Myriad of graph-based algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a large-scale distributed environment to scale to massive data sets. To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). Different from traditional iterative computations, which iteratively update the result based on the result from the previous iteration, DAIC updates the result by accumulating the “changes” between iterations. By DAIC, we can process only the “changes” to avoid the negligible updates. Furthermore, we can perform DAIC asynchronously to bypass the high-cost synchronous barriers in heterogeneous distributed environments. Based on the DAIC model, we design and implement an asynchronous graph processing framework, Maiter. We evaluate Maiter on local cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves as much as 60 × speedup over Hadoop and outperforms other state-of-the-art frameworks.

Iterative computations are pervasive among data analysis applications, including web search, online social network analysis, recommendation systems, and so on. These applications typically involve data sets of massive scale. Fast convergence of the iterative computations on the massive data set is essential for these applications. In this paper, we explore the opportunity for accelerating iterative computations by prioritization. Instead of performing computations on all data points without discrimination, we prioritize the computations that help convergence the most, so that the convergence speed of iterative process is significantly improved. We develop a distributed computing framework, PrIter, which supports the prioritized execution of iterative computations. PrIter either stores intermediate data in memory for fast convergence or stores intermediate data in files for scaling to larger data sets. We evaluate PrIter on a local cluster of machines as well as on Amazon EC2 Cloud. The results show that PrIter achieves up to 50 × speedup over Hadoop for a series of iterative algorithms. In addition, PrIter is shown better performance for iterative computations than other state-of-the-art distributed frameworks such as Spark and Piccolo.

Iterative computations are pervasive among data analysis applications in the cloud, including Web search, online social network analysis, recommendation systems, and so on. These cloud applications typically involve data sets of massive scale. Fast convergence of the iterative computation on the massive data set is essential for these applications. In this paper, we explore the opportunity for accelerating iterative computations and propose a distributed computing framework, PrIter, which enables fast iterative computation by providing the support of prioritized iteration. Instead of performing computations on all data records without discrimination, PrIter prioritizes the computations that help convergence the most, so that the convergence speed of iterative process is significantly improved. We evaluate PrIter on a local cluster of machines as well as on Amazon EC2 Cloud. The results show that PrIter achieves up to 50x speedup over Hadoop for a series of iterative algorithms.

Network virtualization is becoming an irresistible trend for building future Internet. It enables multiple virtual networks running on the same physical substrate, by which network service can be deployed on a customized virtual network to get better performance. In this paper, we propose a method to achieve more reliable live streaming service by deploying it over multiple virtual networks, namely MultiNet. Rather than running streaming service relying on a single network alone, MultiNet provides network resources redundancy for better reliability, avoiding unexpected interrupt due to network errors, such as link down, router crash, or transient routing failure. In addition, it balances traffic load between networks as some network congests, reducing packet losses significantly. To verify this idea, we implement MultiNet prototype exploiting some open source components. And several experiments on this prototype are studied both for illustrating MultiNet's benefits and for better understanding of MultiNet.

We focus on the problem of packet distribution for live media streaming over multipath networks. After proposing multipath live streaming model and an in-depth analysis of it, we suggest that traffic load should be allocated to paths in proportion to the paths' available bandwidths, considering complicated network status changing as well as burst of media sending rate, that can minimize the bandwidth overload probability. Moreover, weighted size-aware packet distribution algorithm is proposed to avoid the actual traffic deviation from expected due to variance of packet sizes. Simulation results show that the proposed algorithm outperforms other traditional algorithms, especially on reducing late packets, which has negative impact on real-time requirement.

We present the design of session initiation protocol (SIP) conference based on application layer multicast. Based on the character of SIP conference, we offered a new algorithm to create an application layer multicast tree. In this application layer multicast tree, the two key factors of both bandwidth and delay in streaming media are taken into consideration. The simulation and the experiments show that the design is practical and has a great improvement on the video quality and scalability