HeonChang Yu

Markov Chain Based Monitoring Service for Fault Tolerance in Mobile Cloud Computing

Mobile cloud computing is a combination of mobile computing and cloud computing, and provides cloud computing environment through various mobile devices. Recently, due to rapid expansion of smart phone market and wireless communication environment, mobile devices are considered as resource for large scale distributed processing. But mobile devices have several problems, such as unstable wireless connection, limitation of power capacity, low communication bandwidth and frequent location changes. As resource providers, mobile devices can join and leave the distributed computing environment unpredictably. This interrupts the undergoing operation, and the delay or failure of completing the operation may cause a system failure. Because of low reliability and no-guarantee of completing an operation, it is difficult to use a mobile device as a resource. That means that mobile devices are volatile. Therefore, we should consider volatility, one of dynamic characteristics of mobile devices, for stable resource provision. In this paper, we propose a monitoring technique based on the Markov Chain model, which analyzes and predicts resource states. With the proposed monitoring technique and state prediction, a cloud system will get more resistant to the fault problem caused by the volatility of mobile devices. The proposed technique diminishes the volatility of a mobile device through modeling the patterns of past states and making a prediction of future state of a mobile device.

Volunteer availability based fault tolerant scheduling mechanism in desktop grid computing environment

Fault tolerance is essential to the further development of desktop grid computing system in order to guarantee continuous and reliable execution of tasks in spite of failures. In a desktop grid computing environment, volunteers are often susceptible to volunteer autonomy failures such as volatility failure and interference failure in the middle of execution of tasks because a desktop grid computing maximally respects autonomy of volunteers. The failures result in an independent livelock problem (i.e. the delay and blocking of the entire execution of a job). Therefore, the failures should be considered in a scheduling mechanism. In This work, in order to tolerate volunteer autonomy failures, we propose a new fault tolerant scheduling mechanism. First, we specify a volunteer autonomy failures and an independent livelock problem. Then, we propose a volunteer availability which reflects the degree of volunteer autonomy failures. Finally, we propose a fault tolerant scheduling mechanism based on volunteer availability (which is called VAFTSM).

A resource management and fault tolerance services in grid computing

In grid computing, resource management and fault tolerance services are important issues. The availability of the selected resources for job execution is a primary factor that determines the computing performance. In this paper, we propose a resource manager for optimal resource selection. Our resource manager automatically selects the set of optimal resources among candidate resources that achieves optimal performance using a genetic algorithm. Typically, the probability of a failure is higher in the grid computing than in a traditional parallel computing and the failure of resources affects job execution fatally. Therefore, a fault tolerance service is essential in computational grids. And grid services are often expected to meet some minimum levels of Quality of Service (QoS) for a desirable operation. To address this issue, we also propose a fault tolerance service that satisfies QoS requirements. We extend the definition of failures from the conventional notion of failures in distribute systems in order to provide a fault tolerance service that deals with various types of resource failures, which include process failures, processor failures, and network failures. We also design and implement a fault detector and a fault manager. The implementation and simulation results indicate that our approaches are promising in that (1) the resource manager finds the optimal set of resources that guarantees efficient job execution, (2) the fault detector detects the occurrence of resource failures and (3) the fault manager guarantees that the submitted jobs complete and the performance of job execution is improved due to job migration even if some failures occur.

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.

Resource allocation techniques based on availability and movement reliability for mobile cloud computing

The researches on utilizing mobile devices as resources in mobile cloud environments have been gained attentions recently because of the enhanced computer power of mobile devices with the advent of dual cores chips. In this paper, propose a resource allocation technique which offers reliable resource allocation considering the availability of mobile resources and movement reliability of mobile resources. We also demonstrate the performance of our technique through the experiments.

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.

Fault tolerance and QoS scheduling using CAN in mobile social cloud computing

The performance of mobile devices including smart phones and laptops is steadily rising as prices plummet sharply. So, mobile devices are changing from being a mere interface for requesting services to becoming computing resources for providing and sharing services due to immeasurably improved performance.With the increasing number of mobile device users, the utilization rate of SNS (Social Networking Service) is also soaring. Applying SNS to the existing computing environment enables members of social network to share computing services without further authentication.To use mobile device as a computing resource, temporary network disconnection caused by user mobility and various HW/SW faults causing service disruption should be considered. Also these issues must be resolved to support mobile users and to provide user requirements for services.Accordingly, we propose fault tolerance and QoS (Quality of Services) scheduling using CAN (Content Addressable Network) in Mobile Social Cloud Computing (MSCC). MSCC is a computing environment that integrates social network-based cloud computing and mobile devices. In the computing environment, a mobile user can, through mobile devices, become a member of a social network through real world relationships. Essentially, members of a social network share cloud service or data with other members without further authentication by using their mobile device. We use CAN as the underlying MSCC to logically manage the locations of mobile devices. Fault tolerance and QoS scheduling consists of four sub-scheduling algorithms: malicious-user filtering, cloud service delivery, QoS provisioning, and replication and load-balancing. Under the proposed scheduling, a mobile device is used as a resource for providing cloud services, faults caused from user mobility or other reasons are tolerated and user requirements for QoS are considered.We simulate scheduling both with and without CAN. The simulation results show that our proposed scheduling algorithm enhances cloud service execution time, finish time and reliability and reduces the cloud service error rate.

Using virtual platform in embedded system education

This article presents our cost‐effective curriculum on embedded systems. Education on embedded systems requires coverage of both hardware and software aspects of the systems. Our curriculum uses one monolithic environment, virtual platform, to introduce all the layers of the system components (i.e., from hardware to operating systems to user applications). It is cost‐effective since a hardware system is replaced by a virtual platform. Correspondingly, hardware boards and a lab space are not required. Yet, students are able to make modifications easily on hardware and software components of interest, fully exercising the system. Students responded to the course survey that they are knowledgeable on how embedded systems work after taking the course. Especially, students responded that the virtual platform is effective to use, in place of a hardware platform to learn embedded systems. The course materials are available to the public from a website at Korea University.

Unstructured deadlock detection technique with scalability and complexity-efficiency in clouds

To detect deadlock in distributed systems, the initiator should construct an efficient explicit or implicit global wait-for graph. In this paper, we present an unstructured deadlock detection algorithm using a gossip protocol in cloud computing environments, where constituting nodes may join and leave at any time. Because of the inherit properties of a gossip protocol, we argue that our proposed deadlock detection algorithm is scalable, fault-tolerant, and efficient, retaining safety and liveness properties. The correctness proof of the algorithm is also provided. The message complexity of our proposed algorithm is On, where n is the number of nodes. Our performance evaluation with scalable settings shows that our approach has a significant advantage over previous deadlock detection algorithms in terms of solving scalability, fault-tolerance, and complexity-efficiency issues. Copyright

Adaptive workflow scheduling strategy in service-based grids

During the past several years, the grid application executed same jobs on one or more hosts in parallel, but the recent grid application is requested to execute different jobs linearly. That is, the grid application takes the form of workflow application. In general, efficient scheduling of workflow applications is based on heuristic scheduling method. The heuristic considering relation between hosts would improve execution time in workflow applications. But because of the heterogeneity and dynamic nature of grid resources, it is hard to predict the performance of grid application. In addition, it is necessary to deal with users QoS as like performance guarantee. In this paper, we propose a service model for predicting performance and an adaptive workflow scheduling strategy, which uses maximum flow algorithms for the relation of services and users QoS. Experimental results show that the performance of our proposed scheduling strategy is better than common-used greedy strategies.