Abstract
5G systems are putting increasing pressure on Telecom operators to enhance users’ experience, leading to the development of more techniques with the aim of improving service quality. However, it is essential to take into consideration not only users’ demands but also service providers’ interests. In this work, we explore policies that satisfy both views. We first formulate a mathematical model to compute End-to-End (E2E) delay experienced by mobile users in Multi-access Edge Computing (MEC) environments. Then, dynamic Virtual Machine (VM) allocation policies are presented, with the objective of satisfying mobile users Quality of Service (QoS) requirements, while optimally using the cloud resources by exploiting VM resource reuse.Thus, maximizing the service providers’ profit should be ensured while providing the service required by users. We further demonstrate the benefits of these policies in comparison with previous works.
Highlights
Virtualization is being widely employed with the boom of Cloud Computing (CC)
Identify the main contributors to the delay in Multi-access Edge Computing (MEC) environments and formulate a pragmatic mathematical model for E2E delay experienced by mobile users
For a MEC scenario, we argue that this may not be true as there is little time to perform analysis steps, or that the analysis will incur in unacceptable overhead due to the frequent updates, over a network that is not so powerful as a large datacenter
Summary
Virtualization is being widely employed with the boom of Cloud Computing (CC). MEC is becoming the emerging trend in CC. 5G telecom operators promise improved latency-sensitive services for mobile users, and envision moving data centers or just computational resources closer to their clients, as the means to enable such level of performance. 5G telecom operators promise improved latency-sensitive services for mobile users, and envision moving data centers or just computational resources closer to their clients, as the means to enable such level of performance. This near-user deployment directly exposes the services to clients’ mobility, making mobility management more challenging and calling for careful service management to keep Service Level Agreement (SLA) limits preserved. Some of these techniques address task offloading and optimal service migration, in addition to improved handover mechanisms. Comes the need to come up with an optimal service migration policy, which satisfies the usersneeds, and provides optimal use of the cloud resources, from the point of view the of service providers
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