A Cost Driven Approach to Information Collection for Mobile Environments

1. A Cost Driven Approach to Information Collection for Mobile Environments Qi Han Nalini Venkatasubramanian Department of Information and Computer Science University of California-Irvine

2. QoS Aware Information Infrastructure Battlefield Battlefield Visualization Visualization Data servers Data servers QoS Enabled Wide Area Network CollaborativeMultimedia Application Mobile hosts • Quality of Service enhanced resource management at all levels - storage management, networks, applications, middleware

3. Motivation • Advanced level of tetherless mobile multimedia services requires • The development of a wireless network that supports integrated multimedia services • Focus of prior work • The development of intelligent network management middleware services that provides agile interfaces to mobile multimedia services • Our objective: to provide support for mobility and QoS management at the middleware layer independent of the underlying specific network architecture

4. Issues Effective middleware infrastructure that must adapt to changing network conditions Resource provisioning algorithms that utilize current system resource availability information to ensure that applications meet their QoS requirements Additional Challenges In mobile environments, system conditions are constantly changing Maintaining accurate and current system information is important to efficient execution of resource provisioning algorithms QoS-based Resource Provisioning

5. The Information Collection Problem • Goal • To provide information good enough for resource provisioning tasks such as admission control, load balancing etc. • Need an information collection mechanism that • is aware of multiple levels of imprecision in data • is aware of quality requirements of applications • makes optimum use of the system (network and server) resources while tolerating imprecision of the information • Collected Parameters • Network link status, Data server capacity (Remote disk bandwidth, Processor capacity), Mobile host status

6. Directory Enabled Network Information Collection • Provide directory service as an information base for QoS-provisioning algorithms • feasible servers for requests, available network and server resources • Uses distributed probes to monitor traffic and collect dynamic load state information • Directory Enabled Information Collection • Information Acquisition • Directory Organization and Manipulation • Approximation and Cost • Scalability: Hierarchical directory organization + Caching

7. Former Information Collection Approaches for Non-mobile Environments • Instantaneous snapshot based techniques (SS) • Monitoring module samples residue capacity of network link periodically and updates directory with latest value • Static range based intervals • Partition link capacity into static intervals and update directory with the interval number • Throttle (TR) • the directory holds a range-based representation of the monitored parameter, with upper and lower bounds that can vary dynamically • Time Series (MA) • time series models are used to predict future trends in sample values with some defined level of confidence

8. Challenges in Information Collection Problem for Mobile Environments • Inherent tradeoff between information accuracy and system performance • Solutions for non-mobile environments are not appropriate for mobile environments • Increased dynamicity • Constant change of client access points to fixed network

9. Our Approach • Dynamic range-based representation • Mobile host Aggregation driven collection • Source and consumer-initiated triggers and updates • 2 phase information collection process • Address the tradeoff between accuracy of directory information and the update overhead costs for mobile environments

10. mobile host fixed host server router Information Collection Framework Information Consumer Mobility management Server selection Information Repository QoS management Mobile QoS management … Information Mediator Location management Information collection Information Source

11. Components of Information Collection Framework • Information source • Managed entities: server, link, mobile or stationary host… • Information consumer • Consumers data collected from sources • Information mediator • Decision point of the information collection • Information repository • Holds system state information about sources

12. AutoSeC (Automatic Service Composition) Framework

13. Aggregate Mobility Model Region i Xregion Mobile host j at (xj(t),yj(t)) Yregion Ymax Aggregation of Region i at time t: Xmax

14. Resource Utilization Factor • Resource utilization factor for network links: • Resource utilization factor for servers:

15. Generalized Aggregation Based Information Collection (Gen-ABIC) • Use a range R:=[L,U] to represent the monitored parameter • Phase 1: • Derives the aggregate mobility patterns • Utilizes the aggregation status and current resource utilization status to adjust the collection parameters such as sampling frequency SF and range size R • Phase 2: • Utilizes feedback from the sources (source-initiated triggers and updates) and consumers (consumer-initiated triggers and updates) for further customization of the collection process

16. New range Range adjustment Accuracy not enough: consumer-initiated trigger New range: source-initiated update New range: consumer-initiated update Value out of range: source-initiated trigger Noise filtering Range relaxation Range tightening Thrashing avoidance Change confirmed Change confirmed State Diagram of Information Collection Process Current range Current range Information source Directory service Information consumer Information mediator Regular probing

17. Cost Factors in an Information Collection Process • Regular sampling overhead Crs • Regular directory update overhead Cru • Source/consumer-initiated trigger overhead Cst and Cct • Source/consumer-initiated directory update overhead Csu and Ccu consumer Cct Ccu Directory service Cru mediator Csu Crs Cst source

18. Optimal Range Size to Minimize the Cost • To minimize the overall cost, a good range size is needed to reduce the need for further updates • To avoid source-initiated triggers and updates, R should be big enough • Pst=Kst/R2 , Psu=Ksu/R2 • To avoid consumer-initiated triggers and updates, R should be small enough • Pct=Kct*R , Pcu=Kcu *R • To minimize Cost :

19. The CDIC Algorithm CDIC Algorithm( ) /* invoked periodically */ /* Phase 1: aggregation driven coarse-grained adjustment of parameters /* Compute host aggregation level; Compute resource utilization level; switch ( resource utilization) { casehigh: set SF and R to be minimum; caselow: set SF and R to be minimum; casemedium: increase/decrease SF and R based on current aggregation level; } /*Phase 2: fine-grained adjustment of range size */ Calculate Kst, Ksu, Kct, Kcu based on monitored cost factors appropriately; Set R to be optimal which minimizes the cost.

20. Issues of CDIC • The model parameters such as Pst, Psu, Pct, Pct need to be monitored • Monitoring complexity affects the system performance to a great extent • User QoS may be compromised • Utilizing mobile host aggregation status to drive the information collection process could sacrifice some individual requests’ QoS, but overall system performance is improved

21. Optimized Cost Driven Information Collection (Opt-CDIC) • Further reduce communication overhead without sacrificing the overall QoS • Selective triggering • Turn off consumer-initiated triggering • Lazy sampling • Reduce sampling frequency when • The number of source-initiated triggers in a given period is less than a pre-determined value • The range is relaxed to exceed a certain value

22. Simulation Environments • Request model • Request arrival as a Poisson distribution • Request holding time is exponentially distributed • Traffic model • Uniform pattern • Non-uniform pattern • Mobility model • Incremental individual mobility model • High mobility and low mobility • Four Scenarios

23. Simulation Objectives • Analyze the impact of information collection mechanisms on the overall resource provisioning performance • Information collection mechanisms • SS, SR, TR, Gen-ABIC, CDIC, Opt-CDIC • Resource provisioning algorithm • CPSS (Comined Path and Server Selection) • Performance Metrics • Request completion ratio • Overhead involved • Overall efficiency

24. Simulation Results (Comparison of SS, SR, TR, Gen-ABIC under HM-NUT) • Completion ratio • Gen-ABIC shows the highest completion ratio • SS, SR and TR exhibit similar completion ratios • Overhead • Increases with the increase of the number of requests • SS introduces the highest overhead, while Gen-ABIC has the least overhead • Overall Efficiency • Gen-ABIC shows the highest overall efficiency

25. Simulation Results (Comparison of SS, SR, TR, Gen-ABIC under HM-NUT)

26. Simulation Results (Comparison of SS, SR, TR, Gen-ABIC under HM-NUT)

27. Simulation Results (Comparison of Gen-ABIC, CDIC and Opt-CDIC under HM-NUT) • For completion ratio • Gen-ABIC is marginally higher than Opt-CDIC, but much higher than CDIC • Decreases with an increase of the number of requests in the system • For overhead • CDIC is the highest, and Opt-CDIC is the lowest • For overall efficiency • Opt-CDIC is the highest

28. Simulation Results (Comparison of Gen-ABIC, CDIC and Opt-CDIC under HM-NUT)

29. Simulation Results (Comparison of Gen-ABIC, CDIC and Opt-CDIC under HM-NUT)

30. Simulation Results (Comparison of Gen-ABIC, CDIC and Opt-CDIC under LM-UT)

31. Simulation Results (Comparison of Gen-ABIC, CDIC and Opt-CDIC under LM-UT)

32. Conclusions • Coarse assignment of collection parameters (e.g. SF and R) is adequate to render satisfactory completion ratios under most traffic workloads and mobility patterns • Optimization of turning off consumer-initiated triggers and lazy sampling help reduce overhead to a great extent without lowering the completion ratio • Therefore, Opt-CDIC is a desirable strategy to collect network and server information in mobile environments

33. Future Work • Enhance AutoSeC for mobile environments by integrating Opt-CDIC with the other resource provisioning algorithms • Develop a scalable information collection architecture suitable for wide-area environments that incorporates distributed directory services