Sin-seok Seo

Usage pattern analysis of smartphones

Recently, mobile traffic has increased tremendously due to the deployment of smart devices such as smartphones and smart tablets. These devices use various types of access networks such as 3G, WiFi, and mobile WiMAX. Network service providers also provide these access networks with various types of plans. There is a growing need to manage these smart devices and mobile networks. However, research on mobile network management has focused on the performance of the network itself. Few research has focused on applying the usage patterns of smartphone users to mobile network management. In this paper, we present an analysis of smartphone usage patterns. We define the five possible states of a smartphone based on such a phones basic operations. We collected real usage log data from real smartphone users over a two month period. We show that all users have their own usage pattern. We present a case study in order to show how to apply usage pattern information to power management of smartphones. We also discuss how to apply such information to mobile device management and network management.

Personalized Battery Lifetime Prediction for Mobile Devices based on Usage Patterns

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device’s available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world. Category: Embedded computing

Autonomic personalized handover decisions for mobile services in heterogeneous wireless networks

In this paper, we present an autonomic management method to provide personalized handover decisions for customized mobility management in heterogeneous wireless networks. A handover decision is a significant problem, especially in a heterogeneous network environment. This is exacerbated when the goal is to provide personalized services for mobile users. Personalized handover decisions should not only consider received signal strength, which is a traditional handover decision factor, but also context information, user preferences, user profiles, and other non-functional requirements. We present two metrics for evaluating access points: access point acceptance value and access point satisfaction value. Our algorithm uses a combination of functional and non-functional metrics to select the access point that has the maximum satisfaction value. In our simulation study, we show that our decision algorithm is better than other decision algorithms in terms of end user satisfaction.

User-Centric Prediction for Battery Lifetime of Mobile Devices

Today, mobile devices are being used for various applications such as making voice/video calls, browsing Internet and so on. The operating time and battery consumption spent in those activities affect the battery life of mobile devices. In this paper, we propose a method for predicting the battery lifetime of mobile devices based on usage patterns. We define the possible states of mobile devices based on their operating functions and develop a method of predicting battery lifetime based on average battery consumption and duration of each state.

FAST: A fuzzy-based adaptive scheduling technique for IEEE 802.16 networks

Since the IEEE 802.16 first standard was proposed in 2004 to provide broadband wireless service, the standard has not only been widely studied, but also broadly commercialized. The current IEEE 802.16-2009 standard document specifies five Quality of Service classes. As is typical with most standards, IEEE 802.16 does not require the use of a specific scheduler. In this paper, we first evaluate the performance of four popular schedulers. By analyzing the results, we highlight that no single scheduler type performs the best in all traffic situations; however, we shown that there exist the most favorable scheduler type in each situation. Based on this rationale, our idea is to propose an adaptive scheduling schema where the scheduler is dynamically chosen based on the current traffic context, such as the number of flows of each Quality of Service class. We investigate this approach and evaluate its performance against existing static schemas. The results show that our approach presents some interesting performances in terms of throughput, delay, and packet loss ratio regarding state of art approaches.

Flow-level traffic matrix generation for various data center networks

The number of data centers deployed by governments, enterprises, and universities has been increased affected by the development of cloud computing technologies to reduce CAPAX and OPEX. Many architectures or topologies for data center networks have been proposed to address the diverse purposes and requirements. However, the construction of data centers incurs significant costs. Moreover, there are many technologies that can affect the structure of the data center. Before building a data center, it must be confirmed that it possesses the characteristics necessary to satisfy requirements. Efficient ways to find and confirm network characteristics include simulation and tests using a traffic generation method. Our proposed method is designed to generate network traffic that address many characteristics of data center networks explored by several studies. The proposed method generates network traffic utilizing flow-level traffic matrix, not directly generates packets. We used Python programming language to create traffic matrix and iPerf to generate network packets. To evaluate it, we compared the generation results to real network traffic collected from a data center network. The result shows that the generated traffic is similar with the real network traffic.

A cognitive model‐based approach for autonomic fault management in OpenFlow networks

SUMMARY Autonomic network management is an approach to the management of complex networks and services that incorporates the detection, diagnosis and reconfiguration, as well as optimization, of their performance. A control loop is fundamental as it facilitates the capture of the current state of the networks and the reconfiguration of network elements without human intervention. For new networking architectures such as software-defined networking and OpenFlow networks, in which the control plane is moved onto a centralized controller, an efficient control loop and decision making are more crucial. In this paper, we propose a cognitive control loop based on a cognitive model for efficient problem resolving and accurate decision making. In contrast to existing control loops, the proposed control loop provides reactive, deliberative and reflective loops for managing systems based on analysis of current status. In order to validate the proposed control loop, we applied it to fault management in OpenFlow networks and found that the protection mechanism provides fast recovery from single failures in OpenFlow networks, but it cannot cover multiple-failure cases. We therefore also propose a fast flow setup (FFS) algorithm for our control loop to manage multiple-failure scenarios. The proposed control loop adaptively uses protection and FFS based on analysis of failure situations. We evaluate the proposed control loop and the FFS algorithm by conducting failure recovery experiments and comparing its recovery time to those of existing methods. Copyright

The design of a quality of experience model for providing high quality multimedia services

In the last decade, networks have evolved from simple data packet forwarding to platforms that support complex multimedia services, such as network-based personal video recording and broadcast TV. Each of these services has significant quality demands: they are very sensitive to packet loss and jitter, and require a substantial amount of bandwidth. As the quality perceived by the end user gives the most accurate view on the streamed service quality, operators are increasing their focus on this type of metric, commonly described as Quality of Experience. This paper presents the design of a Quality of Experience information model that defines important metrics for measuring service quality. Based on these metrics, we define a novel control loop that represents the relationships among Quality of Experience, the Customer, and network services.

Software defined networking-based traffic engineering for data center networks

Todays Data Center Networks (DCNs) contain tens of thousands of hosts with significant bandwidth requirements as the needs for cloud computing, multimedia contents, and big data analysis are increasing. However, the existing DCN technologies accompany the following two problems. First, power consumptions of a DCN is constant regardless of the utilization of network resources. Second, due to a static routing scheme, a few links in DCNs are experiencing congestions while other majority links are being underutilized. To overcome these limitations of the current DCNs, we propose a Software Defined Networking (SDN)-based Traffic Engineering (TE), which consists of optimal topology composition and traffic load balancing. We can reduce the power consumptions of the DCN by turning off links and switches that are not included in the optimal subset topology. To diminish network congestions, the traffic load balancing distributes ever-changing traffic demands over the found optimal subset topology. Simulation results revealed that the proposed SDN-based TE approach can reduce power consumptions of a DCN about 41% and Maximum Link Utilization (MLU) about 60% on average in comparison with a static routing scheme.

Autonomic fault management based on cognitive control loops

This paper presents an efficient fault management approach based on cognitive control loops in order to support autonomic network management for the Future Internet. The cognitive control loops determines urgency of network alarms, processes urgent alarms more quickly, and then infers root causes of the problems based on learning and reasoning. We show that we reduce a number of alarms by correlation and detect alarm priorities using an ontology model based on the policy.