Network and service governance for the management of future networks

Future Network & MobileSummit 2013 Conference Proceedings Paul Cunningham and Miriam Cunningham (Eds) IIMC International Information Management Corporation, 2013 ISBN: 978-1-905824-37-3

Network and Service Governance for the Management of Future Networks Beatriz FUENTES1, Eleni PATOUNI2, Teemu RAUTIO3, Evangelos KOSMATOS2, Roi ARAPAGLOU2, George KATSIKAS2 1

Telefónica I+D, Distrito Telefónica Ronda de la Comunicación s/n Edificio Sur 3-Planta 3, Madrid, 28050, Spain, Tel: +34 91 3129546, Fax: +34 914023137, Email: [email protected] 2 National & Kapodistrian University of Athens, Panepistimiopolis, Athens, 157-84, Greece, Tel: +30 210 7275238, Fax: +30 210 7275601, Email: {elenip, vkosmatos, roiar, katsikas}@di.uoa.gr 3 VTT Technical Research Centre of Finland, Kaitoväylä 1, Oulu FI-90571, Finland, Tel: + 358 20 722 2322, Fax: + 358 20 722 2320, Email: [email protected] Abstract: The emergence of new technologies and services forces the network operator to manage uniformly and efficiently the complex network environment of Future Networks. The interworking of multiple underlying heterogeneous network domains with proprietary network management systems is currently a tedious task, and this fact will be exacerbated in the near future. To this end, we address the challenge of dynamic, efficient administration of Future Networks introducing the Network Governance paradigm; the latter automates the dynamic joint network and service management while fulfilling different QoS requirements for the users. The proposed governance framework supports the dynamic definition of operators’ business goals, their translation to network policies and management actions and their enforcement onto the network. Moreover, an experimental case study on management of FFTH and WLAN testbeds has been implemented through two main mechanisms: policy-driven wireless access load balancing and self-diagnosis and monitoring, thereby proving the feasibility and applicability of the introduced concepts. The results show the performance gains including e.g. the QoS optimisation for packet loss and delay for a user class. Keywords: Governance, Policy-based Management, Policy Translation, SelfManaged Networks, FTTH, WLAN, QoS

1. Introduction Nowadays the telecommunication industry has been dominated by the complexity inherent to the adoption of new technologies, the continuous evolution of the service portfolio and the quantitative growth in the number of customers, increased by a factor of 105 in one century. This constant expansion of the telecommunication business has been accompanied by the corresponding evolution of the network operational and maintenance processes. The explosion of the Internet of Things, enabling every device with seamless network connectivity and data exchange with other devices in the ecosystem, poses new challenges in the network management field. The most conservative predictions throw a number of 1 trillion connected devices by 2025 [1]. The evolution of the network management systems to accommodate the requirements of the future networks still forms a major research activity. The current trend pushes for autonomic mobile networks with self-* capabilities. The research community supports that autonomic networks will fully automate the provisioning and runtime phases, while maintaining QoS guarantees with minimal human Copyright © 2013 The authors

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intervention. However, still some efforts have to be devoted to solve the question of how an intelligent – in terms of decision-making capabilities network, can be controlled by humans. This paper introduces a Governance framework for the management of autonomic networks and services, focusing on mechanisms that address the gap between high-level specification of performance objectives and existing resource management infrastructures. The proposed approach provides operators with means for decision-oriented operational tasks based on the use of policies rather than low-level command execution. Thus, it is expected to decrease the human intervention and the degree of specialization required for deploying new services, configuring and operating the network. Furthermore, the use of the governance framework is demonstrated for the management of WLAN and FTTH (FiberTo-The-Home) access networks. The rest of the paper is structured as follows. Section 2 introduces the governance framework and supporting mechanisms. In section 3 a semantic-based approach for policy translation is detailed. Section 4 presents a case study validating the applicability and efficiency of the framework within a testbed comprising the two different abovementioned access networks. The experimental evaluation focuses on the following policy-driven scenarios based on the governance framework: load balancing in the wireless access domain and automated self-diagnosis and monitoring in the FTTH domain. Finally, some conclusions are presented in section 5, summarizing the outcome of this paper.

2. Governance Framework Description Network and service governance is defined as a management framework that enables the operator to adjust the features of the demanded service/infrastructure using a high level language. These high level directives should be translated into low-level policy rules, which in turn should be enforced to control the behaviour of self-managed resources. The required autonomic functions for dynamic resources administration are encapsulated into Network Empowerment Mechanisms (NEMs) – each NEM is described by a manifest. The NEM manifest forms semantic information that specifies the managed equipment, the acquired inputs, the produced outputs and the kind of actions that a NEM can implement. Based on the manifest, Governance facilitates the seamless deployment, management and supervision of NEMs. The main functions of the proposed framework are depicted in Figure 1. The Human to Network (H2N) interface provides a friendly tool for the human operator to dynamically define high-level business objectives and user requirements, which will be later on translated into technology-specific terms. Such interface alleviates the human operator from the need to deal with technical details. High-level objectives may include definition of new services, user classes (gold, silver, bronze) and their QoS levels etc. Next, the business goals are forwarded to the Policy Derivation & Management (PDM) function.

Figure 1: Functions of Network & Service Governance Framework Copyright © 2013 The authors


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PDM is in charge of the following functions: (i) providing storage for the policies and facilitating the management of the Policy Repository, (ii) checking whether the different policies have conflicts and resolving them according to the well-defined conflict resolution mechanisms, (iii) translating the policies to lower level policies, (iv) analyzing if the network, in its current status, can accommodate the request, and optimizing it otherwise, and (v) evaluating if the enforcement of the network actions fulfils the defined business goals and service requirements. PDM follows the Policy Continuum approach, composed of a set of levels. Our approach considers three different levels: Business Level, Service Level and NEM Level. Each level corresponds to different user types that need and/or use slightly different information. For example, a business user deals with services information and QoS guarantees and is not aware of the network mechanisms needed for delivering the defined QoS. Conversely, network operators deal with the set of commands that will force the network elements (e.g. routers) to deliver the QoS defined at the business level. These are completely different representations of the same policy. For each level of the Continuum, the above PDM described operations are deployed, following their customisation. The NEM Management function is in charge of managing the lifecycle of the autonomic NEMs, allowing to the human network operator to gain full control over them. Finally, the Enforcement function provides the means for communicating NEM policies to the network mechanisms, offering an abstraction for the interconnection with the selfmanaged resources. In general, the key challenge addressed by the introduction of the governance framework is to achieve the highest level of automation possible in the policy translation process. Fully automated approaches are highly specialized to particular applications domains and require experts on both the application domain and low-level formal representations. In our approach, we exploit the properties of semantics and reasoning techniques that allow the interoperability between semantically equivalent, but differently instantiated models. Thus the mapping from Business to NEM policies is supported by the policy semantic description. Semantic translation rules are used to allow the data interchange between the different levels of the policy continuum. The detailed implementation of this mechanism is described in the next section.

3. Policy Translation Mechanism Using Ontologies In this direction, the policy translation operation part of PDM aims at simplifying the network, as well as the service management by automatically translating the business parameters imposed by the operator into NEM-enforceable policies; the latter are customised to the peculiarities of each heterogeneous technology and service. A variety of policy translation mechanisms have been proposed so far, some of them are analysed in [2]. In general, the translation mechanisms can be classified to the following policy-refinement categories: goal-oriented, classification-based, ontology-based prescription-based and case-based. The study in [3] relies on goal-oriented policy refinement, which use the KAOS [4] formalization technology to perform goal elaboration. In [5] a set of domain-specific and domain-independent elaboration patterns are used to refine a goal into sub-goals. In [6] a policy refinement method is presented based on goal requirement and modal checking technologies. The study in [7] utilizes classification-based policy refinement, which deploys classification of statistical data for policy refinement. Ontology-based refinement is based on the use of ontologies to describe concepts and the relationship among them. For example, in several research activities the use of OWL (Web Ontology Language) [8] is adopted for policy description. An ontology-based policy refinement using SWRL (Semantic Web Rule Language) rules is proposed in [9]. The authors propose a methodology which uses OWL for the description of the policies within Copyright © 2013 The authors


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each level. Moreover, OWL links with SWRL rules are used to allow the data interchange between the different levels. In [10], the SEMPR architecture is proposed; the latter uses Web services and automatic Web services composition as a complementary technique to policy translation. In [11] a policy refinement approach based on prescription and ontology is presented. The authors in [12] introduce a case-based reasoning approach where knowledge acquired from the past system behaviours is utilised to predict its present and future behaviours. The majority of the aforementioned policy translation studies concentrate on translating a business policy into a set of technology-specific policies, which are generated ‘from scratch’. This approach, although generic enough, lacks practical feasibility as it requires the operator to provide a vast quantity of information which is very difficult or even impossible to collect. The proposed policy translation methodology overcomes this hurdle by using a set of reusable policy templates, which guide the translation process through all its phases. The novelty of our approach is that it orchestrates distinct mechanisms (NEM entities) in a seamless and harmonized manner, as a result of policy-based objectives, expressed in OWL. In addition, it enables the automatic integration of newly deployed NEMs, through the use of XML and OWL. Specifically, it is based on a three-fold approach in order to maximize automation, retain low complexity and high preciseness, while being highly reusable. Firstly, the operator’s business parameters are classified into categories (e.g. QoE, Energy Efficiency), while a set of KPIs are assigned to each category. Secondly, the translation process is led by a set of guidelines which are described in policy templates, stored in a policy template pool. Thirdly, the modelling of policies and translation process is realised with the use on ontologies (OWL) and respective rules (SWRL). OWL is one of the most common languages used for the realization of policy-based network management systems. OWL supports the feature of automatic reasoning and data analysis. One of its most prominent characteristics is the reduced computational overhead. The study in [13] presents a Network Management system for heterogeneous multi-tier network environments. Among others, it thoroughly examines the computational complexity and the additional delay of incorporating ontology into management systems, concluding that the overhead of introducing ontology-based mechanisms in a network management system is acceptable given the inherent benefits. The translation process adopts the Policy Continuum approach as described in the previous section. The policies of all levels are described in OWL. The policies in business level are modelled using the ontology reflecting the business level, close to natural language, while the policies of other levels are modelled using the policy language based on Shared Information and Data (SID) information model. The ontologies of different levels are linked in OWL by means of interoperability relationships between classes, which express the interrelation between subsequent levels. The translation is realised by the implementation of respective algorithms expressed in SWRL, which transform, generate and deliver data from one level to the subsequent level. The translation process, illustrated in Figure 2 comprises the following three steps: 1. The initial High Level Parameters are classified into a High Level Parameters’ Category (HLP Category) based on operator selections. 2. The initial translation from Business Level Policies to Service Level Policies is realised based on HLP Category to KPIs mapping, the selected combination of high level semantics and the mapping of available service to KPI values (e.g. KPIDelay < 50 msec). For each HLP Category a series of service policy templates are extracted from the policy template pool. The selection of the appropriate policy templates is done based on the set of KPIs involved and the initial classification. In general each policy template is a policy skeleton which contains the policy structure and the policy variables, without

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variable values. During the translation process the missing values are filled and the policies instances are generated. 3. On the second translation level, each service-level policy is further translated to a set of operational NEM-level policies. The translation is realised by using a set of NEM templates including KPI/parameter related information and then filling them with respective values. In this step, the involved KPIs/parameters are mapped to the available NEM operations, inputs and outputs based on the NEM Manifest.

Figure 2: Policy translation process

In its general form a NEM policy is described by the following expression: [ON event] IF condition THEN action The “event” part of the policy may include an event that can be detected by a specific NEM. The “condition” part usually expresses a mathematical condition including a set of parameters and KPIs that can be monitored by a specific NEM and their values. The “action” part is described using NEM operations and settings of NEM parameters. The action expresses the NEM reaction in case of an event fulfilling the defined condition. An indicative example of an SWRL rule for the different steps of the translation process is given below. As it can be seen, the first SWRL rule maps the Business level class of the ontology (Availability, Speed, Reliability, Security) into Service Level characteristics. The second one maps the Excellent QoS Level into the pre-defined OLT profile and associated parameters corresponding to the business parameters. Policy(?p) & hasAvailability(?p, ?a) & Availability_Excellent(?a) & hasReliability(?p, ?r) & Reliability_Excellent(?r) & hasSpeed(?p, ?s) & Speed_Excellent(?s) -> hasQoS_Level(?p, ?q) & QoS_Level_Excellent(?q) Policy(?p) & hasQoS_Level(?p, ?q) & QoS_Level_Excellent(?q) & hasUC(?p, 1) & hasToS(?p, 3) -> hasAction(?p, "ENT-PROFILE-BW:ALCANTARAMADRID.3.1:1:0:profile_100:CIR=50000,EIR=50000,DT=80;")

Figure 3: Example of SWRL rule for the 3 steps of the translation process

4. Case Study: Application of Governance for Management of FTTH & WLAN The purpose of this case study is to evaluate the innovation of the network and service governance framework and in particular the mechanisms for policy translation. To this end the governance framework has been fully implemented, complemented by two specific network functionalities: a) the FTTH self-diagnosis & monitoring and b) the wireless access



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load balancing functionality for the dynamic relocation of users between WLAN Access Points (APs). A set of experiments have been conducted targeting the following goals: x The provision of automated decision oriented operational tasks: it is shown how the definition of high level business goals translated into policy rules results in automated actions in the running network functionalities. x The distinguished QoS provision to the different users according to their QoS requirements. For example, a prioritized policy-driven load-balancing of the user traffic is implemented targeting their optimal serving. In detail, the load-balancing actions fulfil the policy rules that are derived from the PDM function based on the business objectives and parameters defined using the H2N interface. Specifically, the mobile users class gold, silver and bronze) is taken into account in terms of their profile and QoS requirements. Table 1 presents an exemplary subset of the defined business rules based on existing and emerging network management systems. It should be noted that a subset of the policy rules is activated at the same time depending on the requirements for the users QoS and QoE. x

The applicability of the framework in heterogeneous environments of both fixed and mobile network segment. Specifically, a FTTH and WLAN testbed are considered. The governance framework forms an umbrella framework responsible for the dynamic management of network operations and services within both testbeds. The presented framework can be scaled for the simultaneous management of large-scale testbeds. This can be realised by the derivation and translation of additional business policies through the proposed translation process with the use of SWRL and OWL.

Policy Rule ID 1

2

3

4

5

Policy Rule Description A user assigned with user class ‘Gold’ consuming a service with service type ‘Streaming’ via a specific technology ‘Wi-Fi’ using a device of device type ‘Any’ consuming data volume ‘Any’ when an event is occurring of event type ‘Any’ experiences Availability Level ‘Excellent’, experiences Reliability level ‘Excellent’, experiences Speed level ‘Excellent’ experiences Security level ‘Excellent’. A user assigned with user class ‘Bronze’ consuming a service with service type ‘Streaming’ via a specific technology ‘Wi-Fi’ using a device of device type ‘Any’ consuming data volume ‘Any’ when an event is occurring of event type ‘Any’ experiences Availability Level ‘Excellent’, experiences Reliability level ‘Excellent’, experiences Speed level ‘Excellent’ experiences Security level ‘Excellent’. A user assigned with user class ‘Bronze’ consuming a service with service type ‘Streaming’ via a specific technology ‘Wi-Fi’ using a device of device type ‘Any’ consuming data volume ‘Any’ when an event is occurring of event type ‘Any’ experiences Availability Level ‘Normal’, experiences Reliability level ‘Normal’, experiences Speed level ‘Normal’ experiences Security level ‘Normal’. A user assigned with user class ‘Gold’ consuming a service with service type ‘IP-TV’ via a specific technology ‘FTTH’ using a device of device type ‘Any’ consuming data volume ‘Any’ when an event is occurring of event type ‘Any’ experiences Availability Level ‘Normal’, experiences Reliability level ‘Normal’, experiences Speed level ‘Normal’ experiences Security level ‘Normal’. A user assigned with user class ‘Silver’ consuming a service with service type ‘Internet’ via a specific technology ‘FTTH’ using a device of device type ‘Any’ consuming data volume ‘Any’ when an event is occurring of event type ‘Any’ experiences Availability Level ‘Excellent’, experiences Reliability level ‘Normal’, experiences Speed level ‘Normal’ experiences Security level ‘Excellent’.

Table 1: Example of Policy Rules defined within the Governance Framework

4.1 Testbed Description Figure 4 sketches the integrated FTTH and WLAN testbed used for showcasing the application of the governance framework in heterogeneous environments. Respective information flows for network monitoring, business goal creation and policy enforcement are visualised. The FTTH testbed represents a typical fiber access network deployment, consisting of a node at the Central Office (CO), called optical line terminal (OLT), the equipment at the customer premises, and the fibers and splitters between them. At the customer home, an optical network terminal (ONT) receives the fiber cable, a router provides wifi connectivity, Copyright © 2013 The authors


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and a PC and TV represent the customer devices. The video service is provided by a dedicated server at the Central Office. A reflectometric probe is used to detect the cut fibers and the exact distance where the failure occurs. On top of this infrastructure, monitoring and diagnosis NEMs have been deployed, able to gather data from the different network elements and probes in the FTTH access segment. Based on the gathered information, the diagnosis NEM finds the most probable root cause of failure and the corresponding probability, using Bayesian inference. These NEMs have been implemented as JADE (Java Agent Development Framework) [14] agents, where each agent embeds the behaviour of a NEM (monitoring or diagnosis). The WLAN testbed consist of: two wireless IEEE 802.11 access points, three laptops as server devices for running pieces of software at a network side (one piece per IP subnet, which are represented as light grey clouds in Figure 4) and two laptops as a wireless enduser devices (Gold and Bronze users). Three different IP-subnets (two Access networks and ISP/Service Provider network) are connected together with a router. The Wireless Access Load Balancing NEM described more detailed in [15] is capable of monitoring characteristics of the two wireless access points, end-user devices as well as services, with help of a Distributed Decision Engine (DDE). The collected information is further processed into single values using a fuzzy-based approach; the latter illustrate quality of this service in this network segment for this particular user. Based on predefined policies enforced by Governance, wireless end-users are grouped into user classes, which are, for example, based on customer subscriptions. In possible access point overloading situations, QoS for prioritized traffic (i.e. higher user class) is guaranteed by moving/removing user(s) with lower user class to another access point. In this example, we have two end-users with two different employed services; a gold user with video streaming service and a bronze user with file downloading service. Additionally, third end-user device is used for introducing extra traffic load in to the access point. The Governance framework has been implemented in Java [16], comprising elements described in section 2.

Figure 4: Topology of the FTTH and WLAN testbeds under the Governance umbrella



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4.2 Policy-driven Wireless Access load Balancing

8

9

7

8

DownlinkPktLoss[%]

DownlinkDelay[10ms]

In order to test the applicability and efficiency of the governance framework, we conducted an experimental evaluation within the WLAN testbed, comprised of two separated test runs. During the first run, the policy rules with ID 1 and 2 of Table I are activated. Such policies do not differentiate the QoS requirements of gold and bronze users. Thus, using these policies, we simulate the absence of the governance framework and autonomic mechanisms underneath (acting as a reality in today’s networks) that will result in the bandwidth resources saturation. Since the users are not differentiated in terms of QoS requirements, no load balancing actions are triggered and the signed QoS levels will not be satisfied for the users. During the second run, policies 1 and 3 are activated- the latter differentiate the QoS between gold/bronze users and therefore will result in load balancing actions in the network finally ensuring the signed SLAs. Figure 5 presents the end-to-end QoS metrics (delay and packet loss respectively) for the Gold users’ video stream. The measurements have been conducted using the Qosmetsolution [17] for both test runs; the blue curves represent the first run (application of policies with ID 1 and 2) whereas the red curves represent the second run (policies with ID 3 and 4). In both test runs, the Gold user consumes a video streaming service (approx. 2 Mbits/s for entire capturing duration) over the first access point (AP) and Bronze user downloads a file over both APs. At timestamp of 40 seconds, third end-user inserts congestive UDP traffic into the first AP and maintains it until next 60 seconds. When considering the absence of user classes, the video QoS received by Gold user is notably impaired for whole duration of the congestion - the latter is proved by increased delay and packet loss values in the figure. In the latter test case, Bronze user gets informed about congestion in the first AP, and releases bandwidth in it within few seconds delay. Then, the QoS of first AP begins to improve and after a stabilization period of approximately 30 seconds period it is completely normalized (the delay and packet loss decrease). Finally the QoS for the video stream reaches the same level before the congestion. As a consequence, the time period corresponding to impaired video stream QoS is minimized into less than a half compared to the reference case.

6 5 4 3 2 1

7 6 5 4 3 2 1

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0 0

10 20 30 40 50 60 70 80 90 100 110

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Withuserclassdifferentation

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Figure 5: Downlink delay and packet loss for the Gold users class without (blue) and with (red) governancedefined user class differentiation.

4.3 Policy-driven Self-Diagnosis and Monitoring The second demo scenario presents the use of the governance framework for controlling the self-monitoring and diagnosis functionality in the FTTH testbed, where a video service is offered to customers. Initially a set of policy rules specifying the QoS levels for the Copyright © 2013 The authors


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different user classes are communicated via GOV framework to the specific NEM (policies with ID 4 and 5), which triggers the continuous monitoring process. When a failure occurs causing the interruption of the video service, e.g. a fiber cut or an ONT misconfiguration, a Bayesian diagnosis process takes place, which produces a list of the most probable root cause of failures and their attached probability. In case of a fiber cut, the measurements of the attenuation versus the distance from the central office can be used to precisely estimate the location of the fiber-cut disconnection. Figure 6 a) presents the attenuation versus distance before the fiber-cut whereas Figure 5Figure 6 b) presents the same parameter values after the fiber-cut in the distribution segment has been diagnosed. As shown, the attenuation decreased dramatically after the first splitter. Next, corrective actions, such as ONT reconfigurations can then be triggered to adjust the network configuration parameters following the network and service conditions. A notification is also triggered to notify the NOC (Network Operation Center) operator via the Human to Network interface about the current network status and the derived actions.

Figure 6 Results of self-diagnosis in the FTTH testbed: a) attenuation versus distance before the fiber-cut, b) attenuation versus distance once the fiber cut has been diagnosed

The presented case study highlights the use of the Governance framework and NEMs for the management of the network elements in the different testbeds. It has been shown how Governance functions can facilitate the dynamic definition of business goals, their translation through the different levels of the Policy Continuum and the enforcement of the derived policies. Together with the self-management algorithms embedded into the NEMs, the case study demonstrated how the presented management approach can guarantee the QoS levels delivered to the customer.

5. Conclusions The telecommunications industry needs to adapt its network management schemes in order to address the scalability and complexity requirements of future networks. In this paper we have discussed part of the architectural Unified Management Framework specified in UniverSelf project, the so-called Governance core block that effectively facilitates the joint network and service management. Network governance framework is an open and extensible umbrella concept enabling the incorporation of new underlying mechanisms (NEMs) dynamically. The dynamic definition of suitable business goals overcomes the limitation of proprietary management systems, thus allowing its applicability to any underlying networking environment and mechanisms. This paper has shown the validation of the framework through the integration with dedicated NEMs for the management of FTTH and WLAN access networks. Future work involves the evolution of the governance



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framework versus more complicated case studies aiming to validate the key processes of the management of legacy and future networks.

Acknowledgement 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.

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