59 research outputs found
Towards More Efficient 5G Networks via Dynamic Traffic Scheduling
Department of Electrical EngineeringThe 5G communications adopt various advanced technologies such as mobile edge computing and unlicensed band operations, to meet the goal of 5G services such as enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). Specifically, by placing the cloud resources at the edge of the radio access network, so-called mobile edge cloud, mobile devices can be served with lower latency compared to traditional remote-cloud based services. In addition, by utilizing unlicensed spectrum, 5G can mitigate the scarce spectrum resources problem thus leading to realize higher throughput services.
To enhance user-experienced service quality, however, aforementioned approaches should be more fine-tuned by considering various network performance metrics altogether. For instance, the mechanisms for mobile edge computing, e.g., computation offloading to the edge cloud, should not be optimized in a specific metric's perspective like latency, since actual user satisfaction comes from multi-domain factors including latency, throughput, monetary cost, etc. Moreover, blindly combining unlicensed spectrum resources with licensed ones does not always guarantee the performance enhancement, since it is crucial for unlicensed band operations to achieve peaceful but efficient coexistence with other competing technologies (e.g., Wi-Fi).
This dissertation proposes a focused resource management framework for more efficient 5G network operations as follows. First, Quality-of-Experience is adopted to quantify user satisfaction in mobile edge computing, and the optimal transmission scheduling algorithm is derived to maximize user QoE in computation offloading scenarios. Next, regarding unlicensed band operations, two efficient mechanisms are introduced to improve the coexistence performance between LTE-LAA and Wi-Fi networks. In particular, we develop a dynamic energy-detection thresholding algorithm for LTE-LAA so that LTE-LAA devices can detect Wi-Fi frames in a lightweight way. In addition, we propose AI-based network configuration for an LTE-LAA network with which an LTE-LAA operator can fine-tune its coexistence parameters (e.g., CAA threshold) to better protect coexisting Wi-Fi while achieving enhanced performance than the legacy LTE-LAA in the standards. Via extensive evaluations using computer simulations and a USRP-based testbed, we have verified that the proposed framework can enhance the efficiency of 5G.clos
Multi-access edge computing: A survey
Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture.We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture
Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments
In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, transmission delays between edge servers and clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, so that their tasks are completed with minimum energy consumption and processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay
How to Place Your Apps in the Fog -- State of the Art and Open Challenges
Fog computing aims at extending the Cloud towards the IoT so to achieve
improved QoS and to empower latency-sensitive and bandwidth-hungry
applications. The Fog calls for novel models and algorithms to distribute
multi-service applications in such a way that data processing occurs wherever
it is best-placed, based on both functional and non-functional requirements.
This survey reviews the existing methodologies to solve the application
placement problem in the Fog, while pursuing three main objectives. First, it
offers a comprehensive overview on the currently employed algorithms, on the
availability of open-source prototypes, and on the size of test use cases.
Second, it classifies the literature based on the application and Fog
infrastructure characteristics that are captured by available models, with a
focus on the considered constraints and the optimised metrics. Finally, it
identifies some open challenges in application placement in the Fog
5G Multi-access Edge Computing: Security, Dependability, and Performance
The main innovation of the Fifth Generation (5G) of mobile networks is the
ability to provide novel services with new and stricter requirements. One of
the technologies that enable the new 5G services is the Multi-access Edge
Computing (MEC). MEC is a system composed of multiple devices with computing
and storage capabilities that are deployed at the edge of the network, i.e.,
close to the end users. MEC reduces latency and enables contextual information
and real-time awareness of the local environment. MEC also allows cloud
offloading and the reduction of traffic congestion. Performance is not the only
requirement that the new 5G services have. New mission-critical applications
also require high security and dependability. These three aspects (security,
dependability, and performance) are rarely addressed together. This survey
fills this gap and presents 5G MEC by addressing all these three aspects.
First, we overview the background knowledge on MEC by referring to the current
standardization efforts. Second, we individually present each aspect by
introducing the related taxonomy (important for the not expert on the aspect),
the state of the art, and the challenges on 5G MEC. Finally, we discuss the
challenges of jointly addressing the three aspects.Comment: 33 pages, 11 figures, 15 tables. This paper is under review at IEEE
Communications Surveys & Tutorials. Copyright IEEE 202
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