Yanlei Shang

Goodput-Aware Load Distribution for Real-Time Traffic over Multipath Networks

Load distribution is a key research issue in deploying the limited network resources available to support traffic transmissions. Developing an effective solution is critical for enhancing traffic performance and network utilization. In this paper, we investigate the problem of load distribution for real-time traffic over multipath networks. Due to the path diversity and unreliability in heterogeneous overlay networks, large end-to-end delay and consecutive packet losses can significantly degrade the traffic flows goodput, whereas existing studies mainlyfocus on the delay or throughput performance. To address the challenging problems, we propose a Goodput-Aware Load distribuTiON (GALTON) model that includes three phases: (1) path status estimation to accurately sense the quality of each transport link, (2) flow rate assignment to optimize the aggregate goodput of input traffic, and (3) deadline-constrained packet interleaving to mitigate consecutive losses. We present a mathematical formulation for multipath load distribution and derive the solution based on utility theory. The performance of the proposed model is evaluated through semi-physical emulations in Exata involving both real Internet traffic traces and H.264 video streaming. Experimental results show that GALTON outperforms existing traffic distribution models in terms of goodput, video Peak Signal-to-Noise Ratio (PSNR), end-to-end delay, and aggregate loss rate.

Joint source-channel coding and optimization for mobile video streaming in heterogeneous wireless networks

This paper investigates mobile video delivery in a heterogeneous wireless network from a video server to a multi-homed client. Joint source-channel coding (JSCC) has proven to be an effective solution for video transmission over bandwidth-limited, error-prone wireless networks. However, one major problem with the existing JSCC approaches is that they consider the network between the server and the client as a single transport link. The situation becomes more complicated in the context of multiple available links because involving a low-bandwidth, highly lossy, or long-delay wireless network in the transmission will only degrade the video quality. To address the critical problem, we propose a novel flow rate allocation-based JSCC (FRA-JSCC) approach that includes three key phases: (1) forward error correction redundancy estimation under loss requirement, (2) source rate adaption under delay constraint, and (3) dynamic rate allocation to minimize end-to-end video distortion. We present a mathematical formulation of JSCC to optimize video quality over multiple wireless channels and provide comprehensive analysis for channel distortion. We evaluate the performance of FRA-JSCC through emulations in Exata and compare it with the existing schemes. Experimental results show that FRA-JSCC outperforms the competing models in improving the video peak signal-to-noise ratio as well as in reducing the end-to-end delay.

A cost-aware auto-scaling approach using the workload prediction in service clouds

Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.

A novel scheduling approach to concurrent multipath transmission of high definition video in overlay networks

Recent advancements in network infrastructures provide increased opportunities to support video delivery over multiple communication paths. However, the high definition (HD) video transmissions still pose crucial challenges due to the high throughput demands and large-size video frames. Motivated by optimizing the delay performance for concurrent multipath transmission of HD video, we propose a novel scheduling approach dubbed FSWG (Frame Splitting based on Weibull distribution and Graph theory) that aims to minimize the end-to-end frame delay while alleviating out-of-order arrivals. First, we analytically construct a delay performance model for HD video streaming in multipath overlay networks based on Weibull distribution and graph theory. Second, we formulate the frame splitting over parallel paths as a constrained optimization problem of minimizing total frame delay and derive its solution based on the water filling algorithm. Third, we design a multipath video transmission system to implement the proposed scheduling approach. The performance evaluation is conducted through extensive simulations in QualNet using H.264 video streaming. Experimental results show that FSWG outperforms the existing schemes in terms of Mean Opinion Score (MOS), Peak Signal-to-Noise Ratio (PSNR), and delay performance metrics.

SPMLD: Sub-packet based multipath load distribution for real-time multimedia traffic

Load distribution is vital to the performance of multipath transport. The task becomes more challenging in real-time multimedia applications (RTMA), which impose stringent delay requirements. Two key issues to be addressed are: 1) how to minimize end-to-end delay, and 2) how to alleviate packet reordering that incurs additional recovery time at the receiver. In this paper, we propose SPMLD, a new model that splits traffic at the granularity of sub-packet. The Sub-Packet based Multipath Load Distribution (SPMLD) model aims to minimize total packet delay by effectively aggregating multiple parallel paths as a single virtual path. First, we formulate the packet splitting over multiple paths as a constrained optimization problem and derive its solution based on progressive approximation method. Second, in the solution, we analyze queuing delay by introducing D/M/1 model and obtain the expression of dynamic packet splitting ratio for each path. The performances of SPMLD are evaluated through extensive simulations in QualNet using real-time H.264 video streaming. Experimental results demonstrate that: SPMLD outperforms previous flow and packet based load distribution models in total packet delay, end-to-end delay and seldom induces packet reordering. Besides, SPMLDs extra overhead is tiny compared to the input video streaming.

Robust bandwidth aggregation for real-time video delivery in integrated heterogeneous wireless networks

Bandwidth aggregation is the process of integrating the limited channel resources available in heterogeneous wireless networks. Optimizing this process is an important step towards improving the throughput and reliability for the bandwidth-demanding video applications. In this paper, we investigate the bandwidth aggregation for real-time video delivery in heterogeneous wireless networks from a video server to a multihomed client. Forward Error Correction (FEC) coding is commonly adopted for data protection in implementing loss-resilient wireless video transmission systems. However, the inherent channel unreliability, along with the video traffic variability, can significantly degrade the FEC performance. To address the critical issues, we propose a ROBust BandwIdth Aggregation (ROBBIA) scheme that includes three phases: (1) FEC redundancy adaption, (2) transmission rate assignment, and (3) path interleaving. We present a mathematical formulation of the transmission scheduling to minimize end-to-end video distortion and provide comprehensive analysis for the channel distortion. We conduct the performance evaluation in the Exata and simulation results show that ROBBIA outperforms existing bandwidth aggregation approaches in improving video quality in terms of PSNR (Peak Signal-to-Noise Ratio).

An adaptive prediction approach based on workload pattern discrimination in the cloud

Generally speaking, the workloads are changing rapidly on the Internet, but there is still regularity of changing patterns. Currently, workload prediction has become a promising tool to facilitate automatic scaling of resource management, and thus reducing the cost and improving resource utilization in the cloud. Most current predication methods of workload are based on a single model. However, because the network traffics are usually mixed and inseparable, it is hard to get the satisfactory prediction performance by means of a single model. To solve this problem, an adaptive approach for work load prediction is proposed in this paper. This approach firstly categorizes the workloads into different classes which are automatically assigned for different prediction models according to workload features. Furthermore, the workload classification problem is transformed into a task assignment one by establishing a mixed 01 integer programming model, and an online solution is provided. We used Google Cluster trace to evaluate the proposed approach. The experimental results demonstrate that the proposed approach improves the platform cumulative relative prediction errors by 29.06%, 8.42% and 40.86% respectively in comparison with the time-series prediction methods (Autoregressive Integrated Moving Average (ARIMA), Support Vector Machines (SVMs) and Linear Regression (LR).

Optimizing Workload Category for Adaptive Workload Prediction in Service Clouds

It is important to predict the total workload for facilitating auto scaling resource management in service cloud platforms. Currently, most prediction methods use a single prediction model to predict workloads. However, they cannot get satisfactory prediction performance due to varying workload patterns in service clouds. In this paper, we propose a novel prediction approach, which categorizes the workloads and assigns different prediction models according to the workload features. The key idea is that we convert workload classification into a 0–1 programming problem. We formulate an optimization problem to maximize prediction precision, and then present an optimization algorithm. We use real traces of typical online services to evaluate prediction method accuracy. The experimental results indicate that the optimizing workload category is effective and proposed prediction method outperforms single ones especially in terms of the platform cumulative absolute prediction error. Further, the uniformity of prediction error is also improved.

A novel transmission scheme to inter destination video synchronisation

Inter destination video synchronisation (IDVS) is a key technology in emerging interactive multimedia applications. It is essential to ensure the synchronous experiences of users in such applications. However, one inevitable barrier for IDVS is the packet transfer delay differences (PTDDs) among different destinations. Existing researches have tried to eliminate the side effects of PTDD passively and schedule the video packets in a ‘back-to-back’ fashion. In this paper, the authors propose to proactively leverage such differences to design a transmission scheme that enhances video quality while ensuring the synchronous arrival of packets. Based on the network measurements from a real multimedia conferencing system and the Planetlab, they find that the PTDD is between the ranges of 100–250 ms. Motivated by this observation, they propose a scheme named asynchronous departure for synchronous arrival (ADSA), which inserts intervals between consecutive packets according to the synchronisation reference. To prove the superiority of ADSA, they carry out analysis based on Gilbert loss model and continuous time Markov chain. They conduct performance evaluation through emulations in Exata and experimental results show that ADSA improves the video peak signal-to-noise ratio by up to 9.1 and 6.9 dB compared with existing latest and earliest schemes, respectively.

Predicting of Job Failure in Compute Cloud Based on Online Extreme Learning Machine: A Comparative Study

Early prediction of job failures and specific disposal steps in advance could significantly improve the efficiency of resource utilization in large-scale data center. The existing machine learning-based prediction methods commonly adopt offline working pattern, which cannot be used for online prediction in practical operations, in which data arrive sequentially. To solve this problem, a new method based on online sequential extreme learning machine (OS-ELM) is proposed in this paper to predict online job termination status. With this method, real-time data are collected according to the sequence of job arriving, the job status could be predicted and the operation model is thus updated based on these data. The method with online incremental learning strategy has fast learning speed and good generalization. Comparative study using Google trace data shows that prediction accuracy of the proposed method is 93% with updating model in 0.01 s. Compared with some state-of-the-art methods, such, as support vector machine (SVM), ELM, and OS-SVM, the method developed in this paper has many advantages, such as less time-consuming in establishing and updating the model, higher prediction accuracy and precision, and better false negative performance.