Yifeng He

Optimal Scheduling for Charging and Discharging of Electric Vehicles

The vehicle electrification will have a significant impact on the power grid due to the increase in electricity consumption. It is important to perform intelligent scheduling for charging and discharging of electric vehicles (EVs). However, there are two major challenges in the scheduling problem. First, it is challenging to find the globally optimal scheduling solution which can minimize the total cost. Second, it is difficult to find a distributed scheduling scheme which can handle a large population and the random arrivals of the EVs. In this paper, we propose a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging. We first formulate a global scheduling optimization problem, in which the charging powers are optimized to minimize the total cost of all EVs which perform charging and discharging during the day. The globally optimal solution provides the globally minimal total cost. However, the globally optimal scheduling scheme is impractical since it requires the information on the future base loads and the arrival times and the charging periods of the EVs that will arrive in the future time of the day. To develop a practical scheduling scheme, we then formulate a local scheduling optimization problem, which aims to minimize the total cost of the EVs in the current ongoing EV set in the local group. The locally optimal scheduling scheme is not only scalable to a large EV population but also resilient to the dynamic EV arrivals. Through simulations, we demonstrate that the locally optimal scheduling scheme can achieve a close performance compared to the globally optimal scheduling scheme.

Optimal resource allocation for multimedia cloud based on queuing model

Multimedia cloud, as a specific cloud paradigm, addresses how cloud can effectively process multimedia services and provide QoS provisioning for multimedia applications. There are two major challenges in multimedia cloud. The first challenge is the service response time in multimedia cloud, and the second challenge is the cost of cloud resources. In this paper, we optimize resource allocation for multimedia cloud based on queuing model. Specifically, we optimize the resource allocation in both single-class service case and multiple-class service case. In each case, we formulate and solve the response time minimization problem and resource cost minimization problem, respectively. Simulation results demonstrate that the proposed optimal allocation scheme can optimally utilize the cloud resources to achieve a minimal mean response time or a minimal resource cost.

Distributed Algorithms for Network Lifetime Maximization in Wireless Visual Sensor Networks

Network lifetime maximization is a critical issue in wireless sensor networks since each sensor has a limited energy supply. In contrast with conventional sensor networks, video sensor nodes compress the video before transmission. The encoding process demands a high power consumption, and thus raises a great challenge to the maintenance of a long network lifetime. In this paper, we examine a strategy for maximizing the network lifetime in wireless visual sensor networks by jointly optimizing the source rates, the encoding powers, and the routing scheme. Fully distributed algorithms are developed using the Lagrangian duality to solve the lifetime maximization problem. We also examine the relationship between the collected video quality and the maximal network lifetime. Through extensive numerical simulations, we demonstrate that the proposed algorithm can achieve a much longer network lifetime compared to the scheme optimized for the conventional wireless sensor networks.

Optimal Resource Allocation for Pervasive Health Monitoring Systems with Body Sensor Networks

Pervasive health monitoring is an eHealth service, which plays an important role in prevention and early detection of diseases. There are two major challenges in pervasive health monitoring systems with Body Sensor Networks (BSNs). The first challenge is the sustainable power supply for BSNs. The second challenge is Quality of Service (QoS) guarantee for the delivery of data streams. In this paper, we optimize the resource allocations to provide a sustainable and high-quality service in health monitoring systems. Specifically, we formulate and solve two resource optimization problems, respectively. In the first optimization problem, steady-rate optimization problem, we optimize the source rate at each sensor to minimize the rate fluctuation with respect to the average sustainable rate, subject to the requirement of uninterrupted service. The first optimization problem is solved by a proposed analytical solution. The second optimization problem is formulated based on the optimal source rates of the sensors obtained in the steady-rate optimization problem. In the second optimization problem, we jointly optimize the transmission power and the transmission rate at each aggregator to provide QoS guarantee to data delivery. The second optimization problem is converted into a convex optimization problem, which is then solved efficiently. In the simulations, we demonstrate that the proposed optimized scheme enables the pervasive health monitoring system to provide a sustainable service with guaranteed low delay and low Packet Loss Rate (PLR) to subscribers.

Optimal Prefetching Scheme in P2P VoD Applications With Guided Seeks

Most existing peer-to-peer (P2P) video-on-demand (VoD) systems have been designed and optimized for the sequential playback. In practice, users often want to seek to the positions they are interested in. Such frequent seeks raise greater challenges to the design of the prefetching scheme. In this work, we first propose the concept of guided seeks. With the guidance, users can perform more efficient seeks to the desired positions. The guidance can be obtained from collective seeking statistics of other peers who have watched the same title in the previous and/or concurrent sessions. However, it is very challenging to aggregate the statistics efficiently, timely and in a completely distributed way. We design the hybrid sketches that not only capture the seeking statistics at significantly reduced space and time complexity, but also adapt to the popularity of the video. From the collected seeking statistics, we estimate the segment access probability, based on which we further develop an optimal prefetching scheme and an optimal cache replacement policy to minimize the expected seeking delay at every viewing position. Through extensive simulations, we demonstrate that the proposed prefetching framework significantly reduces the seeking delay compared to the sequential prefetching scheme.

Optimal resource allocation for multimedia cloud in priority service scheme

Multimedia cloud is an emerging computing paradigm that can effectively process multimedia applications and provide multi-QoS provisions for customers. Two major challenges exist in multimedia cloud: the resource cost and the service response time. In this paper, we employ the proposed queuing model to optimize the resource allocation for multimedia cloud in priority service scheme. Specifically, we formulate and solve the resource cost minimization problem and the service response time minimization problem respectively. The simulation results demonstrate that the proposed optimal resource allocation method can greatly enhance the performance of multimedia cloud data center in terms of resource cost and service response time.

Distributed Throughput Maximization in P2P VoD Applications

In peer-to-peer (P2P) video-on-demand (VoD) systems, a scalable source coding is a promising solution to provide heterogeneous peers with different video quality. In this paper, we present a systematic study on the throughput maximization problem in P2P VoD applications. We apply network coding to scalable P2P systems to eliminate the delivery redundancy. Since each peer receives distinct packets, a peer with a higher throughput can reconstruct the video at a higher quality. We maximize the throughput in the existing buffer-forwarding P2P VoD systems using a fully distributed algorithm. We demonstrate in the simulations that the proposed distributed algorithm achieves a higher throughput compared to the proportional allocation scheme or the equal allocation scheme. The existing buffer-forwarding architecture has a limitation in total upload capacity. Therefore we propose a hybrid-forwarding P2P VoD architecture to improve the throughput by combining the buffer-forwarding approach with the storage-forwarding approach. The throughput maximization problem in the hybrid-forwarding architecture is also solved using a fully distributed algorithm. We demonstrate that the proposed hybrid-forwarding architecture greatly improves the throughput compared to the existing buffer-forwarding architecture. In addition, by adjusting the priority weight at each peer, we can implement the differentiated throughput among different users within a video session in the buffer-forwarding architecture, and the differentiated throughput among different video sessions in the hybrid-forwarding architecture.

Improving the streaming capacity in P2P VoD systems with helpers

Peer-to-Peer (P2P) Video-on-Demand (VoD) is a promising solution to provide video service to a large number of users. Streaming capacity in a P2P VoD system is defined as the maximal streaming rate that every user can receive. Due to the upload bottleneck, the streaming capacity in the P2P VoD system is limited. In this paper, we introduce helpers in the P2P VoD system and then optimize the helper resources to improve the streaming capacity. Specifically, we first optimize the helper assignment using a greedy algorithm. Then we develop a proximal distributed algorithm to maximize the streaming capacity by optimizing the link rates. Through simulations, we demonstrate that the P2P VoD system with optimized helpers can obtain a much higher streaming capacity compared to the P2P VoD system without any helper or the one with randomly assigned helpers.

Optimal allocation of virtual machines for cloud-based multimedia applications

With the emergence of cloud computing, cloud-based multimedia applications have been increasingly adopted in recent years. There are two major challenges for multimedia application providers: the round trip time (RTT) requirement and the resource cost. In this paper, we study the virtual machine (VM) allocation problem for multimedia application providers to minimize the resource cost under RTT requirements. Specifically, we propose the optimal VM allocation schemes for single-site cloud and multi-site cloud, respectively. Moreover, we propose the greedy algorithms to efficiently allocate VMs in each case. Simulation results demonstrate that the proposed optimal VM allocation schemes can optimally allocate VMs to achieve a minimal resource cost.

All-Optical Demultiplexing of WLAN and Cellular CDMA Radio Signals

Subcarrier multiplexed transmission of multimedia radio signals over fiber is often done to deliver broadband services cost effectively. These signals need to be demultiplexed, preferably in the optical domain, to avoid loss and noise due to optical-to-electrical conversion. However, it is challenging to optically isolate signals at subgigahertz range due to the need for very narrow optical bandpass filters with high selectivity and low insertion loss and distortion. We developed such a novel subpicometer all-optical bandpass filter by creating a resonance cavity using two closely matched fiber Bragg gratings. This filter has a bandwidth of 120 MHz at -3 dB, 360 MHz at -10 dB, and 1.5 GHz at -20 dB. Experimental results show that this filter optically separates two RF signals spaced as close as 50 MHz without significant distortion. This paper analytically and experimentally investigates the scenario when this filter was used with 2.4-GHz (wireless local area network) and 900-MHz (cellular wireless) radio signals. The bit-error rate of the underlying baseband data is related to the linearity and isolation of the filter.