�������� Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments Axel Busch, Qais Noorshams, Samuel Kounev, February 4 th , 2015 Anne Koziolek, Ralf Reussner, Erich Amrehn ICPE 2015 , Austin, TX busch@kit.edu ARCHITECTURE-DRIVEN REQUIREMENTS ENGINEERING GROUP INSTITUTE FOR PROGRAM STRUCTURES AND DATA ORGANIZATION, FACULTY OF INFORMATICS KIT – University of the State of Baden-Wuerttemberg and www.kit.edu National Research Center of the Helmholtz Association
Motivation App Kunden Speicher-System ● ● ● ● ● ● 15 Response Time (ms) ● ● ● ● ● ● ● ● ● ● 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 100 ● ● ● ● ● ● ● ● ● ● ● 80 ● ● ● ● ● ● ● ● s t s ● 60 e 60 ● u ● ● ● ● ● q ● e R ● ● s u ● ● 40 o 40 Request Size (KB) ● ● e ● ● n a l t ● ● u m ● ● i S 20 20 0 0 Many measurements to perform Invasive instrumentation needed Time consuming model development Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 2
Motivation 2. 3. 1. App Fully automated workload characterization Lightweight approach Non-invasive instrumentation No need to install the full software stack No need to develop any complex models by hand Applicable in virtualized environments Fast estimation of performance behaviour in typical scenarios Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 3
Methodology 1. Workload Characterization I/O-intensive Workload Workload workload Monitoring Metrics Set Characteristics e.g., file server, & Extraction Model mail server Workload Characterization Input Model Process Output Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 4
Methodology 2. Workload Emulation Workload Results of Workload Generator Characteristics emulated Model Workload Workload Emulation e.g., Response Time Monitoring Parameters Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 5
Metrics set optional Workload Mix mandatory alternative Workload Average Request Files Request Mix Intensity Request Size Access Pattern File Set Read Write File Sizes Sequential Random Size Proportion Proportion (simplified) (Experimental Evaluation of the Performance-Influencing Factors of Virtualized Storage Systems. Q. Noorshams, S. Kounev, and R. Reussner. In EPEW ’12, volume 7587 of LNCS. Springer, 2012.) Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 6
File size & File set size File size File size File size Size File 1 File 2 File n ... File set size T X Z T P n ( t ) ι =1 φ ι ( t ) fileSetSize avg = dt T : � re φ ι ( t ) he size of the ι -th file at time t 0 nd n ( t ) : � z he number of files at time t , � T � n ( t ) P ι =1 φ ι ( t ) fileSize avg = : � Let [0 , T ] , T > 0 b e observation period. dt T · n ( t ) 0 � Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 7
Workload Intensity Application System Z T χ ( t ) workloadIntensity avg = : � re χ ( t ) he workload intensity nd) at time t . , dt T 0 : � e observation period. Let [0 , T ] , T > 0 b Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 8
Request Mix R R W R W System R R R # readRequests reqMix = # readRequests + # writeRequests W W Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 9
Request Size R R W R W System Read Req Write Req Request Size Read Request Size Write P | Γ | − 1 Γ j j =0 , Γ contains all observed request sizes requestSize = | Γ | ( Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 10
Access Pattern Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 11
Access Pattern 1. Searching for consecutive block access 2. Counts the number of consecutive blocks 3. Results in number of consecutive block accesses in percent Algorithm 1 Access Pattern Recognition Algorithm ops ← Number of operations // Iterate through requests while i < req do for j such that i < j < req do block end = R i 2 // End block of request R i block start = R j 1 // Start block of request R j if block end = block start then req seq req seq + 2 // Count both R i , R j R R \ { R i , R j } continue while; end if end for i i + 1 end while return req seq req Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 12
Application Workload Results of Workload Generator Characteristics emulated Model Workload Workload Emulation e.g., Response Time Monitoring Parameters Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 13
System Setup IBM System z IBM DS8700 LPAR1 LPAR2 Storage Controller App. App. Volatile Non-Volatile Fibre Channel Cache Cache z/Linux z/Linux Switched Fibre Channel PR/SM (Hypervisor) Processors, Memory Harddisks (RAID) Workload characterization performed on an IBM System z and DS8700 storage system Both systems represent high-end virtualized environments for critical business applications Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 14
System Setup IBM System z IBM DS8700 LPAR1 LPAR2 Storage Controller App. App. Volatile Non-Volatile Fibre Channel Cache Cache z/Linux z/Linux Switched Fibre Channel PR/SM (Hypervisor) Processors, Memory Harddisks (RAID) System z configuration: DS8700 configuration: Debian z/Linux VM (=LPAR) 50 GB volatile cache (VC) 2 IFLs (cores) ~2760 MIPS 2 GB non-volatile cache (NVC), i.e., battery-backed cache 4 GB RAM RAID5 array with 7 HDDs (15k r/min) with 1 hot spare disk Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 15
Tools Filebench as storage system benchmark Used for generating workloads to be characterized https://github.com/Filebench-Revise F FSB Flexible File System Benchmark as application layer I/O benchmark Used for emulating workloads https://github.com/FFSB-Prime/ffsb Storage Performance Analyzer as measurement coordinator http://research.spec.org/tools/overview/spa.html Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 16
Systematic experiments SPA extension allowing automatized Workload characterization results [avg]: Workload execution Workload results synchronized Monitoring mechanisms File server Mail server Workload characteristics extraction File size 17 KiB 130 KiB File set size 684 MiB 1163 MiB Controller Machine Workload intensity 16 50 SUT Benchmark Harness Request mix 56 % 42 % SSH Request size (r) 14 KiB 103 KiB Benchmark Benchmark Driver Request size (w) 15 KiB 79 KiB Benchmark Controller Access pattern (r) 29 % 97 % Monitor Driver Monitor Access pattern (w) 57 % 99 % Measurements performed using 1 min warm up + 20x 5 min SQLite Database benchmark time Low standard deviations Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 17
Evaluation Scenarios Workload characterization approach evaluated by two case studies I) Workload Characterization How accurate is the estimation of the workload characterization approach? II) Scenarios a) Migration scenario How accurate is the estimation in migration scenarios? b) Consolidation scenario How accurate is the estimation in consolidation scenarios? Motivation Approach Case Study Conclusion 15-02-04 Axel Busch – Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments 18
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