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:  Mobility Management in MEC Networks. 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  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:
Mobility management has been extensively investigated assuming a single mobility anchor per mobile network, which becomes a single point of failure. Distributed mobility anchors can also be deployed within different geographical areas, so that when a mobile device crosses the boundary, handover (HO) requests are triggered to guarantee high data rate and low bit error rate. However, these policies cannot be directly applied to MEC systems with moving users, since they neglect the effects of the computation resources at edge servers on the HO policies. In current mobile networks, HO is handled as follows: when the mobile node signal strength becomes insufficient (weak), HO is initiated either by the mobile node (mobile-initiated HO) or by the network (network-initiated HO). The current serving BS communicates with neighbor BS, target BS, via the X2 interface. In the meantime, IP packets are buffered to minimize packet loss. Once the signaling is over, the mobile node is handed over to the target BS, and a new link is formed. Mainly, the goal of the mobility management protocol is to minimize packet loss and to select the target BS for the UE; that is, only the HO target (BS to connect to) is considered in this case. In MEC systems, computational capacity, current workloads, computational costs, and energy side information are crucial in determining the HO target (offloading target in this case). Thus, it is important to extend HO and mobility management procedures to MEC networks, keeping these additional variables into account. The mobility patterns observations in BIB003 BIB001 BIB004 BIB002 have shown that it is possible (and sensible) to make use of the observed information in designing MEC mobility management procedures. However, this introduces the use of distributed databases to keep and process the observed mobility information, to extract relevant patterns. This might pose a challenge, as it might add to the delay constraints, as decision making will rely on drawing conclusions from the observed states and to being able to predict the device movement around the edge.