Erasure codes have been considered as one of the most promising techniques for data reliability enhancement and storage efficiency in modern distributed storage systems. However, erasure codes often suffer from a time-consuming coding process which makes them nearly impractical. The opportunity to solve this problem probably rely on the parallelization of erasure-code-based application on the modern multi-/many-core processors to fully take advantage of the adequate hardware resources on those platforms. However, the complicated data allocation and limited I/O throughput pose a great challenge on the parallelization. To address this challenge, we propose a general multi-threaded parallel coding approach in this work. The approach consists of a general multi-threaded parallel coding model named as MTPerasure, and two detailed parallel coding algorithms, named as sdaParallel and ddaParallel, respectively, adapting to different I/O circumstances. MTPerasure is a general parallel coding model focusing on the high level data allocation, and it is applicable for all erasure codes and can be implemented without any modifications of the low level coding algorithms. The sdaParallel divides the data into several parts and the data parts are allocated to different threads statically in order to eliminate synchronization latency among multiple threads, which improves the parallel coding performance under the dummy I/O mode. The ddaParallel employs two threads to execute the I/O reading and writing on the basis of small pieces independently, which increases the I/O throughput. Furthermore, the data pieces are assigned to the coding thread dynamically. A special thread scheduling algorithm is also proposed to reduce thread migration latency. To evaluate our proposal, we parallelize the popular open source library jerasure based on our approach. And a detailed performance comparison with the original sequential coding program indicates that the proposed parallel approach outperforms the original sequential program by an extraordinary speedups from 1.4x up to 7x, and achieves better utilization of the computation and I/O resources.

This paper describes a near-optimal allocation method for web-based multi-player online role-playing games (MORPGs), which must be able to cope with a large number of users and high frequency of user requests. Our previous work introduced a dynamic data reallocation method. It uses multiple web servers and divides the entire game world into small blocks. Each ownership of block is allocated to a web server. Additionally, the ownership is reallocated to the other web server according to the user's requests. Furthermore, this block allocation was formulated as a combinational optimization problem. And a simulation based experiment with an exact algorithm showed that our system could achieve 31% better than an ad-hoc approach. However, the exact algorithm takes too much time to solve a problem when the problem size is large. This paper proposes a meta-heuristic approach based on a tabu search to solve a problem quickly. A simulation result shows that our tabu search algorithm can generate solutions, whose average correctness is only 1% different from that of the exact algorithm. In addition, the average calculation time for 50 users on a system with five web servers is about 25.67 msec while the exact algorithm takes about 162 msec. An evaluation for a web-based MORPG system with our tabu search shows that it could achieve 420 users capacity while 320 for our previous system.

We have investigated the bottleneck in web-based MORPG system and proposed a load-distribution method using multiple web servers. This technique uses a dynamic data allocation method, called the moving home. This paper describes the evaluation of our method using 4, 8, 16 web servers. We evaluated it on both the single-server system and multi-server system. And we confirm that the effect of the moving home through the comparison between the multi-server system without the moving home and that with the moving home. Our experimental result shows that the upper bound of the number of avatars in the eight-server system with the moving home becomes 380 by contrast that in the single-server system is 200.

Data broadcast has become a promising approach to achieving information dissemination in wireless environments due to the limited channel bandwidth and the power constraints of portable devices. In this paper, a restricted dynamic programming approach which generates broadcast programs is proposed to partition data items over multiple channels near optimally. In our approach, a function to predict the optimal average expected delay, in terms of the number of channels, the summation of the access frequencies of data items, and the ratio of the data items is developed by employing curve fitting. Applying this function, we can find a cut point that may be very close to the optimal cut. Thus, the search space in dynamic programming can be restricted to the interval around a determined cut point. Therefore, our approach only takes O(N log K) time, where N is number of data items and K is the number of broadcast channels. Simulation results show that the solution obtained by our proposed algorithm is near-optimal.