Five Trends Leading to Opportunities in Multi-Cloud Global Application Delivery
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1 Five Trends Leading to Opportunities in Multi-Cloud Global Application Delivery Washington University in Saint Louis Saint Louis, MO Research Seminar at Cisco, San Jose, CA March 18, 2016 These slides are available on-line at: 1
2 Overview Why Multi-Cloud? 1. Cloud Computing 2. Software Defined Networking (SDN) 3. Mobile Traffic Explosion 4. Smart Everything 5. Multi-Cloud Hierarchy OpenADN Multi-Cloud Management Service Function Placement Problem 2
3 1. Cloud Computing Cloud computing was invented in 2006 Then: Cloud = Large Data Center Multiple VMs managed by a cloud management system (OpenStack) Today: Cloud = Computing using virtual resources Cloud = Cloud in a server with multiple VMs. Each VM with Multiple Containers Multiple Services 3
4 2. Software Defined Networking (SDN) SDN was invented in 2009 Then: SDN: OpenFlow Southbound Separation of control and data planes Centralization of Control Now: SDN = Disaggregation of hw/sw Commodity hardware Software that runs on commodity hw Open Source Software Service industry Controller replaced by Orchestrator Control Plane Data Plane Data Plane Orchestrator SW HW SW HW 4
5 3. Mobile Traffic Explosion Backbone Traffic =N X Access Rate= X X N enb X With small cells, N=# of edge points can be large While 5G radio access technologies may see only 1000 increase, backhaul may see much larger increase in capacity, highly dynamic, local Need dynamic capacity management Network Function Virtualization (NFV) Need aggregation Computing in the Edge 5
6 NFV in a Multi-Cloud Multi-Tenant Environment Service Provider 1 Service Provider 2 Infrastructure Provider 1 Infrastructure Provider 2 Infrastructure Provider 3 Residential Gateway CGNAT Set Top Box Residential Gateway CGNAT Set Top Box RNC IMS MME Hardware RNC IMS MME Hardware 6
7 Any Function Virtualization (FV) Network function virtualization of interest to Network service providers But the same concept can be used by any other industry, e.g., financial industry, banks, stock brokers, retailers, mobile games, Everyone can benefit from: Functional decomposition of there industry Virtualization of those functions Service chaining those virtual functions (VFs) or Apps 7
8 Networking App Market: Lower CapEx Virtual IP Multimedia System 8
9 What s Smart? 4. Smart Everything Old: Smart = Can think fast High compute power Then: Smart = Can remember everything High storage Now: Smart = Can communicate Good Networking Smart Grid, Smart Meters, Smart Cars, Smart homes, Smart Cities, Smart Factories, Smart Smoke Detectors, Think Communicate Not-Smart Smart 9
10 Mobile Edge Computing To service mobile users/iot, the computation needs to come to edge Mobile Edge Computing Users Micro-Clouds Network 10
11 5. Multi-Cloud Hierarchy Wide area clouds, local area clouds (home routers with cloud features), Personal area clouds (cars), body area clouds (smart phone) Fog Computing Users Micro-Clouds Regional Clouds Local Clouds Network 11
12 Mobile Healthcare Use Case Home sensors for patient monitoring Medical Application Service Provider (Insurance Co) Multi-Cloud Mobile Application Deployment and Optimization Platform Hospital Cloud SDN Controller Insurance Co Cloud Body Area Network for mobile patient 5G Carrier 12 Mobile Doctor
13 Domain 2.0 Today: Virtualization of switches Ref: AT&T, Domain 2.0 White paper, 13
14 Domain 2.1 Tomorrow: Virtualization of Clouds Multi-Cloud Operating System Cloud Cloud Cloud Cloud Cloud 1. Latency-Sensitive 2. Restricted Bandwidth 3. Policy-based automation 14
15 OpenADN Multi-Cloud Management Application Service Developers Application Architects Application Deployment Administrators Application Service Providers MCAD Application Service (AAS) abstraction MCAD Application Workflow (AAW) abstraction MCAD Application Cloud (AAC) abstraction Northbound Interface Multi-Cloud Application Delivery (MCAD) Platform MCAD Resource Driver (OpenStack) MCAD Resource Driver (OpenDaylight) MCAD Resource Driver (EC2) Southbound Interface Virtual Hosts Virtual Storage OpenStack Virtual Networ k Virtual WAN Services OpenDaylight Virtual Hosts Virtual Storage EC2 Virtual Networ k Resource Providers Enterprise Datacenter Cloud Datacenter Carrier Network Network POP Micro-Datacenters 15
16 Key aspects of OpenADN Architecture Global and local controllers All services, servers, instances accessed by name Can be used by any global enterprise or carrier Global Controller (GC) Name Server OpenADN Modules Local Controller (LC) OpenStack Virtual WAN Controller Open Daylight Local Controller (LC) EC2 Virtual VM-1 VM-N Network VM-1 VM-N Virtual Network Enterprise Data center Cloud Data Center ISP Network 16
17 Challenges in Multi-Cloud Deployment Dynamic: Forwarding changes with state of the servers, links Heterogeneous: Different cloud providers, different services, different policies Distributed Control: Equipment belongs to infrastructure provider Data belongs to Tenants Massive Scale: Millions of enterprise applications sharing networks provided by many ISPs using cloud services from many CSPs 17
18 Service Function Placement Problem VNF 2A VNF 3 VNF 1 VNF 2B VNF 4 Cloud 2 Cloud 1 Cloud 3 18
19 Challenges in Service Placement Delay constraints WAN links bottleneck: Need to model link queues Complexity: NP-complete Need efficient heuristics Affinity: VNF1 and VNF2 should be co-located Significant communication exchanges Duplicate memory pages in VMs (same OS and Libraries) Anti-Affinity: VNF1 and VNF2 should not be placed on the same physical server. CPU-intensive applications VMs belonging to different users in a cloud may cause security risk such as cross-vm attacks Duplicate VMs used to improve fault tolerance and availability 19
20 Sample Service Chains VFs and 3 possible service chains based on business logic 20 20
21 User Clusters A cluster of end users are modeled as one To Cloud To core routers Aggregation Router Access Routers 21
22 VM configurations Resource configurations from Amazon EC2 22
23 Topology UI 23
24 Analysis Results Integer Linear Programming (ILP) to find the optimal solution Exponential complexity (NP-Complete) Takes days Not usable in real-time deployment scenarios Used two heuristics: Greedy (First Fit) Affinity-based approach 24 24
25 Results: Greedy vs. Affinity-based For cluster size 100 at 60% traffic load, total delays are s for Greedy vs s for Affinity 25 25
26 Summary 1. Clouds getting smaller, SDN definition changing to disaggregation, Carriers and enterprises moving to clouds, Internet of things are leading to clouds everywhere => multicloud applications. 2. Our multi-cloud application management system (MCAD) allows policy-based deployment and management of multicloud application. Handles heterogeneous clouds and respects resource ownerships 3. Service function placement problem is NP complete. Challenges included delay constraints, WAN Link bottlenecks, and affinity 4. We have developed an efficient heuristic for placement that is 2X faster than greedy based heuristics. 26
27 References Deval Bhamare, Raj Jain, Mohammed Samaka, Gabor Vaszkun, Aiman Erbad, "Multi-Cloud Distribution of Virtual Functions and Dynamic Service Deployment: OpenADN Perspective," Proceedings of 2nd IEEE International Workshop on Software Defined Systems (SDS 2015), Tempe, AZ, March 9-13, 2015, 6 pp. Subharthi Paul, Raj Jain, Mohammed Samaka, Aiman Erbaud, "Service Chaining for NFV and Delivery of other Applications in a Global Multi-Cloud Environment," ADCOM 2015, Chennai, India, September 19, 2015, Raj Jain, Mohammed Samaka, "Application Deployment in Future Global Multi-Cloud Environment," The 16th Annual Global Information Technology Management Association (GITMA) World Conference, Saint Louis, MO, June 23, 2015, 27
28 References (Cont) Subharthi Paul, Raj Jain, Mohammed Samaka, Jianli Pan, "Application Delivery in Multi-Cloud Environments using Software Defined Networking," Computer Networks Special Issue on cloud networking and communications, Available online 22 Feb 2014, Raj Jain and Subharthi Paul, "Network Virtualization and Software Defined Networking for Cloud Computing - A Survey," IEEE Communications Magazine, Nov 2013, pp , 28
29 Acronyms ATM Asynchronous Transfer Mode ECN Explicit congestion notification EFCI Explicit Forward Congestion Indication FECN Forward Explicit Congestion Notification GB Gigabyte IEEE Institution of Electrical and Electronic Engineering IETF Internet Engineering Task Force IoT Internet of Things IP Internet Protocol IRTF Internet Research Task Force ITU International Telecommunications Union LAN Local Area Network LTE Long Term Evolution MHz Mega Hertz OpenADN Open Application Delivery Networking SDN Software Defined Networking 29
30 Acronyms (Cont) TCP Transmission Control Protocol TV Television VM Virtual Machine WAN Wide Area Network WiFi Wireless Fidelity WiMAX Worldwide Interoperability for Microwave Access 30
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