IEEE_CCNC_Presentation_Peyman
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This document proposes an information centric networking (ICN) approach for contextualized edge services. It discusses objectives like scalability, availability, and interoperability. It presents a service centric edge cloud platform with key components like an ICN service platform, orchestrator, and APIs. The platform supports open interfaces, context-aware and personalized services, and rapid service composition through network function virtualization. It describes aspects like context management, service composition methodology involving abstraction, composition and implementation phases, and functional service composition using a service-logic graph. Resource allocation and optimization problems in mapping services to infrastructure are also covered.
IEEE_CCNC_Presentation_Peyman
- 1. An Information Centric Networking Approach Towards Contextualized Edge Service Peyman TalebiFard, Ravishankar Ravindran, Asit Charkraborti, Jianli Pan, Anu Mercian, Guoqiang Wang and Victor C.M. Leung
- 2. Outline • Introduction • Objectives and Motivations • Service Centric Edge Cloud Platform • Service Orchestration at the Edge Cloud • Conclusion
- 3. Objectives and Motivations • Scalability • Availability • Interoperability, • Portable - Re-use - Decouple • Context-aware and personalized • Rapid service composition through NFV • Improved operational efficiency • Reduced CAPEX • Standardized and open interfaces and APIs • Performance monitoring of virtualized resources and the infrastructure • Resilience to network failures
- 4. Context-aware Service Composition – Context as a Service by leveraging • Processing • Network • Storage – Context providers and context consumers
- 5. Types of Contextual Information
- 6. Service Centric Edge Cloud Framework • Aimed for ICN-based applications such as conferencing or IoT applications • Leverage name based content dissemination, in- network caching, receiver-oriented interest and data multicasting, content level integrity, and privacy. • Support open-APIs • Adapt to user dynamism through adaptation to changes of context, • Capable of dynamic functional composition and enable different ICN protocols and related services for future deployments and experimentation.
- 8. Functional Components of the Platform • ICN Service Platform • ICN Service Orchestrator • ICN Service API
- 10. ICN Service Platform • ISRs: implement the ICN forwarding plane. • Virtualized services • The platform specific service function – ICN Service Access Point (ICN-SAP) per ISR – ICN Service Profile Manager (ICN-SPM). – ICN Service Access Layer (ICN-SAL) which resides in the User Entity (UE).
- 12. ICN Service Orchestrator • Interfaces with the ICN service owners (operator or third party) to program the ICN service platform’s resources through the ICN S-UNI. • Invokes relevant ICN service controller and ICN network controller functions to meet service objectives. • The S-UNI allows service owners to express service requirements during provisioning and request dynamic changes to compute/storage/bandwidth resources to adapt to service load conditions. • Converts service requirements to compute, storage, service connectivity, and bandwidth requirements to meet service objectives.
- 14. ICN Service API • Interfacing with external entities such as the user and the ASPs for service access and management • Interfacing internally to promote inter- operability.
- 15. SAL-SAP Interaction • Service discovery • Service request management – For example as a result of context change. • Service publish
- 16. Context Management • Context sensing and collection: collecting the required contextual information from different context sources such as social communities, devices, network, and other sensors. • Context processing: is the inference of a situation from raw data that are collected from different entities. – Requires reasoning • Inter-domain context handling and aggregation – managing and aggregating the context information collected from various sources and representing that in an easy to use and understand to be shared with other entities.
- 17. Service Resource Distribution to a Particular Service Interest • Best Case All services that a customer requires is available at the localized service pool. • Worst Case None of the requested services are not available at the local service pool. Each requested service resource has to be obtained from distinct locations/domains • Average Case There are some service resources available at the local service resource pool, and the rest of the service resources are located at one or more location
- 18. Service Composition Methodology • Abstraction and Identification Phase • Composition and Specification Phase • Implementation and Realization Phase
- 19. Abstraction & Integration (XML) Composition (XML / WSDL) Implementation NDL: Network Description Language RDF: Resource Description Framework VNE: Virtual Network Embedding ISR: ICN Service Router VIMA: Virtual Infrastructure Management AgentLogical links for composition Mapping Virtual / Physical Infrastructure Overlay Virtual Network Embedding (VNE) (Stage 3) NDL / RDF S1 S3 S4 S5 S6 S2 S8S7 AS ISR1 ISR2 ISR3 Functional Abstraction of service (Stage 1) Composition (Stage 2) Objective Service S1 S2 S3 S4 S5 VIMA
- 20. <Services> <inputs> <outputs> <Description> … <ServiceRequest> <Context> <Abstract Service Definition . XML> <request.XML> CLM or Look up Solve VNE Composition Optimization Processing S2 S3 S4S5 S1 <New Service> Service Request Management
- 21. Service-Logic Graph • Presents an internal relationship between individual services after decomposition; it shows the order of service implementations, on the service name basis • The graph is created based on pre-defined policies – Since mobility requires service to be context adaptive, in- network/transport/gateway services are appended to the consumer’s major request, with dynamically configuration to adapt contextual updates. – A general policy for creating context-aware service logic is that in- network services follow the major service requests from consumer end. • The service logic could be series or parallel or a mix of series and parallel.
- 22. Name: serviceName Type: service Selector: Interest PDU Name: serviceName Type: service SIDx Data PDU ASPx Content Item Repository … Any Object with unique Identifier
- 23. VNE Phase • Goal: Finding the best set of nodes and links for running a service • Minimize Cost function: – F( bw , resource (cpu) ) – Link capacity constraints – Compute resource constraint – SLA requirements
- 24. Virtual Network Embedding (VNE) VNRi = ( Ni , Li ) Demand function: demi : ( Ni ᴜ Li ) R VNR1 = (N1 , L1) VNR2 = (N2 , L2) VNR3 = (N3 , L3) S3 S3 S3 S2 S2 S1 S1 S1 ISR 1 ISR 3 ISR 2 VNRVNR VNR VNR : Virtual Network Request for service I Ni : Set of virtual nodes Li : Set of virtual links Virtual Network Embedding
- 25. Conf. Service Conf. Service Conf. Service L3/L2 ISR-1 Internet ICN Network Based Conferencing Service ICN Service Orchestrator ISR-2 ICN A-UNI ICN A-UNI ICN S-UNI
- 26. Summary and Conclusion • Context-aware service personalization. • ICN-based Service centric framework. • Service composition. • ICN based platform as the enabler of a semantic based intelligent networking of information.
- 28. Service Layer Protocols ICN (CCN/NDN/MobilityFirst/NetInf etc.) SERVAL OpenADN Naming (Cleanslate) Flexible (Flat, Hierarchical) (Incremental*) Flat Service ID (Incremental*)Application tag/Application Level Switching Name Resolution Coupled/de-coupled. Caching/Multicasting Online Resolution. No Caching Consideration Online Resolution. No Caching Consideration Heterogenous (Anycast, Routing, L2 ) Transport Agnostic, highly adaptable (Ad hoc) (inherent features) Adaptation at SERVAL level Adaptation at OpenADN Level (SDN) - IP Based. Application API Get ()/Put()/Interest/Data (Receiver Oriented) Session based (TCP/UDP) Session based (TCP/UDP) Security Content Level Session Level (Segmented) Session Level (Segmented) Context/Service Orchestration Context-centric/Service Composability/Natural Extension No specific Consideration, but Application Meta-tags Mobility Best-effort/Late binding (control plane) Handles Service level mobility (*not content/host.device) (No consideration) IP-Based
- 29. SAL SAP (Context management, Semantic Engine) ICN Service query (interest) Discover entities Return the URI of the entities Request the information of the entities Return the requested information Collect data for service instantiation Return the results Iteration 1 of Class Composition Apply service logic to the results Response the results Analysis Publish the new service
- 30. App -1 Layer -3/Layer -2 ICN Service Access Layer Content Context Sensing Service Discovery Service Publish Service Request Management Mobility Location Social Device App -2 App -N Context Mgmt. ICN APP -SAL API Home Router Smart TV Smart Phone ICN Service Platform Layer -3/Layer -2 Service Resource Management Service Request Management Service Monitoring Service Context processing Service Publish ICN SAP - Service API Service -1 Service -2 … Service -N Service Access Point UNI -API ICN Service Platform L3/L2 … ICN Service Gateway ICN Service -1 ICN Service -2 Device Scope ICN Service Gateway
- 31. Service Context management • Service Context Management module in the SAL: • Interacts with the SAL modules as well as the underlying infrastructure and access network. • i.e Interaction with the service controller may not be sufficient • Example: Context change may be triggered from service controller • Abstracts the information and interaction with the device hardware as well and the access network technology to detect location, mobility and topological changes
- 32. Context Sensing Module • As part of the SAL context Sensing Module is in charge of triggering on-the-fly functional composition based on the changes in the context from the end-user device or based on the user behavior. – The triggered action is interpreted in the SAP. • This phase is responsible for interpretation and discovery of functions that match the context of already instantiated service. • Context sensing module can operate in two modes of operation. – It can be queried to provide the structured context data format for composing a new service. – It can trigger actions based on the critical level of the action as it pertains to the already running service.
- 33. Provisioning and Monitoring • Provisioning should be done to dynamically control the allocation and migration of IT resource pool, Network, Compute, Storage. • Service / Application health monitoring
- 34. Workflow Languages • BPEL as a common example • BPEL is an XML based language for process oriented service composition • In BPEL, processes are composed • Message exchange among partners are based on WSDL interface.
- 35. Workflow Languages • Defining a process involves: – Creation of a .bpel source file – Process interface description based on WSDL – XML optional descriptor of partner services
- 36. Distance based Similarity for Service Selection •Composition specification can result in different compositions depending on the context •Implicit specifications results in larger degree of freedom •Explicit descriptions makes the selections more limited S1 S2 In(S1) Out(S1) In(S2) Measure of similarity: Sim (out(S1), { In(Si) | Si in Set of Candidate Services})
- 37. Service Discovery and Matching (SAP) • Function : Sim( x , y ) • Matching two objects semantically • Exact Plug-in Subsume disjoint Z= Sim (x , y) 1 1/2 < z < 1 0< z < 1/2 0 Interpretation x ≡ y x plugin y X subsume y x ≠ y
- 38. Formation of Service Logic Graph Abstraction & Integration Composition (WSDL) Translation ( BPEL) S1 S2 In(S1) Out(S1) In(S2)
- 39. Service/Context Ontology • Impact on composition of services • Impact on naming and (context-aware) name resolution in ICN.
- 40. Example: Emergency Dispatch Service Emergency Dispatch directionService findNearestPoI call getLocation lookup
- 41. Example: Emergency Dispatch Service • Step 1: Finding services that yield a connectivity (i.e. valid causal links) – A causal link l : (s_x, sim, s_y) is causal iff Sim(s_x , s_y) > 0
- 42. Example: Emergency Dispatch Service • For the case of call : – Valid causal link would select any candidate with the following input parameters: • Context vector C: URI sip: voipNum phoneNum Connection movementSpeed Device Location Etc. Input Parameters Descriptions Cost, quality, capability, typeOfCall, Transcoding , Other Features…
- 43. Example: Emergency Dispatch Service • A user with the following situation • The selection match (normalized) weight vector would be: User ‘s Situation GPRS connection, WiFi enabled device, looking for a hospital, patient with critical condition. Connection Cost Device TypeOfCall dropCallRate 0.6 0 0.3 0.2 0.1 Highest priority Non-important Attribute Weight
- 44. Resource Allocation • Discrete optimization • Resources can also be service components • Sometimes utilization of a service is desirable (elastic utility -> maximize revenue) • Utilize the semantics and model as a social network of Service Routers • Mining social value of information w.r.t targeted users • Monotone • Submodular (diminishing return)
- 45. Resource Allocation • Problem can be formulated in terms of allocation of services running on distributed elements. • Among a set of ICN routers • Where to instantiate a service for a maximized profit. – Intrinsic value – Network value (influence on adjacent nodes)
- 46. Optimization Problem Considerations • Determining the coefficients for each of the SLA parameters in the utility function – Based on Context information – Based on a sensitivity analysis
- 47. CLM Example • A user wants to contact the customer service of business X and speak with language Y. lookup call bName lang num num
- 48. CLM Example Service S1 (lookup) S2 (lookup) S3 (call) S4 (lookup) Input bName(r3,c3) bName(r3,c3) lang(r2,c2) Num(r4,c4) name Output Num(r4,c4) Num(r4,c4) Call(r6,c6) Num(r4,c4) i/j 1 2 3 4 5 6 Row i Addr lang bName num url call Col j Addr lang bName num url call
- 49. CLM Example C1 C2 C3 C4 C5 C6 1 2 { (S2,1) } 3 { (S1,1) , (S2,1) , (S4,2/3) } 4 { (S3, 1) } 5 6 Input 2 & 3 6 Input 2 & 3 4 Input 4 6