Abstract
A distributed system is a paradigm indispensable to the current world due to countless requests with every passing second. In distributed systems, reliability is of extreme importance. In this regard, data replication plays a vital role in making systems more reliable by increasing the availability of the access operations at a lower cost. However, availability and cost both cannot be achieved at the same time. Certainly, there are compromises between these objectives, thereby making different application-scenarios that may not be easily satisfied by contemporary strategies. This requires designing new strategies and the question still stands which strategy is the best for a given scenario or application class assuming a certain workload, its distribution across a network, availability of the individual replicas, and cost of the access operations. For this, the research exploits the heterogeneity between the strategies to generate new data replication strategies automatically through genetic programming. It uses and extends this genetic programming-based automatic mechanism to subsequently demonstrate its usefulness by reducing the cost significantly while not comprising too much on the availabilities of the access operations. It generates replication strategies there are innovative and such combinations have not been explored yet.
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Bokhari, S.M.A., Theel, O. (2021). Designing New Data Replication Strategies Automatically. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2020. Lecture Notes in Computer Science(), vol 12613. Springer, Cham. https://doi.org/10.1007/978-3-030-71158-0_15
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