Distributed Heuristic Algorithm for Migration and Replication of Self-organized Services in Future Networks


  • Manar AL-jabr Department of Auto Control and Computers, College of Mechanical and Electrical Engineering, Al-Baath University, Homs, Syria
  • Ali Diab Department of Auto Control and Computers, College of Mechanical and Electrical Engineering, Al-Baath University, Homs, Syria https://orcid.org/0000-0001-7718-7924
  • Jomana AL-Diab Department of Auto Control and Computers, College of Mechanical and Electrical Engineering, Al-Baath University, Homs, Syria




Communication Cost, Heuristic, Migration, Replication, Service Placement


Nowadays, the mobile communication networks have become a consistent part of our everyday life by transforming huge amount of data through communicating devices, that leads to new challenges. According to the Cisco Networking Index, more than 29.3 billion networked devices will be connected to the network during the year 2023. It is obvious that the existing infrastructures in current networks will not be able to support all the generated data due to the bandwidth limits, processing and transmission overhead. To cope with these issues, future mobile communication networks must achieve high requirements to reduce the amount of transferred data, decrease latency and computation costs. One of the essential challenging tasks in this subject area is the optimal self-organized service placement. In this paper a heuristic-based algorithm for service placement in future networks was presented. This algorithm achieves the ideal placement of services replicas by monitoring the load within the server and its neighborhood, choosing the node that contributes with the highest received load, and finally replicating or migrating the service to it based on specific criteria, so that the distance of requests coming from clients becomes as small as possible because of placing services within nearby locations. It was proved that our proposed algorithm achieves an improved performance by meeting the services within a shorter time, a smaller bandwidth, and thus a lower communication cost. It was compared with the traditional client-server approach and the random placement algorithm. Experimental results showed that the heuristic algorithm outperforms other approaches and meets the optimal performance with different network sizes and varying load scenarios.


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