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Towards a Highly Available Model for Processing Service Requests Based on Distributed Hash Tables

Author

Listed:
  • Voichiţa Iancu

    (Department of Computer Science and Engineering, Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, 060042 Bucharest, Romania)

  • Nicolae Ţăpuş

    (Department of Computer Science and Engineering, Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, 060042 Bucharest, Romania)

Abstract

This work aims to identify techniques leading to a highly available request processing service by using the natural decentralization and the dispersion power of the hash function involved in a Distributed Hash Table (DHT). High availability is present mainly in systems that: scale well, are balanced and are fault tolerant. These are essential features of the Distributed Hash Tables (DHTs), which have been used mainly for storage purposes. The novelty of this paper’s approach is essentially based on hash functions and decentralized Distributed Hash Tables (DHTs), which lead to highly available data solutions, which a main building block to obtain an improved platform that offers high availability for processing clients’ requests. It is achieved by using a database constructed also on a DHT, which gives high availability to its data. Further, the model requires no changes in the interface, that the request processing service already has towards its clients. Subsequently, the DHT layer is added, for the service to run on top of it, and also a load balancing front end, in order to make it highly available, towards its clients. The paper shows, via experimental validation, the good qualities of the new request processing service, by arguing its improved scalability, load balancing and fault tolerance model.

Suggested Citation

  • Voichiţa Iancu & Nicolae Ţăpuş, 2022. "Towards a Highly Available Model for Processing Service Requests Based on Distributed Hash Tables," Mathematics, MDPI, vol. 10(5), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:831-:d:764791
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