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Bhamare, Deval
Publications (5 of 5) Show all publications
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. IEEE
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, IEEE, 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.

Place, publisher, year, edition, pages
IEEE, 2019
Series
IEEE International Conference on Communications, ISSN 1550-3607, E-ISSN 1938-1883
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)000492038804033 ()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: 2019-12-18Bibliographically 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, Unleashing 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, Unleashing the Power of Network Softwarization / [ed] Christian Jacquenet, Filip De Turck, Prosper Chemouil, Flavio Esposito, Olivier Festor, Walter Cerroni, Stefano Secci, IEEE, 2019Conference paper, Published 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, 20140037
Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-12-12Bibliographically approved
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: 2019-12-12
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
Gupta, L., Jain, R., Erbad, A. & Bhamare, D. (2019). The P-ART framework for placement of virtual network services in a multi-cloud environment. Computer Communications, 139, 103-122
Open this publication in new window or tab >>The P-ART framework for placement of virtual network services in a multi-cloud environment
2019 (English)In: Computer Communications, ISSN 0140-3664, E-ISSN 1873-703X, Vol. 139, p. 103-122Article in journal (Refereed) Published
Abstract [en]

Carriers' network services are distributed, dynamic, and investment intensive. Deploying them as virtual network services (VNS) brings the promise of low-cost agile deployments, which reduce time to market new services. If these virtual services are hosted dynamically over multiple clouds, greater flexibility in optimizing performance and cost can be achieved. On the flip side, when orchestrated over multiple clouds, the stringent performance norms for carrier services become difficult to meet, necessitating novel and innovative placement strategies. In selecting the appropriate combination of clouds for placement, it is important to look ahead and visualize the environment that will exist at the time a virtual network service is actually activated. This serves multiple purposes - clouds can be selected to optimize the cost, the chosen performance parameters can be kept within the defined limits, and the speed of placement can be increased. In this paper, we propose the P-ART (Predictive-Adaptive Real Time) framework that relies on predictive-deductive features to achieve these objectives. With so much riding on predictions, we include in our framework a novel concept-drift compensation technique to make the predictions closer to reality by taking care of long-term traffic variations. At the same time, near real-time update of the prediction models takes care of sudden short-term variations. These predictions are then used by a new randomized placement heuristic that carries out a fast cloud selection using a least-cost latency-constrained policy. An empirical analysis carried out using datasets from a queuing-theoretic model and also through implementation on CloudLab, proves the effectiveness of the PART framework. The placement system works fast, placing thousands of functions in a sub-minute time frame with a high acceptance ratio, making it suitable for dynamic placement. We expect the framework to be an important step in making the deployment of carrier-grade VNS on multi-cloud systems, using network function virtualization (NFV), a reality.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2019
Keywords
Virtual network services, Network function virtualization, Service function chain, Virtual network function, Multi-cloud systems, Machine learning, Dynamic placement
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-72408 (URN)10.1016/j.comcom.2019.03.003 (DOI)000468709900008 ()
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-12-09Bibliographically approved
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