Baoliu Ye

On Efficient Resource Allocation for Cognitive and Cooperative Communications

Cooperative communication (CC) can offer high channel capacity and reliability in an efficient and low-cost way by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of multiple source-destination pairs. Unfortunately, all prior work has made an unrealistic assumption that spectrum resources are unlimited and each source-destination pair can communicate over a dedicated channel with no mutual interference. In this paper, we study the problem of maximizing the minimum transmission rate among multiple source-destination pairs using CC in a cognitive radio network (CRN). We jointly consider the relay assignment and channel allocation under a finite set of available channels, where the interference must be considered. In order to improve the spectrum efficiency, we exploit the network coding opportunities existing in CC that can further increase the capacity. Such max-min rate problems for cognitive and cooperative communications are proved to be NP-hard and the corresponding MINLP (Mixed-Integer Nonlinear Programming) formulations are developed. Moreover, we apply the reformulation and linearization techniques to the original optimization problems with nonlinear and nonconvex objective functions such that our proposed algorithms can produce high competitive solutions in a timely manner. Extensive simulations are conducted to show that the proposed algorithms can achieve high spectrum efficiency in terms of providing a much improved max-min transmission rate under various network settings.

A Truthful QoS-Aware Spectrum Auction with Spatial Reuse for Large-Scale Networks

In cognitive radio networks (CRNs), a wireless user with primary access right on a channel (called primary user) has prioritized access to the channel and the user with secondary access right (called secondary user) can use the channel when the primary user is idle. Spectrum auction has emerged as a promising approach to address the access allocation problem in CRNs. A significant challenge in designing such auction is providing truthfulness to avoid market manipulation. In most previous work, the primary access rights on channels are pre-determined before the auction and bidders can only compete for the secondary access rights. However, a users requirement on spectrum access rights relies on their QoS demands. Therefore, it is much desirable to allocate spectrum access rights on the basis of QoS demands as well as to exploit the resulting spatial spectrum reuse opportunities. To solve this problem, we propose TRUMP, a truthful spectrum auction mechanism, by taking into consideration both QoS demands and spectrum spatial reuse, which can drastically improve spectrum utilization. The theoretical analysis proves that TRUMP achieves truthfulness and individual rationality with polynomial-time complexity. Our extensive simulation results show that our proposals outperform previous work in terms of both social welfare and spectrum utilization.

Joint Optimization of Rule Placement and Traffic Engineering for QoS Provisioning in Software Defined Network

Software-Defined Network (SDN) is a promising network paradigm that separates the control plane and data plane in the network. It has shown great advantages in simplifying network management such that new functions can be easily supported without physical access to the network switches. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules for high-speed packet processing in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we propose a rule multiplexing scheme, in which the same set of rules deployed on each node apply to the whole flow of a session going through but towards different paths. Based on this scheme, we study the rule placement problem with the objective of minimizing rule space occupation for multiple unicast sessions under QoS constraints. We formulate the optimization problem jointly considering routing engineering and rule placement under both existing and our rule multiplexing schemes. Via an extensive review of the state-of-the-art work, to the best of our knowledge, we are the first to study the non-routing-rule placement problem. Finally, extensive simulations are conducted to show that our proposals significantly outperform existing solutions.

Capacity maximization in cooperative CRNs: Joint relay assignment and channel allocation

Cooperative communication (CC) can offer high channel capacity and reliability in an efficient and low-cost way by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of multiple source-destination pairs. Unfortunately, all prior work has made an unrealistic assumption that each source-destination pair communicates over a dedicated channel with no mutual interference. In this paper, we study the problem of capacity maximization using cooperative communication in a cognitive radio network by jointly considering the relay assignment and channel allocation under a finite set of available channels, where the interference must be considered. It is proved to be NP-hard and a heuristic algorithm is proposed. Moreover, we exploit the network coding opportunities existing in CC that can further increase the capacity. Extensive simulations are conducted to show that the proposed algorithms can achieve high total capacity under various network settings.

Expander graph based overlapped chunked codes

Chunked codes are a variation of random linear network codes with low computational complexities. In chunked codes, the packets in a file are grouped into small (non-overlapped or overlapped) chunks, and random linear encoding operations are performed within each chunk. Previous studies show that when the chunk size is lower bounded by some increasing function of the file length, chunked codes asymptotically achieve the min-cut capacity. However, in most real applications, the chunk size is required to be a small constant due to the computational constraints of network devices. In this case, it remains unknown which rates can be achieved by chunked codes. In this paper, we address the analysis and design of chunked codes with fixed constant chunk sizes. We first highlight the importance of precoding for chunked codes to achieve constant rates, and then present an analysis of non-overlapped chunked (NOC) codes with precoding. We further introduce a new class of chunked codes, called EOC codes, which are based on expander graphs to form overlapped chunks. Numerical and simulation results show that EOC codes achieve significantly higher rates than NOC codes, and also outperform other state-of-the-art overlapped chunked codes.

Leverage parking cars in a two-tier data center

A large number of data centers have been deployed and available for public renting with the rapid development of cloud computing recently. Meanwhile, the proliferation of automotive electronics has made rich resources in various forms of computation and communication. It is challenging but of great significance to make use of these resources in an efficient way. In this paper, we propose a two-tier data center architecture that leverages the excessive storage resources in parking lots. Such resources form an auxiliary vehicular data center (VDC) such that the pressure on the conventional data center can be mitigated and the total communication cost be reduced. After modeling the dynamics of available resources in a parking lot with a finite capacity, we propose three VDC management policies (i.e., non-replication, simple replication and network coding based replication) and derive their total communication cost in closed form. The high efficiency of the two-tier data center architecture and the accuracy of our analysis are validated via extensive simulations.

DOTA: A Double Truthful Auction for spectrum allocation in dynamic spectrum access

Spectrum auctions have been proposed as an effective approach to fairly and efficiently trade the scarce spectrum resource among wireless users. The most significant challenge of the auction design to provide economic robustness, particularly truthfulness, under the local-dependent interference constraints. However, existing designs either do not consider spectrum reuse or are based on the impractical assumption that each user requests at most one channel. In this paper, we address this problem by proposing DOTA, a DOuble Truthful Auction for dynamic spectrum access. DOTA is economic-robust in terms of truthfulness, individual rationality, and no-deficit. It achieves improved utilization by exploiting spectrum reuse as well as dealing with the interference constraints. Moreover, DOTA minimizes the network transaction overhead and provides flexible channel bidding including range bidding and strict bidding.

UMP-PerComp: A Ubiquitous Multiprocessor Network-Based Pipeline Processing Framework for Pervasive Computing Environments

Pervasive computing provides an attractive vision for the future of computing where contextual intelligent services will be available anywhere at any time. However, how to integrate distributed resources existed among heterogeneous devices to build pervasive applications that can constantly adapt to the highly dynamic computing environment is still a challenge. By analyzing the pipelining feature within a user task and exploiting the parallelism among ubiquitous processors, we propose UMP-PerComp, a ubiquitous multiprocessor-based pipeline processing architecture to support high performance pervasive application development. UMP-PerComp is an integrated comprehensive framework that makes the development of pervasive applications easier. It includes a set of common services such as context management, resource management, service migration, topology management, that help to build applications and directly simplify the task decoupling and service composition. We describe the design and implementation of UMP-PerComp in this paper. Besides, we develop a distributed JPEG encoding application successfully on the UMP-PerComp. Our experience confirms that UMP-PerComp is a flexible, scalable and practicable framework with high performance.

A degree-constrained QoS-aware routing algorithm for application layer multicast

Application layer multicast (ALM) provides a low-cost solution for multicast over the Internet. It overcomes the deployment hurdle of IP multicast by moving all multicast related functions from network routers to end-hosts. However, since packet replication is performed on end-hosts, the system performance of an ALM is limited by the bandwidth of end-hosts. Therefore, degree-constrained QoS-aware multicast routing becomes one of the key concerns for implementing realtime multicast services, such as continuous streaming applications. In this paper, we claim that the QoS gained by most users will be better evaluated using the overall latency, and we explore the optimization of Degree-Constrained Minimum Overall Latency Spanning Tree (DCMOLST). The process for optimizing the overall latency is divided into two phases, i.e., the initialization phase and the dynamic adjustment phase. In the former phase, we present a heuristic DCMOLST algorithm which negotiates both transmission delay and node bandwidth simultaneously, so as to avoid QoS degradation caused by any single metrics. In the later phase, we define a set of distributed iterative optimizing operations to swap the position between nearby end-hosts for further optimization. Experimental results show that the proposed degree-constrained QoS-aware routing algorithm could improve the overall performance of application layer multicast services.

Prediction-Based Prefetching to Support VCR-like Operations in Gossip-Based P2P VoD Systems

Supporting free VCR-like operations in P2P VoD streaming systems is challenging. The uncertainty of frequent VCR operations makes it difficult to provide high quality realtime streaming services over distributed self-organized P2P overlay networks. Recently, prefetching has emerged as a promising approach to smooth the streaming quality. However, how to efficiently and effectively prefetch suitable segments is still an open issue. In this paper, we propose PREP, a PREdiction-based Prefetching scheme to support VCR-like operations over gossip-based P2P on-demand streaming systems. By employing the reinforcement learning technique, PREP transforms users’ streaming service procedure into a set of abstract states and presents an online prediction model to predict a user’s VCR behavior via analyzing the large volumes of user viewing logs collected on the tracker. We further present a distributed data scheduling algorithm to proactively prefetch segments according to the predicted VCR behavior. Moreover, PREP takes advantage of the inherent peer collaboration of gossip protocol to optimize the response latency. Through comprehensive simulations, we demonstrate the efficiency of PREP by gaining the accumulated hit ratio close to 75% while reducing the response latency close to 70% with only less than 15% extra stress on the server side.