Dunren Che

From Big Data to Big Data Mining: Challenges, Issues, and Opportunities

While big data has become a highlighted buzzword since last year, big data mining, i.e., mining from big data, has almost immediately followed up as an emerging, interrelated research area. This paper provides an overview of big data mining and discusses the related challenges and the new opportunities. The discussion includes a review of state-of-the-art frameworks and platforms for processing and managing big data as well as the efforts expected on big data mining. We address broad issues related to big data and/or big data mining, and point out opportunities and research topics as they shall duly flesh out. We hope our effort will help reshape the subject area of todays data mining technology toward solving tomorrows bigger challenges emerging in accordance with big data.

Enhanced First-Fit Decreasing Algorithm for Energy-Aware Job Scheduling in Cloud

With the emerging of many data centers around the globe, heavy loads of large-scale commercial and scientific applications executed in the cloud call for efficient cloud resource management strategies to save energy without compromising the performance and system throughput. According to the statistics from the Data Centre Dynamic (DCD) organization, the expected energy consumption by computer servers would increase by 19% in 2013 compared with the previous year. Such trend may continue for many years. Moreover, the estimated energy consumption of computers in the U.S. was about 2% out of the total electricity consumption in 2010, which makes IT industry the second pollution contributor after aviation. In this paper, a novel approach for scheduling, sharing and migrating Virtual Machines (VMs) for a bag of cloud tasks is designed and developed to reduce energy consumption with guaranteed certain execution time and high system throughput. This approach is derived from an Enhanced First Fit Decreasing (EFFD) algorithm combined with our VM reuse strategy. Furthermore, virtual machine migration method is introduced to dynamically monitor the cloud situation for necessary migration. Our simulation results using Cloud Report show that EFFD with our VM reuse strategy gains higher resource utilization rate and lower energy consumption than Greedy, Round Robin (RR) and FDD without VM reuse.

Improving Relevance Prediction for Focused Web Crawlers

A key issue in designing a focused Web crawler is how to determine whether an unvisited URL is relevant to the search topic. Effective relevance prediction can help avoid downloading and visiting many irrelevant pages. In this paper, we propose a new learning-based approach to improve relevance prediction in focused Web crawlers. For this study, we chose Naïve Bayesian as the base prediction model, which however can be easily switched to a different prediction model. Experimental result shows that our approach is valid and more efficient than related approaches.

An Innovative Energy-Aware Cloud Task Scheduling Framework

With the increased popularity of cloud computing, the number and scales of cloud data centers have kept growing at unprecedented speeds. In the meanwhile, the energy consumption by the data centers has kept commensurately increasing as well. Therefore, the focus of cloud resource management and scheduling has relatively shifted from mere performance to also energy efficiency. In this paper, we present a novel, Energy-Aware Task Scheduling framework that makes integrated exploitation of the two well-known energy saving techniques, DVFS and VM Reuse, on cloud task scheduling in a data center. We present our scheduling approach and framework via a specific algorithm, called EATS-FFD, that assumes FFD as its base scheduling policy. With minor modification, the presented framework can be made to work with a different base scheduling policy, resulting in a correspondingly different scheduling algorithm. Our approach achieves better energy-efficiency without sacrificing system QoS. The effectiveness of our approach is evaluated under various experimental scenarios using the Cloud Report tool running on the open source CloudSim platform.

A "No Data Center" Solution to Cloud Computing

Current Cloud Computing is primarily based on proprietary data centers, where hundreds of thousands of dedicated servers are setup to host the cloud services. In addition to the huge number of dedicated servers deployed in data centers, there are billions of underutilized Personal Computers (PCs), usually used only for a few hours per day, owned by individuals and organizations worldwide. The vast untapped compute and storage capacities of the underutilized PCs can be consolidated as alternative cloud fabrics to provision broad cloud services, primarily infrastructure as a service. This approach, thus referred to as no data center approach, complements the data center based cloud provision model. In this paper, we present our opportunistic Cloud Computing system, called cuCloud, that runs on scavenged resources of underutilized PCs within an organization/community. Our system demonstrates that the no data center solution indeed works. Besides proving our concept, model, and philosophy, our experimental results are highly encouraging.

cuCloud: Volunteer Computing as a Service (VCaaS) System

Emerging cloud systems, such as volunteer clouds and mobile clouds, are getting momentum among the current topics that dominate the research landscape of Cloud Computing. Volunteer cloud computing is an economical, secure, and greener alternative solution to the current Cloud Computing model that is based on data centers, where tens of thousands of dedicated servers are setup to back the cloud services. This paper presents cuCloud, a Volunteer Computing as a Service (VCaaS) system that is based on the spare resources of personal computers owned by individuals and/or organizations. The paper addresses the design and implementation issues of cuCloud, including the technical details of its integration with the well-known open source IaaS cloud management system, CloudStack. The paper also presents the empirical performance evidence of cuCloud in comparison with Amazon EC2 using a big-data application based on Hadoop.

Development of MapReduce and MPI Programs for Motif Search

As one of the important problems in molecular biology, motif search is computationally expensive, especially when the size of DNA sequences is large. Extended from a graduate course project in parallel and distributed computing (PDC), this paper investigates two different programming frameworks, namely MapReduce and MPI on motif finding. We implemented a serial algorithm, a MapReduce based algorithm, and a MPI program to calculate the best motif in given DNA sequences. The experimental results demonstrate that our MPI program outperformed both the MapReduce-based algorithm and the serial program with superior efficiency.

Scheduling data processing flows under budget constraint on the cloud

Cloud computing is emerging as a promising paradigm for large-scale data-intensive queries modeled as complex Directed Acyclic Graph (DAG)-structured dataflows with arbitrary data operators as nodes and producer-consumer interactions as directed edges. The optimization problem of scheduling dataflows on the Cloud is a very complex and challenging task which is similar to query optimization. Optimization must satisfy a variety of objectives and constraints, while taking into account the particular characteristics of the underlying Cloud environment. In addition to achieving minimum completion time, the commercialization of Clouds requires policies to take users economic concerns as well. In this paper, we formulate scheduling of dataflows onto Cloud resources under the objective of minimizing the completion time under certain budget constraint. A heuristic scheduling algorithm, Layer-oriented Resource Allocation within Budget constraint (LRA-B) is proposed and evaluated. Experiments are conducted on numerous dataflows and Cloud environment configurations, and the overall results are quite promising and indicate the effectiveness of our algorithm.