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Green, Trust, and Computation Offloading Perspectives for Optimizing Network Management and Mobile Services

This presentation focuses on the research areas of green network management, computation offloading, and trust management, with a goal of optimizing network management and mobile services. The main themes explored include green network management, computation offloading, and trust management. The speaker's academic career, teaching experiences, research focuses, and perspectives are discussed.

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Green, Trust, and Computation Offloading Perspectives for Optimizing Network Management and Mobile Services

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  1. HABILITATIONADIRIGERDESRECHERCHES Green, Trust and Computation Offloading Perspectives for Optimizing Network Management and Mobile Services ZeinabMOVAHEDI

  2. Outline • Academic career • Research focuses • Thematic 1: Green Network Management • Thematic 2: Computation Offloading • Thematic 3: Trust Management • Conclusion and Perspectives Soutenance HDR – Z. MOVAHEDI

  3. My Career DoctoratInformatique, Télécommunicationet Electroniquede Paris (Réseaux) UPMC Pr. Guy Pujolle Novembre 2011 Licence Informatique Université Paris 8 2005 Master Recherche Informatique (Réseaux) UPMC Co-habilité: ENST 2007 Assistant professor, School of Computer Engineering, IUST Scientific collaborator with Sharif University of Technology, Qom University, E-learning and Pardis Centers of IUST Responsible for international collaborations of School of Computer Engineering, IUST Member of research council of ICT research Institute Member of smart city headquarters, Qom city 2012 Soutenance HDR – Z. MOVAHEDI

  4. Teaching experiences • Advanced topics in computer networks • Advanced computer networks • Wireless communications • Advanced wireless networks • Telecommunication and computer network management • Master level • Advanced topics in network software • PhD level • Network security • Advanced computer programming • Fundamentals of computer and programming • Internet engineering • Imperative programming and data structures • Imperative programming and algorithmic • BSc level Soutenance HDR – Z. MOVAHEDI

  5. Research supervisions Soutenance HDR – Z. MOVAHEDI

  6. Research focuses IoT Mobile Cloud/Edge/Fog Computing (MCC) Wired Networks (Autonomous Systems) Software Defined Netwoking (SDN) Trust Management Green Network Management Soutenance HDR – Z. MOVAHEDI Wireless Networks

  7. Outline • Academic career • Research focuses • Thematic 1: Green Network Management • Thematic 2: Computation Offloading • Thematic 3: Trust Management • Conclusion and Perspectives Soutenance HDR – Z. MOVAHEDI Soutenance HDR – Z. MOVAHEDI

  8. Green Network ManagementICT carbon footprint ICT today: about 2% of global emissions Gtons CO2 Potential direct telecom equipment energy savings by 2020 If business-as-usual 1.4 0.8 0.5 0.5 Zero Growth Line 2002 2007 2020 Soutenance HDR – Z. MOVAHEDI Source: GeSI – SMART 2020: Enabling the Low Carbon Economy in the Information Age

  9. Green Network ManagementDifferent approaches Sleep-Scheduling Re-Engineering Interface Proxying Adaptive Link Rate Soutenance HDR – Z. MOVAHEDI 1 Gbps 10 Mbps F. Dabaghi, Z. Movahedi, and R. Langar. ”A survey on green routing protocols using sleep-scheduling in wired networks.” Journal of Network and Computer Applications 77 (2017): 106-122.

  10. Green Network ManagementSleep-scheduling objective • find a minimum connected subgraph of network topology which respects the tradeoff between energy conservation and network performance. • Modeled as ILP, NP-complete Energy QoS Soutenance HDR – Z. MOVAHEDI Soutenance HDR – Z. MOVAHEDI

  11. Green Network Managementgeneral related work dynamic operation QoS Overhead & Complexity Traffic-unaware heuristics Traffic-aware heuristics Soutenance HDR – Z. MOVAHEDI Contribution 1: Dynamic green network management architecture

  12. Green Network Managementproposed architecture Knowledge component State enforcement component Knowledge Repository Policy Repository Knowledge collection Dynamic sleep-scheduling management Cognitive Knowledge monitoring engine Dynamic mode selection engine Soutenance HDR – Z. MOVAHEDI monitor Switch-off management Switch-on management

  13. Proposed traffic-unaware green approaches Soutenance HDR – Z. MOVAHEDI Soutenance HDR – Z. MOVAHEDI

  14. Green Network ManagementContr. 2&3: existing traffic-unaware green algorithms DGAS VCGA Implicitly QoS-aware • Switches off only links • Lacks dynamic operation • Does not consider the changes in shortest paths • Does not appropriately approximate the potential amount of flow on nodes QoS-unaware Soutenance HDR – Z. MOVAHEDI

  15. Green Network Managementproposed traffic-unaware algorithms: dynamic operation Knowledge component Dynamic sleep-scheduling management Knowledge Repository Policy Repository Dynamic mode selection engine Execute time-based traffic policy traffic = low traffic = high Switch-off management module Switch-on management module Soutenance HDR – Z. MOVAHEDI Nodes switch-off Algo. Awaken sleeping nodes/links according to day’s hours Linkes switch-off Algo.

  16. Green Network ManagementContr. 2: Distributed Green Algorithm based on Sleep-scheduling (DGAS) Considers two topological information: Occurrence & maximum length path change Switch off nodes one-by-one if Connectivity is OK & Paths length change < ε Sort nodes in increasing order of occurrence Nodes switch-off Begin from MST of residual topology inversely weighted based on original occurrence Sort off-links in decreasing order of occurrence Add links until Paths length change <ε Links switch-off Soutenance HDR – Z. MOVAHEDI

  17. Green Network ManagementContr. 3: Vertex Cover based Green Algorithm (VCGA) DGAS VCGA Minimum Vertex Cover Avoid sleeping of high occurrent links during node switch-off process NP-complete Soutenance HDR – Z. MOVAHEDI

  18. Proposed traffic-aware green approaches Soutenance HDR – Z. MOVAHEDI Soutenance HDR – Z. MOVAHEDI

  19. Green Network Managementexistingtraffic-aware green algorithms OGAS OGAS-DALR • Local traffic information • Local-aware decisions may be inappropriate with respect to the entire network • Global traffic information • High complexity • Gradually sleep-scheduling upon flow arrival • Dynamic solutions: Lack of predictive decisions and hence network instability due to subsequent route changes Online Offline Soutenance HDR – Z. MOVAHEDI • Validity of offline traffic matrix? • Traffic matrix needs to be recalculated when network topology changes

  20. Green Network Managementcontr. 4: Online Green Algorithm based on Sleep-scheduling (OGAS) Knowledge component Dynamic sleep-scheduling management Old Links MLU in net. New Links MLU in net. LSA dissemination policy Dynamic mode selection engine Network MLU policy Network MLU Execute network MLU policy Knowledge Repository Policy Repository If MLU < Knowledge collection If MLU > Cognitive Knowledge monitoring engine Switch-off management Switch-on management Execute LSA dissemination policy Soutenance HDR – Z. MOVAHEDI If (new_Link_MLU - old_Link_MLU) > Monitor (disseminate)

  21. Green Network Managementcontr. 4: Online Green Algorithm based on Sleep-scheduling (OGAS) Switch-on management c more occurrent sleeping nodes are stored in active node set Each sleeping link with connected nodes member of active nodes set is re-switched on with probability proportional to occurrence/max occurrence of sleeping links Switch-off management Sort nodes in increasing order of occurrence Switch on nodes in active nodes set if at least one of its links is switched on A low-occurrent node and all its links are switched off if its average MLU < network MLU

  22. Green Network ManagementContr. 5: Adaptive Link Rate extension OGAS Reduce link capacity according to its utilization range proportional to adaptation coefficient ALR At first, apply a sleep-scheduling algorithm and then adapt links' capacity to match the real network usage Soutenance HDR – Z. MOVAHEDI

  23. Outline • Academic career • Research focuses • Thematic 1: Green Network Management • Thematic 2: Computation Offloading • Thematic 3: Trust Management • Conclusion and Perspectives Soutenance HDR – Z. MOVAHEDI Soutenance HDR – Z. MOVAHEDI

  24. Mobile Cloud Computing + = Soutenance HDR – Z. MOVAHEDI

  25. Mobile Cloud ComputingComputation offloading concept local Remote site 1 X = {, …, S_FacePreview 50 • Relation Graph of application’s components Inter-site Invocation Cost X = argmin(Cost(X)) Cost(X) = 9066 90660 D_FaceDetection2 324 D_FaceDetection1 648 Computation Cost 45330 D_ImageCapture 89 160 80 S_FaceDetectionLib 728

  26. Mobile Cloud Computingexistingmobile computation offloading algorithms • Reduces decision cost (time and energy) • Self-adapts offloading decision according to bandwidth changes FACO Near-optimal algorithms • Appropriate for large graph Unacceptable decision cost for larger graphs Ignore the impact of underlying network change ILP Optimal algorithms • Appropriate for small and mid-size graph Soutenance HDR – Z. MOVAHEDI Bounding value set in least bandwidth B&B

  27. Mobile Cloud ComputingContribution 1: FACO architecture Soutenance HDR – Z. MOVAHEDI

  28. Mobile Cloud ComputingContribution 1: FACO decision planner 70 • Based on B & B • Heaviest nodes higher in tree • Put at first the execution site leading to less cost • Initial bounding function min(, • DFS search strategy 240 100 50 90 L 50+7=57 • Bandwidth 2M • Cloud CPU 10 times faster 90 L 9 R 0+70=70 100/2=50 0 0 100/2=50 70 L 7 R 7 R 70 L Soutenance HDR – Z. MOVAHEDI 240/2=120 120 0 0 120 240/2=120 0 0 50 L 5 R 50 L 5 R 50 L 5 R 50 L 5 R

  29. Proposed multi-site computation offloading solution Soutenance HDR – Z. MOVAHEDI Soutenance HDR – Z. MOVAHEDI

  30. Mobile Cloud ComputingContribution 2: Efficient Multisite Computation Offloading Public/Private cloud 2 X = argmin(Cost(X)) Public/Private cloud K local Soutenance HDR – Z. MOVAHEDI Remote site 1 Remote site m X = {, …,

  31. Mobile Cloud Computingcontribution 2: existing multisite mobile computation offloading algorithms • Executes in runtime • Reduces decision cost (time and energy) • Have more chance to converge to optimal solution EMCO Near-optimal algorithms May get stuck in local optimum • Ant colony optimization Optimal algorithms Ignores runtime parameters, e.g. bandwidth • Static decision Soutenance HDR – Z. MOVAHEDI High decision time • B&B

  32. Mobile Cloud ComputingContribution 2: Efficient Multisite mobile Computation Offloading (EMCO) Component 5 Component 1 Component 3 Component 4 Component 2 Initialization Selection Component 5 Component 1 Component 3 Component 4 Component 2 Cross-over Random Mask Chromosome Component 5 Component 1 Component 3 Component 4 Component 2 While (No further Improvement || max iteration reached Mutation Soutenance HDR – Z. MOVAHEDI Component 5 Component 1 Component 3 Component 4 Component 2

  33. Proposed mobility-aware offloading solution Soutenance HDR – Z. MOVAHEDI Soutenance HDR – Z. MOVAHEDI

  34. Mobile Cloud ComputingContribution 3: Mobility-aware and Fault Tolerant Offloading Soutenance HDR – Z. MOVAHEDI

  35. Mobile Cloud ComputingContribution 3: existing mobility-aware offloading solutions • Fine-grain offloading • Considers probabilistic nature of mobility and failure • Fault-tolerance feature MAFO As offloading decision factor • Ignores the probabilistic nature of mobility or failure time prediction • Ignores the fault-tolerance • Some of them are coarse-grain offloading Selecting appropriate cloud based on location-time workflow Considers the connection time to current AP in calculating cost AP selection Considers the extra cost imposed by missing components following a failure Soutenance HDR – Z. MOVAHEDI

  36. Mobile Cloud ComputingContribution 3: Mobility-aware and Fault Tolerant Offloading • Markov chain of user mobility profiled with user pause time and bandwidth • Extracted based on history of user mobility & its experience Pt=20 Bw=256 Pt=20 Bw=256 Pt=20 Bw=256 AP1 AP3 AP2 0.2 0.9 0.8 AP4 AP5 C 0.6 Pt=20 Bw=256 • Genetic algorithm almost similar to EMCO • Fitness calculated based on user mobility Markov Chain 0.1 AP6 0.3 Pt=20 Bw=256 Pt=20 Bw=256 Soutenance HDR – Z. MOVAHEDI

  37. Outline • Academic career • Research focuses • Thematic 1: Green Network Management • Thematic 2: Computation Offloading • Thematic 3: Trust Management • Conclusion and Perspectives Soutenance HDR – Z. MOVAHEDI Soutenance HDR – Z. MOVAHEDI

  38. Trust Management Trust management based on local evidence &received recommendations • Mobile Ad hoc Network A is good E B A is simply bad? A is double-face? C is lier ? D A F C Soutenance HDR – Z. MOVAHEDI A is bad

  39. Mobile Cloud Computingexisting trust management solutions • Enforces local observation to improve the detection time of double-face neighbors • Estimates the recommendation expected to receive from a neighbor TRTMS • What happens if a node is good with respect to network operation but bad with regard to its recommendations (lying)? Existing solutions consider the trust of a recommender as the trust of its recommendation Soutenance HDR – Z. MOVAHEDI Z. Movahedi, Z. Hosseini, F. Bayan, G. Pujolle, "Trust-Distortion Resistant Trust Management Frameworks on Mobile Ad hoc Networks: A Survey", Communications Surveys & Tutorials, IEEE, vol. 18, no. 2, pp. 1287-1309, second quarter 2016

  40. Trust Managementcontribution 1: trust-distortion resistant trust management scheme (TRTMS) OTMS Trust Level Computation Trust Establishment Knowledge Collection Knowledge Repository Policy Base Policies Knowledge Base LTT GTT VTT Direct Trust Monitoring Module Indirect Trust Monitoring Module Soutenance HDR – Z. MOVAHEDI Collecting local information Exchanging remote information Network Interface

  41. Trust Managementcontribution 1: trust-distortion resistant trust management scheme (OTMS) • Direct Trust Monitoring Modified watchdog Local Trust Table (LTT) Virtual Trust Table (VTT) of each neighbor E B Improves the detection of double-face locally • piggybacking • Global Trust Table (GTT) • Indirect Trust Monitoring Minimizes the overhead D • deviation(Received recom., expected recom from VTT) A • Bad-mouthing detection F C Soutenance HDR – Z. MOVAHEDI A behaved good overally regarding its neighbors including myself

  42. Trust Managementcontribution 2: Green Trust-distortion Resistant Trust Management Scheme (GTRTMS) Behavior of a neighbor with regard to each of its neighbors Targeted Local Trust Table (TLTT) Trust of a node is the minimum of its TLTs • Direct Trust Monitoring • Duration of promiscuous overhearing adjusted based on average network trust change and the local monitoring policy • Cognitive direct Trust Monitoring • Based on average network trust, local traffic rate and dissemination policy • Self-adaptive • piggybacking • Cognitive indirect Trust Monitoring Soutenance HDR – Z. MOVAHEDI

  43. Outline • Academic career • Research focuses • Thematic 1: Green Network Management • Thematic 2: Computation Offloading • Thematic 3: Trust Management • Conclusion and Perspectives Soutenance HDR – Z. MOVAHEDI Soutenance HDR – Z. MOVAHEDI

  44. Conclusion Dynamic Green Network Management Architecture Traffic-unaware sleep-scheduling DGAS • Green Network Management VCGA Traffic-aware sleep-scheduling OGAS OGAS-DALR Fast & self-adaptation FACO • Mobile Cloud Computing Multisite EMCO Mobility-aware & fault-tolerance MAFO Soutenance HDR – Z. MOVAHEDI • Trust Management Resistance to simultaneous trust-distortion attacks TRTMS Green feature GTRTMS

  45. Perspectives Simultaneous load-balancing & energy saving (sleep-scheduling ) in SDN • Green Network Management Energy saving in SDN architecture by sleep-scheduling TCAMs through reducing the number of rules in flow table Reducing the cost of offloading decision update Mobility-aware offloading decision • Mobile Cloud Computing Selecting appropriate AP for offloading Optimizing computation offloading using 5G architecture (C-RAN & Edge) Channel assignment between multiple users with offloading tasks Assignment of users with offloading tasks to appropriate edge clouds Soutenance HDR – Z. MOVAHEDI • Trust Management Via central Cloud D2D and IoT trust management Direct D2D

  46. Participation in projects Project collaborations • Gazsmart metering National GazCompany Sept 2017 – present • IoT for power industries Ministry of Energy Sept 2016 – Sept 2017 • IaaS, SaaS and PaaS projects National Center of Cyberspace Jan 2016 – Jan 2017 • Autonomic Internet (AutoI) FP7 European project Jan 2008 – June 2010 Project heading • Mobile Cloud Computing in 5G Hubert Curien Partnerships (PHC) with UPMC Jan 2016 – Jan 2018 • Mobility-aware Computation Offloading in Mobile Cloud Computing IUST April 2017 – April 2018 • Multisite Computation Offloading in Mobile Cloud Computing IUST April 2016 – April 2017 • Green Computation Offloading in Mobile Cloud Computing IUST April 2015 – April 2016 • Green Algorithms for Resource Allocation Management in Cloud Computing IUST April 2014 – April 2015 • Green Routing Protocols for Self-Organizing Networks IUST April 2013 – April 2014 • Trust Management Scheme for Self-Organizing Networks and Internet of Things (IoT) IUST April 2012 – April 2013 Soutenance HDR – Z. MOVAHEDI

  47. Thank you

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