DDS-Enabled Cloud Management Support for Fast Task Offloading ...

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

DDS-Enabled Cloud Management Support for Fast Task Offloading IEEE ISCC 2012, Cappadocia Turkey

Antonio Corradi 1 Luca Foschini 1 Javier Povedano-Molina2 Juan M. Lopez-Soler2 1 Dipartimento

di Elettronica, Informatica, e Sistemistica Universit` a di Bologna (Italy)

2 Departamento

de Teor´ıa de la Se˜ nal, Telem´ atica y Comunicaciones Universidad de Granada (Spain)

2nd July, 2012 Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Agenda Cloud Monitoring and Management DARGOS Data-Centric Publish-Subscribe Architecture Experimental Results Testbed Description Results Conclusions and Future Work

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Conclusions and Future Work

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Cloud monitoring

Cloud monitoring systems can be categorized: I

Architectural model: Centralized vs. Decentralized

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Communication model: Pull vs. Push

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Deployment model: Agent-based vs. Agentless

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Resource monitoring in Clouds I I

A typical approach: centralized pull Central node queries and stores remote resource usage I I

pros: easy to implement cons: central point of failure, request-reply, scalability in N:M scenarios, support for different update rates, no notifications

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Centralized Cloud management

Centralized architecture (OpenStack) Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Conclusions and Future Work

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Centralized Cloud management (II)

Virtual Machine instantiation Typical Cloud scenario (OpenStack) Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Types of loads in Clouds

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Services I I

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Long term duration Load is (almost) stable (e.g. Web server, Databases, ...)

Tasks I I

Short duration (from seconds to few minutes) Load of each task is unknown a priori

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Cloud resource monitoring in dynamic scenarios I

Short-mid tasks with dynamic load I I I

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Require an accurate and reliable snapshot of resources available (real-time update) I

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Bag of Tasks (BoT) Media transcoding Computation offloading

CPU load, memory usage, system load, hypervisor,...

Different goals: maximize throughput, minimize power consumption, ...

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

DARGOS I I I I I

Distributed Architecture for Resource manaGement and mOnitoring in cloudS A distributed monitoring system “Argos Panoptes“: Argos the ”100 eyed“ guardian Uses a Publish Subscribe approach Used to collect real time monitoring data for taking scheduling decisions

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

DCPS

Data-Centric Publish-Subscribe I I I I I

Entities share a data model instead using interfaces Producers publish data conforming this data model Subscribers receive data matching their interests Publishers and subscribers are decoupled in space and time Middleware can manage data samples

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

DCPS

Data Distribution Service (DDS) I

OMG Specification for Data-Centric Publish-Subscribe I I

I I I I I I I I

Data model Wire protocol

Entities exchange Topics (e.g. temperature, 2D position, ...) Topics are defined by their name and data type Topic samples can contain key data to identify them Publishers pushes Topic updates into Subscribers local cache QoS control and management Partition mechanisms Unicast and multicast support Adopted in time critical systems (avionics, stock exchange quotations,...)

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

DCPS

Data Distribution Service

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Experimental Results

Conclusions and Future Work

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Architecture

DARGOS Entities I

DARGOS has two kinds of entities: I

Node Monitoring Agent (NMA): collect and publishs local resource usage I I

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Installed at each node (e.g. CPU, system load, memory,...) 1 resource, 1 topic

Cloud Monitoring Supervisor (CMS): interested in remote monitoring data I I

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Discovers and subscribes remote resources Define their own requirements (reliability, acceptable deadlines) Installed in every application interested in resource data (schedulers, dashboard,...)

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Architecture

DARGOS scenario

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Experimental Results

Conclusions and Future Work

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Architecture

Node Monitoring Agents (NMA)

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Collect local resource data and publishes as DARGOS Topics DARGOS NMAs have two operation modes: I

Periodic I I

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NMA pushes periodically resource usage information Maximizes Accuraccy

Event based I

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NMA pushes resource information under certain conditions (e.g. resource usage delta exceeds threshold) Maximizes Scalability and bandwidth saving

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Architecture

Periodic vs. Event based Periodic

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Period=1 second

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Samples published=10

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Event-based

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Samples sent when usage changes range

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Samples published=5

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Architecture

Cloud Monitoring Supervisor (CMS) I

CMS discovers available nodes and their available sensors (DARGOS Topics)

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CMS subscribe to sensor information of interest (CPU, memory,...)

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Applications that use CMS: Cloud dashboards, schedulers Each CMS define their own quality of service (QoS) requirements

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Reliability or best effort (RELIABILITY) Maximum allowable delay between updates (DEADLINE) Maximum refresh rate (TIME BASED FILTER)

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CMSs establish subscription contracts with NMAs

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On QoS violations, a CMS can trigger actions

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Architecture

DARGOS-based Cloud management

Cloud scenario (OpenStack+DARGOS) Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Conclusions and Future Work

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Testbed Description

Experimental testbed

I I

Testbed with DARGOS-enabled OpenStack Cloud DARGOS based OpenStack scheduler I I

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Server Consolidation Load balancing

Run multiple tasks with random durations and loads

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Testbed Description

Testbed description

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OpenStack Cloud fabric

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DARGOS enabled scheduler service

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Three DARGOS enabled compute nodes

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RTI DDS 4.5d middleware

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1Gbps switch

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Conclusions and Future Work

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Results

Results (VM per node)

OpenStack out-of-the-box scheduler

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

OpenStack DARGOS-based scheduler (consolidation)

Cloud Monitoring and Management

DARGOS

Experimental Results

Results

Results (bandwidth)

Bandwidth consumption per protocol Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Conclusions and Future Work

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Conclusions I

Typical centralized Cloud monitoring systems are not suitable for dynamic scenarios

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DARGOS is a decentralized Cloud monitoring system suitable for fast task

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DARGOS can also satisfy service scenarios

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DARGOS is more robust than centralized systems

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The Data-Centric Publish-Subscribe model used by DARGOS makes possible to manage task oriented Clouds accurately and reliably

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DARGOS introduce low overhead while maintaining accuracy

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Experimental Results

Conclusions and Future Work

Future Directions

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Include more sofisticated scheduling algorithm (e.g. include historical data)

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Extend the notification mechanism to include alarms or complex events

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Add customized action registration to automatically react to certain events (e.g. live migrations)

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Cloud Monitoring and Management

DARGOS

Q&A

Thank You

Corradi, Foschini, Povedano-Molina, Lopez-Soler DDS-Enabled Cloud Management Support for Fast Task Offloading

Experimental Results

Conclusions and Future Work