Virtual Machine Migration for Mobile Edge Services
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Solution Overview
Problem
Ensuring a satisfactory level of service continuity for vehicles in motion is challenging due to changes in geographic coverage areas, which can lead to interruptions in service as vehicles move away from host servers.
Innovation Solution
A remote execution process that calculates a vector of probabilities for potential migration times and selects the optimal time for migration to minimize energy costs and ensure service continuity, while also determining the number of virtual machine replications needed for each selected migration time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full replication across all available host servers is performed, then service continuity is guaranteed, but computational resource consumption and replication energy cost increase significantly
Solution Approach 1:
The patent applies partial replication by selecting only the most probable next host servers based on mobility prediction, rather than replicating across all available servers. The system calculates a probability vector for migration to different host servers and performs replication only to the top k most likely targets, thereby reducing energy consumption while maintaining acceptable service continuity.
Solution Approach 2:
The system dynamically adjusts the number of replications and migration timing based on calculated probability vectors and predicted mobility patterns. By changing the replication strategy from static full-replication to dynamic partial-replication based on predicted parameters, the system optimizes the balance between service continuity and energy consumption.
2Use of energy by moving object
If single predicted host server is chosen for migration, then resource consumption is limited, but service interruption risk increases in case of prediction error
Solution Approach 1:
The patent implements partial replication by selecting the top k most probable host servers based on mobility prediction, rather than replicating to all servers or only one. This partial action approach reduces resource consumption compared to full replication while providing better service continuity than single-server migration by preparing multiple backup execution instances.
Solution Approach 2:
The system performs beforehand cushioning by pre-replicating virtual machines to multiple predicted host servers before the vehicle actually moves. This creates a cushion of available execution instances that can handle service requests even if the mobility prediction is not entirely accurate, thereby reducing service interruption risk while managing resources efficiently.
3Use of energy by stationary object
If migration is delayed until necessary, then replication energy cost is reduced, but service availability may be compromised when vehicle leaves coverage area
Solution Approach 1:
The patent applies preliminary action by triggering migration and replication before the vehicle actually leaves the current host server's coverage area. The system uses mobility prediction to forecast when the vehicle will exit coverage and initiates replication in advance, ensuring service availability is maintained while optimizing energy consumption by performing replication only when and where necessary based on predicted movement patterns.
Data Source
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AI summary
The invention relates to a method and a system for remote execution of services requested by at least one mobile device via a cellular communications network according to a communication protocol which is at least fourth-generation, a service being executed by a virtual machine of a current host server. The method comprises, for a given service request: - a) calculating (52) a probabilities vector containing K components, each component being associated with a given moment in time from a set of K moments in time; - b) selecting (54) a migration moment in time based on the calculated probabilities vector; - c) determining (56) a number of replications of the virtual machine to be carried out at the selected migration moment in time in order to minimise a replication energy cost while providing a level of service continuity, and calculating (56) a predicted gain for the migration.