PARVIS - Performance management of VIrtualized Systems

Transcription

1 PARVIS - Performance management of VIrtualized Systems Danilo Ardagna joint work with Mara Tanelli and Marco Lovera, Politecnico di Milano ardagna@elet.polimi.it Milan, November

2 Data Centers, Virtualization, and Cloud Computing 2 Nowadays, large Data Centers provide computational capacity on demand to many customers by sharing a pool of IT resources: IBM Cloud Amazon EC2 Windows Azure Issues: Workload variability and QoS guarantees Energy consumption

3 Workload variability 3 Requests rates may change by order of magnitudes with a business day

4 Workload variability 4 Requests rates may change by order of magnitudes with a business day Traffic surges

5 Workload variability 5 Requests rates may change by order of magnitudes with a business day Traffic surges Sport events

6 Data Center energy consumption: An environmental problem... About 0.5% of global electric power consumption is due to DC In developed country: UK: % USA: 1.5% From the environmental point of view: 2% of global CO 2 emissions % European IT Energy Consumption Cellular phone Network Telecom Network Server & Data Center 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% Source: EU Commission

7 Data Center energy consumption: but first an economic one Company Server Electric Power (TWh) Cost ebay 16K 0.06 $3.7M Microsoft >200K 0.6 >$36M Google >500K 6 >$38M IT costs Number of Servers (M units) 12% 35% New Servers costs Energy and cooling costs 28% 25% Source: EU Commission Source: Microsoft Research

8 DC Inefficiencies Courtesy of IBM

9 Virtualization of Physical Resources 9 Virtualization, proposed in early 70s, is driving again the interest both of industry and academia Enabling technology for server consolidation and cloud computing Advantages: Physical resources are partitioned among competing running VMs, improved security and reliability, performance isolation Resource allocation parameters can be updated by in few milliseconds without introducing any system overhead

10 Virtualization of Physical Resources 10 Hardware resources (CPU, RAM, ecc...) are partitioned and shared among multiple virtual machines (VMs) The virtual machine monitor (VMM) governs the access to the physical resources among running VMs

11 Virtualization of Physical Resources: Research Challenges 11 Performance modelling of virtualized environments is challenging Traditional queueing network models are inadequate to model virtualized systems performance at a very finegrained time scale

12 PARVIS goals 12 Develop novel resource allocation policies virtualized cloud infrastructures via an interdisciplinary approach: Performance evaluation and optimization methods for the long-term management of the physical infrastructure System identification and control engineering methods to derive load-dependent black-box models of virtualized systems and to design short-term control systems

13 PARVIS Data Center: Autonomic resource management Application 1 Application 2 Application 3 Free Server Pool Internet

14 PARVIS Reference framework Infrastructure controller VM 1 VM 2 VM n App 1 App 2 S.O. S.O. App n S.O. Virtual Machine Monitor Infrastructure controller Long term time horizon Queuing network models à Non linear optimization Time scale: ten minutes/hour Workload partitioning Performance goals of individual servers Fine grained performance and energy consumption goals Local controller System Controller DFS CPU weights Admission control Performance metrics Performance goals Local controller Short term time horizon Dynamic models à Control theory Time scale: minute/seconds

15 PARVIS Reference framework 15 Infrastructure controller: Mixed Integer Non Linear Problem Local Search Local controller: Linear Parameter Varying Models Model Predictive Controllers D. Ardagna, B. Panicucci, M. Trubian, L. Zhang Energy-Aware Autonomic Resource Allocation in Multi-tier Virtualized Environments. IEEE Transactions on Services Computing. To Appear. M. Tanelli, D. Ardagna, M. Lovera. Identification of LPV state space models for Autonomic Web service systems. IEEE Transactions on Control Systems Technology. To Appear. B. Addis, D. Ardagna, B. Panicucci, L. Zhang. Autonomic Management of Cloud Service Centers with Availability Guarantees. In Cloud 2010 Proceedings. D. Ardagna, M. Tanelli, M. Lovera, L. Zhang. Black-box Performance Models for Virtualized Web Service Applications. Proceedings of the 1st Joint WOSP/SIPEW International Conference on Performance Engineering. WOSP/SIPEW2010. ACM DL.

16 SLA Service Level Agreement 16 Revenues are a function of average response times Revenues Average response time soft-constraint Average response time

17 Service Center Performance Model 17 Open queueing network model: heterogeneous service centers and a delay center VMM modelling: GPS (Generalized Processor Sharing) scheduling Hexogenous arrival rate A class k request Session becomes modelling a request k with probability p k,k or terminates Service centers model physical servers which support VMs execution

18 Optimization Problem 18 Objective: maximize SLA revenues minus energy costs Decision variables: x i server i ON/OFF (binary variable) server i arrival rate for the VM operating at tier j of request class k λ k i,j server i CPU capacity fraction devoted to the VM operating at tier j of request class k φ k i,j assignment of the VM operating at tier j of request class k to server i (binary variable) z k i,j f i,h server i operating frequency (binary variable)

19 Local search 19 Heuristic solution based on problem decomposition: Initial solution: Assign VMs to physical servers (problem equivalent to a special case of a CFLP, Capacitated Facility Location Problem) Optimum load balancing and capacity allocation (fixed point iteration) The solution is then enhanced by a local search: Switch servers ON and OFF Change VMs placement Change servers CPU frequency

20 LPV State Space Models 20 Linear Parameter Varying (LPV) systems are a class of time-varying systems In discrete-time state space form: p k u k LPV System y k Time varying systems, the dynamics of which are functions of a measurable, time varying parameter vector p.

21 LPV state-space models 21 R k 1, R k 2 Arrival rate λ k 1 λ k 2 Arrival rate W k 1, W k 2 s k 1, s k 2 φ 2 k φ 1 k u k p k LPV System y k Virtualized system identification: Scheduling parameters: arrival rates, requests service times Ouput variables: requests response times Control variables: VMM parameters

22 Infrastructure controller: IBM Tivoli comparison 22 Real log traces (Politecnico di Milano Web site), 10 requests classes Comparison with IBM Tivoli resource allocation policies Scenario 1: users come from the same time zone Scenario 2: users come from different time zones

23 IBM Tivoli comparison scenario 1 23 Our solution IBM Tivoli IBM Tivoli Our solution

24 IBM Tivoli comparison scenario 2 24 Our solution IBM Tivoli IBM Tivoli Our solution

25 System Identification - Experimental setting 25 Two reference scenarios: A Micro benchmarking instrumented Web application SPECweb2005 industrial benchmark VMM monitor: Xen 3.0 and Xen 3.3 Validation: Synthetic workload inspired by a real-world. Log trace from a large financial system

26 Micro-benchmarking Web Service Application Experiments 26 Number of VMs varied between 2 and 4 For system identification purposes request arrival rates vary stepwise every 1 minute Each request consumes s i k CPU time varied between 0.06 s and 1.1 s. 1,440 intervals (24 hours) Parametrization [s i k ρ ki ]

27 Micro-benchmarking Web Service Application Experiments 27

28 SPECweb2005 Experiment 28 Two VMs running the banking and e-commerce loads The number of users N i k accessing each of the two VMs varied stepwise every 1 minute, with values between 10 and 220 Proportional assignment scheme: 1,440 intervals (24 hours) Parametrization [N k i ρ k ]

29 SPECweb2005 Experiment 29

30 PARVIS future work 30 Analysis of real applications Local controller design Integration of the two approaches

31 Akwnoledgements Mara Tanelli, Marco Lovera, Barbara Panicucci, Marco Bergamasco, Alessandro Barenghi, Alessandro Colleoni, Bernardetta Addis, Giuliana Carello, Antonio Capone, Politecnico di Milano Folco Bombardieri, Danilo Ghirardelli, Gianluca Pisati, Giovanni Pirotta, Marco Caldirola, Mauro Speroni, Paolo Sala, Marco Casiero, Stefano Vettor, Massimo Bergami, Politecnico di Milano students Marco Trubian, Università degli Studi di Milano Li Zhang, IBM Research Massimo Leoni, Carla Milani, IBM Italy