Jianhua Tang

Cross-Layer Resource Allocation With Elastic Service Scaling in Cloud Radio Access Network

Cloud radio access network (C-RAN) aims to improve spectrum and energy efficiency of wireless networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We propose and investigate a cross-layer resource allocation model for C-RAN to minimize the overall system power consumption in the BBU pool, fiber links and the remote radio heads (RRHs). We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which jointly considers elastic service scaling, RRH selection, and joint beamforming. The MINLP is however a combinatorial optimization problem and NP-hard. We relax the original MINLP problem into an extended sum-utility maximization (ESUM) problem, and propose two different solution approaches. We also propose a low-complexity Shaping-and-Pruning (SP) algorithm to obtain a sparse solution for the active RRH set. Simulation results suggest that the average sparsity of the solution given by our SP algorithm is close to that obtained by a recently proposed greedy selection algorithm, which has higher computational complexity. Furthermore, our proposed cross-layer resource allocation is more energy efficient than the greedy selection and successive selection algorithms.

Dynamic Request Redirection and Elastic Service Scaling in Cloud-Centric Media Networks

We consider the problem of optimally redirecting user requests in a cloud-centric media network (CCMN) to multiple destination Virtual Machines (VMs), which elastically scale their service capacities in order to minimize a cost function that includes service response times, computing costs, and routing costs. We also allow the request arrival process to switch between normal and flash crowd modes to model user requests to a CCMN. We quantify the trade-offs in flash crowd detection delay and false alarm frequency, request allocation rates, and service capacities at the VMs. We show that under each request arrival mode (normal or flash crowd), the optimal redirection policy can be found in terms of a price for each VM, which is a function of the VMs service cost, with requests redirected to VMs in order of nondecreasing prices, and no redirection to VMs with prices above a threshold price. Applying our proposed strategy to a YouTube request trace data set shows that our strategy outperforms various benchmark strategies. We also present simulation results when various arrival traffic characteristics are varied, which again suggest that our proposed strategy performs well under these conditions.

The role of cloud computing in content-centric mobile networking

Due to the ever growing popularity of smart handheld devices, the demand for multimedia services has had an upsurge in mobile networks over the past several years, and traditional mobile networking is turning into a content-centric mobile networking (CCMN). To maintain high quality of service for multimedia services, especially video services, caching is regarded as one of the most effective techniques. In this article, we first examine the limitations of caching techniques in conventional CCMN, including the core network and RAN. Then, by leveraging cloud computing into both core network and RAN caching, a cloud CCMN architecture is discussed, which overcomes the limitations of conventional CCMN architecture. Specifically, we elaborate on a cloud content delivery network in the core network and cloud RAN with caching as a service on the RAN side. Furthermore, we review the main problems that have been studied under cloud CCMN architecture. Recent results show that cloud CCMN is a promising architecture to serve the foreseen multimedia traffic. Lastly, we conclude this article by presenting some open challenges in cloud CCMN.

Cross-layer resource allocation in cloud radio access network

Cloud radio access network (C-RAN) aims to improve the spectrum and energy efficiency of wireless communication networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We investigate a cross-layer resource allocation model for C-RAN to minimize the overall system power consumption in both the BBU pool and the remote radio heads (RRHs), while guaranteeing the cross-layer QoS. We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which is however NP-hard. By relaxing the original MINLP problem to a quasi weighted sum-rate maximization (QWSRM) problem, we utilize a branch and bound method to solve the QWSRM problem, and propose a low-complexity bisection search algorithm to obtain a sparse solution for RRH selection problem. Simulation results suggest that our cross-layer approach achieves more energy savings than the recently proposed greedy selection and successive selection algorithms for optimal RRH selection.

Joint resource segmentation and transmission rate adaptation in Cloud RAN with Caching as a Service

By introducing Caching as a Service (CaaS) in Cloud radio access network (C-RAN), the joint resource segmentation and transmission rate adaptation problem is investigated in this paper. Specifically, in the baseband unit (BBU) pool of C-RAN, we optimally segment computation and storage resources to different types of virtual machines (VMs), and in the remote radio heads (RRHs), we adjust the beamformers to obtain the cache-based adaptive rate (CBAR). We aim to minimize the system cost, which includes server cost, VM cost and wireless transmission cost. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which contains l0-norm terms in the objective function and nonconvex constraints. We propose a three-step solution approach, i.e., a general smooth function approximation step, a weighted minimum mean square error (WMMSE) reformulation step and an integer recovery step. Simulation results show that our proposed integer recovery algorithms recover the integer variable values effectively.

System Cost Minimization in Cloud RAN With Limited Fronthaul Capacity

Cloud radio access network (C-RAN) is emerging as a potential alternative for the next generation RAN by merging RAN and cloud computing together. In this paper, we consider the baseband unit (BBU) pool of C-RAN as a collection of virtual machines (VMs). We allow each user equipment (UE) to associate with multiple VMs in the BBU pool, and each remote radio head (RRH) can only serve a limited number of UEs. Under this model, we jointly optimize the VM activation in the BBU pool and sparse beamforming in the coordinated RRH cluster, which is constrained by limited fronthaul capacity, to minimize the system cost of C-RAN. We formulate this problem as a mixed-integer nonlinear programming problem, and then propose efficient methods to optimize the number of active VMs, as well as the sparse beamforming vectors. Moreover, we derive a closed-form solution for the beamforming vectors. Simulation results suggest that our proposed algorithms have better performance than the benchmark algorithms in terms of both system cost and robustness.

Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling

In this paper, we propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. We aim to minimize both total tasks’ execution latency and the MD’s energy consumption by jointly optimizing the task allocation decision and the MD’s central process unit (CPU) frequency. This paper considers two cases for the MD, i.e., fixed CPU frequency and elastic CPU frequency. Since these problems are NP-hard, we propose a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search-based approach and an SDR-based approach for the elastic CPU frequency case. Our simulation results show that the SDR-based algorithms achieve near optimal performance. Performance improvement can be obtained with the proposed scheme in terms of energy consumption and tasks’ execution latency when multiple edge devices and elastic CPU frequency are considered. Finally, we show that the MD’s flexible CPU range can have an impact on the task allocation.

Cooperative transmission meets computation provisioning in downlink C-RAN

Cloud radio access network (C-RAN), regarded as a promising green network architecture, facilitates cooperative transmission among remote radio heads (RRHs) while enabling flexible computation provisioning in the virtualized baseband unit pool. By jointly optimizing cooperative transmission, i.e., transmit power allocation with zero-forcing precoding adopted, and computation provisioning, i.e., virtual machine assignment, this paper minimizes the system power consumption comprised of transmit power and processing power in downlink C-RAN. Specifically, subject to per-RRH power constraint (PRPC) and per-MU quality of service constraint, the system power consumption minimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem. To solve the challenging MINLP, we reformulate the MINLP as a minimum weight perfect matching problem to get the initial solution without considering the PRPC. On this basis, a power-aware greedy algorithm is further devised to modify the solution such that the PRPC is satisfied. Finally, extensive simulations show the superiority of the proposed scheme on system power saving and the tradeoff between transmit power and processing power.

Exploiting Hybrid Clustering and Computation Provisioning for Green C-RAN

By migrating baseband processing functionalities into a centralized cloud-based baseband unit (BBU) pool, cloud radio access network (C-RAN) facilitates cooperative transmission among remote radio heads (RRHs) and enables flexible computation provisioning in the BBU pool. In C-RAN, due to the high amount of data transfer from the BBU pool to RRHs through fronthauls, limited fronthaul capacity becomes a key factor when designing cooperative transmission schemes among RRHs. Meanwhile, as computational resources are provisioned to mobile users (MUs) for baseband processing in the form of virtual machines (VMs) in the BBU pool, an effective VM assignment strategy is also with great significance. In this paper, we propose a holistic framework for green C-RAN under the constraint of limited fronthaul capacity, where we jointly optimize hybrid clustering and computation provisioning to appropriately provide a cluster of RRHs and a VM to each MU for cooperative transmission and baseband processing, aiming at minimizing the system power consumption. The system power minimization problem is formulated as an integer non-linear programming problem, which is hard to tackle. For tractability purpose, we transform this problem to an equivalent hybrid clustering problem embedded with a series of VM assignment problems. On this basis, we first achieve the optimal solution for system power minimization with high computational complexity, and then, a greedy algorithm is proposed to solve the hybrid clustering problem for practical implementation. Finally, the simulation results demonstrate that the proposed joint optimization of hybrid clustering and computation provisioning can significantly reduce the system power consumption.

Towards system cost minimization in cloud radio access network

Cloud radio access network (C-RAN) is regarded as a promising solution of future mobile communication infrastructures. The key character of C-RAN is migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool, which consists of many software defined virtual machines (VMs). In this paper, we take the joint consideration of VM activation in the BBU pool and joint transmission (JT) in the coordinated remote radio heads (RRHs) cluster to minimize the system cost of C-RAN, such that the system delay constraint can be satisfied. We propose two different approaches to solve this problem, i.e., integer search (IS) approach and joint optimization (JO) approach. Simulation results suggest that our proposed algorithm is more cost-effective than the benchmark algorithms.