Design of Self-Adaptive System Observation over Internet of Things

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1 , pp Design of Self-Adaptive System Observation over Internet of Things Young-Joo Kim 1, Jong-Soo Seok 1, Moon Soo Lee 1, Jeong-Si Kim 1, and YungJoon Jung 1 1 Electronics and Telecommunications Research Institute, Embedded SW Platform Research Section 218, Deajeon, Korea {kr.yjkim, jsseok, msless, sikim00, jjing}@etri.re.kr Abstract. This paper proposes a design of a self-adaptive system observation. The observation considers an optimal environment for guaranteeing low-power and high-performance regarding systems and applications. The component of the optimal environment consists of two parts: a monitoring part and a controlling part. The monitoring part provides static and dynamic information regarding systems and applications in real-time. The controlling part supports to control system resources in real-time. We shows indisputable potential of the self-adaptive system observation through a simple video transport system. The system always keeps user-defined QoS because of assistance of the system observation even if there are a lot of overheads in a system due to some causes. Keywords: IoT, Self-adaptive, Low-power, High-performance, monitoring, controlling 1 Introduction Most applications are required to customize whenever embedded devices such as IoT [1,2,3,4] devices are changed or new applications are served on them. These applications, however, are not optimized in the aspect of performance, power, and so on since IoT device systems do not effectively provide static/dynamic information regarding systems and applications for properly changing system resources and applications execution flow. Therefore, in this paper we proposes a design of a self-adaptive [5,6] system observation. The observation considers provides an optimal environment for guaranteeing low-power and high-performance regarding systems and applications. The component of the optimal environment consists of two parts: a monitoring part and a controlling part. The monitoring part provides static and dynamic information regarding systems and applications in real-time. Static information means values fixed in systems and applications, and dynamic information means values changed during an execution of systems and applications. The controlling part supports to control system resources (e.g. CPU, Memory, Network, etc) in real-time. Several control functions are defined: CPU on/off, CPU frequency control, cache memory control, and network bandwidth control. We shows indisputable potential of the self-adaptive system observation through a simple video transport system. The system always ISSN: ASTL Copyright 2015 SERSC

2 keeps user-defined QoS because of assistance of the system observation even if there are a lot of overheads in a system due to some causes. This remainder of this paper is structured as follows. In section 2, we describe the self-adaptive system observation. In section 3, we show the observation s potential through developed video transport system. Finally, we offer concluding remarks, and outline our directions for future research. 2 Self-Adaptive System Observation In this paper, we present the self-adaptive system observation which consists of the monitoring part and the controlling part. At first, the system observation performs gathering static/dynamic information regarding applications and systems necessary for self-adaptive decision, and then these information is provided to external modules and external devices. The self-adaptive system observation also has the light-weight characteristic in the aspect of CPU utilization so that it can be ported to diverse IoT devices such as embedded systems. Fig. 1 shows the block diagram of the self-adaptive system observation. In this figure, whenever applications are executed on a system, the system observation monitors status of these applications and the system in real-time, and traces these information into application information DB and system information DB respectively. The traced information will be utilized to decide system s optimization by considering high-performance, low-power, and so on. Fig. 1. Block Diagram of Self-adaptive System Observation consisted of Monitoring Part and Controlling Part 166 Copyright 2015 SERSC

3 2.1 Monitoring Part It consists of four modules: program register, program information receiver, system information gatherer, and data coordinator. Program register and program information receiver are related to collection of program information. When a program executes on a system, the program is registered into the self-adaptive system observation by program register, and then program information receiver gathers static/dynamic information for the registered program. We define it as application information. Table 1 is to summarize application information. Table 1. Applications Information Static Information Program ID Program Name Program Path Max QoS Min QoS Target QoS Application Sampling Time Log File Dynamic Information Allocated Core The number of Thread Thread List Program Status Program Time Program/memory Utilization Network Information Heartbeat Rate System information gatherer collects static/dynamic information in real-time for system resources. We define it as system information. System static information means fixed values regarding HW resources (e.g. CPU, Memory, Network) and it is set just once when a system works. Generally, system static information has a unique value in the system as long as HW specific isn t changed. The value is managed through once collection. Whereas, system dynamic information means changeable values according to system s status. Most these values can be different according to system condition, and they are updated periodically every the setup time for the information collection. The system static information shows in Table 2 regarding CPU Core, CPU frequency, memory, and network. The system static information is fixed information of HW. If the HW capability is changed, system static information is also updated. And these information maintains data structure corresponding to each HW system respectively. The system static information is updated just once whenever the self-adaptive system observation executes. The system dynamic information shows in Table 2 regarding CPU core s current state, CPU Utilization, memory, and network. The dynamic information can be changed according to system s current state or users change system s resource values so that dynamic information is changed in real-time. Data coordinator reorganizes information which is collected from program information receiver and system information gatherer, and then these information is saved in application information DB and system information DB respectively. Dynamic information for applications and systems is divided into two data structures: instant data and calculation data. The instant data (e.g. CPU activity) can be used in other modules immediately, while the calculation data (e.g. utilization) can t be used Copyright 2015 SERSC 167

4 immediately because of demanding additional operations. Static information regarding applications and systems has one data structure corresponding to instant data. These data are classified with application and system in real-time. Table 2. System Information Static Information The number of Core Max freq. of Core Min freq. of Core Core MIPS Available frequency System Memory Network Interface Dynamic Information Core activity status Core current freq. The number of thread The number of process Core utilization System memory Network Packet 2.2 Controlling Part It consists of four modules: cpu control, memory control, network control, and control execution. This part provides a handling environment to control system resources such as CPU, memory, and network. - CPU: Core on/off, Core frequency, DVFS - Memory: Cache drop, Minimum memory set - Network: Bandwidth control, Packet drop In order to control CPU and memory, control execution handles proc file system. In order to control network, control execution uses network control commands (e.g tc command) or kernel model programs (e.g. network stack). Also the controlling part provides monitoring interfaces and controlling interfaces such as libraries. Table 3 is to show monitoring interfaces and controlling interfaces. Therefore, the self-adaptive system observation helps applications to execute optimally by using static/dynamic information regarding applications and systems. Table 3. Monitoring Interfaces and Controlling Interfaces Monitoring Interface CPU Utilization (System) Memory Utilization (System) Network Tx/Rx (System) Core Utilization (PID) Memory Utilization (PID) Network Tx/Rx (PID) Controlling Interface CPU On/Off Core Frequency DVFS Swap Memory Cache Drop Packet Drop 168 Copyright 2015 SERSC

5 3 Implementation In this chapter, we introduce a simple video transport system for potential test. At first, we describe the overall structure of the system, and then we shows an implemented system. The simple video transport system is composed of the structure of Fig. 2. Fig. 2. Simple Video Transport System for Potential Test of Self-adaptive System Observation with Simple QoS Manager Video sources are generated from an Edison board [7, 8, 9, 10] with cam in real-time. The generated sources are transferred into STB (Set-Top Box) through Wi-Fi. A video streaming server of STB receives these video sources and decodes them in realtime. Finally, the decoded sources are transferred into a mobile device through Wi-Fi. In this structure, if the number of video source increases steadily, it is not easy for a typical video transport system to provide high bitrate videos generated from the Edison board to the mobile device. The reason is that system resources (e.g. the number of core, memory, network bandwidth) allocated in the streaming server are insufficient. Whereas, the suggested video transport system can provide high bitrate videos to the mobile device because our system has a middleware such as the selfadaptive system observation. The middleware can handle system resources by using application and system information collected by the self-adaptive system observation. Therefore, the suggested system always keeps QoS defined by user even if there are a lot of overheads in a system due to some causes. Copyright 2015 SERSC 169

6 Fig. 3. Simple Video Transport System which implements the system of Fig. Fig. 3 is to shows an implemented system of Fig. 2. In this figure, two Edison boards are capturing video images and are encoding them. Embedded board receives these images and processes them. In the board, the self-adaptive system observation is ported. The tool which works in the notebook shows information collected by the system observation in real-time, where information means static/dynamic information regarding the video streaming server and embedded board. The mobile device is offered original images of target systems without loss of images. The tool and UI show a regular QoS in graph with red color. 4 Conclusion In this paper, we propose a design of the self-adaptive system observation which is able to use on IoT devices. We describe the inner structure of the system observation in detail. Through implemented simple video transport system, we show potential of this design. The self-adaptive system observation can be used in small-scale devices (e.g. Edison board and IoT devices) and large-scale devices (e.g. Home gateway and STB). In the future, we will conduct various experiments with the self-adaptive system observation. Through this experiment, we will complement the self-adaptive system observation and prove its efficiency. Acknowledgments. This work was supported by ICT R&D program of MSIP/IITP. [B , The research and development of the self-adaptive software framework for various IoT devices] 170 Copyright 2015 SERSC

7 References 1. Höller, J., Tsiatsis, V., Mulligan, C., Karnouskos, S., Avesand, Boyle, S. D.: From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence. Elsevier (2014) 2. "Internet of Things Global Standards Initiative". ITU. Retrieved26 June (2015) 3. Weiser, M.: "The Computer for the 21st Century". Scientific American 265 (3): Zanella, A., Bui, N., Castellani, A., Vangelista, L., and Zorzi, M.: "Internet of Things for Smart Cities". IEEE Internet of Things Journal, vol 1(1) (2014) 5. Macias-Escriva, F. D., Haber, R., Toro, R., and Hernandez, V: Self-adaptive systems: A survey of current approaches, research challenges and applications, Expert Systems with Applications, vol. 40(18), pp , Elsevier (2013) 6. Cheng, B. H., Lemos, R., Giese, H., Inverardi, P., and Magee, J.: Software Engineering for Self-Adaptive Systems: A Research Roadmap, LNCS 5525, pp. 1-26, Spirnger (2009) Intel Edison Boards, Intel Edison Breakout Board Hardware Guide, Jan. (2015) 10. Intel Edison Boards, Intel Edison Board Support Package User Guide, Revision 001, Sep. (2014) Copyright 2015 SERSC 171