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Bauschert, T., D’Andreagiovanni, F., Kassler, A. & Wang, C. (2019). A matheuristic for green and robust 5G virtual network function placement. In: Paul Kaufmann, Pedro A. Castillo (Ed.), Applications of Evolutionary Computation: . Paper presented at 22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019 (pp. 430-438). Cham: Springer
Open this publication in new window or tab >>A matheuristic for green and robust 5G virtual network function placement
2019 (English)In: Applications of Evolutionary Computation / [ed] Paul Kaufmann, Pedro A. Castillo, Cham: Springer, 2019, p. 430-438Conference paper, Published paper (Refereed)
Abstract [en]

We investigate the problem of optimally placing virtual network functions in 5G-based virtualized infrastructures according to a green paradigm that pursues energy-efficiency. This optimization problem can be modelled as an articulated 0-1 Linear Program based on a flow model. Since the problem can prove hard to be solved by a state-of-the-art optimization software, even for instances of moderate size, we propose a new fast matheuristic for its solution. Preliminary computational tests on a set of realistic instances return encouraging results, showing that our algorithm can find better solutions in considerably less time than a state-of-the-art solver.

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11454
Keywords
5G, Matheuristic, Robust Optimization, Traffic uncertainty, Virtual Network Function, 5G mobile communication systems, Energy efficiency, Linear programming, Transfer functions, Computational tests, Optimization problems, Optimization software, State of the art, Traffic uncertainties, Virtual networks, Network function virtualization
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-72518 (URN)10.1007/978-3-030-16692-2_29 (DOI)2-s2.0-85065708641 (Scopus ID)978-3-030-16691-5 (ISBN)978-3-030-16692-2 (ISBN)
Conference
22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019
Available from: 2019-06-13 Created: 2019-06-13 Last updated: 2019-11-11Bibliographically approved
Bhamare, D., Kassler, A., Vestin, J., Khoshkholghi, M. A. & Taheri, J. (2019). IntOpt: In-Band Network Telemetry Optimization for NFV Service Chain Monitoring. In: 2019 IEEE International Conference on Communications (ICC) Próceedings: . Paper presented at IEEE ICC 2019: IEEE International Conference on Communications 2019 Shanghai, China 20-24 May.
Open this publication in new window or tab >>IntOpt: In-Band Network Telemetry Optimization for NFV Service Chain Monitoring
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2019 (English)In: 2019 IEEE International Conference on Communications (ICC) Próceedings, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Managing and scaling virtual network function(VNF) service chains require the collection and analysis ofnetwork statistics and states in real time. Existing networkfunction virtualization (NFV) monitoring frameworks either donot have the capabilities to express the range of telemetryitems needed to perform management or do not scale tolarge traffic volumes and rates. We present IntOpt, a scalableand expressive telemetry system designed for flexible VNFservice chain network monitoring using active probing. IntOptallows to specify monitoring requirements for individual servicechain, which are mapped to telemetry item collection jobsthat fetch the required telemetry items from P4 (programmingprotocol-independent packet processors) programmable dataplaneelements. In our approach, the SDN controller creates theminimal number of monitoring flows to monitor the deployedservice chains as per their telemetry demands in the network.We propose a simulated annealing based random greedy metaheuristic(SARG) to minimize the overhead due to activeprobing and collection of telemetry items. Using P4-FPGA, webenchmark the overhead for telemetry collection and compareour simulated annealing based approach with a na¨ıve approachwhile optimally deploying telemetry collection probes. Ournumerical evaluation shows that the proposed approach canreduce the monitoring overhead by 39% and the total delays by57%. Such optimization may as well enable existing expressivemonitoring frameworks to scale for larger real-time networks.

Keywords
In-band Network Telemetry, Monitoring, P4, Service Function Chain, Software Defined Networks
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-74631 (URN)10.1109/ICC.2019.8761722 (DOI)978-1-5386-8089-6 (ISBN)978-1-5386-8088-9 (ISBN)
Conference
IEEE ICC 2019: IEEE International Conference on Communications 2019 Shanghai, China 20-24 May
Projects
HITS, 4707
Funder
Knowledge Foundation
Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-09-19Bibliographically approved
Santos, R., Koslowski, K., Daube, J., Ghazzai, H., Kassler, A., Sakaguchi, K. & Haustein, T. (2019). mmWave Backhaul Testbed Configurability Using Software-Defined Networking. Wireless Communications & Mobile Computing, 1-24, Article ID 8342167.
Open this publication in new window or tab >>mmWave Backhaul Testbed Configurability Using Software-Defined Networking
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2019 (English)In: Wireless Communications & Mobile Computing, ISSN 1530-8669, E-ISSN 1530-8677, p. 1-24, article id 8342167Article in journal (Refereed) Published
Abstract [en]

Future mobile data traffic predictions expect a significant increase in user data traffic, requiring new forms of mobile network infrastructures. Fifth generation (5G) communication standards propose the densification of small cell access base stations (BSs) in order to provide multigigabit and low latency connectivity. This densification requires a high capacity backhaul network. Using optical links to connect all the small cells is economically not feasible for large scale radio access networks where multiple BSs are deployed. A wireless backhaul formed by a mesh of millimeter-wave (mmWave) links is an attractive mobile backhaul solution, as flexible wireless (multihop) paths can be formed to interconnect all the access BSs. Moreover, a wireless backhaul allows the dynamic reconfiguration of the backhaul topology to match varying traffic demands or adaptively power on/off small cells for green backhaul operation. However, conducting and precisely controlling reconfiguration experiments over real mmWave multihop networks is a challenging task. In this paper, we develop a Software-Defined Networking (SDN) based approach to enable such a dynamic backhaul reconfiguration and use real-world mmWave equipment to setup a SDN-enabled mmWave testbed to conduct various reconfiguration experiments. In our approach, the SDN control plane is not only responsible for configuring the forwarding plane but also for the link configuration, antenna alignment, and adaptive mesh node power on/off operations. We implement the SDN-based reconfiguration operations in a testbed with four nodes, each equipped with multiple mmWave interfaces that can be mechanically steered to connect to different neighbors. We evaluate the impact of various reconfiguration operations on existing user traffic using a set of extensive testbed measurements. Moreover, we measure the impact of the channel assignment on existing traffic, showing that a setup with an optimal channel assignment between the mesh links can result in a 44% throughput increase, when compared to a suboptimal configuration.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2019
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-71786 (URN)10.1155/2019/8342167 (DOI)000465345700001 ()
Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-05-09Bibliographically approved
Alizadeh Noghani, K., Kassler, A. & Taheri, J. (2019). On the Cost-Optimality Trade-off for Service Function Chain Reconfiguration. In: : . Paper presented at IEEE CloudNet 2019 - 8th IEEE International Conference on Cloud Networking, Coimbra, Portugal, 4-6 Nov. 2019. IEEE
Open this publication in new window or tab >>On the Cost-Optimality Trade-off for Service Function Chain Reconfiguration
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Optimal placement of Virtual Network Functions (VNFs) in virtualized data centers enhances the overall performance of Service Function Chains (SFCs) and decreases the operational costs for mobile network operators. Maintaining an optimal placement of VNFs under changing load requires a dynamic reconfiguration that includes adding or removing VNF instances, changing the resource allocation of VNFs, and re-routing corresponding service flows. However, such reconfiguration may lead to notable service disruptions and impose additional overhead on the VNF infrastructure, especially when reconfiguration entails state or VNF migration. On the other hand, not changing the existing placement may lead to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of the resulting placement and service flow (re)routing. We model different reconfiguration costs related to the migration of stateful VNFs and solve a joint optimization problem that aims to minimize both the total cost of the VNF placement and the reconfiguration cost necessary for repairing a suboptimal placement. Numerical results show that a small number of reconfiguration operations can significantly reduce the operational cost of the VNF infrastructure; however, too much reconfiguration may not pay off should heavy costs be involved.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Joint optimization problem, reconfiguration, virtual network function, VNF migration
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-75574 (URN)
Conference
IEEE CloudNet 2019 - 8th IEEE International Conference on Cloud Networking, Coimbra, Portugal, 4-6 Nov. 2019
Projects
HITS
Funder
Knowledge Foundation
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-14Bibliographically approved
Khoshkholghi, M. A., Taheri, J., Bhamare, D. & Kassler, A. (2019). Optimized Service Chain Placement Using Genetic Algorithm. In: Christian Jacquenet, Filip De Turck, Prosper Chemouil, Flavio Esposito, Olivier Festor, Walter Cerroni, Stefano Secci (Ed.), Proceedings of the 2019 IEEE Conference on Network Softwarization NetSoft 2019, Unleasing the Power of Network Softwarization: . Paper presented at Network Softwarization (NetSoft), IEEE Conference on 24-28 June Paris, France. IEEE
Open this publication in new window or tab >>Optimized Service Chain Placement Using Genetic Algorithm
2019 (English)In: Proceedings of the 2019 IEEE Conference on Network Softwarization NetSoft 2019, Unleasing the Power of Network Softwarization / [ed] Christian Jacquenet, Filip De Turck, Prosper Chemouil, Flavio Esposito, Olivier Festor, Walter Cerroni, Stefano Secci, IEEE, 2019Conference paper (Refereed)
Abstract [en]

Network Function Virtualization (NFV) is anemerging technology to consolidate network functions onto highvolume storages, servers and switches located anywhere in thenetwork. Virtual Network Functions (VNFs) are chainedtogether to provide a specific network service. Therefore, aneffective service chain placement strategy is required tooptimize the resource allocation and consequently to reduce theoperating cost of the substrate network. To this end, we proposefour genetic-based algorithms using roulette wheel andtournament selection techniques in order to place service chainsconsidering two different placement strategies. Since mappingof service chains sequentially (One-at-a-time strategy) may leadto suboptimal placement, we also propose Simultaneous strategythat places all service chains at the same time to improveperformance. Our goal in this work is to reduce deployment costof VNFs while satisfying constraints. We consider Geantnetwork as the substrate network along with its characteristicsextracted from SndLib. The proposed algorithms are able toplace service chains with any type of service graph. Theperformance benefits of the proposed algorithms arehighlighted through extensive simulations.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Network Function Virtualization, Optimization, Genetic Algorithm, Service Chain Placement
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-74619 (URN)10.1109/NETSOFT.2019.8806644 (DOI)978-1-5386-9376-6 (ISBN)978-1-5386-9377-3 (ISBN)
Conference
Network Softwarization (NetSoft), IEEE Conference on 24-28 June Paris, France
Projects
HITS, 4707
Funder
Knowledge Foundation
Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-09-04
Vestin, J., Kassler, A., Bhamare, D., Grinnemo, K.-J., Andersson, J.-O. & Pongracz, G. (2019). Programmable Event Detection for In-Band Network Telemetry. In: : . Paper presented at IEEE Cloud Net 4-6 november.
Open this publication in new window or tab >>Programmable Event Detection for In-Band Network Telemetry
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2019 (English)Conference paper (Refereed)
Abstract [en]

In-Band Network Telemetry (INT) is a novel framework for collecting telemetry items and switch internal state information from the data plane at line rate. With the suppor programmable data planes and programming language P4,switches parse telemetry instruction headers and determine which telemetry items to attach using custom metadata. At the network edge, telemetry information is removed and the original packets are forwarded while telemetry reports are sent to a distributed stream processor for further processing by a network monitoring platform. In order to avoid excessive load on the stream processor, telemetry items should not be sent for each individual packet but rather when certain events are triggered. In this paper, we develop a programmable INT event detection mechanism in P4 that allows customization of which events to report to the monitoring system, on a per-flow basis, from the control plane. At the stream processor, we implement a fast INT report collector using the kernel bypass technique AF XDP, which parses telemetry reports and streams them to a distributed Kafka cluster, which can apply machine learning, visualization and further monitoring tasks. In our evaluation, we use realworld traces from different data center workloads and show that our approach is highly scalable and significantly reduces the network overhead and stream processor load due to effective event pre-filtering inside the switch data plane. While the INT report collector can process around 3 Mpps telemetry reports per core, using event pre-filtering increases the capacity by 10-15x.

National Category
Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-75832 (URN)
Conference
IEEE Cloud Net 4-6 november
Projects
HITS, 4707
Funder
Knowledge Foundation
Available from: 2019-11-27 Created: 2019-11-27 Last updated: 2019-11-27
Aboueata, N., Alrasbi, S., Erbad, A., Kassler, A. & Bhamare, D. (2019). Supervised machine learning techniques for efficient network intrusion detection. In: Proceedings - International Conference on Computer Communications and Networks, ICCCN: . Paper presented at 28th International Conference on Computer Communications and Networks, ICCCN 2019, 29 July 2019 through 1 August 2019. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Supervised machine learning techniques for efficient network intrusion detection
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2019 (English)In: Proceedings - International Conference on Computer Communications and Networks, ICCCN, Institute of Electrical and Electronics Engineers Inc. , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in telecommunication industry. Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) are being widely deployed and utilized by end users, including many private as well as public organizations. Despite its wide-spread acceptance, security is still the biggest threat in cloud computing environments. Users of cloud services are under constant fear of data loss, security breaches, information theft and availability issues. Recently, learning-based methods for security applications are gaining popularity in the literature with the advents in machine learning (ML) techniques. In this work, we explore applicability of two well-known machine learning approaches, which are, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), to detect intrusions or anomalous behavior in the cloud environment. We have developed ML models using ANN and SVM techniques and have compared their performances. We have used UNSW-NB-15 dataset to train and test the models. In addition, we have performed feature engineering and parameter tuning to find out optimal set of features with maximum accuracy to reduce the training time and complexity of the ML models. We observe that with proper features set, SVM and ANN techniques have been able to achieve anomaly detection accuracy of 91% and 92% respectively, which is higher compared against that of the one achieved in the literature, with reduced number of features needed to train the models.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Artificial Neural Networks, Cloud Computing, Intrusion Detection, Support Vector Machines, Anomaly detection, Computer networks, Cost effectiveness, Machine learning, Neural networks, Platform as a Service (PaaS), Software as a service (SaaS), Statistical tests, Supervised learning, Telecommunication industry, Web services, Cloud computing environments, Feature engineerings, Learning-based methods, Machine learning approaches, Network intrusion detection, Security application, Supervised machine learning, Virtualized data centers, Infrastructure as a service (IaaS)
National Category
Computer Sciences
Identifiers
urn:nbn:se:kau:diva-75727 (URN)10.1109/ICCCN.2019.8847179 (DOI)2-s2.0-85073165460 (Scopus ID)9781728118567 (ISBN)
Conference
28th International Conference on Computer Communications and Networks, ICCCN 2019, 29 July 2019 through 1 August 2019
Available from: 2019-11-12 Created: 2019-11-12 Last updated: 2019-11-13Bibliographically approved
Alizadeh Noghani, K., Ghazzai, H. & Kassler, A. (2018). A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks. In: 2018 IEEE Global Communications Conference (GLOBECOM): . Paper presented at 2018 IEEE Global Communications Conference (GLOBECOM) Abu Dhabi, United Arab Emirates, 9-13 dec (pp. 1-7). IEEE
Open this publication in new window or tab >>A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks
2018 (English)In: 2018 IEEE Global Communications Conference (GLOBECOM), IEEE, 2018, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

With the emergence of millimeter-Wave (mmWave) communication technology, the capacity of mobile backhaul networks can be significantly increased. On the other hand, Mobile Edge Computing (MEC) provides an appropriate infrastructure to offload latency-sensitive tasks. However, the amount of resources in MEC servers is typically limited. Therefore, it is important to intelligently manage the MEC task offloading by optimizing the backhaul bandwidth and edge server resource allocation in order to decrease the overall latency of the offloaded tasks. This paper investigates the task allocation problem in MEC environment, where the mmWave technology is used in the backhaul network. We formulate a Mixed Integer NonLinear Programming (MINLP) problem with the goal to minimize the total task serving time. Its objective is to determine an optimized network topology, identify which server is used to process a given offloaded task, find the path of each user task, and determine the allocated bandwidth to each task on mmWave backhaul links. Because the problem is difficult to solve, we develop a two-step approach. First, a Mixed Integer Linear Program (MILP) determining the network topology and the routing paths is optimally solved. Then, the fractions of bandwidth allocated to each user task are optimized by solving a quasi-convex problem. Numerical results illustrate the obtained topology and routing paths for selected scenarios and show that optimizing the bandwidth allocation significantly improves the total serving time, particularly for bandwidth-intensive tasks.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE Global Communications Conference (GLOBECOM), ISSN 2576-6813, E-ISSN 2576-6813
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-71436 (URN)10.1109/GLOCOM.2018.8647559 (DOI)000465774302096 ()978-1-5386-4727-1 (ISBN)978-1-5386-6976-1 (ISBN)
Conference
2018 IEEE Global Communications Conference (GLOBECOM) Abu Dhabi, United Arab Emirates, 9-13 dec
Projects
Socra, 4840
Funder
Knowledge Foundation
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-11-10Bibliographically approved
Marotta, A., Avallone, S. & Kassler, A. (2018). A Joint Power Efficient Server and Network Consolidation approach for virtualized data centers. Computer Networks, 130, 65-80
Open this publication in new window or tab >>A Joint Power Efficient Server and Network Consolidation approach for virtualized data centers
2018 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 130, p. 65-80Article in journal (Refereed) Published
Abstract [en]

Cloud computing and virtualization are enabling technologies for designing energy-aware resource management mechanisms in virtualized data centers. Indeed, one of the main challenges of big data centers is to decrease the power consumption, both to cut costs and to reduce the environmental impact. To this extent, Virtual Machine (VM) consolidation is often used to smartly reallocate the VMs with the objective of reducing the power consumption, by exploiting the VM live migration. The consolidation problem consists in finding the set of migrations that allow to keep turned on the minimum number of servers needed to host all the VMs. However, most of the proposed consolidation approaches do not consider the network related consumption, which represents about 10–20% of the total energy consumed by IT equipment in real data centers. This paper proposes a novel joint server and network consolidation model that takes into account the power efficiency of both the switches forwarding the traffic and the servers hosting the VMs. It powers down switch ports and routes traffic along the most energy efficient path towards the least energy consuming server under QoS constraints. Since the model is complex, a fast Simulated Annealing based Resource Consolidation algorithm (SARC) is proposed. Our numerical results demonstrate that our approach is able to save on average 50% of the network related power consumption compared to a network unaware consolidation.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Cloud, Virtualization, Power, Green computing, Simulated annealing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-65324 (URN)10.1016/j.comnet.2017.11.003 (DOI)000424179900006 ()
Projects
HITS
Funder
Knowledge Foundation
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2019-10-29Bibliographically approved
Gokan Khan, M., Taheri, J., Kassler, A. & Darula, M. (2018). Automated Analysis and Profiling of VirtualNetwork Functions: the NFV-Inspector Approach. In: 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN): . Paper presented at IEEE Conference on Network Function Virtulization and Software defined Networks, Verona, Italy, 27-29 November 2018. IEEE
Open this publication in new window or tab >>Automated Analysis and Profiling of VirtualNetwork Functions: the NFV-Inspector Approach
2018 (English)In: 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Discovering insights about Virtual Network Function (VNFs) resource demand characteristics will enable cloud vendors to optimize their underlying Network Function Virtualization (NFV) system orchestration and dramatically mitigate CapEx and OpEx spendings. However, analyzing large-scale NFV systems, especially in mobile network environments, is a challenging task and requires tailor-made approaches for each particular application. In this demo, we showcase NFV-Inspector, an open source and extensible VNF analysis platform that is capable of systematically benchmark and profile NFV deployments. Based on its pluggable framework, NFV-Inspector classifies VNFs resource demand characteristics and correlate their Key Performance Indicators (KPIs) with system-level Quality of Service (QoS) measurements. 

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Classification, Network Function Virtualization, Platform, Profiling, Quality of Service
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-71388 (URN)10.1109/NFV-SDN.2018.8725697 (DOI)000475896900023 ()978-1-5386-8281-4 (ISBN)978-1-5386-8282-1 (ISBN)
Conference
IEEE Conference on Network Function Virtulization and Software defined Networks, Verona, Italy, 27-29 November 2018
Projects
NFV Optimizer, 5276
Funder
Knowledge Foundation, 20160182
Note

Available from: 2019-02-28 Created: 2019-02-28 Last updated: 2019-08-06Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9446-8143

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