Dapeng Oliver Wu

Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.

The Impact of Node Selfishness on Multicasting in Delay Tolerant Networks

Due to the uncertainty of transmission opportunities between mobile nodes, delay tolerant networks (DTNs) exploit the opportunistic forwarding mechanism. This mechanism requires nodes to forward messages in a cooperative and selfish way. However, in the real word, most of the nodes exhibit selfish behaviors, such as individual and social selfishness. In this paper, we are the first to investigate how the selfish behaviors of nodes affect the performance of DTN multicast. We consider two typical multicast relaying schemes, namely, two-hop relaying and epidemic relaying, and study their performance in terms of average message transmission delay and transmission cost. Specifically, we model the message delivery process under selfish behaviors by a 3-D continuous time Markov chain; under this model, we derive closed-form formulas for the message transmission delay and cost. Then, we evaluate the accuracy of the proposed Markov chain model by comparing the theoretical results with the simulation results obtained by simulating the message dissemination under both two-hop and epidemic relaying with different network sizes and mobility models. Our study shows that different selfish behaviors may have different impacts on different performance metrics. In addition, selfish behaviors influence epidemic relaying more than two-hop relaying. Furthermore, our results show that the performance of multicast with selfish nodes depends on the multicast group size.

Energy-efficient scheduling policy for collaborative execution in mobile cloud computing

In this paper, we investigate the scheduling policy for collaborative execution in mobile cloud computing. A mobile application is represented by a sequence of fine-grained tasks formulating a linear topology, and each of them is executed either on the mobile device or offloaded onto the cloud side for execution. The design objective is to minimize the energy consumed by the mobile device, while meeting a time deadline. We formulate this minimum-energy task scheduling problem as a constrained shortest path problem on a directed acyclic graph, and adapt the canonical “LARAC” algorithm to solving this problem approximately. Numerical simulation suggests that a one-climb offloading policy is energy efficient for the Markovian stochastic channel, in which at most one migration from mobile device to the cloud is taken place for the collaborative task execution. Moreover, compared to standalone mobile execution and cloud execution, the optimal collaborative execution strategy can significantly save the energy consumed on the mobile device.

TOSS: Traffic offloading by social network service-based opportunistic sharing in mobile social networks

The ever increasing traffic demand becomes a serious concern of mobile network operators. To solve this traffic explosion problem, there have been many efforts to offload the traffic from cellular links to direct communications among users. In this paper, we propose the framework of Traffic Offloading assisted by Social network services (SNS) via opportunistic Sharing in mobile social networks, TOSS, to offload SNS-based cellular traffic by user-to-user sharing. First we select a subset of users who are to receive the same content as initial seeds depending on their content spreading impacts in online SNSs and their mobility patterns in offline mobile social networks (MSNs). Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct, Device-to-device in LTE) with each other. The observation of SNS user activities reveals that individual users have distinct access patterns, which allows TOSS to exploit the user-dependent access delay between the content generation time and each users access time for traffic offloading purposes. We model and analyze the traffic offloading and content spreading among users by taking into account various options in linking SNS and MSN trace data. The trace-driven evaluation demonstrates that TOSS can reduce up to 86.5% of the cellular traffic while satisfying the access delay requirements of all users.

Linear Rate Control and Optimum Statistical Multiplexing for H.264 Video Broadcast

The H.264 video coding standard achieves significantly improved video compression efficiency and finds important applications in digital video broadcast. To enable H.264 video encoding for digital TV broadcast and maximize its broadcast efficiency, there are two important issues that need to be adequately addressed. First, we need to understand the complex coding mechanism of an H.264 video encoder and develop a model to analyze and control its rate-distortion (R-D) behavior in an accurate and robust manner. Second, the R-D behaviors of individual channels in the broadcast system should be jointly controlled and optimized under bandwidth and buffer constraints so as to maximize the overall broadcast quality. In this paper, we develop a linear rate model and a linear rate control scheme for H.264 video coding. We develop an optimum statistical multiplexing system to allocate bits across video programs (each being encoded by an H.264 encoder) and video frames so that the overall video broadcast quality is maximized. We study the bandwidth and buffer constraints in video broadcast and formulate the optimum statistical multiplexing into a constrained mathematical optimization problem. Realizing that it is impossible to find a close-form solution for global optima, we propose a simple yet efficient algorithm to find a near-optimum solution for joint rate allocation under buffer constraints. Our extensive simulation results demonstrate that the proposed statistical multiplexing system achieves about 40-50% saving in bandwidth, provides a smooth video quality change across programs and frames, and maintains robust decoder buffer control.

Collaborative Task Execution in Mobile Cloud Computing Under a Stochastic Wireless Channel

This paper investigates collaborative task execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading tasks to the cloud. We formulate the collaborative task execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative task execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local execution and the remote execution, the collaborative task execution can significantly save the energy consumption on the mobile device, prolonging its battery life.

ROSE: Robustness Strategy for Scale-Free Wireless Sensor Networks

Due to the recent proliferation of cyber-attacks, improving the robustness of wireless sensor networks (WSNs), so that they can withstand node failures has become a critical issue. Scale-free WSNs are important, because they tolerate random attacks very well; however, they can be vulnerable to malicious attacks, which particularly target certain important nodes. To address this shortcoming, this paper first presents a new modeling strategy to generate scale-free network topologies, which considers the constraints in WSNs, such as the communication range and the threshold on the maximum node degree. Then, ROSE, a novel robustness enhancing algorithm for scale-free WSNs, is proposed. Given a scale-free topology, ROSE exploits the position and degree information of nodes to rearrange the edges to resemble an onion-like structure, which has been proven to be robust against malicious attacks. Meanwhile, ROSE keeps the degree of each node in the topology unchanged such that the resulting topology remains scale-free. The extensive experimental results verify that our new modeling strategy indeed generates scale-free network topologies for WSNs, and ROSE can significantly improve the robustness of the network topologies generated by our modeling strategy. Moreover, we compare ROSE with two existing robustness enhancing algorithms, showing that ROSE outperforms both.

Toward Transcoding as a Service in a Multimedia Cloud: Energy-Efficient Job-Dispatching Algorithm

In this paper, we investigate the energy-efficient job-dispatching algorithm for transcoding as a service (TaaS) in a multimedia cloud. We aim to minimize the energy consumption of service engines in the cloud while achieving low delay for TaaS. We formulate the job-dispatching problem as a constrained optimization problem under the framework of Lyapunov optimization. Using the drift-plus-penalty function, we propose an online algorithm that dispatches the transcoding jobs to service engines, with an objective to Reduce Energy consumption while achieving the QUEue STability (REQUEST). We first characterize the fundamental tradeoff between energy consumption and queue delay for the REQUEST algorithm numerically and obtain its performance bound theoretically. Second, we study the robustness of the REQUEST algorithm, with numerical results indicating that the REQUEST algorithm is robust to the inaccuracy of estimating the transcoding time. Third, we compare the performance of the REQUEST algorithm with the other two algorithms, i.e., the Round Robin and Random Rate algorithms. By simulation and real trace data, we show that by appropriately choosing the control variable, the REQUEST algorithm outperforms the Round Robin and Random Rate algorithms, with smaller time average energy consumption and time average queue length. The proposed REQUEST algorithm can be applied in cloud-assisted multimedia transcoding service.

Delay-Constrained Optimal Link Scheduling in Wireless Sensor Networks

We consider the optimal link scheduling problem in wireless sensor networks. The optimal link scheduler under our consideration is intended to assign time slots to different users to minimize channel usage subject to constraints on data rate, delay bound, and delay bound violation probability; we study the problem under fading channels and a signal-to-interference-plus-noise-ratio (SINR)-based interference model. To the best of our knowledge, this problem has not been studied previously. We use the effective capacity model to formulate the optimal link scheduling as a mixed-integer optimization problem. We first discuss a simple case, namely, the scheduling with a fixed power allocation, and then extend to the case with variable transmit power. Moreover, because the mixed-integer optimization problem is NP-hard, we propose a computationally feasible column-generation-based iterative algorithm to search for a suboptimal solution to the problem. Finally, we design a medium access control (MAC) protocol to implement our optimal link scheduling strategy in practical wireless networks. Simulation results demonstrate that our proposed scheme achieves a larger throughput, a larger admission region, and a higher power efficiency than a benchmark time-division multiple-access (TDMA) system.

Just FUN: a joint fountain coding and network coding approach to loss-tolerant information spreading

To address the problem of information spreading over lossy communication channels, this paper proposes a joint FoUntain coding and Network coding (FUN) approach. Different from the Transmission Control Protocol (TCP), our FUN approach is a mechanism of Forward Error Correction (FEC), which does not use retransmission for recovery of lost packets. The novelty of our FUN approach lies in combining the best features of fountain coding, intra-session network coding, and cross-next-hop network coding. As such, our FUN approach is capable of achieving unprecedented high throughput over lossy channels. Experimental results demonstrate that our FUN approach achieves higher throughput than the existing schemes for multihop wireless networks.