Eunyoung Lee

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.

Two-phase grouping-based resource management for big data processing in mobile cloud computing

Big data is generated from recent social network services, and distributed processing techniques have been studied to analyze it. In particular, because of the fast spread of mobile devices, a huge amount data is generated in a mobile environment. The distributed processing technologies such as MapReduce are applied to mobile devices, thanks to the improved computing power of mobile devices. However, mobile devices have several problems such as the movement problem and the utilization problem. Especially, the utilization problem and the movement problem of mobile devices cause system faults more frequently because of dynamic changes, and system faults prevent applications using mobile devices from being processed reliably. Therefore, to cope with these significant problems of mobile devices, we propose a grouping technique based on the utilization and movement rates. In our proposed scheme, mobile devices are separated into groups by cut-off points based on entropy values. We also propose a two-phase grouping method in order to reduce the overhead of group management. The experimental result shows that our algorithm outperforms traditional grouping techniques with maintaining stable big data processing and managing reliable resource. Copyright

A Workflow Scheduling Technique for Task Distribution in Spot Instance-Based Cloud

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. In this paper, we propose the workflow scheduling scheme that reduces the task waiting time when an instance occurs the out-of-bid situation. And, our scheme executes user’s job within selected instances and expands the suggested user budget. The simulation results reveal that, compared to various instance types, our scheme achieves performance improvements in terms of an average execution time of 66.86% over shortest execution time in each task time interval. And, the cost in our scheme is higher than an instance with low performance and is lower than an instance with high performance. Therefore, our scheme is difficult to optimize cost for task execution.

Task Classification Based Energy-Aware Consolidation in Clouds

We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.

Monitoring service using markov chain model in mobile grid environment

Recently mobile devices become considered as grid resources as the technology of mobile devices and wireless communication network improves But mobile devices have several problems such as instability of wireless communication, intermittent connection, limitation of power supply, and low communication bandwidth These problems make it difficult to use mobile grid computing resources for stable job processing effectively For stable resource participation of mobile devices, a monitoring scheme that collects and analyzes dynamic information of mobile devices, such as CPU, memory, storage, network and location, is required But, if the time interval of monitoring is very short, overhead of collecting information increases The scheme, however, cannot keep correct state information in dynamic environments if the interval is very long This paper proposes a monitoring service scheme that adjusts the time interval of monitoring according to the state information predicted by Markov Chain model.

Task Balanced Workflow Scheduling Technique considering Task Processing Rate in Spot Market

Recently, the cloud computing is a computing paradigm that constitutes an advanced computing environment that evolved from the distributed computing. And the cloud computing provides acquired computing resources in a pay-as-you-go manner. For example, Amazon EC2 offers the Infrastructure-as-a-Service (IaaS) instances in three different ways with different price, reliability, and various performances of instances. Our study is based on the environment using spot instances. Spot instances can significantly decrease costs compared to reserved and on-demand instances. However, spot instances give a more unreliable environment than other instances. In this paper, we propose the workflow scheduling scheme that reduces the out-of-bid situation. Consequently, the total task completion time is decreased. The simulation results reveal that, compared to various instance types, our scheme achieves performance improvements in terms of an average combined metric of 12.76% over workflow scheme without considering the processing rate. However, the cost in our scheme is higher than an instance with low performance and is lower than an instance with high performance.

Plans and Strategies for UBcN Networks and Services

The broadcasting & telecommunication services in the future will be converged and be serviced on mobile devices. However, the current ICT infrastructure does not fully meet the future demand for those converged, realistic, intelligent, and personalized services. The Korean government is going to establish a high speed next generation network called UBcN (Ultra-Broadband Convergence Network) by 2013. The Korean government has announced a multi-year plan to establish an UBcN network and to discover and stimulate new converged services for an UBcN in January, 2009. The author of this paper has taken part in formulating development plans since the early stages of planning. In this paper, Korea`s development plans for the next generation network and their development strategies are analyzed and discussed based on the author`s experience. The paper also discusses the expected impacts of the plan for the future ICT industry, and the implications of government-driven development plans.

An Estimation-Based Task Load Balancing Scheduling in Spot Clouds

Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. Cloud computing based on the spot market helps a user to obtain resources at a lower cost. However, these resources may be unreliable. In this paper, we propose an estimation-based distributed task workflow scheduling scheme that reduces the estimated generation compared to Genetic Algorithm (GA). Moreover, our scheme executes a user’s job within selected instances and stretches the user’s cost. The simulation results, based on a before-and-after estimation comparison, reveal that the task size is determined based on the performance of each instance and the task is distributed among the different instances. Therefore, our proposed estimation-based task load balancing scheduling technique achieves the task load balancing according to the performance of instances.

Entropy-Based Grouping Techniques for Resource Management in Mobile Cloud Computing

Recently, research on utilizing mobile devices as resources in mobile cloud environments has been gaining attention because of the enhanced computing power of mobile devices, with the advent of quad-core chips. Such research is also motivated by the advance of communication networks as well as the growing population of users of smart phones, tablet PCs, and other mobile devices. This trend has led researchers to investigate the utilization of mobile devices in cloud computing. However, mobile devices have several problems such as characteristics of the mobility, low memory, low battery, and low communication bandwidth. Especially, the mobility of mobile device causes system faults more frequently, and system faults prevent application using mobile devices from being processed reliably. Therefore, groups are classified according to the availability and mobility to manage reliable resource. In this paper, we make groups of mobile devices by measuring the behavior of mobile devices and calculating the entropy.

Strategies for IT Convergence Services in Rural Areas

The digital divide refers to the gap between people with effective access to digital and information technology and those with very limited or no access at all. The inequality of accessing information could cause the inequality of opportunity between different social groups. This inevitably results in social problems. The Korea government has been conducting a project resolving the digital divide between urban areas and rural areas since 2010. In this paper, we introduce the rural BcN project of the Korea government with the motivation and development plans. After that, we propose the strategies for boosting broadcast-communication services and the strategies for enhancing user experience.