Joon-Min Gil

A Target Coverage Scheduling Scheme Based on Genetic Algorithms in Directional Sensor Networks

As a promising tool for monitoring the physical world, directional sensor networks (DSNs) consisting of a large number of directional sensors are attracting increasing attention. As directional sensors in DSNs have limited battery power and restricted angles of sensing range, maximizing the network lifetime while monitoring all the targets in a given area remains a challenge. A major technique to conserve the energy of directional sensors is to use a node wake-up scheduling protocol by which some sensors remain active to provide sensing services, while the others are inactive to conserve their energy. In this paper, we first address a Maximum Set Covers for DSNs (MSCD) problem, which is known to be NP-complete, and present a greedy algorithm-based target coverage scheduling scheme that can solve this problem by heuristics. This scheme is used as a baseline for comparison. We then propose a target coverage scheduling scheme based on a genetic algorithm that can find the optimal cover sets to extend the network lifetime while monitoring all targets by the evolutionary global search technique. To verify and evaluate these schemes, we conducted simulations and showed that the schemes can contribute to extending the network lifetime. Simulation results indicated that the genetic algorithm-based scheduling scheme had better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.

A unified scheme of shot boundary detection and anchor shot detection in news video story parsing

In this paper, we propose an efficient one-pass algorithm for shot boundary detection and a cost-effective anchor shot detection method with search space reduction, which are unified scheme in news video story parsing. First, we present the desired requirements for shot boundary detection from the perspective of news video story parsing, and propose a new shot boundary detection method, based on singular value decomposition, and a newly developed algorithm, viz., Kernel-ART, which meets all of these requirements. Second, we propose a new anchor shot detection system, viz., MASD, which is able to detect anchor person cost-effectively by reducing the search space. It consists of skin color detector, face detector, and support vector data descriptions with non-negative matrix factorization sequentially. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

MJSA: Markov job scheduler based on availability in desktop grid computing environment

In a desktop grid computing environment, voluntary desktops (i.e., resource providers) are free to leave and join independently in the middle of execution. To develop a reliable desktop grid computing system, a scheduling scheme must consider the dynamic nature (i.e., volatility) of volunteers. Existing desktop grid computing systems, however, do not consider volatility in their scheduling procedures. As a result, job execution is often suspended, resulting in delayed completion time and degraded performance and reliability. To solve these limitations, we propose the Markov Job Scheduler based on Availability (MJSA) supporting three advanced scheduling schemes: OPTIMIST, PESSIMIST, and REALIST. These scheduling schemes are based on stochastic modeling of desktop availability. In the OPTIMIST scheme, in which time constraints are relaxed, the MJSA provides reliable resource selection at low cost. In the PESSIMIST scheme, where time constraints are rigid, the MJSA enables stable makespan in strictly time. Finally, in the REALIST scheme, where time constraints are only partially relaxed, the MJSA provides enhanced cost efficiency. In conclusion, the MJSA improves performance and reliability by adapting the appropriate scheduling scheme when selecting volunteers according to the needs of applications.

Adaptive group scheduling mechanism using mobile agents in peer-to-peer grid computing environment

Peer-to-peer grid computing is an attractive computing paradigm for high throughput applications. However, both volatility due to the autonomy of volunteers (i.e., resource providers) and the heterogeneous properties of volunteers are challenging problems in the scheduling procedure. Therefore, it is necessary to develop a scheduling mechanism that adapts to a dynamic peer-to-peer grid computing environment. In this paper, we propose a Mobile Agent based Adaptive Group Scheduling Mechanism (MAAGSM). The MAAGSM classifies and constructs volunteer groups to perform a scheduling mechanism according to the properties of volunteers such as volunteer autonomy failures, volunteer availability, and volunteering service time. In addition, the MAAGSM exploits a mobile agent technology to adaptively conduct various scheduling, fault tolerance, and replication algorithms suitable for each volunteer group. Furthermore, we demonstrate that the MAAGSM improves performance by evaluating the scheduling mechanism in Korea@Home.

Group-based dynamic computational replication mechanism in peer-to-peer grid computing

A peer-to-peer grid computing is complicated by heterogeneous capabilities, failures, volatility, and lack of trust because it is based on desktop computers at the edge of the Internet. In order to improve the reliability of computation and gain better performance, a replication mechanism must adapt to these distinct features. In other words, it is required to classify volunteers into groups that have similar properties and then dynamically apply different replication algorithms to each group. However, existing mechanisms do not provide such a replication mechanism on a per group basis. As a result, they cause a high overhead and poor performance. To solve the problems, we propose a new group-based computational replication mechanism to adapt to an unstable, untrusted, dynamic peer-to-peer grid computing environment. Our mechanism can reduce the number of redundancy and therefore complete many tasks by adaptively replicating computations on the basis of the properties of volunteer group such as availability, credibility, and volunteering service time.

An efficient checkpointing scheme using price history of spot instances in cloud computing environment

The cloud computing is a computing paradigm that users can rent computing resources from service providers as much as they require. A spot instance in cloud computing helps a user to utilize resources with less expensive cost, even if it is unreliable. When a user performs tasks with unreliable spot instances, failures inevitably lead to the delay of task completion time and cause a seriously deterioration in the QoS of users. Therefore, we propose a price history based checkpointing scheme to avoid the delay of task completion time. The proposed checkpointing scheme reduces the number of checkpoint trials and improves the performance of task execution. The simulation results show that our scheme outperforms the existing checkpointing schemes in terms of the reduction of both the number of checkpoint trials and total costs per spot instance for users bid.

Scalable and leaderless Byzantine consensus in cloud computing environments

Traditional Byzantine consensus in distributed systems requires n ≥ 3f + 1, where n is the number of nodes. In this paper, we present a scalable and leaderless Byzantine consensus implementation based on gossip, requiring only n ≥ 2f + 1 nodes. Unlike conventional distributed systems, the network topology of cloud computing systems is often not fully connected, but loosely coupled and layered. Hence, we revisit the Byzantine consensus problem in cloud computing environments, in which each node maintains some number of neighbors, called local view. The message complexity of our Byzantine consensus scheme is O(n), instead of O(n2). Experimental results and correctness proof show that our Byzantine consensus scheme can solve the Byzantine consensus problem safely in a scalable way without a bottleneck and a leader in cloud computing environments.

Data center selection based on neuro-fuzzy inference systems in cloud computing environments

A high-quality service for applications in cloud computing environments is guaranteed by making efficient use of resources in data centers. Applications should be allocated to resources suitable for the load of data centers to achieve this. The complex and dynamic nature of the load prevents the proper selection of one of multiple data centers and fails to meet the demands of resources in applications. An incorrect data center selection seriously lowers resource utilization in the data center and accordingly deteriorates the quality of services for applications. This paper proposes a neuro-fuzzy inference-based prediction scheme to select one of multiple data centers in accordance with application workloads. This scheme is used to aggressively capture the time-varying load of data centers by learning and predicting the availability of resources therein. Therefore, it predicts not only the present load but also the future load of data centers in the process of determining a suitable data center. By an autonomic control for data center selection, our scheme can also provide load balancing between data centers. Moreover, we present performance evaluations with experiments based on Xen testbeds to demonstrate the effectiveness of our scheme. The experimental results show that our scheme is superior to other selection schemes with regard to the entire and changed loads of data centers.

Performance Evaluation of a Simple Cluster-Based Aggregation and Routing in Wireless Sensor Networks

In future ubiquitous networks, sensor nodes should collect various environmental data and parameters. Because sensor nodes tend to have small and often irreplaceable batteries with limited power capacity, energy-efficient aggregation and routing are essential to achieve to a prolonged network lifetime. We propose a simple cluster-based data aggregation and routing algorithm (SCAR) that decreases the incurred overhead during the selection of cluster heads in wireless sensor networks. The performance results show that SCAR can prolong network lifetime via energy conservation and achieve energy-balancing when nodes are fixed or have limited mobility.

A genetic algorithm method for sender-based dynamic load balancing algorithm in distributed systems

In a sender-initiated load balancing algorithm, the overloaded processor continues to send unnecessary request messages for load transfer until underloaded processor is found while the system load is heavy. Therefore, it yields inefficient inter-processor communications, low cpu utilization, and low system throughput. To solve these problems, we propose an improved genetic algorithm method for sender-initiated load balancing in distributed systems, and define a suitable fitness function. In this scheme, the processors that the request messages are transfered to are determined by genetic algorithm. The method also decreases unnecessary request messages. Compared with the conventional sender-initiated algorithms, we show that the proposed algorithm performs better.