Télécom SudParis. Djamal Zeghlache Professor. Département Réseaux et Services Multimédia Mobiles

Transcription

1 Télécom SudParis Djamal Zeghlache Professor Département Réseaux et Services Multimédia Mobiles Resource Management Group (in wireless, fixed and computer networks)

2 Département RS2M Méthodes, modèles et optimisation Test et Validation de Protocoles et de Services Services Vérification de Propriété, Protocoles Modèles Méthodes Monitoring Optimisation Combinatoire, Apprentissage et Traitement Distribué de l Information Gestion de ressources, programmabilité et configuration des réseaux Optimisation Apprentissage Prédiction Analyse Architecture de Services et Services Multimédia Mobiles Architecture Réseaux Mobiles & Gestion de Ressources IoT Réseaux Emergents (IoT, capteurs, Ad Hoc, LTE, SF) Architectures e de Réseaux et de Services

3 Equipe (personnes) concernée (s) Cadre Gestion de ressources, programmabilité et configuration 2 E/C, 6 doctorants, 1 Post Doc, 1 CIFRE Collaborations avec d autres E/C (RS2M, RST, INF et UMR CNRS 5157/Samovar

4 Inter-cloud networking framework Work by Marouen Mechtri - Doctoral Student 4

5 Cloud Broker Framework VLAN 6 VLAN 5

6 Cloud Networking Gateway Manager M. Mechtri - Doctorant The CNG Manager has: Northbound interface towards client requesting connectivity based on the OCCI specification and service model. The CNG Manager Core selects the appropriate drivers, in line with user expressed networking requirements. Southbound interface interacting with transport technologies through specific drivers. 6

7 Cloud Networking Gateway and Gateway Manager implementations (M. Mechtri) Code available with readme at o You can also find links to the following (Mechtri et Ghribi) o o o Installation of Openstack IceHouse release with Flat and Neutron networking, Installation of Heat, Deployment of Dockers 7

8 Orchestration Automatisation (avec Dpt INF-TSP) QoS & QoE feedback Users / Consumers / Tenants End users services and Applications Service Requests Service and SLA descriptions and requests (e.g. WS-Agreement and USDL) Service Manager (reconfiguration decisions) Service Life-Cycle Management Analyzer {RuleSet} Alerts/notifications SLA violations, degradations, anomalies SLA & Performance Monitoring of Service Instances (+analysis) Autonomic Manager (SLA injection & Mngt) Monitoring configuration & Probe Deployment Notifications/Message Exchange MAPE Reconfiguration Manager {StrategySet} Infrastructure Resources/services provisioning (Includes scheduling and smart placement) Monitoring configuration & Probe Deployment Alerts/notifications SLA violations, degradations, anomalies SLA & Performance Monitoring of Service Instances (analysis) Analyzer {RuleSet} Cloud services provisioning Network services provisioning Extended OCCI Interface for IaaS & Paas. Cloud Provider (Heterogeneous PaaS & IaaS) Cloud Manager Networking Controler (Nox/Pox/SDN/Any other) Managed Resources and Service Instances OCNI Interface e.g. Software Gateway in a VM. Network Providers Heterogeneous Networks & Networking technologies OCNI Interface SDN Controler & Network API Networking technologies drivers Tunnel Driver (e.g. OpenVPN, IPSEC, GRE ) NFV Driver (e.g. Firewall,DNS,NAT ) OpenFlow Driver NOX Driver Other Drivers (e.g. hardware driver) Extended OCCI Interface.. Cloud Provider (Heterogeneous PaaS & IaaS) Cloud Manager OCNI Interface VLAN or Networking Controler VM i Probe PaaS - Containers In-network services Probes/Monitor Application flows VM j Probe PaaS - Containers 8 OpenNebula.org (4.2) Journée e.g. Linux Cloud Bridge, sur Nantes Septembre VLANs, VXLANs , Mines Nantes. e.g. OpenVSwitch Resource Management Group (in wireless, fixed and computer e.g. OpenStack networks) Neutron- D. Zeghlache Configurable substrate network

9 Smart placement (Collaborations: M. Hadji à l ISx depuis et doctorants de l équipe) 9

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11 Energy based VM placement algorithm Objective, conditions & constraints Objective o initial VM placement leading to minimum number of used servers (or containers) Mathematical Programming Formulation o modelled as a bin packing problem with a minimum power consumption objective Variable comment Bound on the number of used servers m Pj,Max P j, current e j x ij n Number of servers Server power consumption limit Current power consumption Boolean = 1 if j hosts VM Boolean = 1 if VM I assigned to server j Number of requested VMs 11

12 VM placement algorithm Model variables 12

13 VM Migration algorithm (Energy centric) Objective o Optimize data center power consumption using dynamic VM consolidation Mathematical Programming Formulation o Based on linear integer programming formulation Z ijk = 1 if VM k migrated from server i to j y i = 1 if server i idle and = 0 if at least one VM is active m = number of non idle servers m < m P k = power cost when migrating VM k q i is the total number of VMs hosted on server i and candidate for migration into destination servers, especially server j in equation 13

14 VM Migration algorithm Maximize number of empty servers to shut them down by migrating VM to achieve consolidation if a VMk is migrated from a server i (source) to a server j (destination), it can not be migrated to any other server l (l j). Ensuing migrations forbidden Destination VM power budget limit has to be respected Non idle servers candidate for migration have to be entirely emptied Equivalent total number of empty servers Do not migrate a VM whose job is about to end. 14

15 VM Migration algorithm A server candidate to a migration should not migrate its own VMs A VM can not be migrated to many servers at the same time 15

16 Exact energy efficient graph precoloring Algorithm Notations Variable comment G Global graph of requests. V Vertex set of G. E The set of all edges in the graph G. E The set of intra VM edges. C j,id A virtual resource unit VRU or color where j is the server to which it belongs and where id is its associated id. C j Cluster of colors containing colors that belong to the same server j. w j Performance per watt (PPW) of the server j. Z c Boolean = 1 if color c is used and 0 otherwise. x u c y j Boolean = 1 if node u is reserved to color c and 0 otherwise. Boolean = 1 if at least one color belonging to C j is used and 0 otherwise. n Total number of nodes in the graph G. m Number of servers of the data center. 16

17 Objective: Exact energy efficient graph precoloring Algorithm Advanced resource reservation (with minimum number of resources) while maximizing energy efficiency. Mathematical Programming Formulation: Minimize the number of used resources while maximizing energy efficiency Ensure that each node is associated to one and only one color Any two nodes connected by an edge must have different colors Ensure that z c is equal to 1 if the color c is assigned to a node u Nodes belonging to the same VM have to be associated to colors of the same color cluster 17

18 Exact 18

19 Exact -To handle and integrate new request arrivals, we use pre-coloring which is more general than classical graph coloring. -Some vertices of the graph have already assigned colors. - The aim is to color uncolored vertices such that all the graph is colored to respect all time overlap and node constraints 19

20 Energy efficient graph precoloring heuristic The EEGP algorithm uses the following steps to find a solution: 1) Find the color cluster Cj with the highest PPW and with free colors. 2) Determine the neighboring RRUs (or graph vertices) directly connected to VM i RRUs. 3) Construct the list (col j,vmi ) of colors C j that are not assigned to Vm i neighboring RRUs. 4) Finally, the algorithm can assign to each RRU Vm i a different color from the col j,vmi 20

21 Joint node and link mapping Models (mapping/selection/placement) M. Mechtri - Doctorant Exact algorithm (Joint node & link mapping) Heuristic algorithm consists of using network patterns to build a pattern based representation of the reference and the input graph. Use the 2 graphs to solve the problem using a bipartite graph matching technique. Heuristic improves convergence by a factor of in convergence time

22 Mathematical Models Exact Virtual Network Mapping Joint node and link selection Mathematical Programming Formulation Placement Inter domain Path Intra domain Path and V T is the set of vertices and E P the set of edges of the physical or reference or target graph, d(i, k), x ij and y ij, k are Boolean variables indicating if a virtual resource is mapped on n, km a physical one (nodes & links) 22

23 Additional Constraints Node mapping Limited storage Link mapping Node & link mapping Latency Localisation or 23 Resource Management Group (in wireless, fixed and computer networks) - D. Zeghlache

24 Additional Constraints Input Graph (IG) Reference Graph (RG) Example of resources localization constraints : virtual resources 0 and 4 in same node virtual resources 1, 2 & 3 in another node 24

25 Heuristic approach Based on graph patterns and bipartite request and reference graph mappings IG Request Virtual resources RG Infrastructure Physical resources 25

26 Performance Results Convergence speed comparison (from hours to ms, a 10 4 improvement factor) Scalability of Heuristic Algorithm (as a function of input and reference graph sizes) 26

27 Fedération S. Rebai - Doctorante 27

28 Fedération Quelques résultats S. Rebai - Doctorante 28

29 Réseaux Logiciels 29

30 Réseaux Logiciels or Software Defined Networking An old Principle that is gradually materializing LB service IP routing service FW service Network Operating System Service Service Service Operating System Service Specialized Packet Forwarding Hardware Service Service Service Service Operating System Specialized Packet Forwarding Hardware Service Operating System Service Specialized Packet Forwarding Hardware Service Service Operating System Specialized Packet Forwarding Hardware Service Service Service Specialized Packet Operating Forwarding System Hardware 30

31 CMBS - Cloud Message Brokering Service Work by Houssem Medhioub, 3rd year graduate student and RA Exchanging information between cloud providers can have several patterns and can occur in different ways like sending information to one or many providers, or sending information related to a specific topic. To satisfy the Cloud networking requirements of the information exchange, CMBS is based on a message exchange pattern that is structured in five layers 31

32 CMBS - Cloud Message Brokering Service Work by Houssem Medhioub, 3rd year graduate student and RA layer 1 - cloud providers discovery space: The role of this layer is simply to exchange information about the type and characteristics of cloud providers. Cloud providers that want to be members of the CloNe DCP should declare themselves through this layer, in essence they advertise or indicate their desire to join. layer 2 - broadcast space: via this layer, a provider can send an information to all known members of the DCP. layer for provider X: via this layer, a provider can send an information to a particular provider (X). layer about topic Y: via this layer, a provider can send an information about a specific topic (Y) to all providers that are subscribed to this topic (concerned about this topic). layer 4 - for provider X about topic Y: via this layer, a provider can send an information about a particular topic (Y) to a specific provider (X). 32

33 OCNI Open Cloud Networking Interface extension of OCCI for cloud networking Work by Houssem Medhioub, 3rd year graduate student and RA 33

34 L2 Switching OSPF Mobility Management Load Balancing

35 An Overview of NOX What is NOX talks to OpenFlow switches provides libraries for common operations connects to GUI (communicates with NOX by way of JSON messages) has an active user community runs a network out of box Core written in C/C++ event-based cooperative threading use of STL and Boost 35

36 OpenFlow packet format (v1.0) 36

37 Link Provisioning OpenFlow Header Action Statistics B Action B 2 Statistics B OpenFlow Switch 2 OpenFlow Protocol Link Provisioning VN View Header Action Statistics B Action B 3 Statistics B OpenFlow Switch 3 OpenFlow Switch 1 Header Action Statistics B Action B 1 Statistics B B 1 2 BA 1 37

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39 39 Cloud Computing Mastère RS Djamal Zeghlache