Chan-Hyun Youn

Wearable 2.0: Enabling Human-Cloud Integration in Next Generation Healthcare Systems

With the rapid development of the Internet of Things, cloud computing, and big data, more comprehensive and powerful applications become available. Meanwhile, people pay more attention to higher QoE and QoS in a “terminal- cloud” integrated system. Specifically, both advanced terminal technologies (e.g., smart clothing) and advanced cloud technologies (e.g., big data analytics and cognitive computing in clouds) are expected to provide people with more reliable and intelligent services. Therefore, in this article we propose a Wearable 2.0 healthcare system to improve QoE and QoS of the next generation healthcare system. In the proposed system, washable smart clothing, which consists of sensors, electrodes, and wires, is the critical component to collect users’ physiological data and receive the analysis results of users’ health and emotional status provided by cloud-based machine intelligence.

A Novel Approach to Optical Switching for Intradatacenter Networking

In this paper, we propose to apply a novel paradigm called labeled optical burst switching with home circuit (LOBS-HC) for intradatacenter networking to provide a high bisection bandwidth and significantly reduce the cost and energy consumption associated with electronic packet switching. The unique features of LOBS-HC that make it more suitable than either optical circuit switching (OCS) or optical packet/burst switching are exploited to enable all-to-all communications with a guaranteed lossless transmission bandwidth between any given pair of pods, while also supporting bursty transmissions through wavelength-sharing among home circuits (HCs) and statistical multiplexing. As a case study, hypercube-like topologies are considered for the interconnection among the pods within a datacenter. In particular, we first propose a simple but efficient HC assignment scheme called complementary HC for 2-D cube or ring, and then extend our works to n-cube and generalized hypercube by applying the concept of spanning balanced tree (SBT) for their HC assignment. Our analysis results show that with such datacenters, the minimum number of wavelengths needed in each case is significantly reduced from that needed with OCS and also, the network cost in terms of wires and transceivers needed is considerably reduced from that incurs with datacenters using electronic packet switching. We then evaluate the traffic performance of such hypercube-based datacenters using LOBS-HC through simulation experiments via the OPNET simulator. The performance results obtained for a variety of communication patterns and traffic models within a datacenter demonstrate the feasibility of the proposed approach.

QoS-constrained resource allocation for a Grid-based multiple source electrocardiogram application

QoS-constrained policy has an advantage to guarantee QoS requirements requested by users. Quorum systems can ensure the consistency and availability of replicated data despite the benign failure of data repositories. We propose a Quorum based resource management scheme, which resource Quorum includes middleware entity and network entity, both can satisfy requirements of application QoS. We also suggest a heuristic configuration algorithm in order to optimize performance and usage cost of Resource Quorum. We evaluate both simulations and experiments based on the electrocardiogram (ECG) application for health care, because this application requires transferring giga-bytes of data and analyzing complicated signal of ECG. Simulation results show that network capabilities are more important than computing capabilities, as both sizes of transferred data and computation task increases. Experimental results show that our scheme can reduce the total execution time of ECG application by using proposed heuristic algorithm compared to policy based management scheme.

Energy Efficiency Analysis of MISO-OFDM Communication Systems Considering Power and Capacity Constraints

In this paper, the energy efficiency of multi-input single-output and orthogonal frequency division multiplexing (MISO-OFDM) communication systems with power and capacity constraints is investigated. By formulating the power allocation problem of MISO-OFDM communication systems, the minimum subchannel transmission power is analyzed with power and capacity constraints. Simulation results indicate that there exists a specific minimum subchannel capacity threshold. Moreover, the energy efficiency of MISO-OFDM communication systems starts to increase only when the minimum subchannel capacity exceeds the specific threshold.

Scalable multicast routing algorithm for delay-variation constrained minimum-cost tree

The delay-constrained heuristics for multicast routing algorithms capable of satisfying the quality of services requirements of real time applications are essential under distributed network environments. However, some of these heuristics may fail to provide a low cost tree as they assume that network links are symmetric. Furthermore, the time required constructing such a tree might be prohibitive, especially for large networks, as they employ a brute-force approach to search for low-cost delay-bounded paths among the route candidates. In this paper, we propose a new efficient algorithm considering delay variation constraints as well as with the total cost minimization scheme of the multicast trees. The extensive simulations show that the proposed algorithm satisfies the QoS requirement for real-time traffic, very short execution time and good scalability applicable to multicast groups in large networks.

A new grid resource management mechanism with resource-aware policy administrator for SLA-constrained applications

Due to the dynamic nature and an uncertainty of Grid computing, system behavior in terms of Quality of Service (QoS) depends crucially on the selection of appropriate subset of the available resources. Thus, a well-organized system architecture that provides high system availability using a scheduling mechanism for Grid applications is required. In this paper, we design an efficient architecture referred to as Resource-Aware Policy Administrator (RAPA) with Service Level Agreement (SLA) and real-time attributes in Grid resources and propose a new Grid resource management mechanism with an admission control scheme to make an optimal resource allocation policy for both satisfying QoS requirements in terms of deadline as well as profit and accepting more jobs. Also, we demonstrate detailed workings of the proposed schemes with the RAPA via a simulation study by using real-life parameters. The simulation results show that the proposed schemes have an effect on accepting more jobs with satisfying the deadline of jobs and increasing service profits and can guarantee high system availability in terms of system performance.

Detection of atrial fibrillation episodes using multiple heart rate variability features in different time periods

Circadian variations of cardiac diseases have been well known. For example, atrial fibrillation (AF) episodes show nocturnal predominance. In this study, we have developed multiple formulas that detect AF episodes in different times of the day. Heart rate variability features were calculated from randomly sampled three min ECG data. Logistic regression analyses were performed to generate three formulas for the entire day, daytime, and evening time. Compared to the first formula that disregarded the time of the day, the second formula for the daytime detection detected AF episodes more accurately (95.2% vs. 99.3%), whereas third formula for the evening time detection did less accurately (93.8%). These results suggest the detection of AF episodes might become more accurate by considering the time-dependent changes of HRV features. In addition, the detection method for the evening time requires further investigation.

An Adaptive Workflow Scheduling Scheme Based on an Estimated Data Processing Rate for Next Generation Sequencing in Cloud Computing

Abstract —The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

A Novel Model for Metabolic Syndrome Risk Quantification Based on Areal Similarity Degree

Metabolic syndrome (MS) refers to a clustering of specific cardiovascular disease (CVD) risk factors whose underlying pathology is thought to be related to insulin resistance. The risk factors include insulin resistance, obesity, dyslipidemia, and hypertension and it is known to increase the risk for CVD and type II diabetes. Since MS helps to identify individuals at high risk for both CVD and type II diabetes, it has become a major public healthcare issue in many countries. There has been much effort to establish diagnostic criteria for MS, but the current diagnostic criteria of MS have weaknesses, such as binary decision based on diagnostic criteria, equal weight among risk factors, and difficulty in estimating the temporal progress of the risk factors. To resolve these problems, this paper proposes a risk quantification model for MS, which is based on areal similarity degree analysis between weighted radar charts comprising MS diagnostic criteria and examination results of risk factors. The clinical effectiveness of the proposed model is extensively evaluated by using data of a large number of subjects obtained from the third Korea National Health and Nutrition Examination Survey. The evaluation results show that the proposed model can quantify the risk of MS and effectively identify a group of subjects who might be classified into a potential risk group for having MS in the future.

Cost adaptive workflow scheduling in cloud computing

In cloud computing, it remains a challenge to allocate virtualized resource with financial cost minimization and acceptable Quality of Service assurance. In general, the VM instance is allocated to cloud service users based on not actual job processing time but the fixed resource allocation time predetermined by cloud pricing policy in contrast to grid environment. In this case, the unnecessary cost dissipation is occurred by the wasted partial instance hours of allocated resource. To address this problem, we propose the heuristic based workflow scheduling scheme considering cloud-pricing model in this paper. Our scheme is composed of two phases: VM packing and MRSR (Multi Requests to Single Resource) phases. In VM-packing phase, preassigned multi tasks are aggregated into the common VM instance sequentially, and these tasks are merged in parallel by MRSR phase. By using our proposed schemes, we are able to reduce the number of required VM instances and achieve the significant cost saving while we guarantee the users SLA (Service Level Agreement) in terms of workflow deadline. Our proposed schemes cannot only reduce the cost by 30% compared to traditional workflow scheduling schemes but also assure users SLA.