Telco Clouds: Modelling and Simulation Jakub Krzywda1, William Tärneberg2, P-O Östberg1, Maria Kihl2, Erik Elmroth1 1 – Umeå University 2 – Lund University MY NAME IS OTHER AUTHORS are… During this presentation I would like to tell you about our APPROACH to MODELING and SIMULATION of telco clouds.

Multi-Model IN THIS PAPER: We propose a MULTI model ->

… that combines: Telecommunication Cloud computing that combines telecommunication and cloud computing infrastructures -> Telecommunication Cloud computing

… to simulate the influence of: to simulate the influence of users mobility and infrastructure setup -> Mobility Infrastructure Setup

… on: on the system performance and costs. Performance Costs

Computation Offloading Mobile Devices high requirements on Quality of Service limited battery lifetime less powerful resources To give some MOTIVATIONS lets take a look on following example: Mobile phones – NOWADAYS not only making CALLS but also for RUNNING APPLICATIONS Applications – RICH USER INTERFACES and high DEMANDS on computational resources and QoS However… limited battery, less powerful resources than in desktops TO ENABLE running this kind of applications we can use COMPUTATION OFFLOADING

Computation Offloading Computations are offloaded from # mobile phones to # data centers: DATA ARE FIRST SENT to # radio base stations and then via # Internet to data centers, processed there and results are sent back

Other motivations But computation offloading is NOT THE ONLY motivation SELF DRIVING CARS INTERNET OF THINGS with MILLIONS of SENSORS sending data through internet

Current Situation Telecommunication Infrastructure HOWEVER in the current situation # Telecommunication infrastructure – such as radio base station and # cloud computing infrastructure such as data centers are # SEPERATED and MANAGED INDEPENDENTLY Cloud Computing Infrastructure

Issues Latency Hardware costs Network Congestion That leads to several issues. For example, # Latency due to distance # with EXPLOSION of Internet of Things - millions of SENSORS sending data for processing/storing – # may cause network congestion # todays telecommunication hardware is EXPENSIVE Network Congestion

Introducing Telco Cloud To solve this Telco Cloud concept WAS PROPOSED

Current Situation Merging Telecommunication and Cloud Infrastructures CURRENTLY Telcommunication and cloud infrastructures are SEPARATED Telco Cloud OPENS THAT BARRIER and MARGES both infrastructures

Telco Cloud Introducing Proximal Data Centers by PLACING additional PROXIMAL Data Centers at the EDGE of MOBILE NETWORK (closer to users)

Benefits Reduce Latency Decrease costs Prevent Network Congestion Merging Telco and Cloud infrastructures to increase QoS (e.g. by reducing latency) prevent network congestion (e.g. by FLITERING some data and PROCESSING them LOCALLY) decrease costs of operation (e.g. by virtualizing some parts of radio base stations and moving them into the cloud) Prevent Network Congestion

Telco Cloud Dynamics Applications Mobility Topology DC Capacity Workload Objectives Setup Applications Mobility Topology DC Capacity Service placement Quality of Service Costs BEFORE MODELLING and specifying parameters IN THIS PAPER WE analyzed the dynamics inside of telco clouds. We identified 3 main ELEMENTS of Telco Cloud: Workload – INPUT, operators have NO INFLUENCE MOBILITY – how user move Objectives – requirements, specification Setup – parameters that can be modified, configuration

Multi-Model OUR MAIN CONTRIBUTION is multi-model BY MULTI-MODEL I MEAN USING EXISTING models of different parts of Telco Cloud: models for DATA CENTERS, NETWORK DELAYS, MOBILITY and REQUEST GENERATION and COMBINING them to simulate the WHOLE SYSTEM

Multi-Model Parameters Objectives Performance Overhead (e.g., migration cost) NOW I will present in details SELECTED PARAMETERS we used for modeling and I will FOCUS on the parameters that have INFLUENCE on the PERFORMANCE OVERHEAD of DISPERSED COMPUTATIONS (e.g., costs of GEOGRAPHICAL migrations)

Multi-Model Parameters Stateful Applications Objectives Workload User’s State Response State 1010 User’s State Size Total Size of User’s Requests Request When modeling WORKLOAD we include a parameter describing USER’S STATE # In case of STATEFUL APPLICATIONS RESULTS of processing # a REQUEST is not only a # RESPONSE but also # USER’S STATE Therefore a RESPONSE doesn’t only depend on the request but also on the user’s state IN OUR MODEL User’s State Size IS PROPORTIONAL to the total size of User’s Requests: More requests -> Bigger User’s State

Multi-Model Parameters Objectives Workload User’s State Mobility User’s State has to follow User’s movements State Next parameter of workload is USER’S MOBILITY that DEFINES how users MOVE around the simulated area, We use TWO DIMENSIONAL, MULTI MODAL MOBILITY MODEL with ON-AVERAGE UNIFORM DISTRIBUTION OF USERS When Mobile Users use Stateful Applications # User’s State has to follow User’s movements and when user crosses the boarder between areas covered by two different Proximal Data Centers # he changes PDC, and his requests will be processed by new PDC then # USER’S STATE MIGRATION is necessary User changes PDC

Multi-Model Parameters Objectives Workload User’s State Mobility Setup Data Center Catchment : Data Center Catchment – # Rato between the NUMBER of Radio Base Stations and the NUMBER of Data Center HOW MANY RADIO BASE STATIONS EACH DATA CENTER HAS TO HANDLE Ratio between RBS and DC

Multi-Model Parameters Objectives Workload User’s State Mobility Setup Data Center Catchment Virtual Machine Life-Cycle To CAPTURE the performance OVERHEAD of migrations we include a model of VIRTUAL MACHINE LIFE-CYCLE

Virtual Machine Life-Cycle AT THE BEGINNING all VMs - INACTIVE state. WHEN 1st request arrives to a DC - VM is initiated process requests OR migrations at the same time (migrations higher priority) | no requests nor migrations -> IDLE # VM is terminated if IDLE state lasts for longer than tidle seconds # VM termination takes tterm seconds

Simulation Showcase ANOTHER CONTRIBUTION is we IMPLEMENTED a PROTOTYPE of TELCO CLOUD SIMULATOR Simulation showcase shows some CAPABILITIES of simulator that we have implemented NOT SOLVING any optimization problem etc.

Simulation Showcase Setup 16 Radio Base Stations (4x4 layout) Catchment of Data Centers varies SQUARES – Radio Base Stations CELLS COLOR RECTANGES – CATCHMENT of Data Centers

Results Small Catchment  Few Devices  Inactivity Bigger Catchment  Longer Stay  More to Migrate Results Static Mobile (Y) TIME SPENT in each VM STATE #(colors) in the system per DC CATCHMENT #(X axis); CONSTANT number of REQUESTS – constant processing time -> Other states are interesting; 30 DEVICES per CELL; LEFT – Static users # (1:1) INACTIVE (dark blue) few users in DC catchment # RIGHT – Mobile users; MIGRATION (orange) higher overhead for smaller catchment however is not proportional – 1:1 – 47%, 1:8 26% REASON: Users’ states do not have the time to grow; Simulation showcase presents CAPABILITIES of simulator not intended to NOT SOLVE any optimization problem etc.

Summary TO SUM UP the presentation I will REMIND our contributions

Telco Cloud Dynamics Workload Objectives Setup We IDENTIFIED fundamental telco cloud DYNAMICS in terms of the RELATIONS between system INPUT, CONFIGURATION and OUTPUT which we call WORKLOAD, SETUP and OBJECTIVES.

Multi-Model We PORPOSED a MULTI MODEL that combines telecommunication and cloud computing INFRASTRUCTURES to simulate the INFLUENCE of users MOBILITY and infrastructure SETUP on the system PERFORMANCE and COSTS.

Who can benefit? Telecommunication Operators and Equipment Developers Model existing infrastructure and plan changes Researchers Testing algorithms for resource management Mobile Applications Developers Testing behavior of apps in Telco Cloud environment Our model and simulator can be beneficial for: TELECOMMUNICATION OPERATORS – to model EXISTING infrastructures and plan FUTURE changes RESEARCHERS – to TEST ALGORITHMS for resource management MOBILE APPLICATIONS DEVELOPERS to test behaviour of applications in Telco Cloud ENVIRONMENT

Limitations Homogeneous requests (at application level) All requests contribute to User’s State Simplified mobile access network model Tangent, non-overlapping, square cells No physical layer, channel provisioning and cell load balancing

Simulation Showcase Setup 16 Radio Base Stations (4x4 layout) Catchment of Data Centers varies Constant capacity of Data Centers #VMs scales with the catchment workload is balanced between VMs 480 Mobile Devices

Simulation requirements Scale (number of) Mobile Devices – hundreds/thousands Radio Base Stations – tens Data Centers – several Length and granularity of simulation (time) Minutes/hours for mobility patterns Milliseconds for latency

Why simulation? No existing Telco Cloud infrastructure Extremely high costs of building infrastructure for testing purposes Huge number of possible setups

When to Offload? Offloading is beneficial when large amounts of computation C are needed with relatively small amounts of communication D Kumar, K.; Yung-Hsiang Lu, "Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?," Computer , vol.43, no.4, pp.51,56, April 2010

Multi-Model OUR MAIN CONTRIBUTION is multi-model WHAT IS MULTI-MODEL– we combine existing models of different parts Details of parameters used for modelling

Multi-Model Parameters Workload Request Generation 1010 HOW REQUESTS ARE GENERATED BY MOBILE DEVICES Number of requests per session Size of requests Inter-request time

Multi-Model Parameters Workload Request Generation Resource Requirements 1010 HOW REQUESTS ARE PROCESSED BY DATA CENTERS CPU cycles used by service Size of memory and storage used by service Size of user’s state produced per request

Multi-Model Parameters Workload Request Generation Resource Requirements Mobility HOW MOBILE DEVICES MOVE Number of Mobile Devices Movements of Mobile Devices

Multi-Model Parameters Workload Setup Mobile Network HOW MOBILE NETWORK LOOKS Number of Radio Base Stations Dimensions of an RBS cell

Multi-Model Parameters Workload Setup Mobile Network Internet WHAT DELEY INTERNET INDUCES Connecting: RBS with DC DCs with each other

Multi-Model Parameters Workload Setup Mobile Network Internet Data Center DCs - Host multiple services in VM Number of data centers Number of servers in data centers Number of CPUs per server CPU speed Memory capacity Storage capacity (combined with resource requirements allows to compute computation time)

Multi-Model Parameters Workload Setup Mobile Network Internet Data Center Service Placement ? Where to place services? Proximal vs Remote? When migrate? ?

Multi-Model Parameters Workload Setup Objectives Quality of Service HOW TO SPECIFY QUALITY OF SERVICE Application response time Application throughput

Multi-Model Parameters Workload Setup Objectives Quality of Service Costs Total cost of infrastructure Increased by computing resource dispersion Reduced by virtualizing some of telecommunication functions