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

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

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

QoS-based Resource Provisioning 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

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

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

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

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

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

Information Source Information Mediator Information Consumer Information Collection Framework Server selectionMobility management QoS managementMobile QoS management Location managementInformation collection mobile host fixed host serverrouter Information Repository …

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

AutoSeC (Automatic Service Composition) Framework

Aggregate Mobility Model X region Region i Mobile host j at (x j (t),y j (t)) Y max X max Aggregation of Region i at time t: Y region

Resource Utilization Factor Resource utilization factor for network links: Resource utilization factor for servers:

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

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

Cost Factors in an Information Collection Process Regular sampling overhead C rs Regular directory update overhead C ru Source/consumer-initiated trigger overhead C st and C ct Source/consumer-initiated directory update overhead C su and C cu consumer mediator source C rs C st C ct C cu C ru C su Directory service

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 P st =K st /R 2, P su =K su /R 2 To avoid consumer-initiated triggers and updates, R should be small enough P ct =K ct *R, P cu =K cu *R To minimize Cost :

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) { case high: set SF and R to be minimum; case low: set SF and R to be minimum; case medium: increase/decrease SF and R based on current aggregation level; } /* Phase 2 : fine-grained adjustment of range size */ Calculate K st, K su, K ct, K cu based on monitored cost factors appropriately; Set R to be optimal which minimizes the cost.

Issues of CDIC The model parameters such as P st, P su, P ct, P ct 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

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

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 High mobilityLow mobility Uniform trafficHM-UTLM-UT Non-uniform trafficHM-NUTLM-NUT

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

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

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

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

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

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

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

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