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EN.601.419/EN.601.619: Cloud Computing Assignment 2

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EN.601.419/EN.601.619: Cloud Computing

Assignment 2

Goal. The goal of this assignment is to review some of the key concepts related to cloud networking
and big data systems.
Submission instructions. This assignment is due at the time listed above by email to
soudeh@cs.jhu.edu. In your submission, explain your answer (e.g., an answer with a numerical
result should show how you derived it).
• Subject: Assignment 2 Your-Name.
• Attachment format: a PDF le for your answers to the questions.
Collaboration policy. You’re encouraged to discuss the assignment and solution strategies with
your classmates. Your submission must clearly mention the names of all the students that you have
discussed/worked with, as well as all the resources you have used. Your solution and submission,
however, must be written by yourself, in your own words. Please see the policy on academic honesty
and cheating stated in the course syllabus.
1. [10 points] We saw that the additive-increase/multiplicative-decrease (AIMD) algorithm, the
simple distributed feedback control algorithm used in almost all congestion control algorithms
today, has some key strengths such as simultaneously optimizing fairness and eciency. Despite inheriting those strengths from AIMD, TCP’s congestion control algorithm is not ideal
for datacenters and we saw in the Jupiter paper that even for lightly loaded datacenters,
managing congestion is still a big open research challenge.
(a) Give a few (at least three) reasons why TCP is not eective for managing congestion in
datacenters.
(b) Describe at least one remedy in the context of datacenters for improving the performance
of TCP.
(c) Is this remedy consistent with, at odds with, or orthogonal to the end-to-end principle?
2. [10 points] Suppose we build a cluster with a fat tree topology using 24-port switches,
following the topology design of the Al-Fares paper (that you read and reviewed).
(a) How many distinct end-to-end shortest paths are there in the physical topology between
a source server A and a destination server B in a dierent pod? (Here, shortest paths are
those that have the minimum number of switches along the path. This question concerns
the physical topology, not routing or forwarding.)
(b) Suppose the cluster runs BGP, with each switch having its own AS number and each
edge switch announcing a distinct attached IP prex covering all its servers. (No other
prexes are announced.) Switches run standard IP forwarding with ECMP across all
shortest paths. How many forwarding rules are in each edge switch? How many are in
each core switch? (Each forwarding rule species a single egress interface for a set of
matching packets.)
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EN.601.419/EN.601.619 Spring 2020
(c) Suppose the switch hardware is capable of performing MPLS forwarding, with IP-inMPLS encapsulation, and the hardware can also still perform IP forwarding. More
specically, a forwarding rule on a switch can either (1) perform IP/ECMP forwarding
as above, (2) match a prex of IP packets and direct it into an MPLS tunnel, with
ECMP-style hashing if there are multiple matching rules; (3) perform MPLS label swap
forwarding; or (4) match an MPLS label, pop the MPLS header and continue forwarding
out an interface via IP. Can you use this hardware to deliver data along the same paths
as ECMP, but with fewer forwarding rules? If so, describe your solution and how many
forwarding rules it needs at each edge switch and at each core switch.
(d) Compare the resilience of your solution with that of ECMP. Specically, suppose one
core switch fails. What plausibly happens within about 1 millisecond, i.e., with only
local reaction at each switch, in ECMP and in your solution? (Note: the goal isn’t to
produce a scheme which is necessarily better or worse than ECMP; the goal is just to
compare.)
3. [15 points] Routes between A and B are asymmetric when the A B path is not the same as
the B A path. How can asymmetric routing occur (a) inside a datacenter with a topology
such as fat-tree that uses ECMP, and (b) between two datacenters even if all networks use
BGP with common business relationship policies (prefer customer over peer over provider for
route selection, and valley-free export)? Give two examples (one for (a) and one for (b)) of
how this could happen.
4. [10 points] BGP routing can be formulated as a game where the selsh players are autonomous systems (ASes). It can be shown that this game has no Nash equilibrium (stable
state) in some cases. That is, the control plane will keep switching routes even though the
physical network is stable. In this problem, we’ll see an example where there are two equilibria,
and dierent sequences of events could lead to one or the other.
Consider the network below:
3 4
1
2
peer link
provider
customer
customer
customer
provider
provider
destination
For simplicity, assume AS 1 is the only destination: it is the only AS that will originate
an announcement message. The ASes have provider/customer/peer business relationships as
shown. They follow the common route selection and export policies … except that AS 1 is
using AS 2 as a backup provider. This means AS 1 has instructed AS 2 to route to AS 1 via
the link 2 → 1 only when no other path is available. (Incidentally, this is possible with BGP’s
community attribute.) The eect is that AS 2 prefers to route through AS 3 in order to reach
AS 1. Assume that at time 0, no routes have been announced by anyone. Shortly after time
0, AS 1 will begin announcing its IP prex.
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EN.601.419/EN.601.619 Spring 2020
(a) Describe a sequence of events (BGP announcement messages and path selection decisions)
that lead to one stable state.
(b) Describe a dierent sequence of events that lead to a dierent stable state.
(c) Suppose the network is now stabilized in state (a). A link fails; the BGP routers reconverge; the link recovers; BGP reconverges again; but now the network is in state (b)
instead of state (a)! (This problem is sometimes known as a BGP wedgie because the
system has gotten stuck in a bad state.) What sequence of events causes this story to
happen? That is, which link failed, and which messages get sent?
5. [15 points] Short questions.
(a) In the original OpenFlow paper that we read, a centralized controller receives the rst
packet of each ow and installs forwarding rules to handle that ow. However, a signicant concern for any centralized design is scalability. Describe a feature of Google’s
Firepath SDN design that allows it to scale to large datacenters.
(b) Consider a cluster of database servers. The time taken for any server to respond to
any request is 10 milliseconds 99.8% of the time, and 200 milliseconds 0.2% of the time.
Assume these times are sampled independently at random for each request. We have a
front end web server that, when a user request arrives, queries 100 database servers in
parallel. When all responses are received, it can reply to the client. The web server itself
is incredibly fast, so all its local processing always takes less than 1 millisecond. What
is the probability that the web server needs ≥ 200 milliseconds to be ready to reply to
the client? (As with all answers, briey show how you calculated the answer.)
(c) In the setting of the previous question, suppose the web server needs to perform two
phases of processing before replying to the client. Phase 1 queries 100 database servers
as described above. But now, after receiving all responses from phase 1, it can begin the
second phase, where it queries another 200 database servers in parallel. Finally, when all
responses are received, it can reply to the client. What is the probability that the web
server needs to wait ≥ 200 milliseconds for its requests to complete?
6. [15 points] In a regular Clos datacenter, among the four types of resources (compute, memory,
storage, and network), which ones are likely to become scalability bottlenecks for MapReduce
and Spark? You will need to explain the workows of these systems to answer this question.
7. [25 points] Peer-validation: One of the nal tasks for the project is to evaluate a peer
project. As you read the nal reports, we need you to validate and comment on the reproducibility of the graphs in it. You will practice validating a project for this assignment using
the Checkpoint 2 slides of a group assigned to you. The group-validator assignments will be
posted on Piazza after the deadline of the second checkpoint.
• Write a brief summary (1 paragraph) of the project assigned to you: What problem are
they going to solve? Why is it important? Their approach? Their progress so far?
• Write a comment or question for that project.
• Please use the following rubric to leave a score on the reproducibility of their results.
Note that this is not their nal grade. The teaching sta will be evaluating the projects
and reproduce the results separately. This is just to give us another perspective and
practice peer-validation.
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EN.601.419/EN.601.619 Spring 2020
(a) Code ran to completion and results matched very well with the report.
(b) Code ran to completion and results matched fairly well with the report.
(c) Code ran to completion but results did not match the ones in the report, or all
results in the report were not produced with the default run conguration.
(d) Code did not run to completion (exceptions, innite loops, no progress/debug output/log output, etc.)
(e) Instructions were so vague or incomplete that the setup could not be completed to
even start the code.
(f) Instructions not found.
• [Optional] Feel free to write some additional feedback in addition to the reproducibility
score. We will also appreciate if you can comment on the quality of analysis and if you
thought it was interesting.
8. [5 points] Optional/Extra credit. One of the reasons that MapReduce is slow is the barrier
between the Map and the Reduce phases, i.e., no Reduce task can start before all Map tasks
nish. Suppose a programmer writes a variant of MapReduce without a barrier, i.e., where
Reduces can start before all Maps are done computing.
• Demonstrate with an example that this Mapreduce run may be incorrect, e.g., it may
generate an incorrect value.
• Show how you can resolve this correctness issue while (partially) preserving the performance improvement of this barrier-less MapReduce.
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