MPhil, Part III, and Part II Project Suggestions (2018-2019)
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For 2019-2020 suggestion, please see 2019-2020 project suggestion page! Please contact Eiko Yoneki (email: eiko.yoneki@cl.cam.ac.uk) if you are interested in any project below. * indicates high recommendation! 1. Tuning Neural Networking Compression with Hierarchical Reinforcement Learning * Contact:
Eiko Yoneki Executing neural network tasks on mobile and edge
devices (e.g. facial recognition on smart phones) is gaining increasing focus in
the systems community due to constraints in CPU, memory and energy usage
on such devices. Consequently, neural networks trained on large servers usually
need to be compressed to be fit for low power mobile execution. Recent work has
explored various techniques to automatically combine various compression and
quantization techniques [1, 2].
This project will investigate hierarchical reinforcement learning [3]
to improve on these existing search techniques by attempting to model
fine-grained dependencies between different compression and quantization
methods. [1] [2]
https://arxiv.org/pdf/1806.03723.pdf [3]
https://ai.google/research/pubs/pub46646 2. Model-based Reinforcement Learning in Computer Systems * Contact:
Eiko Yoneki Reinforcement learning (RL) is gaining interest as a
generic optimisation and control method in data management tasks such as
resource management/scheduling, database tuning, or stream processing. A central
challenge in applying RL to such problems are long decision evaluation times. A
single step in a real system may take multiple minutes, as opposed to simulators
for standard benchmark tasks (e.g. Atari games), which can execute thousands of
steps per second. One way of improving sample efficiency is
model-based RL [1], wherein a model of the environment
is learned and subsequently used to plan and evaluate decisions. The goal of
this project is to investigate recent work in learning to construct environment
models in the context of database administration. The student will use an
existing framework to evaluate RL on automatic index selection, and attempt to
accelerate training and improve result quality by implementing and evaluating
approaches such as Imagination-Augmentation [2,3,4].
[1]
https://arxiv.org/pdf/1708.05866.pdf [2]
https://arxiv.org/abs/1707.06203 [3]
https://deepmind.com/blog/agents-imagine-and-plan/ [4] https://arxiv.org/abs/1707.06170
3. Reinforcement Learning for Automatic Index Selection in SQL Databases ** Contact:
Eiko Yoneki Index selection is a standard database administration
task which is often performed by human experts, typically assisted by profiling
tools [0]. Selecting the optimal set of indices is difficult as it depends on
query structure, data distribution, anad workload distribution. As each index
incurs memory and maintenance cost when inserting new data, the aim of index
selection is to identify the minimal number of indices meeting performance
requirements (query latency). Previous work in this group has implemented a framework
to select compound indices in NoSQL databases such as MongoDB using deep
reinforcement learning and pre-existing log data. The goal of the project is to
investigate the applicability of this framework to indexing semantics [1] in
relational databases such as PostgreSQL.
In general, labelling training data is increasingly the bottleneck in deploying machine learning systems and generating labelling function based on heuristics is a growing research topic. Possibly you can extend a project for the general model building using a weak supervision [3].
[0]
https://stratos.seas.harvard.edu/files/a4-idreos.pdf [1]
https://www.postgresql.org/docs/10/static/indexes-multicolumn.html [2]
https://arxiv.org/pdf/1801.05643.pdf
[3] A. Ratner, S. H. Bach, H. Ehrenberg, J. Fries, S. Wu, and C. Re. Snorkel: Rapid training data creation with weak supervision, VLDB, 2017. https://dawn.cs.stanford.edu/2017/05/08/snorkel/
4. Deep Reinforcement Learning in Networking ** Contact:
Eiko Yoneki Modern network infrastructure is increasingly driven by
software-defined components. This opens up ample opportunities to explore modern
data-driven machine learning methods in traffic protocols. Early work has
explored deep reinforcement learning in routing [0]. Until recently, standard purpose network simulators
(e.g. Ns-3 [1]) were not available via common reinforcement learning benchmark
interfaces such as OpenAI gym. An open source bridge between a gym-like
interface and Ns-3 has now become available [1], and several example tasks are
provided. The aim of this project is to evaluate a baseline deep reinforcement
learning algorithm (e.g. high-performance DQN implementation available in RLgraph
[2]) on some of these tasks, and subsequently implement and explore approaches
from one of the following: Hierarchical RL, model-based RL, Neural combinatorial
optimization/optimal transport theory (guidance can be given depending on
background and student interests). [0]
http://www.cs.huji.ac.il/~schapiram/Learning_to_Route%20(NIPS).pdf [1]
https://arxiv.org/pdf/1810.03943.pdf [2]
https://github.com/rlgraph/rlgraph
5. Convolutional Neural Network for Raspberry Pi Contact:
Eiko Yoneki In this project, you will build Convolutional Neural Network (CNN) Library for raspberry Pi in C++. Raspberry Pi has limited computational capabilities comparing to the conventional desktop computers, and you will build up a CNN library specifically designed for Raspberry Pi. The basis for it was to see if parallel training of a convolutional neural network across multiple Raspberry Pis was feasible and provided any performance benefits compared to training a network on a single, faster machine. You would start from our current PiCNN Library, where the basic functionalities are equipped. Your task is extending our current PiCNN to distributed setting with implementing a distributed parameter server architecture to schedule updates and synchronize them via a python script using Berkeley's Ray project [1] including performance improvement. Required skill sets include Linux, C++, basic knowledge of Computer System’s architecture, basics of Machine Learning, Distributed Systems. [1]
https://github.com/ray-project/tutorial/blob/master/examples/sharded_parameter_server.ipynb
6. Optimisation of JVM Garbage Collection using ML * Contact:
Eiko Yoneki This project extends the Cassandra case study [1,2]
in incremental ways:
1) Cassandra database was
only executed in one setting. We could create an auto-tuner that is able to deal
with a wider variety of settings. This includes:
-
Changing the heap size of the JVM
-
Changing the underlying hardware of Cassandra
-
Changing the underlying hardware of YCSB (Yahoo! Cloud Serving Benchmark)
2) Applying it to other JVM
applications (such as other databases, anything where we have a rigorous
benchmark for throughput and latency)
3) Looking at some of the
other JVM flags (we only tuned three) as well as the other JVM garbage collector For evaluation following two points are evaluated.
1) No GC option as baseline
2) Various GC
implementations (i.e. some are good on certain workloads) [1]
https://www.cl.cam.ac.uk/~ey204/pubs/2017_WWW.pdf [2]
https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-900.html 7. Optimising Neural Network Model over Power Consumption * Contact:
Eiko Yoneki
In this project, you would tune neural network model itself. Pick one typical
Deep Neural Network application (e.g. image classification), and play with
various hyper-parameters, where hyper-parameters might take substantially
different values for the best accuracy or performance.
You would explore various parameters based experiments and build up a
probabilistic model expressing the parameter relationship applying the BOAT
technique (see [1] and [2]).
Also you would explore recent Google’s Hyper parameter tuning on Google Cloud
Platform [3], in comparison to the above approach.
[1]
https://www.cl.cam.ac.uk/~ey204/pubs/2017_WWW.pdf
[2]
https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-900.html
8. Unikernel over Raspberry Pi: Building Data Processing Platform Contact:
Eiko Yoneki [1] Jitsu: Just-In-Time Summoning of Unikernels,
NSDI, 2015. [2] RaDiCS
https://gitlab.com/RaspiUnikernel/RaDiCS. 9. Dynamic Task Scheduling on Heterogeneous CPU/GPU Environment using ML for Parallel Processing Contact:
Eiko Yoneki In this project, various aspects of parallel
processing will be explored using a new generation of CPU/GPU integrated board.
We use NVIDIA’s Jetson X2. This new GPUs makes it possible to cluster the GPU
nodes for different scale of parallel processing. Various applications running
over X2 will be investigated including graph processing and neural network
training using the Tensorflow framework. Graph processing can take advantage of
processor heterogeneity to adapt to structural data patterns. The overall aim of
graph processing can be seen as scheduling irregular tasks to optimise
data-parallel heterogeneous graph processing, by analysing the graph at runtime
and dispatching graph elements to appropriate computation. Various task
scheduling strategies over NN applications could be explored such as mixture of
model parallelism and data parallelism. In comparison to the above approach, several types of
GPU machines in cluster computing environment will be explored. Furthermore, our
recent work, BOAT [1], could be used for optimisation of complex parameter
space. [1] V. Dalibard, M. Schaarschmidt, and E. Yoneki:
BOAT: Building Auto-Tuners with Structured Bayesian Optimization, WWW, 2017.
https://github.com/VDalibard/BOAT.
10. Building Graph Query Function using Functional Programming Contact:
Eiko Yoneki Demand to store and search of online data with graph
structure is emerging. Such data range from online social networks to web links
which requires efficient query processing in a scalable manner. In this project,
you will build a graph query function (layer) to achieve efficient graph data
processing. The graph query function builds on a lazy graph loaded from multiple
sources and performs queries at the same speed or faster than a relational
database. The function should be written in Clojure or another functional
language to support practical lazy loading from data source to an in-memory
graph database. Efficient representations for graph queries should be also
investigated. The evaluation includes comparisons with existing Graph Database
and the query capability comparing with SQL. You could start the project by our
existing work in this area. [1] Microsoft Research, “Trinity project: Distributed
graph database,”
https://research.microsoft.com/en-us/projects/trinity/ [2] Neo Technology, “Java graph database,”
https://neo4j.org/ 11. Approximate
Algorithms Determining Local Clustering Coefficients Anonymously Originator/Supervisor:
Eiko Yoneki (with Amitabha Roy
in Google) Keywords: Sampling, Approximation, Privacy, Cluster
Coefficient Anonymous social networks are a new phenomenon in
an increasingly privacy conscious world. A natural question to ask in this
setting is whether we can continue to apply known principles of network science
in such settings where neighbourhood information is concealed both between nodes
and external observers. This project is to work on approximate algorithms that
determines clustering coefficient in such settings. Clustering coefficients
provide a way to relatively order nodes by importance in a network, determine
densely connected neighbourhoods and distinguishing social graphs from web
graphs. Algorithms to measure clustering coefficients have hitherto required
global knowledge of graph links. This requires individual nodes in the graph to
give up the identity of their neighbours. This project investigates an algorithm
for estimating the clustering coefficient of a vertex by exchanging only
anonymised set summaries of neighbours, from which it is difficult to reverse
engineer individual links. The bulk of the project will consist of working on
and improving sampling techniques to arrive at accurate local clustering
coefficients without exchanging explicit neighbour lists in social networks . [1] P. Flajolet, Eric Fusy, O. Gandouet, and et al.
Hyperloglog: The analysis of a near-optimal cardinality estimation algorithm. In
Proceedings of the International Conference on Analysis of Algorithms, 2007. Contact EmailPlease email to eiko.yoneki@cl.cam.ac.uk for any question. |