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Whitepaper nebucom intelligent application broking and provisioning in a hybrid cloud environment

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Intelligent Application Broking and Provisioning in a Hybrid Cloud Environment

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Whitepaper nebucom intelligent application broking and provisioning in a hybrid cloud environment

  1. 1. Intelligent Application Broking and Provisioning in a Hybrid Cloud Environment Pieter-JanMaenhaut, JorenInghelbrecht, BrunoVolckaert IDLab.ugent.be contact: pieterjan.maenhaut@ugent.be Context The carbon footprintof data centersincreasesfurtherasevermore servicesare hostedin the cloud, leveraging the needfor an efficient management of the available resources.State-of-the-art servers exhibit a linear relationshipbetween resource and power usage, but software optimizations such as intelligentprovisioningcanincrease theenergyefficiency,reducingboththeenergyconsumptionand operational costs. Furthermore, cloud providers struggle with the heterogeneity of virtualization technologies, cloud management platforms and the underlying physical resources, and with the upcoming popularity of container-based virtualization technologies, the question arises how well these technologies perform regarding performance and isolation compared to traditional VM technologies. Within IDLab, the Internet Technology and Data Science Lab, we have been working on the developmentof ahighlyinteroperableplatformthatsupports applicationdeploymentonavarietyof cloud management frameworks. The platform can be used for developing and evaluating multiple intelligent VM/ container / resource allocation algorithms on top of the supported cloudplatforms, and allows researchers to quickly setup experiments without having to dive deep into the complex details of the underlying technologies. Virtualization Technologies In general, virtualization introduces an additional software layer between the host platform and the virtualized resources. Cloudmanagementplatforms such as OpenStack, VMWare vCloudand Amazon AWS make use of different virtualization technologies to manage the available cloud resources. For example, a partition of a physical machine’s computational resources can be assigned to a Virtual Machine(VM) orcontainer, andrentedtoacloud userasasingle computationalunit. Applicationsfrom multiple usersare typically consolidatedonasingle physicalserver, leveraging theneed for a clear data and performance isolation between the different units hosted on the same physical machine. System-Level Virtualization vs OS-Level Virtualization System-level virtualization creates an abstraction of the physical machine’s hardware and executes one or more virtual machines on top of this virtualized hardware. This abstraction layer is typically called the hypervisor, and can either be deployed on a host operating system (OS) or directly on hardware (bare metal).System-level virtualizationoftenfaceshighperformance overheadandsuffers fromI/Olimitations.IthoweverprovidesagoodisolationbetweenVMshosted onthe same machine, which can be a stringent requirement for critical applications. Server consolidation can be achieved by assigning a specific amount of resources to each VM, and efficiently collocating the VMs over a minimumsetof physical servers,withoutviolatinganyof the guaranteedSLAs(e.g.maximumallowed latencyand/orresponse time).A VMcan be savedas a single file,butthese filescanbe big in size as the whole operatingsystemandsoftwarestackisincluded.Furthermore,snapshotscanbe takenof a VM, which eases recovery and backup management.
  2. 2. OS-levelorcontainer-basedvirtualizationoperatesonahigherlevel,since containersshareanOS and possibly libraries and binaries. The container engine provides the necessary abstraction layer for a container to be deployable on different operating systems. OS-level virtualization is based on two importantkernelfeatures,namelycontrol groups(cgroups)andkernelnamespaces,andbothfeatures increase the manageability and security of the containers. Control groups allow processes to be grouped together, and ensure that each group gets a share of memory, CPU and disk I/O. Kernel namespaces ensure isolation between processes executed by different containers. As multiple containersshare the underlyingOS,containerimagesare muchsmaller,makingcontainermigrations much easier than VM migrations. Due to the OS-level virtualization,there is no need to virtualize hardware, and therefore the overhead is limited to a minimum. Since containers are so lightweight, an applicationcanbe orchestratedasmultipleinteractingcomponents(service-orientedarchitecture) and each component can be deployed on a different container. This divide and conquer strategy is also often called a micro service architecture. Many concerns have been raised to the use of containers, in contrast to traditional VMs. As virtualization happens on a higher level, the operating system, it is clear that the attack surface of containers is much larger compared to VMs. As long as containers cannot provide isolation on the same level as VMs, VMs will continue to play an important role within cloudcomputing. Containers howevercan alsobe deployedwithinVMs,meaningthatusingone technologydoesnotexclude the other. Performance Comparison When hostingan applicationinthe cloud,the questionariseswhichvirtualizationtechnologyisbest suited,aseverytechnologyhasitsadvantagesanddrawbacks.Togainmoreinsightsintotheoverhead introducedbybothtechnologies,weranseveral experimentsondifferenttestscenario’s.Anoverview of the scenariosisprovedin Figure 1. In the firstscenario,whichwe referto as Bare Metal (BM), the experimentsare executeddirectlyonthe hardware.In the Bare Metal – Container(BM-CT) andBare Metal – Virtual Machine (BM-VM) scenarios,experimentsare executedinside acontainerandavirtual machine respectively. The last scenario, Bare Metal – Virtual Machines – Container combines both technologies by using a container engine inside a virtual machine. Fig. 1. Overview of test scenarios used for the performance comparison. The evaluated experiment consists of three performance tests. The first test is a CPU benchmarking test, the second measures the disk I/O throughput and the last test determines the maximum RAM copying bandwidth. All tests were executed on a physical machine with 4 CPU cores and 16 GB of RAM.
  3. 3. The CPU benchmarkingtest utilizesSysbench,anopensource tool for evaluating the performance of Linux-basedsystems. Theresultsare illustratedinFigure2.Runningthe CPUbenchmarkinacontainer adds no overhead compared to running it on bare metal. Equivalently, there is no performance difference betweenthe scenariosBM-VMandBM-VM-CT. The performance overheadforusingaVM is 16% (1 thread) up to 30% (16 threads). Fig. 2. Total execution time of the Sysbench CPU test in function of the number of threads for the different scenarios. The two bottom lines represent BM & BM-CT, the two top lines BM-VM & BM-VM-CT. Sysbenchwasalsousedfor measuringthe diskI/Othroughput,andFigure 3 illustratesthe measured throughput (in bytes per second) in function of the number of threads for all evaluated scenarios. Again, there is a clear overlap between BM-VMand BM-BM-CT, and also between BMand BM-CT, indicating that containerizationhas no significant impact on the performance of disk I/O. Using a virtual machine howeverhasa significantimpactondiskI/Operformance,asforthisexperimentthe throughputdropswithafactor30 whenusingsystem-levelvirtualization.Notehoweverthatonlyone hypervisortechnologywasevaluatedinthisexperiment,andusinga differenthypervisortechnology or a different storage technology might result in a lower overhead.
  4. 4. Fig. 3. Throughput of the Sysbench disk I/O test in function of the number of threads for all scenarios. The two bottom lines represent BM-VM & BM-VM-CT, the two top lines BM & BM-CT. For measuring the RAM bandwidth, the open source MWB tool was used, which measures the memorybandwidth availabletouserspace programs.Inthe test,anarrayof a configurable amountof bytes(1- 64 MiB) iscopiedfromone variabletoanotherusingmemcpy. Figure4illustrates theresults, beingthe bandwidth(MiB/s) infunctionof the array size.Again,there isnearperfectoverlapforthe scenarios BMand BM-CT. The same is valid for BM-VMand BM-VM-CT when the array size is larger than or equal to 8 MiB. The drop in memory (RAM) throughput is roughly 20% when using system- level virtualization. Fig. 4. Memory bandwidth (using memcpy) in function of the array size for each scenario. The two bottom lines represent BM-VM & BM-VM-CT, the two top lines BM & BM-CT. In general, we can conclude that using containers introduces a negligible overhead. Using virtual machines however introduces a significant overhead, especially regarding the disk I/O. As a result,
  5. 5. containers should be considered instead of virtual machines for deploying applications requiring a highperformance.Whenhigherisolationandsecurityhoweverare preferredaboveobtainingahigher performance,virtual machinesshouldstill be consideredasusingcontainersonbare metal resultsin a larger attack surface. A Platform for Intelligent Application Broking and Provisioning To facilitate the deployment of applications on top of one or more cloud platforms, we developed a platformforthe intelligentbrokingandprovisioningontopof oneormore virtualizationtechnologies. Figure 5illustratesthe architectureof ourplatform.The platformconsistsof 3mainlayers,theBroking Layer used for selecting the appropriate virtualizationtechnology,the Provisioning Layer used for provisioningthe requiredresourceswithinthe selectedvirtualizationtechnology,andthe Framework Abstraction Layerwhichtranslatesthe genericrequestsfromthe provisioninglayertowardsthe APIs of the differentunderlying frameworks(e.g. OpenStackor Docker). Each layeronlyinteractswith its adjacent layers resulting in a 3-tier architecture. Fig. 5. General 3-tier architecture of the developed platform. The twomostimportantnon-functional requirements of theplatformare modifiabilityandscalability. The use of intermediariesandencapsulationensures thatmultiple external softwaretools,algorithms and frameworks can be easily integrated withinthe platform, without having to modify the existing code base.Additionally, the platformoperatesinacloud environment,andthereforeascalable core architecture isessential. Asaresult,thefront-endisimplementedinanon-blockingway,andmultiple requests can be handled simultaneously. The platform was built to scale out to a container cluster with numerous worker nodes to process the incomingrequests.The platformprovidesa web-based frontend, which interacts with a Django-powered backend. The asynchronous execution of backgroundprocesses(e.g.the applicationbrokingorprovisioningprocess) ishandledthroughDjango Channels,whichoffersthe managementof multipleworkernodesloadbalancedbyanASGIprotocol server. The Broking Layer selects the appropriate technology for hosting the application or one of its componentusingan applicationbrokingalgorithm.Asan example,analgorithmbasedona decision tree model was implemented within the platform.Every application or component that needsto be
  6. 6. deployed is assigned a value between 0 and 1 for 5 different features, being security, CPU, ram, scalabilityandavailability.These valuesare assignedbytheuser,andthealgorithmselectstheoptimal technology based on the values for these metrics, by mapping the features on a 2D latent variable space usingPrincipal ComponentAnalysis.Thisalgorithmonlyservesasanexample,andcanbe easily replaced within the platform by any other broking algorithm. A second algorithm was also added, which selects one of the available provisioning algorithms at random, and can be used a baseline algorithm. The Provisioning Layer invokes a resource provisioning algorithm to select the best suitedhost, and the goal is to minimize the requirednumber of hosts to reduce energyconsumptionandoperational costs.Whenan estimationof the requiredCPUandRAMusage isavailable forthe application(orone of its components),binpackingalgorithmscanbe usedas provisioningalgorithms.Asan example,a heuristic First-Fit Decreasing (FFD) algorithm was implemented within the platform as provisioning algorithm, but again this algorithm can easily be replaced by any other resource provisioning algorithm. A secondalgorithmwasalsoadded,whichselectsarandomhostand/orVM(if applicable), mostly for comparison purposes. Most resource allocation algorithms however require some input from the hosts. Therefore, an additional component called Notifier was implemented, which is deployed onto each host managed bythe platform,andthiscomponentgathersthe actual local resource usageinformationwhichissent back to the platform. The returned results from the different hosts over time can also be visualized within the platform through the web-based frontend. The Framework Abstraction Layer communicates with the selected virtualization technology framework. For example, if Docker is selected by the broking platform, the framework abstraction layerwill be usedto deploythe applicationinside aDockercontaineronthe Dockerhost selectedby the provisioning layer. On the other hand, if a VM is more appropriate, the framework abstraction layer will communicate with a host running VirtualBox or with an OpenStack cluster, to provision a newvirtual machine,anddeploytheapplicationwithinthismachine.Currently,we haveimplemented integration with both VirtualBox and Docker as an example, and are working on the translation towardsan OpenStackcluster. Itshould howeverbe clearthatsupportforadditional frameworkscan easily be integrated within the platform. To verify the correctness of the platform, 3 example applications were provisioned. The first application is a simple Heartbeat application, which sends a log message to the platform on a configurable time interval, and is usedto verifythat the provisioning of applications works correctly within the platform. The two other applications are a CPU- and RAM-intensive application respectively.The CPU-intensiveapplicationexecutesamachinelearningalgorithm,whereasthe RAM- intensiveapplicationperformsbasicimage processing.Bothapplicationsalsosendnotificationstothe platformaftersome of the tasksare finished.Three scenarios were testedusingthe availablebroking andprovisioningalgorithms:deployingonlyCPU-intensiveapplications,deployingonlyRAM-intensive applications,anddeployingbothalternately.Fromthe obtainedresults,we verifiedthatthe platform isworkingcorrectly,andthe example algorithmsalreadyillustratedthebenefitsof usinganintelligent algorithmoverthe baseline random algorithms in terms of performance and server consolidation. Conclusions and Future Work When migrating applications or application components to the cloud, the question arises which virtualizationtechnologyisbestsuitedforprovisioningthe requiredresources.The applicationcanbe directly executed on bare metal, it can be executed within a virtual machine (system-level virtualization) or it can be executed within a container (OS-level virtualization). Both methods have
  7. 7. advantagesanddrawbacks,butsystem-level virtualizationwill typicallyofferahigherlevelof security and performance isolation,whereas OS-level virtualizationwill keep the performance overhead to a minimum. The experiments described in this paper confirm this, as there is a near-perfect overlap between the results obtained from executing the tests on bare metal (bm) and executing the tests inside a container (bm-ct) and also between the results obtained from executing the tests inside a virtual machine (bm-vm) andexecutingthe testsinside acontainerinside avirtual machine (bm-vm- ct). Asa result,containersshouldbe consideredinsteadof VMswhenhighperformance is required. To facilitate the deploymentonaphysical cloudtestbed,we introducedanintelligentplatformforthe brokingand provisioningof applicationsandapplicationcomponentsontop of various virtualization technologies. The platform consists of 3 core layers; the Broking Layer selects the best suited technologybasedonthe characteristicsof the applicationthatneedstobe deployed,the Provisioning Layerselectsasuitable workernodeforhostingthe application,andthe FrameworkAbstractionLayer translatesthe genericrequeststowardsthe specificframework.We implementedanexample broking algorithm and provisioning algorithm within the broking and provisioning layers respectively, and deployed several test applications using the platform to verify the correctness of the platform. In future work, we will be working on the implementation of the OpenStack adapter within the Framework Abstraction Layer, and look at the possibilities for adding a feedback loop betweenthe first two layers. In some cases, it might be beneficial to select a different type of virtualization technology to consolidate servers, e.g. if there are still VM’s available that can host the application although a container would be better suited. The feedback loopcan be used to implement thistype of behavior within the platform.

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