How To Manage Cloud Service Provisioning And Maintenance

Full text

(1)Managing Cloud Service Provisioning and SLA Enforcement via Holistic Monitoring Techniques Vincent C. Emeakaroha Matrikelnr: 0027525. [email protected]. Supervisor: Univ.-Prof. Dr. Schahram Dustdar. Examiner: Prof. Dr. César De Rose.

(2) Problem Domain Overview  What is Cloud computing?  Technology enabling on-demand sharing of computing resources via Internet access  Rapid elasticity, pay-as-you-go, ubiquitous network access  Different types of delivery and deployment models  Provisioning based on Service Level Agreement (SLA).  What is SLA?  A contract between Cloud provider and consumer  Specifies application performance goals  Includes penalties in case of violations 2.

(3) SLA Specification Example SLA Parameters. SLA Objectives. Availability. > 99.999%. Response Time. < 5 ms. CPU. > 90 %. Memory. > 8 GB. Storage. > 80 GB. Throughput. > 2 f/s. Completion Time. < 1 hr.  Transactional (interactive) applications  Web applications.  High performance applications  Computational intensive applications  Bioinformatics workflows 3.

(4) Problem Statement: Mapping High-level SLAs Applications Provisioning (e.g., Availability) Based on SLA. VM. VM. ?. VM. Virtualization Layer Low-level metrics (e.g., Uptime). Physical Resources. Example Cloud 4. VM.

(5) Problem Statement: Application App + SLA. App + SLA. App + SLA. App + SLA. VM. VM. App + SLA. How to ensure single Customer SLA?. Deploy? VM. App + SLA. VM. VM. VM. VM. Virtualization Layer. Virtualization Layer. Physical Resources. Physical Resources. 5. VM.

(6) Problem Statement: Integration Monitoring Techniques. Knowledge Management. Applications Provisioning Based on SLA. VM. VM. VM. VM. Virtualization Layer. Physical Resources. Cloud environment.

(7) PhD Contributions  Contribution 1 & 2  Infrastructural resource monitoring and mapping  Determination of optimal measurement intervals.  Contribution 3  SLA-aware application scheduling and deployment.  Contribution 4  Application level monitoring architecture.  Contribution 5  Integration of monitoring techniques with knowledge management.  In summary  A novel Cloud management infrastructure 7.

(8) Contribution 1: Challenges  How to monitor Cloud resources?  How to manage and enforce SLA agreement based on monitored resource metrics?. Applications Provisioning Based on SLA. VM. VM. VM. VM. Virtualization Layer. Resource Monitoring. Physical Resources. Cloud Architecture 8.

(9) Solution: LoM2HiS Framework  Goals. FoSII Infrastructure LoM2HiS Framework. Service Request/ Response Service Customer. /866"4( /"1#%&'(. Get SLA. Get/Store Values. !"#$%&"'(. Notifications/ Thresholds. )*+,-."(/0+%10#( Push Measured Metrics. Service Provider. Resource status. 90'1(/0+%10#( Raw Metrics :+;#8'1#*&1*#"()"'0*#&"'( <98#4=8#">(. Execute Rules.  Resource monitoring  Metrics mapping  SLA violation detection.  Example mapping rule: ?+0=@"43"(A0.60+"+1(. 23#""4(!52( )"60'%10#7(. Definition of Mappings. " downtime % Availability = $1! ' *100(%) # uptime + downtime &.  Monitoring agent  Gmond Ganglia project. LoM2HiS Framework 9.

(10) Metrics Mappings Rules  Depends on application type  SLA parameters are application dependent.  Two types of mapping rules  Simple mapping rules  Maps 1:1 (e.g., disk space -> storage).  Complex mapping rules  Does not map 1:1 (e.g., table below) !"#$%&'"()"*&+'#(. ,-.(/0&01"*"&#(. !"#$%&'()*+%&') 6$132'7()"*2132'7() +.89'27/:'()1.$;#/;2</$() 1.$;#/;2<"*2). Where Rin =. ,-./0.1/0/23)4,-5). )022+34(!%5"#( " % downtime Av = $1! ' *100(%) uptime + downtime # &. )='7+"$7')>/&')4=5). packetsize bandwidthin − inbytes. R = Rin + Rout (ms) and 10. Rout =. packetsize bandwidthout − outbytes.

(11) Contribution 2: Challenges Cloud Environment Service Interface Service Deployer Service Provisioning Based on SLA. Service Request Cloud Customer. SLA. How often? VM. VM. Monitor Resources VM. VM. How to monitor in real Cloud?. Cloud Resources 11. Cloud Provider.

(12) Solution: DeSVi Architecture  Consists of three core components  VM deployer & configurator  Application deployer  LoM2HiS framework.  Manages service provisioning lifecycle  Resource allocation  SLA violation detection. 12.  One application per VM.

(13) Evaluation Applications Use-case scenario of heterogeneous workloads with three types of POV-Ray applications. Fish. Box. 13. Vase.

(14) Evaluation Testbed.

(15)  30 min execution length  Reference interval  10 sec.  Measurement intervals. Nr. of Detected SLA Violations.  15s, 20s, 25s, 30s, 1min, 2min. Box Pov-Ray Result 200. CPU Memory Storage. 150 100 50 0 0. 20. 40. 60. 80. 100. 120. Measurements Intervals (sec). Nr. of Detected SLA Violations.  Configurations. Nr. of Detected SLA Violations. Monitoring Results Fish Pov-Ray Result 200. CPU Memory Storage. 150 100 50 0 0. 20. 40. 60. 80. 100. 120. Measurements Intervals (sec). Vase Pov-Ray Result 200. CPU Memory Storage. 150 100 50 0 0. 20. 40. 60. 80. 100. 120. Measurements Intervals (sec).

(16) Result Analysis  Cost function definition. C = µ ∗ Cm +. �. ψ�{cpu,memory,storage}. α (ψ) ∗ Cv. µ= number of measurements Cm = cost of measurement α (ψ)= number of undetected SLA violations Cv = cost of missing an SLA violation  Measurement cost depends on system intrusiveness  i.e., overhead caused by measurements.  Cost of missing SLA violations depends on SLA penalties  Profit factors for the provider  Reputation issues 16.

(17) Result Outcomes +,-(./,0,12"34(2" +5((516"7548-941":,2,;941"34(2" '#!".  Optimal intervals depend on application types. !"#$%&'(%. '!!".  Fish: 60s  Box: 25s  Vase: 30s. &!" %!" $!" #!" !" '!(". ')(" #!(" #)(" *!(" %!(" '#!(" )*+#,-*.*/$%0/$*-1+2#%. +,-(./,0,12"34(2" +5((516"7548-941":,2,;941"34(2". +,-(./,0,12"34(2" +5((516"7548-941":,2,;941"34(2". '$!" '#!" '!!" &!" %!" $!" #!" !". '$!" '#!" '!!" &!" %!" $!" #!" !". !"#$%&'(%. !"#$%&'(%. Fish Application. '!(". ')(" #!(" #)(" *!(" %!(" '#!(" )*+#,-*.*/$%0/$*-1+2#%. Box Application. '!(". 17. ')(" #!(" #)(" *!(" %!(" '#!(" )*+#,-*.*/$%0/$*-1+2#%. Vase Application.

(18) PhD Contributions  Contribution 1 & 2  Infrastructural resource monitoring and mapping  Determination of optimal measurement intervals.  Contribution 3  SLA-aware application scheduling and deployment.  Contribution 4  Application level monitoring architecture.  Contribution 5  Integration of monitoring techniques with knowledge management 18.

(19) Contribution 3: Challenges App + SLA. App + SLA.  How can we achieve high resource utilization while maintaining agreed application SLA terms?  Can we schedule applications based on SLAs to optimize their performance?. App + SLA. Deploy? VM. VM. VM. VM. Virtualization Layer. Physical Resources. 19.

(20) Solution: SLA-Aware Scheduling Heuristic Customers.  Scheduling based on multiple SLAs  Load balancer. !. App1 + SLA. App2 + SLA. Appn + SLA.  Similar Next-fit  Fills boxes one after the other. Scheduling Apps / Load balancing. VM. VM. VM. VM. VM. VM.  Differences  Fill all boxes parallel.  On-demand VM starting. 20.

(21) Scheduling Evaluation  Goals  Resource utilization and deployment efficiency  Application performance comparison.  Simulation setups. On-demand VMs  Web App -> 4  HPC App -> 229  Web/HPC Apps -> 215.  60 PMs  370 VMs  1500 service requests !""#. !""# $(&)*#. !""#. !""#. $%&"'#. $"# !"#$%&#'()%*%+(,-.(. )!&*+#. )"#. %$&)*#. 789:;<8=>#. %"#. ?.19=@A.>B#. ,"#. !""#. $%# !""# %"#. %"#. ("# !"#$%&#'()%*%+(,-.(. !""# !""#. +"# *"# )"#. 6789:;7<=#. ("#. >-08<?@-=A#. '"# &"# !"#. "# -./#011#. 234#011#. "#. -./5234#0116#. Fixed Resource Group. ,-.#/00#. 21. 123#/00#. ,-.4123#/005#. On-demand Resource Group.

(22) Application Performance Comparison (&". (&". &$". &$". &#". &#". !"#$%&'$()%. !"#$%&'$()%. Fixed Resources SLA-Aware Scheduler Traditional Task Scheduler. &!" %'" %&". &!" %'" %&". $". $". #". #". !". !" )*+",--" 3*2-452*"678*"". ./0",--". )*+1./0",--2". )*+",--". 048-9*:45"678*"". 3*2-452*"678*". ./0",--". )*+1./0",--2". 048-9*:45"678*". %#". %#". %!". %!". !"#$%&'$()%. !"#$%&'$()%. On-demand Resources SLA-Aware Scheduler Traditional Task Scheduler. $#" $!" #". $#" $!" #". !". !" &'(")**" 0'/*12/'"345'". +,-")**". &'(.+,-")**/". -15*6'712"345'". &'(")**" 0'/*12/'"345'". +,-")**". &'(.+,-")**/". -15*6'712"345'".


(24) Contribution 4: Challenges App + SLA. App + SLA. App + SLA. How to ensure single Customer SLA? VM. VM. VM. Virtualization Layer. Physical Resources. VM.  How do we determine resource consumption of each application?  How can we manage the applications separately to ensure their SLAs?  Can we automatically find optimal measurement intervals?. 24.

(25) Solution: CASViD Architecture  Manages application provisioning  CASViD monitor  Application monitoring.  SLA manager  Violation notifications  Communication with knowledge management.  Automatic optimal measurement intervals determination 25.

(26) CASViD Monitor .  Generic application level monitoring  Monitors application specific metrics based on process ID  Uses SNMP agents  Designed with scalability in mind  Low intrusiveness on systems.

(27)

(28) .

(29)

(30)

(31) .

(32)

(33) .

(34)

(35) . .

(36) .

(37) . . .

(38)

(39) .

(40)

(41) . 26.

(42) Monitoring and Intrusiveness Results  Detected SLA violations. Box Fish Vase. 3.  Outcome:. Intrusion (%). 2.5.  Low intrusion on system  Good overall performance. 2 1.5 1 0.5 0. 0. 2. 4. 6. 8. 10. Monitoring frequency (samples per minute). 12.

(43) Automatic Interval Finding Results. Provider Net Utility ($). Customer.  Importance:. 2.  Effective monitoring  Efficient application management in Clouds. 1.5 1 0.5. 0. 2. 4. 6. 8. Execution Line (min). 10. 12.


(45) Contribution 5: Challenges  How can we address the SLA violation threats / real violations detected by monitoring?  How can we achieve autonomous behaviour in managing resources in Clouds?  How can we avoid resource wastage and save energy / power in Cloud data centers?. 30.

(46) Solution: Cloud Management Infrastructure.  Ensures adequate computational resources  Facilitates efficient management to increase revenue and avoid resource wastage  Realizes autonomic behaviour in Clouds 31.

(47) Case Study: Problem Description  Data analysis in bioinformatics. Recent Trend (NCBI). Data Sample. 32.

(48) Optimizing Workflow Executions Cloud Management Infrastructure Holistic Monitoring Model. Knowledge Management. Million reads. Cloud Environment. Mapping to genome Monitoring Agent. Aligned reads. Not-aligned reads. Splitting to sub-reads Monitoring Agent. Sub-reads Set 1. Sub-reads Set 2. Searching for splice-junctions Monitoring Agent. Reads aligned to the reference. 33. Sub-reads Set n.

(49) Case Study Evaluation 120.  Monitoring results CPU Usage (%). 100.  Three hours execution  One minute measurement interval  Ensure SLA objectives. 40. 20. 40. 60. 80. 100. 120. 19. 7e+06 6e+06 5e+06 4e+06 3e+06 2e+06 1e+06. 140. 160. 180. Execution Time (min). Storage Capacity (GB). Memory Capacity (KB). 60. 0. Memory. 8e+06. 80. 20.  Results from one node 9e+06. CPU. Storage. 18.5 18 17.5 17 16.5 16 15.5 15. 0. 20. 40. 60. 80. 100. 120. Execution Time (min). 140. 160. 0. 180. 34. 20. 40. 60. 80. 100. 120. Execution Time (min). 140. 160. 180.

(50) Resource Allocation Results '((#.  Three scenarios. &'%#. !"#$%&'(%)*". &"(#. 1 Static configuration 2 Knowledge approach 3 Peak provisioning. &((# )"(# )((#. !"#. "(#. '#. (#. $%". !"#$%&"'()*+,). !"#$%&'()*+,-&'".%-&'/". )''#. $!" #$". #!". !". !". !" ()*"". !". !". +,-./0". !" #" !" 12./34,". 16,73/8."$". (#. '#. (#. 340156.#. !'#. 38.951:0#&#. (#. 37/#. 38.951:0#'#. %$# !"# $"# $"#. $$#. &'#. !&#. %'#. !$# $&#. &%# ""#. "'# ('#. !". !". ''#. 15-". *+,##. 0,+,1$2$+/" 16,73/8."#". (#. -./012#. 38.951:0#)#. $&". &". (#. +','-./.,*". '!". ##". (#. *+,##.  Via simulations. #&". $%#. !"#. -./012#. 340156.#. -&.&/010.2). 16,73/8."'". 39.:51;0#)#. 35. 39.:51;0#(#. 39.:51;0#<#. 786#. &%#.

(51) Conclusion  A novel Cloud management infrastructure  Resource monitoring and mapping techniques  SLA-aware application scheduling and deployment heuristic  Application monitoring architecture  Integration of holistic monitoring technique with knowledge management.  SLA enforcement  Transactional applications  Require small measurement intervals.  High performance applications  Tend to larger measurement intervals.  Optimal measurement interval depends on application characteristics 36.

(52) Publications / Collaborations  4 Journal papers  Over 15 conferences and workshop papers  3 Book chapters  High Performance Computing Lab. Pontifical Catholic University (PUCRS), Porto Alegre, Brazil  Clouds Laboratory, Department of Computing and Information System, University of Melbourne, Australia  IBM Research, Sao Paulo, Brazil  Computer Architecture Department, Technical University of Catalunya, Barcelona, Spain  Chair of Bioinformatics, Boku University Vienna, Austria 37.

(53) Vincent C. Emeakaroha [email protected] http://www.infosys.tuwien.ac.at/staff/vincent/. 38.

(54)

No results found