environments, services and network management for green...

Environments, Services and NetworkManagement for Green Clouds

Carlos Becker Westphall

Networks and Management Laboratory

Federal University of Santa Catarina

MARCH 3RD, REUNION ISLAND IARIA GLOBENET 2012 - KEYNOTE SPEECH 1

Summary

1 - Introduction

2 - Motivation

3 - Proposals and Solutions3 - Proposals and Solutions

4 - Case Studies

5 - Results

6 - Conclusions

7 - Future Works



(J. Werner, G. A. Geronimo, C. B. Westphall et al. CLEI EJ 2012)

1 Introduction

• We propose an integrated solution forenvironment, services and networkmanagement based on organization theorymodel.

• This work introduces the system managementmodel, analyses the system’s behavior,describes the operation principles, andpresents case studies and some results.


1 Introduction

• We extended CloudSim to simulate the organizationmodel approach and implemented the migration andreallocation policies using this improved version tovalidate our management solution.

• Organization:

2 introduces a motivating scenario.

3 outlines the system design.

4 presents case studies.

5 presents some results.


2 Motivation

• Our research was motivated by a practicalscenario at our university’s data center.

• Organization theory model for integratedmanagement of the green clouds focusingmanagement of the green clouds focusingon:

• (i) optimizing resource allocation throughpredictive models;


2 Motivation

• (ii) coordinating control over the multipleelements, reducing the infrastructureutilization;

• (iii) promoting the “balance” between local• (iii) promoting the “balance” between localand remote resources; and

• (iv) aggregating energy management ofnetwork devices.


2 Motivation (Concepts & Analysis)

Cloud computing

• This structure describes the most commonimplementation of cloud; and

• It is based on server virtualization• It is based on server virtualizationfunctionalities, where there is a layer thatabstracts the physical resources of the serversand presents them as a set of resources to beshared by VMs.


The NIST Cloud Definition Framework

CommunityCommunityCloudCloud

PrivatePrivateCloudCloud

Public CloudPublic Cloud

Hybrid Clouds

DeploymentModels

ServiceModels

Software as aService (SaaS)

Platform as aService (PaaS)

Infrastructure as aService (IaaS)

11

EssentialCharacteristics

CommonCharacteristics

Resource Pooling

Broad Network Access Rapid Elasticity

Measured Service

On Demand Self-Service

Low Cost Software

Virtualization Service Orientation

Advanced Security

Homogeneity

Massive Scale Resilient Computing

Geographic Distribution

Based upon original chart created by Alex Dowbor


Green cloud

• The green cloud is not very different fromcloud computing, but it infers a concernover the structure and the socialover the structure and the socialresponsibility of energy consumption; and

• Hence aiming to ensure the infrastructuresustainability without breaking contracts.



Analysis

• Table I relates (1) the 3 possiblecombinations between VMs and PMs, with(2) the average activation delay, and (3) the(2) the average activation delay, and (3) thechances of the services not being processed(risk); and

• It also presents the energy consumedaccording to each scenario.



PM State VM State Time Risks Watts Consumption

Down Down 30s High 0Ws None

Up Down 10s Medium 200Ws MediumUp Down 10s Medium 200Ws Medium

Up Up 0s None 215Ws High

RELATION BETWEEN SITUATIONS & RISKS & ACTIVATION DELAY & CONSUMPTION(ASSUNÇÃO, M. D. ET AL. ENERGY 2010)


2 Motivation (Related Works)

• E. Pinheiro, et al. “Load balancing andunbalancing for power and performance incluster-based systems” in Proceedings of theWorkshop on Compilers and OperatingSystems for Low Power. 2001.Systems for Low Power. 2001.

• Pinheiro et al. have proposed a technique formanaging a cluster of physical machines thatminimizes power consumption whilemaintaining the QoS level.



• The main technique to minimize powerconsumption is to adjust the load balancingsystem to consolidate the workload in someresources of the cluster to shut down the idleresources.resources.

• At the end, besides having an economy of 20%compared to fulltime online clusters, it savesless than 6% of the whole consumption of thedata center.



• R. N. Calheiros, et al. “Cloudsim: A toolkit formodeling and simulation of cloud computingenvironments and evaluation of resourceprovisioning algorithms” Software: Practiceprovisioning algorithms” Software: Practiceand Experience. 2011.

• Calheiros et al. have developed a frameworkfor cloud computing simulation. It has fourmain features:



• (i) it allows for modeling and instantiation ofmajor cloud computing infrastructures,

• (ii) it offers a platform providing flexibility ofservice brokers, scheduling and allocationsservice brokers, scheduling and allocationspolicies,

• (iii) its virtualization engine can becustomized, thus providing the capability tosimulate heterogeneous clouds, and



• (iv) it is capable of choosing the schedulingstrategies for the resources.

• R. Buyya, et al. “Intercloud: Utility-orientedfederation of cloud computing environmentsfederation of cloud computing environmentsfor scaling of application services”Proceedings of the 10th InternationalConference on Algorithms and Architecturesfor Parallel Processing. 2010.



• Buyya et al. suggested creating federatedclouds, called Interclouds, which form a cloudcomputing environment to support dynamicexpansion or contraction.expansion or contraction.

• The simulation results revealed that theavailability of these federated clouds reducesthe average turn-around time by more than50%.



• It is shown that a significant benefit for theapplication’s performance is obtained by usingsimple load migration policies.

• R. Buyya, et al. “Energy-Efficient Management ofData Center Resources for Cloud Computing: AData Center Resources for Cloud Computing: AVision, Architectural Elements, and OpenChallenges” in Proceedings of the 2010International Conference on Parallel andDistributed Processing Techniques andApplications.



• Buyya et al. aimed to create architecture of greencloud. In the proposals some simulations areexecuted comparing the outcomes of proposedpolicies, with simulations of DVFS (DynamicVoltage and Frequency Scaling).Voltage and Frequency Scaling).

• They leave other possible research directionsopen, such as optimization problems due to thevirtual network topology, increasing responsetime for the migration of VMs because of thedelay between servers or virtual machines whenthey are not located in the same data centers.



• L. Liu, et al. “Greencloud: a new architecturefor green data center” in Proceedings of the6th international conference industry sessionon autonomic computing. 2009.on autonomic computing. 2009.

• Liu et al. presented the GreenCloudarchitecture to reduce data center powerconsumption while guaranteeing theperformance from user perspective.



• P. Mahavadevan, et al. “On Energy Efficiencyfor Enterprise and Data Center Networks” inIEEE Communications Magazine. 2011.

• Mahadevan et al. described the challenges• Mahadevan et al. described the challengesrelating to life cycle energy management ofnetwork devices, present a sustainabilityanalysis of these devices, and developtechniques to significantly reduce networkoperation power.


2 Motivation (Problem Scenario)

• To understand the problem scenario, weintroduce the elements, interactions, andoperation principles in green clouds.

• The target in green clouds is: how to keep• The target in green clouds is: how to keepresources turned off as long as possible?

• The interactions and operation principles ofthe scenario are:



• (i) there are multiple applications generatingdifferent load requirements over the day;

• (ii) a load “balance” system distributes theload to active servers in the processing pool;load to active servers in the processing pool;

• (iii) the resources are grouped in clusters thatinclude servers and local environmentalcontrol units; and



• (iv) the management system can turn on/offmachines overtime, but the question is whento activate resources on-demand?

• In other words, taking too much delay to• In other words, taking too much delay toactivate resources in response to a surge ofdemand (too reactive) may result in theshortage of processing power for a while.


3 Proposals and Solutions


3 Proposals and Solutions

• The four roles that operations system may beclassified as are: VM management; Serversmanagement; Network management; andEnvironment management.


Environment management.

• The three roles that service system may beclassified as are: Monitor element; Servicescheduler; and Service analyser.

4 Case Studies

• We modeled the system using Norms (NM),Beliefs (BL) and Plan Rules (PR), inferring thatwe would need (NM) to reduce energyconsumption.consumption.

• Based on inferences from NM, BL and PRagents would monitor the system anddetermine actions dynamically.


5 Results

The main components implemented in the improvedversion at CloudSim are as follows:HostMonitor: controls the input and output of physicalmachines; VmMonitor: controls the input and output ofvirtual machines; NewBroker: controls the size ofrequests; SensorGlobal: controls the sensors;requests; SensorGlobal: controls the sensors;CloudletSchedulerSpaceShareByTimeout: controls thesize and simulation time; VmAllocationPolicyExtended:allocation policy; VmSchedulerExtended: allocates thevirtual machines; UtilizationModelFunction: checks theformat of requests; CloudletWaiting: controls the time ofthe request; and DatacenterExtended: controls thedatacenter.


5 Results


(J. Werner, G. A. Geronimo, C. B. Westphall et al. LANOMS 2011)

5 Results


5 Results

Parameter Value

VM – Image size 1GB

VM - RAM 256MB

PM - Engine Xen

PM - RAM 8GB

PROPOSED SCENARIO CHARACTERISTCS(J. Werner, G. A. Geronimo, C. B. Westphall et al. CLEI EJ 2012)

PM - RAM 8GB

PM - Frequency 3.0GHZ

PM - Cores 2


5 Results (consumption)



5 Results (SLA violations)



5 Results (Hybrid strategy)


5 Results (Hybrid strategy)

Strategy Cost Consumption

On-demand - 3.2 % - 23.5 %

REDUCTION OF COST AND POWER CONSUMPTION(J. Werner, G. A. Geronimo, C. B. Westphall et al. CLEI EJ 2012)

Idle resources - 49.0 % - 59.0 %


6 Conclusions

• Tests were realized to prove the validity of thesystem by utilizing the CloudSim simulatorfrom the University of Melbourne in Australia.

• We have implemented improvements related• We have implemented improvements relatedto service-based interaction.

• We implemented migration policies andrelocation of virtual machines by monitoringand controlling the system.


6 Conclusions

We achieved the following results in the testenvironment:

- Dynamic physical orchestration and serviceorchestration led to 87,18% energy savings,orchestration led to 87,18% energy savings,when compared to static approaches; and

- Improvement in load “balancing” and highavailability schemas provide up to 8,03% SLAerror decrease.


7 Future Works

• As future work we intend to simulate otherstrategies to get a more accurate feedback of themodel, using other simulation environment andtesting different approaches of beliefs and planrules.rules.

• Furthermore, we would like to exploit theintegration of other approaches from the field ofartificial intelligence, viz. bayesian networks,advanced strategies of intention reconsideration,and improved coordination in multi-agentsystems.


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