Section: Computer Science and Engineering
Tutor: MIRANDOLA RAFFAELA
Advisor: DI NITTO ELISABETTA Major Research topic
:Optimization Framework for Resource Management of Mobile Edge Computing NetworksAbstract:
The Fifth-generation (5G) and beyond mobile networks aim at satisfying, in different demanding application scenarios, stringent Quality of Service (QoS) requirements, among which latency is one of the key metrics that mobile operators are supposed to optimize for mobile users. Mobile Edge Computing (MEC), an essential techniques utilized in 5G networks, brings cloud-computing capabilities to the edge of the mobile networks, especially in close proximity to mobile users, making it possible to simultaneously address the stringent latency requirements of critical services and ensure highly efficient network operation and service delivery, so as to improve user experience.
MEC services, on one hand, require significant investments from both network operators and service providers in terms of deploying, operating and managing edge clouds, and on the other, provides limited computational and storage resources by design as data centers are deployed in the core of the network. During peak hours, the operator must serve a large amount of tasks from users with high demands, hence the latency requirements of different services can hardly be guaranteed. This issue can be tackled by massively deployed edge clouds that are attached to the base stations and connected to each other in a specific topology, as ultra-dense 5G-and-Beyond networks are built. In this way, the resource limitations can be solved through sharing computational and storage capabilities among multiple MEC units nearby.
In this thesis, we leverage cooperation among interconnected multiple MEC units and investigate joint resource optimization considering multiple aspects of network operations, with the target of enhancing the utilization efficiency of resources to further satisfy improved QoS and reduce network operation cost. Specifically, aggregated mobile traffic and user requests are considered based on their types (e.g., voice, video, web, game, etc.) that are associated with different QoS requirements. We jointly optimize 1) where to process the traffic and requests, 2) how to route network flows and 3) how to allocate and schedule the required resources with regard to communication, computation and storage.
Firstly, to serve mobile traffic, we investigate in the context of hierarchical edge networks and propose a mathematical optimization model to perform a joint slicing of mobile network and edge computation resources. The optimization aims at minimizing the total traffic latency considering operations including transmitting, outsourcing and processing user traffic, under the constraint of user tolerable latency for each class of traffic.
Then, we release the constraints on hierarchical network and fixed computation capacity, and further take into account the overall budget of operators to plan and allocate the computation capabilities in edge network with an arbitrary topology. The main objective, aligned with the first one, is to further operate cost-efficient edge networks through jointly planning the availability of computational resources at the edge, slicing mobile network and edge computation resources, and routing heterogeneous traffic types to various slices. We propose an optimization model to minimize the network operation cost and the total traffic latency in the procedures of transmitting, outsourcing and processing user traffic, under the constraint of user maximum tolerable latency for each class of traffic.
Finally, we focus on serving multiple classes of user requests (with starting time, deadline and duration) which require bandwidth, storage and computation resources. The main objective is to exploit the flexibility of services to requests by shifting the starting time without penalizing utility perceived by users, while, in the meantime, permitting an efficient resource utilization. We propose an optimization framework that jointly considers several key aspects of the resource allocation problem, specifically, through optimizing: admission decision, scheduling of admitted requests (also called calendaring), routing of these flows, the decision of which nodes will serve such requests, as well as the amount of processing and storage capacity reserved on the chosen nodes, with the objective of maximizing the operator’s profit.
The above proposed optimization models are first formulated as mixed-integer nonlinear programming (MINLP) problems, which are NP-hard. To tackle them efficiently, we perform equivalent reformulations from MINLP to mixed-integer quadratically constraint programming (MIQCP), and based on that, further propose effective heuristics to facilitate the solutions of the problems, i.e., Sequential Fixing (SF) for the edge slicing model, Neighbor Exploration and Sequential Fixing (NESF) for the edge planning model and Sequential Fixing and Scheduling (SFS) for the edge calendaring model. We evaluate the performance of our proposed models and heuristics in real-size network scenarios, showing the impact of all the considered parameters (i.e., different types of user traffic or requests, tolerable latency, network topology and bandwidth, computation, storage and link capacities) on the both optimal and approximate solutions. Results obtained demonstrate that near-optimal resource allocation solutions can be achieved by our heuristics in low computing time.