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Autonomic Computing and Networking Pieter Simoens, Steven Latr Filip De Turck, Bart Dhoedt Future Internet Department 17/05/2011 Gent

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Outline Research Context Thin/Smart client computing Autonomic Communications Introduction to Demos

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Why autonomic systems ? Autonomic systems : Managing complex things is difficult

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Autonomic Systems Observation Complexity of ICT-systems is growing Issues -Management gets complex (high opex) -System configuration error-prone and sub-optimal -Difficult to recover from unforeseen situations

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Autonomic Systems Inspiration : The Human Body - Distributed responsibilities - Collaborating control systems - Each system: optimised for specific task - Under control of central system - Learns and adapts online - Governed by high-level goal: Stay Alive

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Autonomic Systems Autonomic systems decrease management complexity by performing low-level configurations themselves The system adapts its behavior to changes in The environment End-user needs Service requirements It is governed by high-level policies Representing business goals Defined and managed by human operators

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Autonomic Computing MAPE control loop (IBM 2001) - Knows itself and its context - Configures, reconfigures, heals and protects itself - Optimizes continuously - Can interact with outside world - Anticipates to balance resources and needs, without involving users "Civilization advances by extending the number of important operations which we can perform without thinking about them. - Alfred North Whitehead

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ACN @ Future Internet Dpt. 1.Autonomic Technologies - Automatic policy translation - Autonomic adaptation - Scalability and multi-agent management - Learning - Design and implementation of an autonomic service platform 2.Autonomic Communication 3.Autonomic Distributed Computing 4.Integrated infrastructures 5.Smart Client Computing 6.Autonomics for IoT - Sensor networks - ICT for Green

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Outline Research Context Thin/Smart client computing Autonomic Communications Introduction to Demos

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Introduction Thin client ? ideally limited to I/O functions (display, network) CPU and storage hosted in the network Rationale : Enhanced software life cycle management Data security, privacy and integrity Increased terminal lifetime Data is available optimized for wired LAN environments, non I/O intensive applications

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Objectives X-layer optimization for better performance wireless link optimizations image transmission optimizations optimized management (profiling, migration, reservations,...) access network core network public hotspot energy-efficient QoE mobilemultimedia intelligent

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12 MobiThin FP7-STREP (call 1, Challenge 1.1 Future Internet) Time frame start : Jan 1 st, 2008 end : June 30 th, 2010

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MobiThin system 13 Build a mobile thin client service in wireless environment for heterogeneous applications

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System Overview 14

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Project Highlights - Integrated System Management Server SLM Thin Client Server SLM (physical host) User Session SLM (VM that runs apps) Channel server side SLM Channel clientside SLM Mobile Device SLM - Fully functional E2E system has been built, based on requirements analyzed at the start of the project - Cross-layer optimizations = the core business of the project 1) wireless X-layer mechanisms (thin client protocol - PHY-MAC) 2) thin client protocol optimizations - scheduled updates - event buffering 3) self-management of the service - VM migration supporting QoS, peak load avoidance, - server consolidation for green computing 4) SLM framework spanning the complete system developed

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Possible actions per level Relocate session to other server, start/stop extra server Redistribution of resources to certain session, compensating over-spenders by under-spenders Choice of channel (= image transmission protocol) Tuning of channel parameters: color depth, UDP/TCP, user event buffering, scheduled updates, streaming (Semi-) Physical changes: display brightness, wireless interface sleep time Management Server SLM Thin Client Server SLM (physical host) User Session SLM (VM that runs apps) Channel serverside SLM Channel clientside SLM Mobile Device SLM

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Server Consolidation When there is low work load on the system, energy can be saved by shutting down redundant thin client servers. When the work load raises, extra thin client servers should be powered on. Server Consolidation Algorithm Decide how many servers are needed in the (near) future based on the system load in a previous time frame t System load

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PCPU load#online servers%rejected users %difference with simulated #online servers 6.2580.713.80.6-0.8 12.574.414.85.2-1.8 2567.218.33.85.9 5058.921.34.81.7 7547.423.74.4-5.3 100502500 P CPU #online servers Max. Energy Savings: 45% Server Consolidation

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MobiThin Gains Successful project, rated Excellent by EU Strong partnership, good prospects for future collaborations Foundation laid for innovative research ideas Good output in publications > 20 accepted publications Best paper award Standardisation through ETSI (ISG-MTC) 2 work items completed

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From Thin to Smart Thin client : Run the whole application on a server Problems Constant and high bandwidth needed Always extra latency introduced Doesn't work well with some multimedia applications (e.g. augmented reality)

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Smart client Only offload parts of the software

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Adapt the deployment to the changing context and the changing optimization goal Smart client

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Outline Research Context Thin/Smart client computing Autonomic Communications Introduction to Demos

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The goal of autonomic communications Optimize the Quality of Experience, maximize the revenue and do it fast! Router> enable Router# configure terminal Router(config)# interface ethernet 1/1 Router(config-if)# ethernet Router(config-line)# exit Router(config)# end Router# From high-level goals To low-level device configurations

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Autonomic Computing Presented by IBM in 2001 Homogeneous components 1 computing environment MAPE control loop Monitor Analyze Plan Execute Autonomic Communications 25 Computing vs. Communications

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Complexity Manage complexity of an Operations Support System Real-time dynamic management Per service or per subscriber management Will we ever be able to tackle such complexity? Parallel with robotics Millions of interactions Trying to mimic human behavior Still in early stages

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Introducing intelligence into the network HOW? Scalable Privacy Trustworthy Intelligent Human-governed Secure

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A federation of autonomic elements (AE) AE distributed reasoning service discovery contract negotiation context exchange

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Research focus Design and implementation of architectural components for federated management of future networks and services loosely coupled management components semantic communication and collaboration policy driven end-to-end federation of management domains

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Research directions semantic inter-domain contract negotiation autonomic cloud management control loop design automated policy translation

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Automatic policy translation

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FP7 ECODE Introducing autonomic behaviour in todays routers FP7 Strep (Call 1.6 New paradigms and experimental facilities) Timeframe Start: September 2008 End: December 2011

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FP7 ECODE Experimental COgnitive Distributed Engine Cognitive engine on top of an existing router

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Integration of learning capability into self-adaptive closed-loop control process Communication systems autonomously interrelated and controlled, dynamically adapting to changing environments Role of learning How to diagnose their own state, own activity/behavior, and environment over time (thus detect, identify, & analyze problems) How (cost-effective) and when (timely) to adapt decisions and to tune react/execute (and thus capable to increase their functionality and performance) When to operate autonomously and to cooperate Augment control paradigm of pre-defined decision making process, and pre-determined execution, with learning component Routing Forwarding Learning Routing Forwarding Routing + Learning Forward + Learning Router Weak coupling Strong Coupling TodayStep 1: overlay Step 2: integrated

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ECODE machine learning in practice Different TCP stacks cause different levels of fairness Cubic Reno Cubic Highspeed Vegas Highspeed Cubic Vegas Reno Vegas

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ECODE machine learning in practice Different TCP stacks different responsiveness Variations due to Different TCP dialects Defective stacks: ignores congestion warnings Profile Based Accountability holding subscribers (i.e. stacks) accountable for their behaviour aggressiveness responsiveness Good zone reward stacks in the good zone punish stacks in the bad zone

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Outline Research Context Thin/Smart client computing Autonomic Communications Introduction to Demos

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Demo 1 hybrid remote display large areas of solid color few colors updates cover small part of screen low update frequency no homogeneous areas fine-grained complex color patterns updates cover whole screen high update frequency office application text editor, spreadsheet, e-mail client multimedia application video streaming, 3D game Encode through remote display protocol (VNC) Encode through video codec (H.264) Motivation: graphical content diversity

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Dynamically switching between protocols Decision on output encoding format based on amount of motion between subsequent frames inefficient transport of multimedia data via a thin client protocol high bandwidth irresponsive user interface video codecs are designed for transport of video minimal bandwidth requirements for a given amount of motion higher client CPU load due to decoding

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Demo set-up

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Demo 2 SLRG inferencing Identification of Shared Link Resource Groups Shared Link Resource Group

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Demo 2 SLRG inferencing Goal: improve recovery time of link failures by learning. OSPF area One node is enabled with SLRG inference Learns

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Demonstration iLab.t setup Using three nodes ctlvhost-0vid OSPF area video outputDemo control Video streaming

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Demonstration video screen Showing three video streams

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Demonstration video screen What to look for? Video interruptions; standard OSPF (left side) and SRG inference enabled OPSF (right side). For learned SRGs compare left and right parts of a stream; compare streams; compare local and remote link failures.

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Demonstration status screen