With the proliferation of Internet of Things (IoT) devices, the load caused by network demands has been increasing. This has direct consequences in terms of increased network requests and thus significantly higher energy consumption. To this end, this paper focuses on network power saving. In a cloud-radio access network with fiber-wireless, we propose a power saving method based on the optical line terminals (OLTs) and optical network units (ONUs), the equipment responsible for data transmission to and from the devices. Our proposed method is based on the novel idea of dividing the sleep phase of the ONUs into time slots, which facilitates synchronization and coordination in the whole system, thus increasing resource efficiency and bringing power economy not only to the ONUs, but also to the OLTs and the network as a whole. This is in contrast to the existing literature, which considers only ONUs or OLTs separately, without focusing on the cooperation within the system. Additionally, our proposed method was created with IoT traffic in mind. and it was designed to guarantee a maximum delay in order to make it applicable to mobile and real-time IoT. Finally, our proposed method allows the network to operate without external power sources for the ONUs by using power over fiber in conjunction with the OLT. Mathematical analyses show that our proposed method can successfully decrease the system’s energy consumption.

Mobile devices have several restrictions due to design choices that guarantee their mobility. A way of surpassing such limitations is to utilize cloud servers called cloudlets on the edge of the network through Mobile Edge Computing. However, as the number of clients and devices grows, the service must also increase its scalability in order to guarantee a latency limit and quality threshold. This can be achieved by deploying and activating more cloudlets, but this solution is expensive due to the cost of the physical servers. The best choice is to optimize the resources of the cloudlets through an intelligent choice of configuration that lowers delay and raises scalability. Thus, in this paper we propose an algorithm that utilizes Virtual Machine Migration and Transmission Power Control, together with a mathematical model of delay in Mobile Edge Computing and a heuristic algorithm called Particle Swarm Optimization, to balance the workload between cloudlets and consequently maximize cost-effectiveness. Our proposal is the first to consider simultaneously communication, computation, and migration in our assumed scale and, due to that, manages to outperform other conventional methods in terms of number of serviced users.

Mobile devices are naturally limited due to their portable sizes and will therefore never be equal to their desktop counterparts. To overcome this, Edge Cloud Computing can be utilized to execute tasks on behalf of the devices, allowing them to run applications that would normally be too demanding. In this service model, it is important to maintain a low Service Delay to keep the service transparent to the user. This can be achieved by focusing on lowering the Transmission Delay and Processing Delay. While existing approaches in the literature focus on one of those two, we postulate that only when considering both delays you can efficiently lower Service Delay and provide quality to all applications. In order to do that while being feasible, we propose a method based on Particle Swarm Optimization for lowering Service Delay in Edge Cloud Computing. Our proposal is shown to be close to optimality while still maintaining a low execution time for multiple cloudlets scenarios. Moreover, our proposal outperforms existing approaches from the literature with single focus on computation or communication, even in situations with high processing and transmission burdens, proving the superiority of a dual focus approach.

Due to physical limitations, mobile devices are restricted in memory, battery, processing, among other characteristics. This results in many applications that cannot be run in such devices. This problem is fixed by Edge Cloud Computing, where the users offload tasks they cannot run to cloudlet servers in the edge of the network. The main requirement of such a system is having a low Service Delay, which would correspond to a high Quality of Service. This paper presents a method for minimizing Service Delay in a scenario with two cloudlet servers. The method has a dual focus on computation and communication elements, controlling Processing Delay through virtual machine migration and improving Transmission Delay with Transmission Power Control. The foundation of the proposal is a mathematical model of the scenario, whose analysis is used on a comparison between the proposed approach and two other conventional methods; these methods have single focus and only make an effort to improve either Transmission Delay or Processing Delay, but not both. As expected, the proposal presents the lowest Service Delay in all study cases, corroborating our conclusion that a dual focus approach is the best way to tackle the Service Delay problem in Edge Cloud Computing.

A concept of Cloud Radio Access Networks (C-RANs) with Power over Fiber (PoF) is expected to work as a post- disaster access network architecture. In this architecture, since external power is supplied to Remote Radio Heads (RRHs) through the optical-fiber cable for data communication, RRHs can operate even when power cables are disrupted due to disasters. This network, however, needs to reduce the power consumption for RRH operation and consider Quality of Experience (QoE) to provide sustainable service with enough user satisfaction. To this end, we propose user position guidance approaches which give advices of the best position to users. Our proposed approaches are able to achieve high sustainability while satisfying the QoE constraint. Furthermore, the effectiveness of our proposed approaches is evaluated by numerical calculation.

There are many applications which cannot be executed by mobile devices due to their limitations in memory, processing, battery, among others. One solution to this would be offloading heavy tasks to cloud servers in the edge of the network, in a service model called Edge Cloud Computing. The main Quality of Service requirement of this model is a low Service Delay, which can be achieved by lowering Transmission Delay and Processing Delay. Works in literature focus on either one of those two types of delay. This paper, however, argues that an approach which combines transmission and processing technologies to lower Service Delay would be more efficient. This idea is defended by an analysis of the service model and existing stochastic modeling of the Edge Cloud Computing system. We conclude that a dual focus approach would be the only way of truly minimizing the Service Delay, therefore being the desired method to improve Quality of Service. We conclude by laying the foundation for a future model that follows such concept.