Sangyoon Oh

Sercon: Server Consolidation Algorithm using Live Migration of Virtual Machines for Green Computing

Abstract Virtualization technologies changed the way data centers of enterprises utilize their server resources. Instead of using dedicated servers for each type of application, virtualization allows viewing resources as a pool of unified resources, thereby reducing complexity and easing manageability. Server consolidation technique, which deals with reducing the number of servers used by consolidating applications, is one of the main applications of virtualization in data centers. The latter technique helps to use computing resources more effectively and has many benefits, such as reducing costs of power, cooling and, hence, contributes to the Green IT initiative. In a dynamic data center environment, where applications encapsulated as virtual machines are mapped to and released from the nodes frequently, reducing the number of server nodes used can be achieved by migrating applications without stopping their services, the technology known as live migration. However, live migration is a costly operation; hence, how to perform periodic server consolidation operation in a migration-aware way is a challenging task. We propose server consolidation algorithm - Sercon, which not only minimizes the overall number of used servers, but also minimizes the number of migrations. We verify the feasibility of our algorithm along with showing its scalability by conducting experiments with eight different test cases.

Real-time performance analysis for publish/subscribe systems

The publish/subscribe communication system has been a popular communication model in many areas. Especially, it is well suited for a distributed real-time system in many ways. However, the research of cost model and analysis of publish/subscribe system in a distributed real-time system have not been suggested yet. In this paper, we present our cost model for publish/subscribe system in a real-time domain, analyze its performance, and compare it with other communication models such as request/reply and polling models. Our empirical result on mobile embedded device shows accordance with cost analysis, which verifies correctness and usefulness of our cost model.

Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing

Mobile systems, such as smartphones, are becoming the primary platform of choice for a users computational needs. However, mobile devices still suffer from limited resources such as battery life and processor performance. To address these limitations, a popular approach used in mobile cloud computing is computation offloading, where resource-intensive mobile components are offloaded to more resourceful cloud servers. Prior studies in this area have focused on a form of offloading where only a single server is considered as the offloading site. Because there is now an environment where mobile devices can access multiple cloud providers, it is possible for mobiles to save more energy by offloading energy-intensive components to multiple cloud servers. The method proposed in this paper differentiates the data- and computation-intensive components of an application and performs a multisite offloading in a data and process-centric manner. In this paper, we present a novel model to describe the energy consumption of a multisite application execution and use a discrete time Markov chain (DTMC) to model fading wireless mobile channels. We adopt a Markov decision process (MDP) framework to formulate the multisite partitioning problem as a delay-constrained, least-cost shortest path problem on a state transition graph. Our proposed Energy-efficient Multisite Offloading Policy (EMOP) algorithm, built on a value iteration algorithm (VIA), finds the efficient solution to the multisite partitioning problem. Numerical simulations show that our algorithm considers the different capabilities of sites to distribute appropriate components such that there is a lower energy cost for data transfer from the mobile to the cloud. A multisite offloading execution using our proposed EMOP algorithm achieved a greater reduction on the energy consumption of mobiles when compared to a single site offloading execution.

Ontology-based quantitative similarity metric for event matching in publish/subscribe system

Naval ships can achieve information superiority using publish/subscribe systems that integrate heterogeneous applications. The performance of publish/subscribe communication systems depends on the effectiveness of the event matching between events and subscribers. Semantic Web technologies, including ontology, provide a platform and tools for event matching in publish/subscribe systems. Since semantic ambiguity exists in the ontologies, however, matching performance is not ideal. To improve event matching performance, we propose an ontology-based quantitative similarity metric where the frequency of keywords is used to measure the quantitative similarity. When compared to another ontology technique for semantic matching, the approximate semantic matching method, our evaluation results show a performance improvement in precision, recall, and F-measure.

MGEScan: a Galaxy-based system for identifying retrotransposons in genomes.

UNLABELLED : MGEScan-long terminal repeat (LTR) and MGEScan-non-LTR are successfully used programs for identifying LTRs and non-LTR retrotransposons in eukaryotic genome sequences. However, these programs are not supported by easy-to-use interfaces nor well suited for data visualization in general data formats. Here, we present MGEScan, a user-friendly system that combines these two programs with a Galaxy workflow system accelerated with MPI and Python threading on compute clusters. MGEScan and Galaxy empower researchers to identify transposable elements in a graphical user interface with ready-to-use workflows. MGEScan also visualizes the custom annotation tracks for mobile genetic elements in public genome browsers. A maximum speed-up of 3.26× is attained for execution time using concurrent processing and MPI on four virtual cores. MGEScan provides four operational modes: as a command line tool, as a Galaxy Toolshed, on a Galaxy-based web server, and on a virtual cluster on the Amazon cloud. AVAILABILITY AND IMPLEMENTATION MGEScan tutorials and source code are available at http://mgescan.readthedocs.org/ CONTACT hatang@indiana.edu or syoh@ajou.ac.kr SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Semantic similarity method for keyword query system on RDF

Keyword query on RDF data is an effective option because it is lightweight and it is not necessary to have prior knowledge on the data schema or a formal query language such as SPARQL. However, optimizing the query processing to produce the most relevant results with only minimum computations is a challenging research issue. Current proposals suffer from several drawbacks, e.g., limited scalability, tight coupling with the existing ontology, and too many computations. To address these problems, we propose a novel approach to keyword search with automatic depth decisions using the relational and semantic similarities. Our approach uses a predicate that represents the semantic relationship between the subject and object. We take advantage of this to narrow down the target RDF data. The semantic similarity score is then calculated for objects with the same predicate. We make a linear combination of two scores to get the similarity score that is used to determine the depth of given keyword query results. We evaluate our algorithm with other approaches in terms of accuracy and query processing performance. The results of our empirical experiments show that our approach outperforms other existing approaches in terms of efficiency and query processing performance.

Beacon-Based Slotted ALOHA for Wireless Networks with Large Propagation Delay

In a wireless network with large propagation delay S-ALOHA(Slotted ALOHA) requires large guard time which causes lower normalized throughput. To reduce the large guard time in the ISA-ALOHA, a time alignment mechanism was proposed under the assumption of propagation delay estimation. In this letter, we propose a framed structure which is able to estimate propagation delay by employing coordinator beaconing. The framed structure consists of a time period for beaconing and a group of multiple time slots for random access. The proposed Beacon-based S-ALOHA(BS-ALOHA) can make packets generated during the time of beaconing evenly distributed over the random access period. Furthermore, we propose an analytical model considering overhead due to coordinator beaconing time and show that BS-ALOHA provides higher normalized throughput than both ALOHA and S-ALOHA employing the large guard time.

An approach to mitigate DoS attack based on routing misbehavior in wireless ad hoc networks

The routing misbehavior is one of the most serious threats to wireless ad hoc networks where nodes are not forwarding messages correctly. Once the attack has been launched, nodes in the network are not able to send messages anymore. Thus it could be a kind of DoS (denial of service) attack. So far, in order to detect the attack, most researches employ a watchdog method although it is not efficient and has a high false positive ratio. In this paper, we propose a novel approach to detect the attacks based on routing misbehavior. It can detect attacks in a more effective way as well as solve problems in existing watchdog. In the proposed method a node in the network cooperates with its neighbor nodes to collect statistics on packets. According to the statistical information, the method determines each nodes misbehavior. The simulation results shows that the proposed method is practical and effective to apply to real domain.

Alternating decision tree algorithm for assessing protein interaction reliability

This paper presents a machine learning approach for assessing the reliability of protein–protein interactions in a high-throughput dataset. We use an alternating decision tree algorithm to distinguish true interacting protein pairs from noisy high-throughput data using various biological attributes of interacting proteins. The alternating decision tree algorithm is used both for identifying discriminating biological features that could be used for assessing protein interaction reliability and for constructing a classifier to identify true positive interacting pairs. Experimental results show that the proposed approach has a good performance in distinguishing true interacting protein pairs from noisy protein–protein interaction data. Moreover, our alternating decision tree classifier supplemented with domain knowledge may be helpful to understand the biological conditions in connection with interacting protein pairs.

Spark-based in-memory DEM creation from 3D LiDAR point clouds

ABSTRACT The significant performance improvement obtained by using Spark in-memory processing for iterative processes has led many researchers in various fields to implement their applications with Spark. In this study, we investigated the use of in-memory processing with Spark for creating a digital elevation model from massive light detection and ranging (LiDAR) point clouds, which can be considered an iterative process. We conducted our experiments on large high-density LiDAR data sets using two well-known interpolation methods: inverse distance weighting (IDW) and Kriging. Here, we designed our in-memory processing to parallelize those methods, and compared our results with the popularly used Hadoop MapReduce-based implementation. Our experiments ran on six servers under a medium-sized high-performance cloud computing environment. The results demonstrated that our Spark-based in-memory computing yielded better performance compared with Hadoop MapReduce, with an average 5.4 times speed increase in IDW, and 4.8 times improvement in Kriging. In addition, we evaluated the characteristics of our method in terms of central processing unit, memory usage, and network activities.