Friedrich C

Khoshkbarforoushha A

Ranjan R

Wang Q

\ua9 2017 VLDB. A data analytics flow typically operates on three layers: ingestion, analytics, and storage, each of which is provided by a data-intensive system. These systems are often available as cloud managed services, enabling the users to have painfree deployment of data analytics flow applications such as click-stream analytics. Despite straightforward orchestration, elasticity management of the flows is challenging. This is due to: a) heterogeneity of workloads and diversity of cloud resources such as queue partitions, compute servers and NoSQL throughputs capacity, b) workload dependencies between the layers, and c) different performance behaviours and resource consumption patterns. In this demonstration, we present Flower, a holistic elasticity management system that exploits advanced optimization and control theory techniques to manage elasticity of complex data analytics flows on clouds. Flower analyzes statistics and data collected from different data-intensive systems to provide the user with a suite of rich functionalities, including: workload dependency analysis, optimal resource share analysis, dynamic resource provisioning, and cross-platform monitoring. We will showcase various features of Flower using a real-world data analytics flow. We will allow the audience to explore Flower by visually defining and configuring a data analytics flow elasticity manager and get hands-on experience with integrated data analytics flow management

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              This work is licensed under a  Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence   Newcastle University ePrints - eprint.ncl.ac.uk  Khoshkbarforoushha A, Ranjan R, Wang Q, Friedrich C.  Flower: A data analytics flow elasticity manager.  In: Proceedings of the 43rd International Conference on Very Large Databases.  28 August-1 September 2017, Munich, Germany: ACM   Copyright: This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit  http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org  Link to paper: http://www.vldb.org/pvldb/vol10/p1893-khoshkbarforoushha.pdf  Date deposited:   20/12/2017 Flower: A Data Analytics Flow Elasticity Manager ∗Alireza Khoshkbarforoushha 1,2 , Rajiv Ranjan 3, Qing Wang 1, Carsten Friedrich 21 The Australian National University, Canberra, Australia2 Data61 CSIRO, Canberra, Australia3 Newcastle University, UK1 qing.wang@anu.edu.au, 2 first.last@data61.csiro.au, 3 raj.ranjan@ncl.ac.ukABSTRACTA data analytics flow typically operates on three layers: in-gestion, analytics, and storage, each of which is provided bya data-intensive system. These systems are often availableas cloud managed services, enabling the users to have pain-free deployment of data analytics flow applications such asclick-stream analytics. Despite straightforward orchestra-tion, elasticity management of the flows is challenging. Thisis due to: a) heterogeneity of workloads and diversity ofcloud resources such as queue partitions, compute serversand NoSQL throughputs capacity, b) workload dependenciesbetween the layers, and c) different performance behavioursand resource consumption patterns.In this demonstration, we present Flower, a holistic elastic-ity management system that exploits advanced optimizationand control theory techniques to manage elasticity of com-plex data analytics flows on clouds. Flower analyzes statis-tics and data collected from different data-intensive systemsto provide the user with a suite of rich functionalities, includ-ing: workload dependency analysis, optimal resource shareanalysis, dynamic resource provisioning, and cross-platformmonitoring. We will showcase various features of Flower us-ing a real-world data analytics flow. We will allow the audi-ence to explore Flower by visually defining and configuring adata analytics flow elasticity manager and get hands-on ex-perience with integrated data analytics flow management.1. INTRODUCTIONRecent analysis of cloud providers’ service portfolios [10]shows that the number of data processing platforms offeredas cloud managed services has surged. This is because theyare well appreciated by the users, releasing them from thehassle of platforms or cluster setup and maintenance. Forexample, Fig. 1 depicts a click-stream data analytics flow inwhich Amazon Kinesis [4] is used for managing the ingestionof streaming data at scale and Apache Storm [6] deployed on∗Flower is an open-source tool and can be downloaded athttps://github.com/Alireza-/FlowerThis work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copyof this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Forany use beyond those covered by this license, obtain permission by emailinginfo@vldb.org.Proceedings of the VLDB Endowment, Vol. 10, No. 12Copyright 2017 VLDB Endowment 2150-8097/17/08.Figure 1: A click-stream data analytics flow.EC2 processes streaming data and persists the aggregatedresults in DynamoDB [3].Despite straightforward orchestration, elasticity manage-ment of an established flow is highly challenging. This isdue to three unique characteristics of cloud-hosted data an-alytics flows. First, data analytics flow applications haveheterogeneous workloads, in which different platforms andworkloads are dependent on each other. For example, Fig.2 shows how the workload dynamics in the ingestion layeris strongly correlated with the analytics layer. To providesmooth elasticity management, these workload dependenciesneed to be detected and considered in resource allocations.Second, data analytics flow applications often deal withimmense data volume which, together with uncertain veloc-ity of data streams, leads to changing resource consump-tion patterns. This mandates an elasticity technique thatcould sustain workload fluctuations time efficiently, mean-ing that resources should be acquired and released as soonas required. However, almost all the auto-scaling systemsoffered by cloud providers such as Amazon [1] use simplerule-based techniques that quickly trigger in response to pre-defined threshold violations. Although these rules can iden-tify fatal conditions, they often fail to adapt to unplannedor unforeseen changes in demand. Moreover, they entailconsiderable manual efforts in tuning each system individu-ally and specifying rules based on available resources, whichrequire solid expertise and planning.Third, a data analytics flow is often deployed on heteroge-neous cloud resources, each of which exhibits different per-formance behaviours and pricing schemes. Resource alloca-tion thus needs to cater for diverse resource requirementsand their cost dimensions to meet the users’ Service LevelObjectives (SLOs). Existing solutions [11, 12, 13] lack aholistic approach for resource management of big data an-alytics workloads. Instead, they focus either on a specificresource type such as Virtual Machines (VMs) or particularworkload like Hadoop. Nevertheless, it is noted in [15] thatthe ability to scale down both web servers and cache tierleads to 65% saving of the peak operational cost, comparedto 45% if we only consider resizing the web tier.18930 50 100 150 200 250 300 350 400 450 500 5500246xm104InputmRecordsm(U)IngestionmLayerm(Kinesis)0 50 100 150 200 250 300 350 400 450 500 5500102030Timem(min)CPU(A)AnalyticsmLayerm(Storm)Figure 2: The data arrival rate at the ingestion layer(Kinesis in Fig. 1) is strongly correlated (coefficient= 0.95) with the CPU load at the analytics layer(Storm).In response to these challenges, we introduce Flower, anew elasticity system that is designed for holistic resourcemanagement and performance monitoring of cloud-hosteddata analytics flows. Flower’s goal is to provide a high leveleasy-to-use system for the admins and DevOps engineers inorder to fulfil the following tasks:• Workload Dependency Analysis. To analyse thedependencies between workloads, Flower applies sta-tistical regression models to workload logs to quan-titatively explain relationships between, for example,resource amount in the ingestion layer (e.g. No. ofShards) and resource amount in the analysics layer(e.g. CPU usage).• Resource Share Analysis. To determine how tobest allocate budget to various types of resources acrossa data analytics flow, Flower uses a resource share ana-lyzer module which uses optimization theory to resolvemaximum share of different resources for each layer ina data analytics flow.• Resource Provisioning. To enable accurate yet timelyresource provisioning, Flower uses advanced control the-ory to automatically reason about resource resizing ac-tions. Our control system is equipped with the novelfeature of having memory of recent controller decisionswhich leads to rapid elasticity of the flow in responseto workload dynamics.• Cross-Platform Monitoring. Flower also provides across-platform monitoring module that allows the ad-mins to observe and control all performance measurespertaining to multiple data processing systems all inone place.In our demonstration, we will set up a data analytics flowon cloud, and then ask our audience to visually build andconfigure a management layer on top of the data analyticsflow and observe its performance live. The audience willthen be able to witness how Flower dynamically responds toworkload changes.2. FLOWER ARCHITECTUREFlower’s architecture consists of several major components,as depicted in Figure 3. In a nutshell, the workflow ofthe system is described as follows. First, dependencies be-tween workloads’ resource usage measures such as Kine-sis Shard utilization, Storm cluster CPU usage, and Dy-namoDB read/write units are analyzed. To this end, weFigure 3: Flower architecture.apply regression techniques to estimate the relationshipsamong variables. The dependency information along withthe cloud services costs and the user’s SLO constitute therequired inputs for the generation of provisioning plan space.The resource share analyzer module is then invoked todetermine the maximum resource shares of each layer. Theresource shares can be determined with respect to arbitrarytime windows. Once the upper bound resource shares foreach layer are identified, an adaptive controller at each ofthe three layers automatically adjusts resource allocationsof that layer.The controllers are regulated based on a number of param-eters, including monitored resource utilization value, desiredresource utilization value, and history of the controller’s de-cisions. In other words, the controllers continuously provi-sion resources to serve the incoming records or input datain order to keep resource utilization of each layer within thespecified desired value. For this purpose, the controllers areequipped with two key components: sensor and actuator.The sensor module is responsible for providing resourceusage stats as per the specified monitoring window. Theactuator is capable of executing the controllers’ commands,such as adding or removing VMs and increasing or decreas-ing number of Shards.3. FLOWER COMPONENTS3.1 Workload Dependency AnalysisTo analyse workload dependencies, we use regression as asimple yet effective machine learning technique that has asound statistical basis. Flower uses linear regression model toestimate relationships between resources in different layers.In mathematical terms, dependency between a resource r ofthe layer L1 and a resource r of the layer L2 is defined as:r(L1) = β0 + β1r(L2) + ε (1)where L1 6= L2 and they belong to either Ingestion (I), An-alytics (A), or Storage (S) layers, i.e. L1, L2 ∈ {I, A, S}. β0is the y-intercept, β1 is the slope, and ε is the error. Forexample, in Fig. 2 the dependency between the ingestionand the analytics layers is formulated as:CPU ' 0.0002 ∗WriteCapacity + 4.8 (2)1894Evidently, Eq. 2 provides a sound starting point for theuser to reason out, for example, how much CPU we requirein the analytics layer to support the maximum write capac-ity of a Kinesis Shard in the ingestion layer - given eachShard supports up to 1,000 records/second for writes. It isworth mentioning that not all the layers are dependent oneach other where, for example, we witnessed no correlationbetween the write capacity in Kinesis and write capacity inDynamoDB for the click-stream analytics flow application.3.2 Resource Share AnalysisGiven the budget and estimated dependencies betweenworkloads, what would be the maximum share of resourcesfor each layer in a data analytics flow? To address sucha question, Flower provides an optimization module that isable to find an optimal solution with respect to the users’SLOs. To this end, it formulates a multi-objective functionas follows:max(r(I)t , r(A)t , r(S)t ) (3)subject to:∑dr(I)t ∗ cd +∑dr(A)t ∗ cd +∑dr(S)t ∗ cd ≤ Budt (4)r(L1)t = β0 + β1r(L2)t + ε (5)where r(I)t , r(A)t , r(S)t ∈ R+ , and variables like r(I)t representsthe resource amount in the layer I at time t. cd refers to thecost dimension d of the resource. The resource shares of thedifferent layers are subject to the following constraints:(4) Budget Constraint : at time t the sum of costs concernedwith different cloud resources across all the layers mustbe within the specified budget Budt.(5) Dependency Constraints: dependency between the lay-ers which is learned via the regression techniques asdiscussed in Section 3.1.In multi-objective optimization, there does not typicallyexist a solution that minimizes or maximizes all objectivefunctions simultaneously. Thus, attention is paid to Paretooptimal solutions, i.e. those that cannot be improved in anyof the objectives without degrading at least one of the otherobjectives. Flower uses NSGA-II algorithm [8] to efficientlysearch the provisioning plan space.Consider the click-stream data analytics flow in Fig. 1 andthe following assumptive dependency constraints as well:5 ∗ r(A)t1 ≥ r(I)t1 , 2 ∗ r(A)t1 ≤ r(I)t1 , 2 ∗ r(I)t1 ≤ r(S)t1 , where r(I)t1 ,r(A)t1 , and r(S)t1 respectively refer to the number of Shardsin ingestion, VMs in analytics, and Write capacity units inthe storage layer at time t1. Given these constraints, thealgorithm finds six Pareto optimal solutions, each represent-ing the resource shares of Kinesis, Storm, and DynamoDBsimultaneously, as shown in Fig. 4. In the end, one solu-tion which is best suited to the problem in practice must beidentified either manually by the user or randomly by thesystem.3.3 Resource ProvisioningFlower has multiple built-in adaptive controllers tailoredto the data ingestion, analytics, and the storage layers. Thisis due to the design principles [9] mandated by practicalFigure 4: Pareto optimal solutionsrequirements of each layer of a data analytics flow. Ouradaptive controllers are defined as follows:uk+1 = uk + lk+1(yk − yr), (6)Here, uk and uk+1 are the current and new actuator values.yk and yr are respectively the current and desired referencesensor measurement. The controller gain lk+1 is adaptivelyupdated according to the following update law:lk+1 =lk + γ(yk − yr), if lmin ≤ lk + γ(yk − yr) ≤ lmaxlmin, if lk + γ(yk − yr) < lminlmax, if lk + γ(yk − yr) > lmax(7)Here, lk is the controller gain at the time k, lmin > 0 andlmax > 0 are the lower bound and the upper bound of thecontroller gain, respectively, and γ > 0 is a controller pa-rameter.Flower’s sensor module periodically collects live data frommultiple sources such as CloudWatch [2] and inserts theminto the actuator module. The controllers regulate the ac-tuator value from the previous time step proportionally tothe deviation between the current and desired values of thesensor variable in the current time step.Our control system, unlike the existing solutions [12, 14],has the feature of updating the gain parameters in multi-stages and keeping the history of the previously computedcontrol gains for rapid elasticity. Our experiments in [9]have shown that our control system outperforms the state ofthe art fixed-gain [12] and quasi-adaptive [14] counterparts.We have also provided a rigorous stability analysis of theresulting controllers in [9].3.4 Cross-Platform MonitoringMany cluster monitoring tools such as Ganglia [5] areavailable to assist administrators. However, they fail to pro-vide a holistic view of performance measure across a dataanalytics flow. One is required to check out different sys-tems and user interfaces in order to track any possible per-formance failures or slowdowns. For example, monitoring ananalytics flow application built upon Storm and DynamoDBsystems requires to track performance statistics in two sep-arate user interfaces.To tackle this issue, Flower introduces a module called all-in-one-place visualizer, which allows users to visually define1895Figure 5: Flower’s flow builder interfacea monitoring layer on top of multiple systems. The mod-ule calls the APIs of the systems, such as CloudWatch andStorm, and consolidates diverse performance measures in anintegrated user interface:4. DEMONSTRATIONThe demonstration will give a walk-through over the keyfeatures of Flower. To this end, a demo infrastructure will beset up on Amazon cloud with three systems: (a) Kinesis, (b)Storm, and (c) DynamoDB. These systems are required todeploy the test data analytics flow - click-stream analayticsas shown in Fig. 1, which is based on the Amazon’s referencearchitecture for click-stream analytics [7]. Once the flow isdeployed, we will use a random multi-threaded click streamgenerator deployed on several EC2 instances to emulate thereal website traffics.We will then ask the audience to follow the steps belowin order to create and run a flow elasticity manager:1. Flow Builder: The attendee first will use Flower’sFlow Builder to drag and drop multiple platforms andcreate a data analytics flow via its graphical user in-terface as shown in Fig. 5.2. Flow Configuration Wizard: Then, the user willfollow a wizard to configure the controllers with in-formation such as resource name (e.g. table name inDynamoDB), desired reference value, and monitoringperiod for the selected systems in Step 1.3. Controller Performance Monitor: Once configu-ration is completed, the user will then be able to runthe service. Flower will accordingly launch visualiza-tions as shown in Fig. 6. The attendees will thenobserve how different controllers change the cloud ser-vices capacities dynamically and the resulting perfor-mance. They will also be able to adjust parameters ofthe controllers, such as elasticity speed, monitoring pe-riod, or even their internal settings and compare theirimpacts on SLOs.In addition to above demonstration, we will also ask theattendees to create a cross-platform visualization dashboardusing similar steps. This will allow them to experience livemonitoring of multiple systems all in one go.5. REFERENCES[1] Amazon auto scaling. https://aws.amazon.com/autoscaling/.[2] Amazon cloudwatch. https://aws.amazon.com/cloudwatch/.[3] Amazon dynamodb. https://aws.amazon.com/dynamodb.[4] Amazon kinesis. http://aws.amazon.com/kinesis.[5] Ganglia monitoring system http://ganglia.sourceforge.net/.[6] Apache storm. http://storm.apache.org/.[7] R. Bhartia. Amazon kinesis and apache storm: Building areal-time sliding-window dashboard over streaming data.Technical report, Amazon Web Services, October 2014.[8] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast andelitist multiobjective genetic algorithm: NSGA-II. TEVC,6(2):182–197, 2002.[9] A. Khoshkbarforoushha, A. Khosravian, and R. Ranjan.Elasticity management of Streaming Data Analytics Flows onclouds, Journal of Computer System Sciences, 2016.[10] A. Khoshkbarforoushha, M. Wang, R. Ranjan, L. Wang,L. Alem, S. U. Khan, and B. Benatallah. Dimensions forevaluating cloud resource orchestration frameworks. Computer,49(2):24–33, 2016.[11] I. Konstantinou, E. Angelou, D. Tsoumakos, C. Boumpouka,N. Koziris, and S. Sioutas. TIRAMOLA: elastic NoSQLprovisioning through a cloud management platform. InSIGMOD, pages 725–728. ACM, 2012.[12] H. C. Lim, S. Babu, and J. S. Chase. Automated control forelastic storage. In ICAC, pages 1–10. ACM, 2010.[13] J. Ortiz, B. Lee, and M. Balazinska. Perfenforcedemonstration: Data analytics with performance guarantees.SIGMOD, 2016.[14] P. Padala, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal,A. Merchant, and K. Salem. Adaptive control of virtualizedresources in utility computing environments. In ACM SIGOPSOperating Systems Review, volume 41, pages 289–302. 2007.[15] T. Zhu, A. Gandhi, M. Harchol-Balter, and M. A. Kozuch.Saving cash by using less cache. In HotCloud, 2012.Figure 6: Elasticity control and monitoring interface1896

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Flower: A data analytics flow elasticity manager

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Flower: A data analytics flow elasticity manager

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