Chun-Chen Hsu

Dynamic resource selection heuristics for a non-reserved bidding-based Grid environment

A Grid system is comprised of large sets of heterogeneous and geographically distributed resources that are aggregated as a virtual computing platform for executing large-scale scientific applications. As the number of resources in Grids increases rapidly, selecting appropriate resources for jobs has become a crucial issue. To avoid single point of failure and server overload problems, bidding provides an alternative means of resource selection in distributed systems. However, under the bidding model, the key challenge of resource selection is that there is no global information system to facilitate optimum decision-making; hence requesters can only obtain partial information revealed by resource providers. To address this problem, we propose a set of resource selection heuristics to minimize the turnaround time in a non-reserved bidding-based Grid environment, while considering the level of information about competing jobs revealed by providers. We also present the results of experiments conducted to evaluate the performance of the proposed heuristics.

Efficient multi-source data transfer in data grids

As the number of data-intensive applications increases in various domains, scientists need to save, retrieve, and analyze increasingly large datasets. The huge volume of data and the long latency of data transfer on the Internet make it very difficult to ensure high-performance access to data grids. Thus, data replication techniques have been widely adopted to solve the latency problem. In this paper, we propose an efficient data replication algorithm for multi-source data transfer, whereby a data replica can be assembled in parallel from multiple distributed data sources and adapted to the variability of network bandwidths. The experimental results show that the proposed algorithm can obtain more aggregated bandwidth, reduce connection overheads, and achieve superior load balance.

Generalized Edge Coloring for Channel Assignment in Wireless Networks

This paper introduces a new graph theory problem called generalized edge coloring (g.e.c). A generalized edge coloring is similar to traditional edge coloring, with the difference that a vertex can be adjacent to up to k edges that share the same color. The concept of generalized edge coloring can be used to formulate the channel assignment problem in multi-channel multi-interface wireless networks. We provide theoretical analysis for this problem. Our theoretical findings can be useful for system developers of wireless networks. We show that when k = 3, there are graphs that do not have generalized edge coloring that could achieve the minimum number of colors for every vertex. On the contrary, when k = 2 we show that if we are given one extra color, we can find a generalized edge coloring that uses the minimum number of colors for each vertex. In addition, we show that for certain classes of graphs we are able to find a generalized edge coloring that uses the minimum number of colors for every vertex without the extra color. These special classes of graphs include bipartite graph, graphs with a power of 2 maximum degree, or graphs with maximum degree no more than 4

Optimizing server placement in hierarchical grid environments

Abstract In this paper, we address some problems related to server placement in Grid environments. Given a hierarchical network with requests from clients and constraints on server capability, the minimum server placement problem attempts to place the minimum number of servers that satisfy requests from clients. Instead of using a heuristic approach, we propose an optimal algorithm based on dynamic programming to solve the problem. We also consider the balanced server placement problem, which tries to place a given number of servers appropriately so that their workloads are as balanced as possible. We prove that an optimal server placement can be achieved by combining the above algorithm with a binary search on workloads. This approach can be further extended to deal with constrains on network capability. The simulation results clearly show the improvement in the number of servers and the maximum workload. Furthermore, as the maximum workload is reduced, the waiting time is reduced accordingly.

Bi-objective Optimization: An Online Algorithm for Job Assignment

We study an online problem that occurs when the capacities of machines are heterogeneous and all jobs are identical. Each job is associated with a subset, called feasible set, of the machines that can be used to process it. The problem involves assigning each job to a single machine in its feasible set, i.e., to find a feasible assignment. The objective is to maximize the throughput, which is the sum of the bandwidths of the jobs; and minimize the total load, which is the sum of the loads of the machines. In the online setting, the jobs arrive one-by-one and an algorithm must make decisions based on the current state without knowledge of future states. By contrast, in the offline setting, all the jobs with their feasible sets are known in advance to an algorithm. Let m denote the total number of machines, α denote the competitive ratio with respect to the throughput and β denote the competitive ratio with respect to the total load. In this paper, our contribution is that we propose an online algorithm that finds a feasible assignment with a throughput-competitive upper bound

GFS: A Distributed File System with Multi-source Data Access and Replication for Grid Computing

\alpha=O(\sqrt{m})

Resource Selection Strategies for a CNP-Based Resource Management Model

, and a total-load-competitive upper bound

Heuristic Algorithms for Replication Transition Problem in the Grid Systems

\beta=O(\sqrt{m})

A high-performance virtual storage system for Taiwan UniGrid

. We also show a lower bound

Towards a service-based collaborative framework for data-intensive grid applications

\alpha\beta=\Omega(\sqrt{m})