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ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 144-148 Policy-based Autonomic Mobile Network Resource Management Architecture Fang Mei 1 , Yanheng Liu 1 , Hui Kang 1* , and Shuangshuang Zhang 1 1. College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China Email: {meifang, yhliu, kanghui}@jlu.edu.cn, zss198906@163.com Abstract—In this paper, we establish a policy-based autonomic mobile network resource management architecture, then put forward the policies definition and hierarchies of the autonomic management system. We also present the various specific policy forms and the working mechanism of autonomic mobile resource distribution processes. Through the simulation experiments, we prove that the autonomic mobile resource management architecture has a lot of distinct advantages and flexibility contrast to the methods of fixed resource reservation and utility maximization resource allocation algorithm. II. POLICY-BASED AUTONOMIC MOBILE NETWORK RESOURCE MANAGEMENT The article will take IBM autonomic management system [5,6] and universal policy management architecture [7] of IETF as prototypes to present the policy-based autonomic mobile network resource management architecture, shows in Fig1, aiming at the characteristic and requirement of the wireless resource management in mobile environment. Index Tems—autonomic communication, mobile network resource allocation, policy, utility function I. INTRODUCTION The rapid development of the multimedia application and the complexity of the network infrastructure management have made it a new research hot spot to seek for a network and communication architecture which have the capacity of self-management, self-configuration, selfoptimization. The key problem of providing end-to-end QoS guarantee is how to manage the network resource effectively in large-scale network environment. Therefore, it is urgently needed to provide an autonomic method which can meet the multidimensional requirement (such as, Service flow types, User requirements, Network status, etc) to manage the network resource dynamically. At present, it has gone deep into trending to adopt the dynamic and self-adapting method to do the resource allocation research which concerning the fixed network and wireless networks. However, the current outcomes are mainly limited in dynamic reservation [1,2] and adjustment algorithm [3,4] , and to realize the dynamism and selfadaptability of the resource allocation through monitoring the QoS of each service in the domain periodically and execute relevant algorithms. In mobile environment, this kind of method that fixed the allocation and adjustment logic inside the network resource management module can not afford enough flexibility and intelligence due to the variability of the network and the mobility of the users. The goal of this article is to establish an autonomic management architecture of the mobile network resource, to realize self-management, self-configuration, selfoptimization by combining policies and utility computing, meanwhile, to improve the utilization rate of the limited resource and guarantee all kinds of QoS of real-time and non real-time services. Figure 1. Policy-based Mobile Resource Autonomic Management Architecture In this architecture, “network resource monitoring module” constitutes the monitor part of the autonomic management system, “policy decision point” constitutes the “analysis” part, all the policies in the “policy repository” made by the administrator constitute the “plan” part, “policy execution point” constitutes the “execution” part, all the experts knowledge in the knowledge base can support the function of these several parts above. These parts together form the “MAPE-K” control ring defined in the IBM Autonomic Computing concept; therefore, this architecture can meet the requirement of an autonomic management system. © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 144
III. AUTONOMIC RESOURCE ALLOCATION PROCESSES A. Determine the SuperiorLevel Management Requirement The service’s QoS should be ensured according to the SLA signed by the user when wireless cell is providing services to mobile users. In SLA, QoE (Quality of Experience) describes the subjective feelings to the service properties of the mobile end users, it can be denoted by a kind of quantization way to indicate the end users’ experiences and feelings about the service and network, and reflect the gaps between the current network and service’s QoS and the expectation of the users [8] . ( ) QE o = f KPI , K, KPI i= 1, K , K (1) i i1 K represents the number of the QoE parameters used to measure the satisfaction degree of the users about the mobile services; n represents the number of the KPI parameters used to evaluate the ith QoE network performance parameter . Wireless access network should also ensure to make the best use of the network resources to maximize the benefits of the wireless service providers at the same time when the quality of the users’ service is ensured, and the benefits can be denoted by the Network Utility Function, NUF. Therefore, the superior level management requirement of the PBMRAM framework can be represented as: in Max ( QoE) & Max ( NUF ) (2) B. Goal-policy Description Further, to transform the superior level management requirement into the policies of each level to serve the guidance effects. Goal policy describes the administrator’s expectations and constrains in the performance and priority of the system. It can be classified to the following categories according to its effect in the system. 1) System Performance Goal Policy. System performance goal policy describes the range of network performance parameters that each type of mobile service should reach after accessing the wireless cells according to the SLA users signed .Specific example is shown in Figure 2. ::= ::= IF < PolicyCondition1 > THEN ::= ::= Figure 2.Example of System Performance Goal Policy 2) System Performance Optimization Policy. System performance optimization policy describes that it should whether or not to degrade the service level and compress the allocated network resource of the users according to the service category and the signed SLA when the resource of wireless access network is not enough; and whether or not to restore the once degraded service and increase the allocated resource when the network load come down and resource is recovered to be enough. Specific example is shown in Figure 3. Figure 3. Example of System Performance Optimization Policy 3) Service Handle Priority Policy. Service handle priority policy describes the priority of all the service types and users when more than one mobile users are applying to access to the wireless cell. Specific example is shown in Figure4. ::= | ::= IF THEN ::= ::= ::= IF THEN ::= Figure 4.Example of Service Handle Priority Policy C. System Utility Analysis Then we will generate the utility policy which describes the system performance optimization assessment model, by combining the state determination knowledge and the network quality parameters performance model instituted by the experts in the knowledge base. The utility functions in the utility policy are the quantization assessments of the user’s QoE and the system’s NUF. 1) Service Categories Utility Functions. The users in mobile environment can be classified into four types: conversational class, stream class, interactive class and background class, each class is related to several rock-bottom parameters of network performance. To simplify the system modeling, here the utility functions of all the classes are hypothetic to be only related to the allocated bandwidth resource. Now, conforming to document[9],we present the utility functions of each service classes used in utility policies in PBMRAM, and the maximum utility value of each class is 1 hypothetically. a) Conversational Class Service. It is a real-time service, and its characteristics are like this. Its demand of data transmitting rate is not high, but it requires the relative small delay and jitter, and low bit error rate. Therefore, it is sensitive to the change of bandwidth, the minimum bandwidth is hypothetically B minl, when the broadband is b 1 , the utility function U 1 (b 1 ) can be represented as: 145
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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symmetric with respect to the origi
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ontology, and the domain dictionary
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Where: G i is the selection field o
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Theorem 2.4([10]). Let L 1 and L 2
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Figure 2. Example of single-step de
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evocation and the fourth group stor
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focused mainly on rule-based forms
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Ⅵ. CONCLUSION Figure 6. The Flask
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EDCF in comparison with DCF, has so
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B. Simulation results and analysis
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others through the selective emotio
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such as weather information, techno
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the opening window, a related page
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1) Process context storage areas. I
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V. CONCLUSION In this thesis, sCPU-
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main types of horizontal search env
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Enterprise Portal security provides
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output, so BP network has been wide
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input to the artificial neural netw
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The reduction process consists of t
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all eight normal vectors are identi
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is B object , and the number of emp
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Architecture (AMBA) a new bus archi
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In order to ensure the smooth proce
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In this paper, we adopt two Sobel o
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four layers.The far right of the gr
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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