Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> (1) Optimization in MEC Systems Using Renewable Energy. <s> Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics. <s> BIB001 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> (1) Optimization in MEC Systems Using Renewable Energy. <s> Mobile edge computing (a.k.a. fog computing) has recently emerged to enable \emph{in-situ} processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in renewable-powered mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning. <s> BIB002 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> (1) Optimization in MEC Systems Using Renewable Energy. <s> Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost , which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm. <s> BIB003 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> (1) Optimization in MEC Systems Using Renewable Energy. <s> Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme. <s> BIB004 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> (1) Optimization in MEC Systems Using Renewable Energy. <s> Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability , under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control. <s> BIB005 </s> Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey <s> (1) Optimization in MEC Systems Using Renewable Energy. <s> This paper proposes a novel joint computation and communication cooperation approach in mobile edge computing (MEC) systems, which enables user cooperation in both computation and communication for improving the MEC performance. In particular, we consider a basic three-node MEC system that consists of a user node, a helper node, and an access point (AP) node attached with an MEC server. We focus on the user's latency-constrained computation over a finite block, and develop a four-slot protocol for implementing the joint computation and communication cooperation. Under this setup, we jointly optimize the computation and communication resource allocation at both the user and the helper, so as to minimize their total energy consumption subject to the user's computation latency constraint. We provide the optimal solution to this problem. Numerical results show that the proposed joint cooperation approach significantly improves the computation capacity and the energy efficiency at the user and helper nodes, as compared to other benchmark schemes without such a joint design. <s> BIB006
The use of renewable energy in MEC systems was investigated in BIB002 BIB003 , focusing on EH powered MEC servers and EH powered mobile devices. In BIB002 , a reinforcement learningbased online algorithm was used to enhance decision making for EH powered MEC systems in determining the amount of workload to be offloaded from the edge servers to the central cloud, as well as the processing speed of the edge server, taking into account the congestion status in the EPC, the computation workload, and the ESI. Furthermore, a Lyapunov optimization technique based on channel state indicator (CSI) and ESI was used to obtain dynamic offloading policies for EH powered mobile devices BIB003 . Both optimization techniques, reinforcement learning (online) and Lyapunov optimization based, were used to study small-scale MEC deployments, without taking into account large scale networks. Further work shall be carried out to scale up and this still poses a challenge to researchers as data demand increases. Robust optimization algorithms for handling large scale deployments of MEC servers have to be developed under the ESI constraint. In BIB006 , a new energy efficient design principle for the BS (colocated with the MEC server) to minimize its energy consumption, while ensuring selfsustainable computation at the mobile devices (through WPT), is investigated using the Lagrangian duality method. A multiuser MEC system consisting of a multiantenna access point and multiple users is assumed. Each mobile device is equipped with two antennas, one for WPT and the other for computational offloading. The antennas operate over different frequency bands such that WPT and computational offloading can be performed simultaneously, without mutual interference. Users rely on their harvested wireless energy to execute the latency-sensitive computational tasks either via local computing or (possibly partial) offloading to the MEC server. The optimal policy under energy harvesting constraints is obtained leveraging the Lagrange duality BIB001 and the ellipsoid methods  . (2) Optimization for MEC Offloading. During the MEC computation offloading process, the energy consumption for processing the task involves the energy spent by the mobile device to transmit the data to the MEC server and that involved in the computation at the server side. To minimize the system energy consumption under latency constraints, a three-stage energy optimization scheme, that is, (i) mobile device classification, (ii) priority determination, and (iii) radio resource allocation, is proposed in BIB004 . This algorithm involves priority assignment and classification type for mobile devices to reduce the problem complexity. The problem is formulated as a special maximum cardinality bin packing program [51] , where mobile devices choose their task allocation mode through binary strategies, taking into account the transmission interference with other terminals and the limited radio resources. The obtained numerical results demonstrate that the approach increases the energy efficiency of the MEC system. A centralized twostage resource allocation optimization scheme is proposed in BIB005 with the objective of minimizing the mobile energy consumption in a MEC offloading system using TDMA and OFDMA. The MEC server is assumed to have knowledge of the local computation energy consumption, of channel gains and fairness indices for all users. The resource allocation is formulated as a convex optimization problem, whose solution is obtained through dual-decomposition coupled with a relaxation constraint on the cloud capacity.