Distributed Placement of Service Facilities in Large-Scale Networks

Laoutaris, Nikolaos; Smaragdakis, Georgios; Οικονόμου, Κωνσταντίνος; Stavrakakis, Ioannis; Bestavros, Azer

doi:10.1109/infcom.2007.248

Cited by 81 publications

(79 citation statements)

References 22 publications

(35 reference statements)

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“…As a consequence, many sub-optimal heuristics, that decrease complexity, have been proposed. They solve the problem using a wide plethora of different techniques, such as: linear program relaxation [7,8], local search [9,10,11,12], dynamic programming [13], greedy principles [14], and many others [15,12,16,17].…”

Section: Related Workmentioning

confidence: 99%

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Network-aware service placement and selection algorithms on large-scale overlay networks

Famaey¹,

Wauters²,

Turck³

et al. 2011

Computer Communications

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Currently many service providers offer their services on a private and proprietary hard-and software infrastructure. These infrastructures often share many similarities. Hence we believe a generic service management architecture, that allows service providers to offer a large array of different services on a single infrastructure or multiple providers to offer their services cooperatively, would provide many advantages over current silo-based approaches. Additionally, by allowing the distributed service management components to cooperate in a peerto-peer overlay network, scalability and resilience of the system could be greatly improved.In this paper we propose an optimal algorithm, based on an Integer Linear Programming (ILP) formulation, and several heuristics to support such a generic overlay-based service management architecture. More specifically, we propose algorithms for dynamically allocating server and network resources to a set of services and selecting a suitable service instance for each client. Service instances are placed on a set of servers, taking into account server resource constraints (e.g. CPU and memory). Unlike existing algorithms for this problem, those proposed in this paper also support Service Level Agreements (SLAs), which take the form of Quality of Service demands such as transmission latency constraints and bandwidth requirements. The optimization goal is to maximise the percentage of satisfied demand (answered requests) and minimise the total number of required * Corresponding author; Tel: +32 9 331 49 38; Fax: +32 9 331 48 99Email addresses: jeroen.famaey@intec.ugent.be (J. Famaey), tim.wauters@intec.ugent.be (T. Wauters), filip.deturck@intec.ugent.be (F. De Turck), bart.dhoedt@intec.ugent.be (B. Dhoedt), piet.demeester@intec.ugent.be (P. Demeester) Preprint submitted to ElsevierMarch 1, 2010 overlay servers, while satisfying the SLAs and resource constraints. Additionally, we propose an extension that allows the algorithms to find overlay routing paths to improve the transmission latency for latency-sensitive services.Extensive simulations were performed to evaluate the performance and scalability of the heuristics. They showed that in many cases the heuristics perform close to optimal and they scale well in terms of network size.

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Section: Related Workmentioning

confidence: 99%

“…The path can be calculated using an altered version of Dijkstra's shortest path algorithm [36], that does not use the forbidden edges and nodes. The path t is then added to C (lines [16][17]. In the second step, p is set to the shortest hop-count path in C and the process is repeated.…”

mentioning

confidence: 99%

Network-aware service placement and selection algorithms on large-scale overlay networks

Famaey¹,

Wauters²,

Turck³

et al. 2011

Computer Communications

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“…The closest works to the one presented here are [26] and [27]. Ragusa et al [26] propose a heuristic approach for partitioning the network into a number of k clusters selfmanaged through mobile agents.…”

Section: Related Workmentioning

confidence: 99%

“…Clusters may be further partitioned or merged in response to dynamic network changes based on empirically selected threshold values. The second approach, proposed by Laoutaris et al [27] solves the service placement problem in a distributed manner reusing existing near-optimal centralized approaches inside suitably defined rballs (i.e., network areas of r hops away from each service facility). As service facilities move inside the network, rballs are partitioned or merged based on the outcome of the centralized approaches.…”

Section: Related Workmentioning

confidence: 99%

“…As service facilities move inside the network, rballs are partitioned or merged based on the outcome of the centralized approaches. The main difference between [26], [27] and the work presented here, amounts to the requirement for local information in order to select more efficient positions for the mobile agents in [26] and to incorporate classical centralized solution approaches in [27]. Instead, the proposed service migration approach relies on strictly local information; this local information is passively collected by the service facility itself, as opposed to requiring the deployment of a mechanism that provides both the demand and topology information within each cluster as in [26] and [27].…”

Section: Related Workmentioning

confidence: 99%

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Scalable service migration in autonomic network environments

Οικονόμου

Stavrakakis

2010

IEEE J. Select. Areas Commun.

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Abstract-Service placement is a key problem in communication networks as it determines how efficiently the user service demands are supported. This problem has been traditionally approached through the formulation and resolution of large optimization problems requiring global knowledge and a continuous recalculation of the solution in case of network changes. Such approaches are not suitable for large-scale and dynamic network environments. In this paper, the problem of determining the optimal location of a service facility is revisited and addressed in a way that is both scalable and deals inherently with network dynamicity. In particular, service migration which enables service facilities to move between neighbor nodes towards more communication cost-effective positions, is based on local information. The migration policies proposed in this work are analytically shown to be capable of moving a service facility between neighbor nodes in a way that the cost of service provision is reduced and -under certain conditions -the service facility reaches the optimal (cost minimizing) location, and locks in there as long as the environment does not change; as network conditions change, the migration process is automatically resumed, thus, naturally responding to network dynamicity under certain conditions. The analytical findings of this work are also supported by simulation results that shed some additional light on the behavior and effectiveness of the proposed policies.

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Cost‐based placement of vDPI functions in NFV infrastructures

et al. 2015

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Summary Network functions virtualization (NFV) is transforming how networks are operated and how network services are delivered. The network is more flexible and adaptable, and it can scale with traffic demands. To manage video traffic in the network, or acquire protection from cyber‐attacks, Deep Packet Inspection (DPI) is increasingly deployed at specific locations in the network. The virtual DPI (vDPI) engines can be dynamically deployed as software on commodity servers within emerging network functions virtualization infrastructures. For a network operator, deploying a set of vDPIs over the network is a matter of finding the appropriate placement that meets the traffic management or cyber‐security targets (such as the number of inspected flows) and operational cost constraints (licence fees, network efficiency or power consumption). In this work, we formulate the vDPI placement problem as a cost minimization problem. The cost captures the different objectives that the operator is pursuing. A placement of vDPIs on the network nodes realizes a trade‐off between these possibly conflicting goals. We cast the problem as a multi‐commodity flow problem and solve it as an integer linear program. We then devise a centrality‐based greedy algorithm and assess its validity by comparing it with the integer linear program optimal solution on a real dataset (GEANT network with 22 nodes and real traffic matrix). We further analyse the scalability of the heuristic by applying it to larger random networks of up to 300 nodes. The results show the network structure and the costs influence time performance. Finally, they demonstrate that the heuristic approximates the optimal on small and medium problem instances well. Copyright © 2015 John Wiley & Sons, Ltd.

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Distributed Placement of Service Facilities in Large-Scale Networks

Cited by 81 publications

References 22 publications

Network-aware service placement and selection algorithms on large-scale overlay networks

Network-aware service placement and selection algorithms on large-scale overlay networks

Scalable service migration in autonomic network environments

Cost‐based placement of vDPI functions in NFV infrastructures

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