Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. In this paper, we propose an online double deep Q networks (DDQN) based learning scheme for task assignment in dynamic MEC networks, which enables multiple distributed edge nodes and a cloud data center to jointly process user tasks to achieve optimal long-term quality of service (QoS). The proposed scheme captures a wide range of dynamic network parameters including non-stationary node computing capabilities, network delay statistics, and task arrivals. It learns themore »
Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks
The proliferation of innovative mobile services such
as augmented reality, networked gaming, and autonomous driving
has spurred a growing need for low-latency access to computing
resources that cannot be met solely by existing centralized
cloud systems. Mobile Edge Computing (MEC) is expected to
be an effective solution to meet the demand for low-latency
services by enabling the execution of computing tasks at the
network-periphery, in proximity to end-users. While a number
of recent studies have addressed the problem of determining
the execution of service tasks and the routing of user requests
to corresponding edge servers, the focus has primarily been
on the efficient utilization of computing resources, neglecting
the fact that non-trivial amounts of data need to be stored to
enable service execution, and that many emerging services exhibit
asymmetric bandwidth requirements. To fill this gap, we study
the joint optimization of service placement and request routing
in MEC-enabled multi-cell networks with multidimensional
(storage-computation-communication) constraints. We show that
this problem generalizes several problems in literature and
propose an algorithm that achieves close-to-optimal performance
using randomized rounding. Evaluation results demonstrate that
our approach can effectively utilize the available resources to
maximize the number of requests served by low-latency edge
cloud servers.
- Award ID(s):
- 1815676
- Publication Date:
- NSF-PAR ID:
- 10121142
- Journal Name:
- IEEE International Conference on Computer Communications
- Sponsoring Org:
- National Science Foundation
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