Model-Driven Geo-Elasticity In Database Clouds

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1 Model-Driven Geo-Elasticity In Database Clouds Tian Guo, Prashant Shenoy College of Information and Computer Sciences University of Massachusetts, Amherst This work is supported by NSF grant , and

2 Data Center and Clouds Data Center Contains large-scale server clusters Uses virtualization for efficient resource sharing Cloud platform is built with data centers Infrastructure, Platform and Software as a service Benefits: Pay-as-you-go, flexible pricing models, elasticity 2

3 Distributed Clouds Distributed Clouds Clouds that are interconnected and geo-dispersed Offers flexible choices of locations for various cloud services 3

4 Geo Distributed Applications Modern applications: geographically diverse users Spatial dynamics in workload (in addition to temporal) Popularity varies across regions Uncorrelated fluctuations due to regional factors Data Centers 4

5 Provisioning Geo Distributed Application Step 1: Manual selection Step 2: Perform local elasticity sub optimal for geo distributed workload 5

6 Geo-Elasticity Provisioning Geo-Elasticity: is the ability to provision for geo distributed applications with dynamic workloads. Handles both temporal and spatial workload fluctuations Makes provisioning decisions within and across cloud locations 6

7 Database Clouds Database clouds Hosts database tenants inside VM Tenants of different sizes DB1 VM1 DB2 VM2 DB3 Database Clouds Multi-tier application Front-end interacts with clients Back-end processes requests Front-end Back-end 7

8 Database Clouds Database clouds Hosts database tenants inside VM Tenants of different sizes DB1 VM1 DB2 VM2 DB3 Database Clouds Multi-tier application Front-end interacts with clients Back-end processes requests IaaS Cloud Database Cloud Front-end Back-end 8

9 DBScale Problem Statement Question 1: Where to provision database servers? How to obtain the temporal and spatial workload? Which data centers are good candidates? Question 2: How many database servers to provision? How many queries a server could handle? 9

10 Outline Motivation Providing Geo-Elasticity in Database Clouds Prototype Evaluation Conclusions 10

11 Obtaining Database Workload Goal: Determine temporal and spatial workload dynamics Problem: Database servers do not directly observe spatial dynamics. Users are from Location 1, 2. Location 1 Location 2 User Requests User Requests Web Server Location 3 DB Server Users are from Location 3. Approach: Inferring spatial dynamics from web server 11

12 Regression Model Goal: Infer database workload using web workload Query rate = a * web request rate + b Solve a and b using least square regression Regression Model Web Requests Web Queries DB DB DB 12

13 Regression Model Goal: Infer database workload using web workload Query rate = a * web request rate + b Solve a and b using least square regression Regression Model Regression Model Web Requests Web Queries DB DB DB Web Requests Web Queries DB DB DB Web Requests Regression Model Web Queries DB DB DB Web Requests Regression Model Web Queries DB DB DB 13

14 Picking Data Centers Threshold-based greedy clustering Example: threshold = 45 Query Rates = 40 Query Rates = 30 Query Rates = 80 Query Rates = 20 Step 1: Sort based on query rates. 14

15 Picking Data Centers Threshold-based greedy clustering Example: threshold = 45 Query Rates = 40 Query Rates = 50 Query Rates = 80 Query Rates = 0 Merging Step 2: Merge to the closet data center. 15

16 Picking Data Centers Threshold-based greedy clustering Example: threshold = 45 Merging Query Rates = 0 Query Rates = 80 Query Rates = 90 Query Rates = 0 Repeat Step 1 and Step 2. 16

17 Picking Data Centers Threshold-based greedy clustering Example: threshold = 45 Cloud 1 Cloud 2 Choose two candidate clouds for four locations. 17

18 Provisioning Database Server Goal: Determine database server capacity Problem: Queries could be CPU and I/O intensive CPU-based provisioning model is not applicable Approach: A two-node open queueing network CPU as M/G/1/PS and I/O as M/G/1/FCFS p io CPU I/O Pio represents the percentage of IO requests Database Server 18

19 How Many Servers To Provision? To calculate the query rate SLA y: 95th percentile of response time E[T] = given SLA y Assuming response time is exponential distributed. Provision for each location with workload 19

Putting it together DBScale is implemented as a middleware on Amazon EC2 IaaS Cloud DBScale Workload Monitor Data Workload Forecaster Performance Monitor Provisioning Engine DBaaS Cloud Global DNS

20 Putting it together DBScale is implemented as a middleware on Amazon EC2 IaaS Cloud DBScale Workload Monitor Data Workload Forecaster Performance Monitor Provisioning Engine DBaaS Cloud Global DNS Geo-Elastic Coordinator Resource Provisioner Geo-elastic Algorithm Consistency Engine Geo-elastic Actuator Supports loosely and tightly coupled provisioning Supports online master-slave configuration or offline batch updates 20

21 Outline Motivation Providing Geo-Elasticity in Database Clouds Prototype Evaluation Conclusions 21

22 Experiment Setup Use Amazon distributed clouds for IaaS and Database Clouds Inject geo-distributed client workload using PlanetLab Nodes Multi-tiered application: TPC-W Read-intensive: Browsing workload Read/Write: Ordering workload Runs DBScale in US east data center 22

23 Regression Model Efficiency Runs TPC-W ordering workload for five hour Training: first four hour; Testing: last one hour DBScale achieves high prediction accuracy 93%. 23

24 Queueing Model Efficiency 30 mins TPC-W browsing workload, five runs DBScale achieves at least 81% prediction accuracies for all server types. 24

25 Reducing the Client Response time Clients in PA, Web server(with cache) in VA. DBScale improves the mean response time by 94%. 25

26 Related Work Resource Modeling Regression based modeling [ Zhang- ICAC 07, Wood - Middleware 08] Queueing based modeling [Bhuvan - ICAC 05] Database Provisioning Provisioning using cost models [Cecchet - VEE 11] Workload-aware multitenant provisioning [Curino - SIGMOD 11] Distributed Cloud Provisioning Geo replication of key-value data stores [S. P N - DSN 14] 26

27 Conclusion DBScale provides geo-elasticity for database clouds. Uses regression model to infer geo-distributed database workload. Considers both cpu- and I/O intensive queries in modeling the database servers. A middleware on top of Amazon EC2. Up to 66% improvement in response time. Future Work Extensive analysis for database systems and workloads Geo-elasticity for different application types 27

28 Questions? 28

29 Extra Slides 29

30 Predicting Database Workload Step 1: Predict web request rates using time-series Step 2: Apply the regression model Web Request Rates Regression Model Database Query Rates Time Time 30

31 Predicting Database Workload Step 1: Predict web request rates using time-series Step 2: Apply the regression model Query Rates = 40 Query Rates = 30 Query Rates = 80 Query Rates = 20 31

32 Reducing the Client Response time Clients in PA; DBScale provisions in VA; Single-site in IRL DBScale improves mean response time by 98% compared to single-site in remote data center. 32

Model-driven Geo-Elasticity In Database Clouds

2015 IEEE 12th International Conference on Autonomic Computing Model-driven Geo-Elasticity In Database Clouds Tian Guo Prashant Shenoy College of Information and Computer Sciences University of Massachusetts