A Dynamic Governance Framework for Efficient Orchestration of

A Dynamic Governance Framework for Efficient Orchestration of HetNet Empowerment Mechanisms Roi Arapoglou, Konstantinos Chatzikokolakis, George Katsikas, Nancy Alonistioti Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens, Greece {roiar, kchatzi, katsikas, nancy}@di.uoa.gr

Abstract—Now days autonomic network management systems are a significant part of Future Internet vision. These systems may be considered as Network Empowerment Mechanisms (NEMs) in an Heterogeneous, Multi-tier Network Environment. Their main purpose is to monitor network manageable entities, perform decision functions, and start possible healing actions. The NEMs deployment and interworking is proven to be a challenging task, as they increase network complexity in Future Internet paradigm. The decisions and the enforced actions of different NEMs may be conflicting for the underlying network environment thus increasing the complexity of the Network Management. The Unified Management Framework (UMF), solves the aforementioned problems by enabling the efficient orchestration of NEM entities. Following the UMF paradigm, in this work we provide the implementation of the Governance functionality based on an ontology implementation. Furthermore, building on the outcomes of the ontology, we implement the conflict detection and resolution functionality, aiming at handling conflicts in policies introduced by the network operator. The afore-described approach enables the dynamic introduction of newly deployed NEMs into the Governance lifecycle, through the use of OWL language, under specific time constraints. Keywords—heterogeneous governance; policies; OWL.

I.

networks;

autonomic

systems;

INTRODUCTION

Broadband connectivity constitutes a commodity, leading to "always connected" paradigm. Towards this direction, smart devices with advanced capabilities demand seamless network connectivity and extravagant data exchange. Connectivity of physical devices leads to the expansion of Internet to Future Internet [2]. Future Internet becomes a living entity with heterogeneous interconnected networks and varying user devices, facing an evolving set of requirements. Numerous challenges need to be handled, given that a unified and coherent solution does not exist. Among those challenges, identity management is of crucial importance. Autonomic Network Management (ANM) is a promising approach towards a complex redundant and cost effective control loop of network lifecycle. Specifically, Autonomic Network Elements, part of ANM system, are enriched with monitor, decision and execution capabilities, referred to as the MAPE-K model [3]. Their purpose is to present selfmanagement (self-configuring, self-optimizing, self-healing, self-protecting) behavior and thus reduce human intervention.

Typically, ANM elements role is not limited to the provision of always best connect services, but a wide range of healing actions may be implemented. Hence, ANM element's role may include traffic delay, load enhancements, reduction of energy consumptions, fault identification events, reconfiguration activities and deployment of control mechanisms. Obviously, ANM elements' tasks should not be considered separately. Moreover, existence and deployment of several ANM elements consists a typical phenomenon in case of Future Internet deployment. Consequently, interleaving and management of such mechanisms becomes a riddle for operators who agonize to provide competitive services to their users. Significant research effort has been focused in the definition of advanced management architectures of such self-x and autonomic deployed mechanisms [4][5][6][7][8]. A plethora of architectures has been proposed, distributed or centralized, enriched with reasoning and decision making mechanisms. Activation/Deactivation of self-x mechanisms is a vital task of the management process. This task becomes even more complicated in case of activation of two or more conflicting self-x mechanisms. Apart from that, the dynamic configuration of newly deployed self-x mechanisms is another important aspect related to the management of self-x mechanisms. Although a plethora of novel architectural schemes and a series of effective decision making algorithms are available, the vast majority considers a static and predefined environment of self-x mechanisms. However, this is contradictive to the always evolving Future Internet landscape. An introduction of a newly deployed mechanism will require manual configuration so as to realize newly deployed optimization functions, study and overcome imminent conflicting actions and integrate considered entities and decision enforcement points. A centralized Unified Management Framework (UMF), is responsible for the global monitoring and dynamic configuration of deployed Future Internet topologies. The considered framework presents a modular and interworking architecture for the effective realization of Future Internet needs. In this framework Governance constitutes an integral part. Its role is to orchestrate self-x mechanisms in a seamless and harmonized manner, as a result of human (network operator) high level policy objectives. The contribution of this work is the provision of the Governance functionality based on an ontology implementation that permits the automatic

integration of newly deployed self-x mechanisms, through the use of XML and Web Ontology Language (OWL) [9]. Furthermore, a conflict detection and resolution mechanism that is built upon the outcome of the ontology is introduced; the aim of this mechanism is to dynamically handle conflicts in policies introduced by the network operator. The remainder of this paper is organized as follows. Section II discusses related work. Section III provides an overview of UMF architecture and its core block functionalities and Section IV focuses on the Governance block. In Section V enhancements regarding self-x mechanisms dynamic deployment are proposed. Section VI provides the experimental results of our studies. Finally, conclusions and future work are discussed in Section VII. II.

Related Work

Autonomic Network Management has been the subject in numerous research efforts. An indicative survey and analysis may be found in [4]. The notion of policy and policy-based management is proposed as a key solution in many of them. Much of the research effort concentrates on effective policy refinement schemes and studies possible solution for the effective and dynamic adaptation of policy-based management schemes. OWL is considered as a prominent language for the realization of policy-based network management systems. The study in [10] presents a Network Management system, designed for heterogeneous multi-tier network environments. In the considered work ontology has been developed and utilized, in order to automate the data analysis, under an acceptable computational overhead. The notion of autonomous reconfiguration has been studied in [11]. More specifically, a policy-based framework is proposed, where dynamic reconfiguration of policy rules is examined. Through the use of Reinforcement Learning methodology, already deployed policies may be updated dynamically. In addition, a two-layer policy based framework is presented and performance evaluation of the proposed framework is provided. However, adaptability of the network control mechanisms and on-the-fly derivation of new policy rules is not taken into consideration. A centralized policy-driven management framework is also presented in [12], where a multi-layer policy-based model has been proposed. The considered layered approach distinguishes high-level management policies from their implementation. Transition among layers triggers semantic and syntactic procedures, which use mapping translators to interface between respective layers. The proposed solution focuses on heterogeneous mobile platforms, in order to achieve always best connect services. The proposed solution in [13] provides an integrated set of ontologies, and its methodology, by focusing on particular sub-domains of network communication systems. Furthermore, a formal language, common to all the subdomains, is defined, in order to derive network specific policies. The main purpose is to design a collection of ontologies that capture a significant portion of the radio and

network domain knowledge, using a formal language with computer-processable semantics. Such language will need to be developed as an extension and/or combination of existing languages that are available in the literature. Finally, the problem which may arise, regarding the management of complex heterogeneous networks is presented in [14]. In this approach the notion of specialized autonomic elements is presented as part of the management process. In addition, the need for a scalable mechanism to facilitate the interactions between autonomic elements has been introduced. Semantic reasoning is proposed as a solution for the communication and collaboration between autonomic elements. The delay introduced by semantic reasoning is evaluated through an implemented prototype. III.

UNIFIED MANAGEMENT FRAMEWORK

Unified Management Framework (UMF) is a novel architecture designed for the increased needs of Future Internet heterogeneous environments. Its main purpose is to orchestrate efficiently a range variety of complex self-x mechanisms and services. Those self-x mechanisms concentrate on specific network operational problems and deficiencies. They do realize the MAPE-K model and in current work are referred to as Network Empowerment Mechanisms (NEM). Orchestration of several NEM entities is not a trivial task. Several prerequisites should be addressed towards the design of UMF core blocks functionality. These prerequisites include i) Governance of Future Internet network behavior, ii)specification of high level business goals, depicting operator-driven needs, iii) effective triggering of proper NEM functionality, based on realization of aforementioned goals, iv) coordination of several NEMs and managed entities reassuring network stability and conflicting actions avoidance, v) knowledge management regarding manipulation of context information reported by NEMs. The considered UMF architecture is depicted in Figure 1. Governance, Coordination and Knowledge constitute the main blocks of UMF architecture. Their purpose is to address the aforementioned prerequisites, realized in a modular and interoperable formation. Specifically, Governance constitutes the intermediate among a potential human operator and the existing Future Internet infrastructure. It provides an effective Human to Network interface, for expressing operator high level business goals in a natural oriented language. Governance presents remarkable reasoning capabilities, by translating the high level goals into NEM specifics' low level operations. Governance functionality and its capabilities are thoroughly investigated in Section IV. Coordination block reinforces cooperation between NEMs currently in operation. More specifically, Coordination role is to communicate NEM affiliation commands and retrieve the respective feedback. Finally, Knowledge block is responsible to manage information flows coming from NEMs and derive Knowledge through the use of predefined probabilistic models. Last but not least, the afore-described UMF core blocks interwork between each other, in order to achieve dynamic, multidimensional unified perception regarding underlying NEM technologies.

such procedure enables supporting taxonomy among the defined concepts and specification of relations in the form of subject-predicate-object (SPO) clauses. Furthermore, the designed ontology should ensure consistency of the included captured concepts and inferring of relations by assignment of meta-properties to existing properties, enabling deductive and inductive reasoning. Thereafter, the selection of Web Ontology Language (OWL) [9] for the ontology foundation and Semantic Web Rule Language (SWRL) [15] for the construction of the conceptual (ontological) rules that enable the information mapping between the different levels (semantic enrichment of the ontology) seems to be the most appropriate solution Fig. 1. UMF core block architecture

IV.

DYNAMIC GOVERNANCE FRAMEWORK

Governance framework, as also mentioned in Section III, constitutes an integral part of UMF architecture. Governance comprises a unique interaction point between human operator and the underlying architecture. Its main parts in sequel are Human to Network Interface, Policy Derivation and Management, Conflict Resolution and Enforcement module. A. Human to Network Interface Initially, Human to Network Interface (H2N) is the intermediate between UMF and network operator. It provides to the user a human friendly way of creating and editing policies, using high level business concepts. Such concepts may be related to the introduction of a new application, sets of user classes consuming network resources, Quality of Service (QoS) levels etc. Considered high level policies, should be further translated into low level directives which in turn are enforced so as to control the behavior of NEM entity. For this reason, the already defined business goals are forwarded to the Policy Derivation & Management block, in order to be mapped from service requirements into network configuration (technology-specific terms) and allow the system to autonomously work out the situation and meet the objectives. The H2N interface also provides feedback mechanisms to the operator by presenting, for instance, visualization of key performance indicators (i.e. latency, load) monitoring values to the network operator.

The translation process adopts the Policy Continuum model, firstly introduced in [16], and is accomplished in every policy level from the Policy Derivation and Management Function, through mapping of the policies’ parameters to information parameters (and respective attributes) of the ontology. According to this approach, a set of three different levels / tiers (as also depicted in Figure 2) are defined (Business Level, Service Level and NEM Level), each of which constitutes a different representation of the initial business goal. The policies of all levels are described by OWL. The policies in business goal level are modeled based on the ontology reflecting considered objects, close to natural language, while the policies of other levels are modeled on the policy language based on SID information model [17]. Ontologies of different levels are linked in OWL by means of interoperability relationships among classes, which express the interrelation between subsequent levels, while SWRL rules are used for the translation of the policies. Policy Template Pool

Service Policies

Business Level

SWRL Translation

GOV Information

Service Definitions

Service Level Hi gh Level NEM Policies

Parameter

SWRL Translation

Definitions

NEM Manifests

NEM Level

B. Policy Derivation And Management The Policy Derivation and Management (PDM) module is responsible, among others, for the effective translation of high level business objectives into low level NEM specific directives. In addition, policy templates are stored and provided for the effective editing of rules. Focusing in translation process, modeling of high level concepts and rules is realized by the use on ontologies (OWL) and ontological rules (SWRL), respectively. Ontologies constitute a means to capture information and organize information and knowledge representation in a reusable and machine-readable format. Consequently, ontologies enable the representation and communication of business, network and NEM information, and the development of reasoning schemes. To this effect, the utilization of ontology-based policy translation is suitable for the relevant UMF mechanisms. These goals are achieved through suitable formulation and realization of the ontology;

Fig. 2. Three-tier Policy Continuum

C. Conflict Resolution In this section a policy conflict validation, detection and resolution strategy is introduced. Such process should be performed in all stages of policy translation between the different policy levels. If the outcome of the policy conflict detection is that the conflict cannot be resolved, a proper mechanism, which will translate the conditions that led to this decision to a human friendly formulation, for the enlightenment of administrators/operators is necessary. Based on this, it may be required the alteration of specific business goals from the operators. Policy validation, policy conflict detection and conflict resolution have attracted research interest over the past years

[18][19][20]. In the proposed solution, a transformation of the problem of anticipating conflicts between policies into an ontology consistency checking problem is realized. This process, which is based on identify-classify-detect-resolve conflict resolution cycle, is performed in the final stage of policy translation (i.e. operation level). A proper mechanism, which translates the conditions that led to this decision to a human friendly formulation, for enlightenment of administrators /operators is necessary. Additionally, a potential that should also be taken into consideration is the requirement for alteration of specific business goals from the operators, in order to allow as many policy rules as possible. Regarding UMF architecture, Policy Conflict Resolution (PCR) module in our case is considered as integral part of Governance Framework. As can be seen in Figure 3 PCR interacts with the Policy Translation module. More specifically, PCR receives as input the outcome of Policy Derivation and Management block. This outcome is formed as a policy set object (also described in the following subsection), that conforms to the TM Forum standards [21]. This object includes all the low level policy rules (i.e. directives that can be understood by the NEMs) that should be applied to the NEMs. The result of the PCR is the production of a conflict-free policy set object. This outcome is passed then to the Enforcement module and so on until they are applied to the NEMs.

Fig. 3. Policy Derivation and Management

As mentioned afore, PCR receives as input a policy set object from the Policy Derivation and Management module. Then PCR is executed in 3 logical phases, identification of newly expressed rules and already deployed rules, conflict detection and suggested actions for resolution and production of conflict free policy set. At the first phase, PCR receives the policy set object and decomposes the list of Policy Rules included in it to two lists, i.e. one with the deployed policy rules and one with the new policy rules, based on a cache memory preserved. Then, during the second phases, a filtering process checking for conflicts regarding the events, the conditions and the actions is followed. It should also be noted that during the conflict detection on conditions of the policy actions a set of conditions that do not lead to conflict and could replace the initial policy conditions is produced. Furthermore, when the algorithm is checking for conflicts on actions a complementary predefined matrix, namely service interdependencies matrix is checked to evaluate whether two actions are conflicting or not. Finally, a conflictfree policy set object is produced and returned to the Policy

Translation module. This policy set contains the modified policy rules that do not contain conflicts. Also, the cache memory with the deployed and new rules is updated. D. Enforcement Enforcement module encapsulates the communication mechanism between NEM and Governance, as well as other UMF core components. Due to the fact that NEM design and development is realized by several network and service operators, there is a strong need for defining an formal information model which encapsulates UMF/NEM interaction (e.g. policies, resources, context information, knowledge). In addition, compatibility issues should be eliminated and a common "communication protocol" between NEM and UMF should be established. Towards this direction, NEM Skin, extensively described in [22], is considered as the endpoint of communication between every specific NEM and the UMF. NEM Skin, and the corresponding interfaces it does provide, guarantees high reusability, openness and extensibility. Regarding the message exchange between UMF (Governance) and NEM Skin, two types of message exchange are considered, namely NEM Manifest and NEM Mandate. V.

NEM DEPLOYMENT APPROACH

Dynamic self-x mechanisms realization is considered as a challenging task, towards the development of a fully automated network management framework. State of the art analysis, also presented in Section II, enumerates several proposed policybased management architectures and the added value they do provide. However, most of them consider a static and predefined scheme of self-x mechanisms existence and focus only in refined activation/deactivation of them. Nevertheless, research efforts also focus on defining a coherent semantic representation of radio and network domain knowledge. In case of new NEM deployment entities, and self-x mechanisms dynamic definition in general, semantic representation may constitute a common basis for the automatic introduction and definition of new NEM entities. Consequently, semantic representation is supposed to eliminate human intervention. Furthermore, advanced reasoning mechanisms should be developed in order to entail NEMs to the policy continuum model. OWL enables the representation of semantic concepts to OWL classes. Deployment of such new NEMs implies instantiation of OWL classes and triggering of reasoning process. Further enhancements in provided ontology are ongoing, towards the realization of dynamic NEM deployment into the considered UMF. VI.

SIMULATION DETAILS AND RESULTS

The scalability of the proposed policy translation process of the governance framework is evaluated, under various numbers of generated policies. In general, the number of policies generated by human operator depends on the heterogeneity of the underlying network. A set of measurements were performed, in order to study the performance of the proposed policy translation process in terms of delay under different load of generated policies. The results of the considered experimentation are illustrated in Figure 4.

The results indicate that the delay of the translation process is tolerable for a low number of deployed policies, while it proportionally increases to the number of generated policies. However, since this procedure could also be executed offline, the introduced delay does not affect the operation of the overall system. It should be noted that the introduction or update of the defined business goals does not require the re-execution of the whole translation procedure; the sole evaluation and translation of the particular business goal is conducted.

[2]

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[5] [6] [7]

[8] [9] [10]

[11]

[12] Fig. 4. Experimental Analysis of policy enforcement [13]

VII. CONCLUSION AND FUTURE WORK In this paper a novel Autonomic Network Management system (UMF) has been presented. A thorough description of UMF parts and its role has been provided, as well as its interaction with network self-x entities (NEMs). Governance Framework has been studied, as integral part of UMF architecture. Its modules have been described, in order to realize the policy based management procedures that it provides. Possible refinements have also been provided, regarding the dynamic deployment of newly introduced NEMs, by the definition of ontologies (OWL language). A dynamic policy conflict detection and resolution mechanism built upon the produced topologies has been introduced. The experimental analysis given highlights the delay introduced during the translation process, with respect to the growing complexity of the network. Future directions of current research will include the definition of a coherent semantic model and the development of the respective ontology, towards the realization of the dynamic deployment of new self-x capabilities into heterogeneous autonomous environments. ACKNOWLEDGMENT The research leading to these results has been performed within the UniverSelf project (www.UniverSelf-project.eu) and received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 257513. REFERENCES [1]

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