Ching-Hsien Hsu

Biocloud: Cloud Computing for Biological, Genomics, and Drug Design

Cloud computing has emerged rapidly as an exciting new paradigm that offers a challenging model of computing and services. Leveraging cloud computing technology, bioinformatics tools can be made available as services to anyone, anywhere, and through any device. The use of large biodatasets, its highly demanding algorithms, and the hardware for sudden computational resources makes large-scale biodata analysis an attractive test case for cloud computing. This special issue aims to foster the dissemination of high quality research in any new idea, method, theory, and technique related to cloud computing and bioinformatics and to showcase the most recent developments and research in cloud computing for biological, genomics, and drug design, considering genomics and drug design on the cloud, biological tools on the cloud, biodatabase on the cloud, cloud-based biocomputing, and all kinds of successful applications. The research papers selected for this special issue represent recent progresses in the aspects, including theoretical studies, practical applications, new analysis and modeling technology, programming methodologies, and experimental prototypes. All of these papers not only provide novel ideas and state-of-the-art techniques in the field but also stimulate future research in the biocloud environments.

Optimizing Energy Consumption with Task Consolidation in Clouds

Task consolidation is a way to maximize utilization of cloud computing resources. Maximizing resource utilization provides various benefits such as the rationalization of maintenance, IT service customization, QoS and reliable services, etc. However, maximizing resource utilization does not mean efficient energy use. Much of the literature shows that energy consumption and resource utilization in clouds are highly coupled. Consequently, some of the literature aims to decrease resource utilization in order to save energy, while others try to reach a balance between resource utilization and energy consumption. In this paper, we present an energy-aware task consolidation (ETC) technique that minimizes energy consumption. ETC achieves this by restricting CPU use below a specified peak threshold. ETC does this by consolidating tasks amongst virtual clusters. In addition, the energy cost model considers network latency when a task migrates to another virtual cluster. To evaluate the performance of ETC we compare it against MaxUtil. MaxUtil is a recently developed greedy algorithm that aims to maximize cloud computing resources. The simulation results show that ETC can significantly reduce power consumption in a cloud system, with 17% improvement over MaxUtil.

Cold-Start Recommendation Using Bi-Clustering and Fusion for Large-Scale Social Recommender Systems

Social recommender systems leverage collaborative filtering (CF) to serve users with content that is of potential interesting to active users. A wide spectrum of CF schemes has been proposed. However, most of them cannot deal with the cold-start problem that denotes a situation that social media sites fail to draw recommendation for new items, users or both. In addition, they regard that all ratings equally contribute to the social media recommendation. This supposition is against the fact that low-level ratings contribute little to suggesting items that are likely to be of interest of users. To this end, we propose bi-clustering and fusion (BiFu)-a newly-fashioned scheme for the cold-start problem based on the BiFu techniques under a cloud computing setting. To identify the rating sources for recommendation, it introduces the concepts of popular items and frequent raters. To reduce the dimensionality of the rating matrix, BiFu leverages the bi-clustering technique. To overcome the data sparsity and rating diversity, it employs the smoothing and fusion technique. Finally, BiFu recommends social media contents from both item and user clusters. Experimental results show that BiFu significantly alleviates the cold-start problem in terms of accuracy and scalability.

Energy-Aware Task Consolidation Technique for Cloud Computing

Task consolidation is a way of maximizing cloud computing resource, which brings many benefits such as better use of resources, rationalization of maintenance, IT service customization, QoS and reliable services, etc. However, maximizing resource utilization does not mean efficient energy usage. Many literature show that energy consumption and resource utilization in clouds are highly coupled. Some research works aim to decrease resource utilization for saving energy while some try to find the balance between resource utilization and energy consumption. In this paper, an energy-aware task consolidation (ETC) technique is presented aims to optimize energy consumption of virtual clusters in cloud data center. Conforming most cloud systems, a 70% principle of CPU utilization is proposed to manage task consolidation among virtual clusters. The simulation results show that ETC can significantly reduce power consumption in managing task consolidation for cloud systems. Up to 17% improvement as compare to a recent work in [10] that aims to maximize resource utilization can be obtained.

A basic-cycle calculation technique for efficient dynamic data redistribution

Array redistribution is usually required to enhance algorithm performance in many parallel programs on distributed memory multicomputers. Since it is performed at run-time, there is a performance trade-off between the efficiency of the new data decomposition for a subsequent phase of an algorithm and the cost of redistributing data among processors. In this paper, we present a basic-cycle calculation technique to efficiently perform BLOCK-CYCLIC(S) to BLOCK-CYCLIC(t) redistribution. The main idea of the basic-cycle calculation technique is, first, to develop closed forms for computing source/destination processors of some specific array elements in a basic-cycle, which is defined as icm(s,t)/gcd(s,t). These closed forms are then used to efficiently determine the communication sets of a basic-cycle. From the source/destination processor/data sets of a basic-cycle, we can efficiently perform a BLOCK-CYCLIC(s) to BLOCK-CYCLIC(t) redistribution. To evaluate the performance of the basic-cycle calculation technique, we have implemented this technique on an IBM SP2 parallel machine, along with the PITFALLS method and the multiphase method. The cost models for these three methods are also presented. The experimental results show that the basic-cycle calculation technique outperforms the PITFALLS method and the multiphase method for most test samples.

Provision of Data-Intensive Services Through Energy- and QoS-Aware Virtual Machine Placement in National Cloud Data Centers

Many data-intensive services (e.g., planet analysis, gene analysis, and so on) are becoming increasingly reliant on national cloud data centers (NCDCs) because of growing scientific collaboration among countries. In NCDCs, tens of thousands of virtual machines (VMs) are assigned to physical servers to provide data-intensive services with a quality-of-service (QoS) guarantee, and consume a massive amount of energy in the process. Although many VM placement schemes have been proposed to solve this problem of energy consumption, most of these assume that all the physical servers are homogeneous. However, the physical server configurations of NCDCs often differ significantly, which leads to varying energy consumption characteristics. In this paper, we explore an alternative VM placement approach to minimize energy consumption during the provision of data-intensive services with a global QoS guarantee in NCDCs. We use an improved particle swarm optimization algorithm to develop an optimal VM placement approach involving a tradeoff between energy consumption and global QoS guarantee for data-intensive services. Experimental results show that our approach significantly outperforms other approaches to energy optimization and global QoS guarantee in NCDCs.

A generalized basic-cycle calculation method for efficient array redistribution

In many scientific applications, dynamic array redistribution is usually required to enhance the performance of an algorithm. In this paper, we present a generalized basic-cycle calculation (GBCC) method to efficiently perform a BLOCK-CYCLIC(s) over P processors to BLOCK-CYCLIC(t) over Q processors array redistribution. In the GBCC method, a processor first computes the source/destination processor/data sets of array elements in the first generalized basic-cycle of the local array it owns. A generalized basic-cycle is defined as lcm(sP, tQ)/(gcd(s,t)/spl times/P) in the source distribution and lcm(sP, tQ)/(gcd(s,t)/spl times/Q) in the destination distribution. From the source/destination processor/data sets of array elements in the first generalized basic-cycle, we can construct packing/unpacking pattern tables to minimize the data-movement operations. Since each generalized basic-cycle has the same communication pattern, based on the packing/unpacking pattern tables, a processor can pack/unpack array elements efficiently. To evaluate the performance of the GBCC method, we have implemented this method on an IBM SP2 parallel machine, along with the PITFALLS method and the ScaLAPACK method. The cost models for these three methods are also presented. The experimental results show that the GBCC method outperforms the PITFALLS method and the ScaLAPACK method for all test samples. A brief description of the extension of the GBCC method to multidimensional array redistributions is also presented.

Multi-user web service selection based on multi-QoS prediction

In order to find best services to meet multi-user’s QoS requirements, some multi-user Web service selection schemes were proposed. However, the unavoidable challenges in these schemes are the efficiency and effect. Most existing schemes are proposed for the single request condition without considering the overload of Web services, which cannot be directly used in this problem. Furthermore, existing methods assumed the QoS information for users are all known and accurate, and in real case, there are always many missing QoS values in history records, which increase the difficulty of the selection. In this paper, we propose a new framework for multi-user Web service selection problem. This framework first predicts the missing multi-QoS values according to the historical QoS experience from users, and then selects the global optimal solution for multi-user by our fast match approach. Comprehensive empirical studies demonstrate the utility of the proposed method.

Collaboration reputation for trustworthy Web service selection in social networks

We construct a Web service collaboration network.We propose a collaboration reputation concept.We present a trustworthy Web service selection method. Traditional trustworthy service selection approaches focus the overall reputation maximization of all selected services in social networks. However, the selected services barely interact with each other in history, which leads to the trustworthiness among services being very low. Hence, to enhance the trustworthiness of Web service selection, a novel concept, collaboration reputation is proposed in this paper. The collaboration reputation is built on a Web service collaboration network consisting of two metrics. One metric, invoking reputation, can be calculated according to other services recommendation. The other metric, invoked reputation, can be assessed by the interaction frequency among Web services. Finally, based on the collaboration reputation, we present a trustworthy Web service selection method to not only solve the simple Web service selection but also the complex selection. Experimental results show that compared with other methods, the efficiency of our method and the solutions trustworthiness are both greatly increased.

A Highly Accurate Prediction Algorithm for Unknown Web Service QoS Values

Quality of service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore the accurate prediction of unknown QoS values is significant for the successful deployment of web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown web service QoS values more accurately than other existing approaches.