1000 processors. Specifically in the case of random id assignments,
for each ring size n repeat the simulation for many different id assignments (e.g., at least - distinct simulations) and record the correctness and the worst, the best, and the average
performance so that you get meaningful results.
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Algorithm 2 HS (non-terminating version)
Messages are triples of the form hID, direction, hopCounti, where direction ∈ {out, in} and
hopCount positive integer.
Code for processor ui
, i ∈ {1, 2, . . . , n}:
Initially:
ui knows its own unique id stored in myIDi
sendClocki containing a message to be forwarded clockwise or null, initially
sendClocki
:= hmyIDi
, out, 1i
sendCounterclocki containing a message to be forwarded counterclockwise or null, initially
sendCounterclocki
:= hmyIDi
, out, 1i
statusi ∈ {“unknown”,“leader”}, initially statusi
:=“unknown”
phasei recording the current phase number, nonnegative integer, initially phasei = 0
1: upon receiving hinID, out, hopCounti from counterclockwise neighbour
2: if inID > myIDi and hopCount > 1 then
3: sendClocki
:= hinID, out, hopCount − 1i
4: else if inID > myIDi and hopCount = 1 then
5: sendCounterclocki
:= hinID, in, 1i
6: else if inID = myIDi then
7: statusi
:=“leader”
8: end if
9:
10: upon receiving hinID, out, hopCounti from clockwise neighbour
11: if inID > myIDi and hopCount > 1 then
12: sendCounterclocki
:= hinID, out, hopCount − 1i
13: else if inID > myIDi and hopCount = 1 then
14: sendClocki
:= hinID, in, 1i
15: else if inID = myIDi then
16: statusi
:=“leader”
17: end if
18:
19: upon receiving hinID, in, 1i from counterclockwise neighbour, in which inID 6= myIDi
20: sendClocki
:= hinID, in, 1i
21:
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22: upon receiving hinID, in, 1i from clockwise neighbour, in which inID 6= myIDi
23: sendCounterclocki
:= hinID, in, 1i
24:
25: upon receiving hinID, in, 1i from both clockwise and counterclockwise neighbours, in
both of which inID = myIDi holds
26: phasei
:= phasei + 1
27: sendClocki
:= hmyIDi
, out, 2
phasei i
28: sendCounterclocki
:= hmyIDi
, out, 2
phasei i
29:
30: // The following to be always executed by all processors, i.e.,
31: // also in round 1 in which no message has been received
32: send hsendClockii to clockwise neighbour
33: send hsendCounterclockii to counterclockwise neighbour
After gathering the simulation data, plot them as follows. In each plot, the x-axis will
represent the (increasing) size of the ring and the y-axis will represent the complexity measure
(e.g., number of rounds or number of messages). You may produce individual plots
depicting the performance of each algorithm (possibly comparing against standard complexity
functions, like n, n log n, or n
2
) and you are required to produce plots comparing the
performance of both algorithms in identical settings. For example, when measuring the total
number of messages in the case of counterclockwise increasing ids, a plot would show at
the same time the performance of both algorithms for increasing ring size n, using curves of
different colours and possibly also a legend with explanations. Then, for each given ring size,
the corresponding point of each curve will represent the total number of messages generated
by the algorithm (indicated on the y-axis). You can use gnuplot, JavaPlot or any other
plotting software that you are familiar with.
The final crucial step is to prepare a concise report (at most 5 pages including plots)
clearly describing your main implementation choices, the main functionality of your simulator,
the set of experiments conducted, and the findings of your experimental evaluation
of the above algorithms. In particular, in the latter part you should try to draw conclusions
about (i) the algorithms’correctness and (ii) the performance (time and messages)
of each algorithm individually (e.g., what was the worst/best/average performance of each
algorithm as a function of n? For example, we know from the lectures that the worst-case
communication complexity of LCR is O(n
2
): can you verify this experimentally?) and when
the two algorithms are being compared against each other (e.g., which one performs better
and in which settings?). - Deadline and Submission Instructions
• The deadline for submitting this assignment is Monday, 15th March 2021, 17:00
UK time (UTC).
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• Submit
(a) The Java source code for all your programs,
(b) A README file (plain text) describing how to compile/run your code to produce
the various results required by the assignment, and
(c) A concise self-contained report (at most 5 pages including everything) describing
your implementation choices, experiments, and conclusions in PDF format.
Compress all of the above files into a single ZIP file (the electronic submission system
won’t accept any other file formats) and specify the filename as Surname-NameID.zip.
It is extremely important that you include in the archive all the files described
above and not just the source code!
• Submission is via the“Assignments”tab of COMP212-202021 course on CANVAS.
Good luck to all!
