8 research outputs found
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.Comment: IEEE Infocom 201
OKpi: All-KPI Network Slicing Through Efficient Resource Allocation
Networks can now process data as well as transporting it; it follows that
they can support multiple services, each requiring different key performance
indicators (KPIs). Because of the former, it is critical to efficiently
allocate network and computing resources to provide the required services, and,
because of the latter, such decisions must jointly consider all KPIs targeted
by a service. Accounting for newly introduced KPIs (e.g., availability and
reliability) requires tailored models and solution strategies, and has been
conspicuously neglected by existing works, which are instead built around
traditional metrics like throughput and latency. We fill this gap by presenting
a novel methodology and resource allocation scheme, named OKpi, which enables
high-quality selection of radio points of access as well as VNF (Virtual
Network Function) placement and data routing, with polynomial computational
complexity. OKpi accounts for all relevant KPIs required by each service, and
for any available resource from the fog to the cloud. We prove several
important properties of OKpi and evaluate its performance in two real-world
scenarios, finding it to closely match the optimum