UAV-Empowered Disaster-Resilient Edge Architecture for Delay-Sensitive Communication

The fifth-generation (5G) communication systems will enable enhanced mobile broadband, ultra-reliable low latency, and massive connectivity services. The broadband and low-latency services are indispensable to public safety (PS) communication during natural or man-made disasters. Recently, the third generation partnership project long term evolution (3GPPLTE) has emerged as a promising candidate to enable broadband PS communications. In this article, first we present six major PS-LTE enabling services and the current status of PS-LTE in 3GPP releases. Then, we discuss the spectrum bands allocated for PS-LTE in major countries by international telecommunication union (ITU). Finally, we propose a disaster resilient three-layered architecture for PS-LTE (DR-PSLTE). This architecture consists of a software-defined network (SDN) layer to provide centralized control, an unmanned air vehicle (UAV) cloudlet layer to facilitate edge computing or to enable emergency communication link, and a radio access layer. The proposed architecture is flexible and combines the benefits of SDNs and edge computing to efficiently meet the delay requirements of various PS-LTE services. Numerical results verified that under the proposed DR-PSLTE architecture, delay is reduced by 20% as compared with the conventional centralized computing architecture.Comment: 9,

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

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

On distributed mobile edge computing

Mobile Cloud Computing (MCC) has been proposed to offload the workloads of mobile applications from mobile devices to the cloud in order to not only reduce energy consumption of mobile devices but also accelerate the execution of mobile applications. Owing to the long End-to-End (E2E) delay between mobile devices and the cloud, offloading the workloads of many interactive mobile applications to the cloud may not be suitable. That is, these mobile applications require a huge amount of computing resources to process their workloads as well as a low E2E delay between mobile devices and computing resources, which cannot be satisfied by the current MCC technology. In order to reduce the E2E delay, a novel cloudlet network architecture is proposed to bring the computing and storage resources from the remote cloud to the mobile edge. In the cloudlet network, each mobile user is associated with a specific Avatar (i.e., a dedicated Virtual Machine (VM) providing computing and storage resources to its mobile user) in the nearby cloudlet via its associated Base Station (BS). Thus, mobile users can offload their workloads to their Avatars with low E2E delay (i.e., one wireless hop). However, mobile users may roam among BSs in the mobile network, and so the E2E delay between mobile users and their Avatars may become worse if the Avatars remain in their original cloudlets. Thus, Avatar handoff is proposed to migrate an Avatar from one cloudlet into another to reduce the E2E delay between the Avatar and its mobile user. The LatEncy aware Avatar handDoff (LEAD) algorithm is designed to determine the location of each mobile user\u27s Avatar in each time slot in order to minimize the average E2E delay among all the mobile users and their Avatars. The performance of LEAD is demonstrated via extensive simulations. The cloudlet network architecture not only facilitates mobile users in offloading their computational tasks but also empowers Internet of Things (IoT). Popular IoT resources are proposed to be cached in nearby brokers, which are considered as application layer middleware nodes hosted by cloudlets in the cloudlet network, to reduce the energy consumption of servers. In addition, an Energy Aware and latency guaranteed dynamic reSourcE caching (EASE) strategy is proposed to enable each broker to cache suitable popular resources such that the energy consumption from the servers is minimized and the average delay of delivering the contents of the resources to the corresponding clients is guaranteed. The performance of EASE is demonstrated via extensive simulations. The future work comprises two parts. First, caching popular IoT resources in nearby brokers may incur unbalanced traffic loads among brokers, thus increasing the average delay of delivering the contents of the resources. Thus, how to balance the traffic loads among brokers to speed up IoT content delivery process requires further investigation. Second, drone assisted mobile access network architecture will be briefly investigated to accelerate communications between mobile users and their Avatars

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

Mobile cloud computing and network function virtualization for 5g systems

The recent growth of the number of smart mobile devices and the emergence of complex multimedia mobile applications have brought new challenges to the design of wireless mobile networks. The envisioned Fifth-Generation (5G) systems are equipped with different technical solutions that can accommodate the increasing demands for high date rate, latency-limited, energy-efficient and reliable mobile communication networks. Mobile Cloud Computing (MCC) is a key technology in 5G systems that enables the offloading of computationally heavy applications, such as for augmented or virtual reality, object recognition, or gaming from mobile devices to cloudlet or cloud servers, which are connected to wireless access points, either directly or through finite-capacity backhaul links. Given the battery-limited nature of mobile devices, mobile cloud computing is deemed to be an important enabler for the provision of such advanced applications. However, computational tasks offloading, and due to the variability of the communication network through which the cloud or cloudlet is accessed, may incur unpredictable energy expenditure or intolerable delay for the communications between mobile devices and the cloud or cloudlet servers. Therefore, the design of a mobile cloud computing system is investigated by jointly optimizing the allocation of radio, computational resources and backhaul resources in both uplink and downlink directions. Moreover, the users selected for cloud offloading need to have an energy consumption that is smaller than the amount required for local computing, which is achieved by means of user scheduling. Motivated by the application-centric drift of 5G systems and the advances in smart devices manufacturing technologies, new brand of mobile applications are developed that are immersive, ubiquitous and highly-collaborative in nature. For example, Augmented Reality (AR) mobile applications have inherent collaborative properties in terms of data collection in the uplink, computing at the cloud, and data delivery in the downlink. Therefore, the optimization of the shared computing and communication resources in MCC not only benefit from the joint allocation of both resources, but also can be more efficiently enhanced by sharing the offloaded data and computations among multiple users. As a result, a resource allocation approach whereby transmitted, received and processed data are shared partially among the users leads to more efficient utilization of the communication and computational resources. As a suggested architecture in 5G systems, MCC decouples the computing functionality from the platform location through the use of software virtualization to allow flexible provisioning of the provided services. Another virtualization-based technology in 5G systems is Network Function Virtualization (NFV) which prescribes the instantiation of network functions on general-purpose network devices, such as servers and switches. While yielding a more flexible and cost-effective network architecture, NFV is potentially limited by the fact that commercial off-the-shelf hardware is less reliable than the dedicated network elements used in conventional cellular deployments. The typical solution for this problem is to duplicate network functions across geographically distributed hardware in order to ensure diversity. For that reason, the development of fault-tolerant virtualization strategies for MCC and NFV is necessary to ensure reliability of the provided services