Famaey, Jeroen

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Ghent University Academic Bibliography

A hierarchical approach toautonomic service managementJeroen FamaeySupervisor(s): Filip De Turck, Bart DhoedtI. INTRODUCTIONAs computer networks grow more complex,heterogeneous and larger in scale, it becomesincreasingly difficult for human administratorsto manage and configure such systems. Ad-ditionally, services offered across the Internet(e.g. YouTube, Facebook, Twitter) have drasti-cally increased in numbers and diversity.To alleviate the problems associated withmanaging large, complex and diverse computernetworks, researchers have proposed the princi-ple of autonomic communication networks [1].The goal of autonomic networking is to allownetworks to manage themselves, based on high-level business goals set by human administra-tors. The autonomic elements use knowledgegathered from the network to adapt their be-haviour under changing conditions, in order tokeep achieving the predefined business goals.Although some progress has been made to-wards a completely self-governing autonomicservice delivery network, several importantchallenges remain to be overcome. Amongthem is scalability, which is defined as the abil-ity of the system to keep functioning properlyin face of increasing network size, number ofservices, and end-users count. In this paper,we introduce a novel autonomic architecturefor deliving services across the Internet, whichaims to solve scalability issues of current auto-nomic network architectures.—————————————————–J. Famaey is with the Department of InformationTechnology, Ghent University (UGent), Gent, Bel-gium. E-mail: jeroen.famaey@intec.ugent.beII. SERVICE DELIVERY ARCHITECTUREThe proposed architecture allows serviceproviders to deliver a set of services with di-verse requirements to their clients across the In-ternet. By way of autonomic networking prin-ciples we plan to reduce complexity for humanadministrators and allow them to manage thesystem by defining high-level business goals.This hides unnecessary low-level details fromthe administrators, while allowing them to fo-cus on more important high-level objectives.As the amount and diversity of network de-vices and services increases, the autonomicsystem will require more knowledge to modelthe network, and more low-level policies tokeep operating within the bounds of the prede-fined goals. To contain this growth and thus im-prove scalability, we propose to group serversand other network devices in a hierarchy of log-ical units or clusters. Within a cluster, knowl-edge, management elements, policies, and ser-vices are shared freely. However, only relevantinformation is propogated to parent or childclusters. This approach limits the amount ofdetail available in each cluster. A possible hier-archy is shown in Fig. 1. The servers in a singledatacenter are clustered together, while the or-ganisation (e.g. a service provider) consists ofa cluster of datacenters.The architecture consists of several impor-tant components, which are shown in Fig. 2.The policy repository maintains the set of poli-cies that govern the behaviour of the autonomicsystem. A policy consists of a set of conditionsand actions [2]. The actions are triggered whenthe conditions are met. High-level business... ......DatacenterServerOrganisationPoliciesKnowledgeAutonomic ElementsFigure 1. The proposed hierarchical architecturegoals are injected by administrators as poli-cies at the higher layers of the cluster hierar-chy. They propagate downwards and are trans-lated to more specific policies at each level.For example, the high-level business goal “de-liver video in high quality to client X”, couldbe translated to “make sure packet-loss remainsbelow Y for client X” at lower levels.The knowledge base contains the informa-tion necessary for the autonomic elements tomodel the context and make sure everything isworking correctly. At the lowest levels of thecluster hierarchy exact information about thenetwork layer and its devices is measured andinserted into the knowledge base. This infor-mation is aggregated and summarised before itis propagated to higher level clusters. Addi-tionally, autonomic elements may use reason-ing techniques to infer new knowledge from ex-isting information.The autonomic management elements to-gether shape the self-governing behaviour ofthe autonomic system. Using the knowledge inthe knowledge base, they make sure the sys-tem operates within the bounds set forth bythe policies. Additionally, autonomic elementsmay themselves infer new knowledge and de-fine new policies, as long as they do not conflictwith the predefined business goals. As the ele-ments continuously monitor the state, the sys-tem is able to dynamically adapt to changes inthe environment and to new or changing busi-ness goals. Important tasks of the autonomicelements include maintaining the services run-ning in the service container and making surethe requested quality of the delivered servicesis achieved.Our current and future work focuses on de-policy repositorybusiness goalsservice containerknowledge baseautonomic managementelementsnetwork layerFigure 2. The main components of the architecturevising protocols and algorithms for creatingand managing the cluster hierarchies, and prop-agation of knowledge, policies and autonomicelements. In addition, we are developing sev-eral autonomic elements necessary for the cor-rect operation of the service delivery architec-ture. Examples include algorithms for serviceplacement and selection [3], server state man-agement [4], and video quality adaptation [5].III. SUMMARYIn summary, this paper introduces a novelapproach to managing services in a scalablemanner. Network devices are grouped in a log-ical hierarchy of clusters, to contain the expo-nential growth of knowledge and policies as thesystem grows in size and complexity.ACKNOWLEDGMENTJ. Famaey is funded by the Institute for thePromotion of Innovation by Science and Tech-nology in Flanders (IWT).REFERENCES[1] S. Dobson, et al., “A survey of autonomic com-munications,” ACM Trans. Auton. Adapt. Syst.,vol. 1, no. 2, pp. 223–259, 2006.[2] D. Agrawal, et al., “Policy-based management ofnetworked computing systems,” IEEE Commun.Mag., vol. 43, no. 10, pp. 69–75, 2005.[3] J. Famaey, et al., “A latency-aware algorithm fordynamic service placement in large-scale over-lays,” in Proceedings of IM’09, 2009, pp. 414–421.[4] J. Famaey, et al., “Dynamic overlay node acti-vation algorithms for large-scale service deploy-ments,” in Proceedings of DSOM’08, 2008, pp.14–27.[5] J. Famaey, et al., “Dynamic QoE optimisation forstreaming content in large-scale future networks,”in Proceedings of ManFI’09, 2009, pp. 128–134.

A hierarchical approach to autonomic service management

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A hierarchical approach to autonomic service management

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