University College London Featured PhD Programmes
The University of Manchester Featured PhD Programmes
Norwich Research Park Featured PhD Programmes
Imperial College London Featured PhD Programmes
University of Reading Featured PhD Programmes

Evolutionary algorithms

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Research areas: Cloud computing; Computer architectures; Cyber Physical Systems; Embedded systems; Evolutionary computation; Real time languages and systems

Background
Application-aware resource management in multiprocessor, high-performance and cloud computing
The mapping of application tasks onto computing platforms has critical impact over the performance and energy-efficiency of computer systems, as the mapping process partitions the application and allocates the platform resources that will execute each of the partitions. This problem has been addressed since the dawn of computing, but its importance has grown recently because of the additional complexity imposed by the potential availability of massive parallelism at the platform level. Many of the existing heuristics can still be used for finding initial mappings, but the dynamic nature of complex systems requires the mapping to be re-done during runtime in order to adapt to the new situation. To cope with such cases, new heuristics must be found so that their impact is minimal regarding (a) the information they need about the platform state, (b) the overhead caused by the re-mapping of the different parts of an application, and (c) the execution overhead of the heuristic itself.

Specific topic for PhD research:

Evolutionary algorithms have been currently used to statically optimise design and mapping of multiprocessor platforms. Timeliness and energy dissipation have been comprehensively addressed, but there is still scope for a further research on optimising other relevant aspects to multicore embedded systems such as reliability and security, or on coping with the issues raised by the latest semiconductor advances, such as variability and silicon aging.
As the computational power of such platforms keeps increasing, and so does the complexity and dynamism of applications they execute, it is natural to assume that evolutionary algorithms could also be employed during runtime to "evolve" optimised configurations and mappings as the dynamics of the system changes. Interesting research questions include the fine-tuning of the evolutionary approach (which could be done statically or also during runtime) and the decision on how much of the platform resources should be allocated to the evolutionary algorithm itself (how many cores, how much of the interconnect, how to bound its utilisation) so that its overhead will always be less than the optimisation benefits it provides.

References

M.N.S.M. Sayuti and L.S. Indrusiak, Real-time low-power task mapping in Networks-on-Chip, in: IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2013 (https://ieeexplore.ieee.org/document/6654616?tp=&arnumber=6654616).
M.N.S.M. Sayuti and L.S. Indrusiak, A Function for Hard Real-Time System Search-Based Task Mapping Optimisation, in: IEEE Int Symposium on Real-Time Distributed Computing (ISORC), 2015 (https://ieeexplore.ieee.org/document/7153791?arnumber=7153791).

Related Subjects

How good is research at University of York in Computer Science and Informatics?

FTE Category A staff submitted: 34.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully





FindAPhD. Copyright 2005-2019
All rights reserved.