AutoSeC : An Integrated Middleware Framework for Dynamic Service Brokering

1. AutoSeC: An Integrated Middleware Framework for Dynamic Service Brokering Qi Han and Nalini Venkatasubramanian Distributed Systems Middleware Group http://www.ics.uci.edu/~dsm Dept. of Information and Computer Science University of California-Irvine

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

3. Global Information Infrastructure • Proliferation of devices • System support for multitude of smart devices that • attach and detach from a distribution infrastructure • produce large volume of information at a high rate • limited by communication and power constraints • Require a customizable global networking backbone.. • Applications (e.g. multimedia) may have QoS requirements should be translated to system level resource requirements • Explore effective middleware infrastructures which can be used to support efficient QoS-based resource provisioning algorithms

4. QoS-based Resource Provisioning • Issues • Degree of network awareness that middleware and applications must have to deal with network conditions • Resource provisioning algorithms utilize current system resource availability information to ensure that applications meet their QoS requirements • Additional Challenges • In highly dynamic (e.g. mobile) environments, system conditions are constantly changing • Maintaining accurate and current system information is important to efficient execution of resource provisioning algorithms

5. Automatic Service Composition (AutoSeC) • Tools needed to securely and dynamically manage an adaptable network infrastructure while ensuring user QoS • a set of network management middleware services is critical to providing these tools • AutoSeC: • dynamically select an appropriate combination of information collection and resource provisioning policies based on current system status

6. AutoSeC Framework

7. Network and Server Information Collection Policies • System Snapshot (SS) • information about the residual capacity of network nodes and server nodes is based on an absolute value obtained from a periodic snapshot • Static Interval (SI) • residual capacity information is maintained using a static range-based representation • 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. Resource Provisioning Policies • Server Selection (SVRS): attempt to choose the best replica and server for a given request • Least Utilization Factor Policy (SVRS-UF): This policy chooses the server with the minimal utilization factor • Shortest Hop Policy (SVRS-HOP): This policy chooses the nearest server in terms of the number of hops. • Combined Path and Server Selection (CPSS) • Given a client request with QoS requirements, we select the server and links that maximize the overall use of resources. • This allows load balancing not only between replicated servers, but also among network links to maximize the request success ratio and system throughput.

9. Performance Evaluation • Objective: • To determine the best combination of information collection policies and resource provisioning policies under varying application workload • All-req-monitored: all the applications have QoS requirements • Not-all-req-monitored: some requests don’t have QoS requirements • Metrics: • Request success ratio • ratio of number of successful requests over the number of whole requests • Information collection overhead: • sampling overhead and directory service update overhead • Overall performance efficiency: • ratio of the number of successful request to the information collection overhead

10. Simulation Environment • Simulation topology • 18 replicated data servers and 30 backbone links • Capacities of network links • from 1.5Mbps to 155Mbps (mean= 64Mbps) • Capacities of servers • based on real ISP data-center settings • Request and traffic generation model • Request arrival as

11. Impact of Information Collection on CPSS • Compare the performance of the four information collection policies with the CPSS algorithm under similar conditions • All-req-monitored: • Snapshot based approach is very sensitive to sampling period • Given the same sampling period, throttle based approach is superior to other three approaches in terms of performance efficiency • Not-all-req-monitored: • Exhibits similar results to above case

12. CPSS, All-Req-Monitored

13. CPSS, Not-All-Req-Monitored

14. Impact of Information Collection on Server Selection • All-req-monitored • The overall performance efficiency of the throttle-based approach is higher than that of MA based one • Static interval based algorithm results in higher request success ratio and overall efficiency than the other three approaches • Not-all-req-monitored • With fewer requests: the static interval based approach yields higher request success ratios and performance efficiency • When more requests arrive, the request success ratio decreases and gets closer to the dynamic range based approaches • In terms of overall performance efficiency, the throttle based algorithm is better than other approaches

15. Impact of Information Collection on Server Selection • All-req-monitored • For both svrs-hop and svrs-uf, throttle-based and MA model based approaches have similar request success ratios, but the overall performance efficiency of the throttle-based approach is higher • Static interval based algorithm results in higher request success ratio and overall efficiency than other three approaches • Only server resource factors are considered in server selection and also all requests are reflected in resource provisioning module, representing residual link bandwidth with a static interval is accurate enough • Not-all-req-monitored • With fewer requests, the static interval based approach yields higher request success ratios and also higher performance efficiency than other other dynamic ranged based approaches; but when more requests arrive, the request success ratio decreases and gets closer to the dynamic range based approaches • With a larger number of request, the success ratio is more sensitive to the application workload change. • In terms of overall performance efficiency, the throttle based algorithm is better than other approaches

16. SVRS-HOP, All-Req-Monitored

17. SVRS-UF, All-Req-Monitored

18. SVRS-HOPNot-All-Req-Monitored

19. SVRS-UFNot-All-Req-Monitored

20. Performance Summary • Both the accuracy and overhead of information collection policies have a significant impact on the performance of resource provisioning process • Although Snapshot based collection can obtain very accurate information, the huge overhead introduced by frequent sampling and directory updates makes it a bad choice • MA based collection does not always perform very well practically, while throttle based algorithm adapts pretty well to the constantly changing environment and turns out to be a very good choice in most cases

21. Optimal Combinations of Information Collection and Resource Provisioning Policies

22. Preliminary Dynamic Service Composition Rules

23. Future Work • To integrate policies for AutoSeC into CompOSE|Q • To study network management middleware services applicable to mobile environment • mobility management • adaptive probing architecture • distributed directory service management