Xiuhua Li

A Framework of Cooperative Cell Caching for the Future Mobile Networks

The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic load. In this article, we mainly focus on a new novel framework of cooperative cell caching for future mobile cellular networks, where the base station of each cell can have certain capability of caching popular contents. Then we carry out necessary theoretical modeling-based analysis. We also propose to utilize prefix-tree aggregation to improve the caching performance among cells, and discuss potential deployment issues for caching in 5G mobile networks. Based on trace-driven simulations, we evaluate the performance of the proposed framework.

Caching-as-a-Service: Virtual caching framework in the cloud-based mobile networks

Over recent years, the demand for rich multimedia services over mobile networks has been soaring at a tremendous pace. However, it is envisioned that traditional dedicated networking equipment in mobile network operators (MNOs) cannot support the phenomenal growth of the traffic load and user demand dynamics, but consume unnecessary energy resource inefficiently. The emerging techniques for mobile content caching and delivery become more and more attractive, by which popular content can be cached inside mobile front-haul and back-haul networks, so that demands to the same content from users in proximity can be easily accommodated without redundant transmissions from the remote resource, thereby eliminating duplicated traffic significantly. While the incorporation between advanced cloud computing technologies and network function virtualization (NFV) techniques has become an essential issue in the evolution process of mobile systems, in this article, we propose the concept of “Caching-as-a-Service” (CaaS), a caching virtualization framework along with the development of Cloud-based Radio Access Networks (C-RAN), and the virtualization of Evolved Packet Core (EPC). Then we study the potential techniques related to the cache virtualization, and discuss technical details of caching virtualization and system optimization for CaaS. We carry out numerical evaluation on proposed framework and show significant improvement on the performance of reducing inter-MNO traffic load and intra-MNO traffic load.

Trust assistance in Sensor-Cloud

Incorporating 1) the ubiquitous data gathering ability of wireless sensor networks (WSNs) and 2) the powerful data storage and data processing capabilities of cloud computing (CC), Sensor-Cloud (SC) is attracting increasing attention from both academia and industry. In this paper, focusing on improving the quality of service (QoS) of SC for users to obtain sensory data from the cloud, we propose Trust-Assisted SC (TASC). In TASC, trusted sensors (i.e., sensors with trust values exceeding a threshold) collect and transmit sensory data to the cloud. Then the cloud selects the trusted data centers (i.e., data centers with trust values exceeding a threshold) to store, process the sensory data and further transmit the processed sensory data to users on demand. With extensive simulation results, we show that the TASC can substantially improve the throughput and response time for users to obtain sensory data from the cloud, compared with SC without trust assistance (SCWTA).

Job Scheduling for Cloud Computing Integrated with Wireless Sensor Network

The powerful data storage and data processing abilities of cloud computing (CC) and the ubiquitous data gathering capability of wireless sensor network (WSN) complement each other in CC-WSN integration, which is attracting growing interest from both academia and industry. However, job scheduling for CC integrated with WSN is a critical and unexplored topic. To fill this gap, this paper first analyzes the characteristics of job scheduling with respect to CC-WSN integration and then studies two traditional and popular job scheduling algorithms (i.e., Min-Min and Max-Min). Further, two novel job scheduling algorithms, namely priority-based two phase Min-Min (PTMM) and priority-based two phase Max-Min (PTAM), are proposed for CC integrated with WSN. Extensive experimental results show that PTMM and PTAM achieve shorter expected completion time than Min-Min and Max-Min, for CC integrated with WSN.

Weighted network traffic offloading in cache-enabled heterogeneous networks

Due to explosive demands of multimedia services from mobile users, the growing network traffic load becomes a severe challenge for mobile network operators (MNOs). To address this problem, content caching is regarded as an effective emerging technique to reduce the duplicated transmissions of the content downloads demanded by mobile users, while heterogeneous networks (HetNets) are regarded as an effective technique to increase the network throughput. Thus, this paper focuses on content caching in HetNets to offload the weighted network traffic, in which we consider the problem of minimizing the weighted expected sum of traffic load of accessing the requested contents. By transforming the irregular problem into a binary integer linear programming problem, we propose a novel suboptimal heuristic algorithm with polynomial-time complexity to solve the problem, instead of using the existing optimal branch-and-bound method with exponential-time complexity. Numerical results demonstrate that our proposed content caching framework can reduce the weighted expected sum of traffic load significantly.

CaaS: Caching as a Service for 5G Networks

In recent years, demands for rich multimedia services over mobile networks have been soaring at a tremendous pace. Traditional dedicated networking equipment may not be able to efficiently support the phenomenal growth of the traffic load and user demand dynamics while consuming an unnecessarily large amount of energy resources. Recently, mobile content caching, whereby popular contents are cached inside the mobile front-haul and back-haul networks so that demands for these contents from users in proximity can be easily accommodated without redundant transmissions from the remote sources, has emerged as an efficient technique for multimedia content delivery. Mobile content caching is particularly suitable for fifth generation (5G) mobile systems that are being designed to incorporate advanced cloud computing technologies and network function virtualization techniques. Therefore, in this paper, we first propose the concept of “Caching-as-a-Service” (CaaS) based on cloud-based radio access networks, and virtualized evolved packet core, which provides the capability to cache anything at anytime, anywhere in the cloud-based 5G mobile systems to satisfy user demands from any service location with high elasticity and adaptivity, and to empower third-party service providers with flexible controllability and programmability. Then, we study the potential techniques related to the virtualization of caching, and discuss the technical details of virtualization and optimization of CaaS in 5G mobile networks. Some novel schemes for CaaS are proposed to target different mobile applications and services. We also explore new opportunities and challenges for further research.

Collaborative Multi-Tier Caching in Heterogeneous Networks: Modeling, Analysis, and Design

To deal with the explosive growth in multimedia service requests in mobile networks, caching contents at the cells (base stations) is regarded as an effective emerging technique to reduce the duplicated transmissions of content downloads, while heterogeneous networks (HetNets) are regarded as an effective technique to increase the network capacity. Yet, the combination of content caching and HetNets for future networks (i.e., 5G) is still not well explored. In this paper, we propose an efficient collaborative multi-tier caching framework in Het-Nets. In particular, based on patterns of user requests, link capacities, heterogenous cache sizes, and the derived system topology, we focus on exploring the maximum capacity of the network infrastructure so as to offload the network traffic and support users’ content requests locally. Due to the NP-hardness of the complex multi-tier caching problem, we approximately decompose it into some subproblems that focus on the caching cooperation at different tiers by utilizing the derived system topology. Our proposed framework is low-complexity and distributed, and can be used for practical engineering implementation. Trace-based simulation results demonstrate the effectiveness of the proposed framework.

Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges

Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.

Joint User Association and Scheduling for Load Balancing in Heterogeneous Networks

This paper investigates the joint user association (UA) and user scheduling (US) for load balancing in a wireless downlink heterogeneous network by formulating a network-wide utility maximization problem. In order to efficiently solve the problem, we first approximate the original non-convex throughput function to a concave function, and demonstrate that the gap for such approximation approaches zero when the number of users is sufficiently large. Then, a distributed algorithm is further proposed to obtain the UA and US solutions by exploiting the convex optimization technique known as alternating direction method of multipliers. A remarkable feature of the proposed algorithm is that apart from load balancing, multiuser diversity is exploited in the association time to further improve system performance. The simulation results show the superior performance of the proposed algorithm and underscore the significant benefits of jointly exploiting multiuser diversity and load balancing.

Pricing Models for Sensor-Cloud

Incorporating ubiquitous wireless sensor networks (WSNs) and powerful cloud computing (CC), Sensor-Cloud (SC) is attracting growing attention from both academia and industry. However, pricing for SC is barely explored. In this paper, filling this gap, five SC pricing models (i.e., SCPM1, SCPM2, SCPM3, SCPM4 and SCPM5) are proposed first. Particularly, they charge a SC user, based on 1) the lease period of the user, 2) the required working time of SC, 3) the SC resources utilized by the user, 4) the volume of sensory data obtained by the user, 5) the SC path that transmits sensory data from the WSN to the user, respectively. Further, analysis is also presented to study and demonstrate the performance of the proposed SCPMs. We believe that the pricing designs and analysis performed in this work could be a very valuable guidance for future researches regarding pricing in SC.