You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey 
Chapter Title:  (3) Optimization for Mobility Management in MEC Systems. 
1. Abstract of Cited Paper (BIB001):  Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while taking the empirically best action as often as possible. A popular measure of a policy's success in addressing this dilemma is the regret, that is the loss due to the fact that the globally optimal policy is not followed all the times. One of the simplest examples of the exploration/exploitation dilemma is the multi-armed bandit problem. Lai and Robbins were the first ones to show that the regret for this problem has to grow at least logarithmically in the number of plays. Since then, policies which asymptotically achieve this regret have been devised by Lai and Robbins and many others. In this work we show that the optimal logarithmic regret is also achievable uniformly over time, with simple and efficient policies, and for all reward distributions with bounded support. 
2. Abstract of Cited Paper (BIB002):  With the explosion in personal mobile devices, computation offloading through opportunistic networks of nearby devices to support sophisticated mobile applications with limited resources of processing and energy is gaining increased attention. In this letter, we investigate the problem of mobility-assisted opportunistic computation offloading by exploiting the contact patterns regulated by these devices' mobility. We first formulate the optimal opportunistic offloading problem with the aid of statistic property of contact rates and then utilize the method of convex optimization to determine the amounts of computation to be offloaded to other devices. Extensive simulations under realistic human mobility demonstrate the efficiency of our scheme, in terms of obtaining superior higher computation success rate over the compared benchmarks under a wide variety of settings. 
3. Abstract of Cited Paper (BIB003):  The last decade has seen a rapid growth in the use of mobile devices all over the world. With an increasing use of mobile devices, mobile applications are becoming more diverse and complex, demanding more computational resources. However, mobile devices are typically resource-limited (i.e., a slower-speed CPU, a smaller memory) due to a variety of reasons. Mobile users will be capable of running applications with heavy computation if they can offload some of their computations to other places, such as a desktop or server machines. However, mobile users are typically subject to dynamically changing network environments, particularly, due to user mobility. This makes it hard to choose good offloading decisions in mobile environments. In general, users’ mobility can provide some hints for upcoming changes to network environments. Motivated by this, we propose a mobility model of each individual user taking advantage of the regularity of his/her mobility pattern, and develop an offloading decision-making technique based on the mobility model. We evaluate our technique through trace-based simulation with real log data traces from 14 Android users. Our evaluation results show that the proposed technique can help boost the performance of mobile devices in terms of response time and energy consumption, when users are highly mobile. 
4. Abstract of Cited Paper (BIB004):  The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users’ local load and availability of cloudlets. We examine users’ mobility patterns and cloudlets’ admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.

Literature Review:
Conventionally, mobile services are provided to users through the operators EPC network; that is, the traffic goes through the EPC from the Web server(s) hosted within the Internet, while mobile users can move across radio cells. However, mobility management in MEC systems poses a significant challenge due to the fact that the systems will be implemented in the heterogeneous networks (HetNets) architecture consisting of multiple macro, small cell, and eNBs. Thus, user's movement will result in frequent handovers among these radio access technologies (RAT). This challenge implies that the way in which mobility is currently handled may no longer be appropriate in MEC systems, and some rethinking about how it can be best handled at the network edge is in order. In BIB004 , a mobility aware offloading decision maker named Mob-aware is proposed. This scheme estimates future network changes based on typical user's mobility patterns. In this paper, it is observed that user mobility possesses some regularity BIB001 , and this can provide relevant information on what kind of changes can occur within the network in the near future. For example, it is possible to predict a sequence of networks to which users will be connected and thus apply some sort of predictive control policy in making offload decisions. The authors of BIB001 developed a mobility pattern model and then used tracebased simulation with real log data traces from 14 Android users to validate the mobility regularity. Along the same lines, in BIB005 user mobility patterns and cloudlets admission control policies were investigated, and the minimization problem for computation and offloading costs was modeled using Markov decision processes (MDP). In BIB003 , the contact patterns regulated by mobile devices mobility were exploited and opportunistic computation offloading strategies were derived using convex optimization techniques. To maximize the edge computation performance for a user, while keeping the user's communication energy consumption below a certain threshold, a user-centric mobility management scheme exploiting Lyapunov optimization  and multiarmed bandits BIB002 has been proposed in  . Based on these optimization techniques, an online algorithm was developed by considering user-side state information (user location, candidate BS, and workload rate) and BS-side state information (background workload, maximum service rate, and uplink channel conditions). The overall scheme is online and was compared against an oracle-based look-ahead algorithm, which has full knowledge of the system state information. The performance of the online algorithm is satisfactory, providing optimal delay performance when the energy constraint is large and achieving a tradeoff between optimal delay and optimal energy consumption otherwise, while still satisfying the energy budget constraint.