Next-generation distributed computing networks (e.g., edge and fog computing) enable the efficient delivery of delay-sensitive, compute-intensive applications by facilitating access to computation resources in close proximity to end users. Many of these applications (e.g., augmented/virtual reality) are also data-intensive: in addition to user-specific (live) data streams, they require access to shared (static) digital objects (e.g., im-age database) to complete the required processing tasks. When required objects are not available at the servers hosting the associated service functions, they must be fetched from other edge locations, incurring additional communication cost and latency. In such settings, overall service delivery performance shall benefit from jointly optimized decisions around (i) routing paths and processing locations for live data streams, together with (ii) cache selection and distribution paths for associated digital objects. In this paper, we address the problem of dynamic control of data-intensive services over edge cloud networks. We characterize the network stability region and design the first throughput-optimal control policy that coordinates processing and routing decisions for both live and static data-streams. Numerical results demonstrate the superior performance (e.g., throughput, delay, and resource consumption) obtained via the novel multi-pipeline flow control mechanism of the proposed policy, compared with state-of-the-art algorithms that lack integrated stream processing and data distribution control.
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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.
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- Award ID(s):
- 1815676
- NSF-PAR ID:
- 10121142
- Date Published:
- Journal Name:
- IEEE International Conference on Computer Communications
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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