A Genetic Programming-based Multi-objective Optimization Approach to Data Replication Strategies for Distributed Systems
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- @InProceedings{Bokhari:2020:CEC,
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author = "Syed Mohtashim Abbas Bokhari and Oliver Theel",
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title = "A Genetic Programming-based Multi-objective
Optimization Approach to Data Replication Strategies
for Distributed Systems",
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booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC
2020",
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year = "2020",
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editor = "Yaochu Jin",
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pages = "paper id24298",
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address = "internet",
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month = "19-24 " # jul,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185598",
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abstract = "Data replication is the core of distributed systems to
enhance their fault tolerance and make services highly
available to the end-users. Data replication masks
run-time failures and hence, makes the system more
reliable. There are many contemporary data replication
strategies for this purpose, but the decision to choose
an appropriate strategy for a certain environment and a
specific scenario is a challenge and full of
compromises. There exists a potentially indefinite
number of scenarios that cannot be covered entirely by
contemporary strategies. It demands designing new data
replication strategies optimized for the given
scenarios. The constraints of such scenarios are often
conflicting in a sense that an increase in one
objective could be sacrificial to the others, which
implies there is no best solution to the problem but
what serves the purpose. In this regard, this research
provides a genetic programming-based multi-objective
optimization approach that endeavors to not only
identify, but also design new data replication
strategies and optimize their conflicting objectives as
a single-valued metric. The research provides an
intelligent, automatic mechanism to generate new
replication strategies as well as easing up the
decision making so that relevant strategies with
satisfactory trade-offs of constraints can easily be
picked and used from the generated solutions at
run-time. Moreover, it makes the notion of hybrid
strategies easier to accomplish which otherwise would
have been very cumbersome to achieve, therefore, to
optimize.",
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notes = "https://wcci2020.org/
University of Oldenburg, Germany.
Also known as \cite{9185598}",
- }
Genetic Programming entries for
Syed Mohtashim Abbas Bokhari
Oliver Theel
Citations