Without steering wheel and driver's seat, the self-driving cars will have new interior outlook and spaces that can be used for enhanced infotainment services. For traveling people, self-driving cars will be new places for engaging in infotainment services. Therefore, self-driving cars should determine themselves the infotainment contents that are likely to entertain their passengers. However, the choice of infotainment contents depends on passengers' features such as age, emotion, and gender. Also, retrieving infotainment contents at data center can hinder infotainment services due to high end-to-end delay. To address these challenges, we propose infotainment caching in self-driving cars, where caching decisions are based on passengers' features obtained using deep learning. First, we proposed deep learning models to predict the contents need to be cached in self-driving cars and close proximity of self-driving cars in multi-access edge computing servers attached to roadside units. Second, we proposed a communication model for retrieving infotainment contents to cache. Third, we proposed a caching model for retrieved contents. Fourth, we proposed a computation model for the cached contents, where cached contents can be served in different formats/qualities based on demands. Finally, we proposed an optimization problem whose goal is to link the proposed models into one optimization problem that minimizes the content downloading delay. To solve the formulated problem, a block successive majorization-minimization technique is applied. The simulation results show that the accuracy of prediction for the contents that need to be cached is 97.82% and our approach can minimize the delay.

Remarkable prevalence of cloud computing has enabled smart cars to provide infotainment services. However, retrieving infotainment contents from long-distance data centers poses a significant delay. Consequently, hindering to offer stringent latency-aware infotainment services. Alternatively, Multi-access Edge Computing is a promising option to meet strict latency requirements. However, it imposes severe resource constraints with respect to caching and computation. Similarly, communication resources utilized to fetch the infotainment contents are scarce. In this paper, we jointly consider communication, caching, and computation (3C) to reduce infotainment content retrieval delay for smart cars. We formulate the problem as a mix-integer, non-linear, and non-convex optimization to minimize the latency. Furthermore, we relax the formulated problem from NP-hard to linear programming. Then, we propose a joint solution (3C) based on the alternative direction method of multipliers technique which operates in a distributed manner. We compare the proposed 3C solution with various approaches, namely, greedy, random, and centralized. Simulation results reveal that the proposed solution reduces delay up to 9% and 28% compared to the greedy and random approaches, respectively.

Mobile data traffic is increasing astronomically. This enormous extent was not only caused by the increasing in the number of mobile devices, but also the growing number of applications running on clouds. Most of these mobile devices have limited resources for computing, they are relying on cloud computing, which is inadequate to realize millisecond-scale latency in the 5G network. To deal with this issue, Mobile Edge Computing (MEC) has been introduced to supplement cloud computing by pushing Computing, Caching, Communication, and Control (4C) to the edges. However, the proposed MEC server at each base station is not enough to deal with 4C, when it operates independently, and without any collaboration with other MEC servers. This results in increasing the delay and making backhaul to continue suffering from huge data exchange between end-users and remote clouds. To address this challenge, we propose collaborative cache allocation and computation offloading, where the MEC servers collaborate for executing computation tasks and data caching. We formulate an optimization problem that aims at maximizing the resource utilization. The simulation results show that our proposal is easy to be implemented in production network.

Internet traffic volume is continuing to increase rapidly. Named Data Networking (NDN) has been introduced to support this Internet traffic growth through caching contents close to consumers. While caching in NDN is beneficial to both Internet Service Providers (ISPs) and Content Providers (CPs), ISPs serve cached contents independently without any coordination with CPs. By authorizing the ISPs to cache and distribute the contents accessible on payments, it becomes impractical for CPs to control content access and payments. In this work, we address these challenges by proposing a joint incentive mechanism and price based cache replacement policy for paid content in NDN that improves the ISP’s and CPs’ profits. We use an auction theory, where the ISP earns profits from caching by alleviating traffic load on transit links and participating in contents selling. Therefore, before the ISP starts selling cached contents, it needs to cache them first. Furthermore, the ISP cache capacity is limited, so we propose Price Based Cache Replacement (PBCR), where PBCR triggers the content that needs to be replaced when the cache storage is full based on both content price and link cost. The simulation results show that our proposal increases the profits of all the network players involved in paid content caching and improves cache hit ratio.

Offloading computation-intensive tasks to edge clouds has become an efficient way to support resource constraint edge devices. However, task offloading delay is an issue largely due to the networks with limited capacities between edge clouds and edge devices. In this paper, we consider task offloading in Open Radio Access Network (O-RAN), which is a new 5G RAN architecture allowing Open Central Unit (O-CU) to be co-located with Open Distributed Unit (DU) at the edge cloud for low-latency services. O-RAN relies on fronthaul network to connect O-RAN Radio Units (O-RUs) and edge clouds that host O-DUs. Consequently, tasks are offloaded onto the edge clouds via wireless and fronthaul networks, which requires routing. Since edge clouds do not have the same available computation resources and tasks’ computation deadlines are different, we need a task distribution approach to multiple edge clouds. Prior work has never addressed this joint problem of task offloading, fronthaul routing, and edge computing. To this end, using segment routing, O-RAN intelligent controllers, and multiple edge clouds, we formulate an optimization problem to minimize offloading, fronthaul routing, and computation delays in O-RAN. To determine the solution of this NP-hard problem, we use Deep Q-Learning assisted by federated learning with a reward function that reduces the Cost of Delay (CoD). The simulation results show that our solution maximizes the reward in minimizing CoD.