Allocation of Communication and Computation Resources in Mobile Networks

Konvergence komunikačních a výpočetních technologií vedlo k vzniku Multi-Access Edge Computing (MEC). MEC poskytuje výpočetní výkon na tzv. hraně mobilních sítí (základnové stanice, jádro mobilní sítě), který lze využít pro optimalizaci mobilních sítí v reálném čase. Optimalizacev reálném čase je umožněna díky nízkému komunikačnímu zpoždění například v porovnání s Mobile Cloud Computing (MCC). Optimalizace mobilních sítí vyžaduje informace o mobilní síti od uživatelských zařízeních, avšak sběr těchto informací využívá komunikační prostředky, které jsou využívány i pro přenos uživatelských dat. Zvyšující se počet uživatelských zařízení, senzorů a taktéž komunikace vozidel tvoří překážku pro sběr informací o mobilních sítích z důvodu omezeného množství komunikačních prostředků. Tudíž je nutné navrhnout řešení, která umožní sběr těchto informací pro potřeby optimalizace mobilních sítí. V této práci je navrženo řešení pro komunikaci vysokého počtu zařízeních, které je postaveno na využití přímé komunikace mezi zařízeními. Pro motivování uživatelů, pro využití přeposílání dat pomocí přímé komunikace mezi uživateli je navrženo přidělování komunikačních prostředků jenž vede na přirozenou spolupráci uživatelů. Dále je provedena analýza spotřeby energie při využití přeposílání dat pomocí přímé komunikace mezi uživateli pro ukázání jejích výhod z pohledu spotřeby energie. Pro další zvýšení počtu komunikujících zařízení je využito mobilních létajících základových stanic (FlyBS). Pro nasazení FlyBS je navržen algoritmus, který hledá pozici FlyBS a asociaci uživatel k FlyBS pro zvýšení spokojenosti uživatelů s poskytovanými datovými propustnostmi. MEC lze využít nejen pro optimalizaci mobilních sítí z pohledu mobilních operátorů, ale taktéž uživateli mobilních sítí. Tito uživatelé mohou využít MEC pro přenost výpočetně náročných úloh z jejich mobilních zařízeních do MEC. Z důvodu mobility uživatel je nutné nalézt vhodně přidělení komunikačních a výpočetních prostředků pro uspokojení uživatelských požadavků. Tudíž je navržen algorithmus pro výběr komunikační cesty mezi uživatelem a MEC, jenž je posléze rozšířen o přidělování výpočetných prostředků společně s komunikačními prostředky. Navržené řešení vede k snížení komunikačního zpoždění o desítky procent.The convergence of communication and computing in the mobile networks has led to an introduction of the Multi-Access Edge Computing (MEC). The MEC combines communication and computing resources at the edge of the mobile network and provides an option to optimize the mobile network in real-time. This is possible due to close proximity of the computation resources in terms of communication delay, in comparison to the Mobile Cloud Computing (MCC). The optimization of the mobile networks requires information about the mobile network and User Equipment (UE). Such information, however, consumes a significant amount of communication resources. The finite communication resources along with the ever increasing number of the UEs and other devices, such as sensors, vehicles pose an obstacle for collecting the required information. Therefore, it is necessary to provide solutions to enable the collection of the required mobile network information from the UEs for the purposes of the mobile network optimization. In this thesis, a solution to enable communication of a large number of devices, exploiting Device-to-Device (D2D) communication for data relaying, is proposed. To motivate the UEs to relay data of other UEs, we propose a resource allocation algorithm that leads to a natural cooperation of the UEs. To show, that the relaying is not only beneficial from the perspective of an increased number of UEs, we provide an analysis of the energy consumed by the D2D communication. To further increase the number of the UEs we exploit a recent concept of the flying base stations (FlyBSs), and we develop a joint algorithm for a positioning of the FlyBS and an association of the UEs to increase the UEs satisfaction with the provided data rates. The MEC can be exploited not only for processing of the collected data to optimize the mobile networks, but also by the mobile users. The mobile users can exploit the MEC for the computation offloading, i.e., transferring the computation from their UEs to the MEC. However, due to the inherent mobility of the UEs, it is necessary to determine communication and computation resource allocation in order to satisfy the UEs requirements. Therefore, we first propose a solution for a selection of the communication path between the UEs and the MEC (communication resource allocation). Then, we also design an algorithm for joint communication and computation resource allocation. The proposed solution then lead to a reduction in the computation offloading delay by tens of percent

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

The edge cloud: A holistic view of communication, computation and caching

The evolution of communication networks shows a clear shift of focus from just improving the communications aspects to enabling new important services, from Industry 4.0 to automated driving, virtual/augmented reality, Internet of Things (IoT), and so on. This trend is evident in the roadmap planned for the deployment of the fifth generation (5G) communication networks. This ambitious goal requires a paradigm shift towards a vision that looks at communication, computation and caching (3C) resources as three components of a single holistic system. The further step is to bring these 3C resources closer to the mobile user, at the edge of the network, to enable very low latency and high reliability services. The scope of this chapter is to show that signal processing techniques can play a key role in this new vision. In particular, we motivate the joint optimization of 3C resources. Then we show how graph-based representations can play a key role in building effective learning methods and devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing: Principles and Applications", P. Djuric and C. Richard Eds., Academic Press, Elsevier, 201

Optimal association of mobile users to multi-access edge computing resources

Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game