RFID 미들웨어 표준 아키텍처에 기반한 적응적 부하 분산 방법
An adaptive load balancing method for RFID middlewares based on the Standard Architecture
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초록
최근 RFID(Radio Frequency Identification) 기술은 사물에 대한 자동적인 인식을 가능케 함으로써 물류, 의료, 식품관리 등과 같은 분야에 적용되고 있다. 부하 분산은 과부하 상태인 노드로부터 부하가 적은 노드로 작업 부하를 이동시켜 시스템의 확장성을 향상시키는 기본 기술이다. 시스템의 부하를 예측하기 어렵고 부하량의 편차가 큰 경우에는 적응적 부하 분산이 효과적인 것으로 알려져 있다.RFID 미들웨어는 많은 수의 리더로부터 수신된 태그 정보를 효율적으로 처리하기 위하여 기존의 부하 분산기술이 도입될 필요가 있다. RFID 시스템이 부하량을 예측하기 힘들고 편차가 큰 환경에 적용될 경우 실행시간에 시스템의 전체 부하량에 따라 적합한 정책으로 변경할 수 있는 적응적 부하 분산 기법을 사용하는 것이 바람직하다. 본 논문에서는 RFID 미들웨어에 적응적 부하 분산 기법을 도입하기 위한 접근 방법과 결과를 제시한다. 먼저 RFID 미들웨어의 작업 부하 모델을 결정한다. 그리고 부하 모델을 바탕으로 다양한 부하 분산 정책을 시스템의 부하 상태 별로 적용하여 시스템의 부하 상태에 적합한 부하 분산 정책을 선택한다.
Abstract
Because of its capability of automatic identification of objects, RFID(Radio Frequency Identification) technologies have extended their application areas to logistics, healthcare, and food management system. Load balancing is a basic technique for improving scalability of systems by moving loads of overloaded middlewares to underloaded ones. Adaptive load balancing has been known to be effective for distributed systems of a large load variance under unpredictable situations. There are needs for applying load balancing to RFID middlewares because they must efficiently treat vast numbers of RFID tags which are collected from multiple RFID readers. Because there can be a large amount of variance in loads of RFID middlewares which are difficult to predict, it is desirable to consider adaptive load balancing approach for RFID middlewares, which can dynamically choose a proper load balancing strategy depending on the current load. This paper proposes an adaptive load balancing approach for RFID middlewares and presents its design and implementation. First we decide a performance model by a experiment with a real RFID middleware. Then, a set of proper load balancing strategies for high/medium/low system loads is determined from a simulation of various load balancing strategies based on the performance model.
참고문헌 (28)
- Ron Weinstein, "RFID: A Technical Overview and Its Application to the Enterprise", IT Professional, 7(3), pp.27~33 (2005)
- 김현, ""컨테이너터미널의 RFID 효과 분석", 박사, pp.2007
- "EPCglobal Inc"[URL]
- Ken Traub, et al., "The EPCglobal Architecture Framework", EPCglobal (2005)
- T.L. Casavant, "A Taxonomy of Scheduling in General-Purpose Distributed Computing Systems", IEEE Transactions on Software Engineering, 14(2), pp.141~154 (1988)
- N.G. Shivaratri, "Load Distributing for Locally Distributed Systems", Computer, 25(2), pp.33~44 (1992)
- 0. Kremien, "Methodical Analysis of Adaptive Load Sharing Algorithms", IEEE Trans. Parallel and Distributed Systems, 3(11), pp.747~760 (1992)
- Chin Lu, "An adaptive load balancing algorithm for heterogeneous distributed systems with multiple task classes", pp.629~636 (1996), Distributed Computing Systems
- Orly Kremien, "Scalable, adaptive load sharing for distributed systems", IEEE Parallel and Distributed Technology, 1(3), pp.62~70 (1993)
- Chang-Jia Wang, "Intelligent job selection for distributed scheduling", Proceedings the 13th International Conference on Distributed Computing Systems, pp.517~524 (1993)
- M. S. Al-Amri, "New job selection and location policies for load-distributing algorithms", International Journal of Network Management, 12(3), pp.165~178 (2002)
- Krueger, P, "Adaptive location policies for global scheduling", IEEE Transactions on Software Engineering, 20(6), pp.432~444 (1994)
- J. Cao, "Agent-based Grid Load Balancing using Performance‐driven Task Scheduling", pp.49~ (2003)
- Corradi, A., "Diffusive load-balancing policies for dynamic applications", IEEE Parallel and Distributed Technology, 7(1), pp.22~31 (1999)
- M. Calzarossa, "Workload Characterization: A Survey", Proc. IEEE, 통권 : 81, pp.1136~1150 (1993)
- S.P. Dandamudi, "Sensitivity evaluation of dynamic load sharing in distributed systems", IEEE Parallel and Distributed Technology, 6(3), pp.62~72 (1998)
- "EPCglobal Inc. The application level events(ALE) specification, version 1.0", ~
- Neil Garde, et al., "Application Level Event(ALE) 1.02 Conformance Requirements Document" (2005)
- Jaiganesh Balasubramanian, "Evaluating the performance of middleware load balancing strategies", Enterprise Distributed Object Computing Conference, pp.135~146 (2004)
- C. C. Myint, "A framework of using mobile agent to achieve efficient load balancing in cluster", In: Proc. of APSITT, pp.66~70 (2005)
- Marvin M. Theimer, "Finding idle machines in a workstation‐based distributed system", IEEE Transactions on Software Engineering, 15(11), pp.1444~1458 (1989)
- W. Zhu, "An experimental study of load balancing on Amoeba", In First Aizu International Symposium on Parallel Algorithms, 1995, 220~226
- D. L. Eager, "Adaptive Load Sharing in Homogeneous Distributed Systems", IEEE Transactions on Software Engineering, 12(5), pp.662~675 (1986)
- P. Krueger, "A Comparison of Preemptive and Non-Preemptive Load Distributing", Proc. IEEE Int’l Conf. Distributed Computing Systems, pp.123~130 (1988)
- M. Kafil, "Optimal Task Assignment in Heterogeneous Distributed Computing Systems", IEEE Concurrency, 6(3), pp.42~51 (1998)
- C. J. Hou, "Load Sharing with Consideration of Future Task Arrivals in Heterogeneous Distributed Real-Time Systems", IEEE Trans. Computers, 44(9), pp.1076~1090 (1994)
- Dejan S. Milojičić, "Process migration", ACM Computing Surveys, 32(3), pp.241~299 (2000)
- R. Payli, "A dynamic load balancing tool for grid computing", In: Proc. of Parallel CFD (2004)