1 / 22

Dynamic hosting management of Web based applications over clouds

Dynamic hosting management of Web based applications over clouds. Impact Lab Arizona State University. HiPC 2011. Talk Outline. Opportunities for green and cost-efficient computing Workload distribution across data centers: overview DAHM: Our contribution DAHM modeling issues

rasia
Download Presentation

Dynamic hosting management of Web based applications over clouds

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Dynamic hosting management of Web based applications over clouds Impact Lab Arizona State University HiPC2011

  2. Talk Outline Opportunities for green and cost-efficient computing Workload distribution across data centers: overview DAHM: Our contribution DAHM modeling issues Problem statement for DAHM DAHM online solutions Simulation-based evaluations of DAHM

  3. Opportunities for green and cost-efficient computing for data centers • Virtualization technology facilities • Application can run independent of data center place and physical server • Heterogeneity of data centers • Dynamic web hosting across data centers to increase cost efficiency • 1- Spatial and temporal variation in electricity price • 2- Energy proportionality of servers • 3- Green energy sources Power consumption over utilization for two different servers • Some data centers purchase green energy [Google report] • Some data centers have built in renewable energy generators • 4- Cooling energy efficiency of data centers • Power Usage Efficiency (PUE)=Total power/IT power • PUE of data centers ranges from 1.7-1.05 • Changing the physical location of hosting servers across data centers over time

  4. Dynamic web hosting: Perquisites and Opportunities • Migration cost • Stateless applications (e.g. search engines) • Stateful (e.g. online game services, cloud based services) • Delay sensitivity of applications • Online game applications • Cloud services for cell phones • SLA • Leveraging spatio-temporal variation of workload through server consolidation • Sacrificing the delay requirement of few portion of users to increase cost efficiency

  5. Workload distribution across data centers: Related Work • Research questions: • Optimal cost saving versus different energy proportionality of servers (Result: cost saving increases withincreasing idle power of servers) • Migration-operation cost tradeoff (Result: cost saving decreases with migration cost) • QoS-operation cost tradeoff (Result: cost saving significantly increases by violating delay requirement of 0.5% of users) ✔​ ✔​ ✔​ ✔​

  6. Contribution • Proposing DAHM, Dynamic Application Hosting Manager to reduce operation cost of applications: • Operation cost: energy cost, SLA cost, bandwidth cost for live migration • Effective data center energy efficiency parameters: • PUE and energy proportionality of data centers • Spatial and temporal variation of electricity price • Application delay and data migration requirement • Sample results: up to 20% cost saving with negligible SLA violations versus static hosting

  7. DAHM Pictorial view and assumptions • Delay model (turn around time) • Delay=service delay+Network delay • Service delay model • Avoiding overutilization of servers • Network delay • Incurring cost per users whose network delay is not guaranteed • Hosting model assumptions • Assigning one or more VM to the application with respect its workload • The workload of each area can be assigned to a single data center • Replication is allowed

  8. DAHM problem • Given : • A set of areas, A={a1,a2, ..a|A|} whose number of online users vary over time: Nt={n1,t , n2,t ,.. N|A|,t} • A set of data centers, S whose available VMs vary over time: St={s1,t , s2,t , s3,t, …s|S|,t}, • The power consumption parameters, PUE and the temporal variation of electricity price, associated with each data center • The associated delay for the pair of (area, data center) • A discrete time system: t1, t2,.. • Decision: • How one can determine the hosting location of the application at each time t, such that operation cost is minimized? • Determining the binary x vector: xijt=1 if at time t, the area j is receiving service from data center i

  9. Cost model1- energy cost • Power consumption : Idle power+ power due to utilization • Idle power: number of VMs • Utilization power: workload, power-utilization model Linear utilization model : (u: utilization, n: number of online users, c:average utilization per each user) linear power-utilization model: • Electricity cost: ei,t • Cooling energy : PUEi Total energy cost:

  10. Cost model2- SLA violation and migration cost SLA violation cost model: a cost incurs per each user if delay is not met Migration cost model: A constant bandwidth cost incurs per each migration

  11. DAHM as optimization problem [Service constraint] [Idle power constraint] [Capacity constraint]

  12. DAHM online Solutions • Optimal offline solution: • Mixed Integer Programming (glpk) • Online heuristics: • OnlineMIP: • MIP-based online algorithm • OnlineGreedy: • Statically rank data centers according to their cost (for each area) • OnlineCOB: : • Statically assign each area to data centers such that delay is guaranteed and load is balanced among all data centers

  13. Simulation based evaluation • Workload model • Number of online users of an entertainment web site hosted at GODADY over states of U.S Total visitors of three states over time Total visitors over time

  14. Data center models • Three data centers • 20 areas • Data center type • Homogenous • Homogenous servers and homogenous PUEems • Heterogeneous • Heterogeneous servers and different PUE DC1 DC3 DC2 Area covered by DC2 Area covered by DC2 Area covered by DC3

  15. Evaluation results: Optimal online cost saving versus different energy proportionality of servers The percentile cost saving of OnlineMIPw.r.t. OnlineCOB under different IPR of servers (homogeneous data center).

  16. Evaluation results: Optimal online cost saving versus different energy proportionality of servers The performance of OnlineMIPw.r.t. OnlineCOB under different IPR of servers (homogeneous data centers).

  17. Evaluation results: Optimal online cost saving versus different energy proportionality of servers The percentile cost saving of OnlineMIPw.r.t. OnlineCOB under different IPR of servers (heterogeneous data enters).

  18. Evaluation results: Optimal online cost saving versus different energy proportionality of servers The performance of OnlineMIPw.r.t. OnlineCOB under different IPR of servers (heterogeneous data center).

  19. Evaluation results: QoS-operation cost tradeoff Tradeoff between performance (delay) violation and total cost saving of DAHM compared to COB-online under different performance violation cost

  20. Summary of results

  21. Lessons learned Cost benefit of DAHM through minimizing total number of VMs over all data centers It is important to incorporating the power performance into the dynamic hosting problem Immense cost saving cLeveragingQoS-cost tradeoff (incorporating SLA)

  22. Future works • Theoretical approximation bound for the greedy solution • Problems with the stochastic nature of renewable energy sources : • Minimizing Battery size, Minimizing brown energy usage given • Stochastic nature of workload • Stochastic nature of renewable energy sources

More Related