Principal investigator: Gregor v. Bochmann (U of Ottawa)

1. Project Overview Quality of Service and Distributed Systems Management.University of Ottawa, of Western Ontario and UQAM, 2000-2001http://beethoven.site.uottawa.ca/DSRG/citr-ec-QoS.htm Principal investigator: Gregor v. Bochmann (U of Ottawa) Co-investigators: Brigitte Kerhervé (UQAM) Hanan Lutfiyya (U of Western Ontario)

2. Background In the context of electronic commerce, standardization of the equipment and software packages is not possible. Furthermore, the Internet is evolving as a collection of heterogeneous networks with different capabilities. Therefore the infrastructure has to be able to deal with subsystems of different capabilities. This is particularly true for the quality of service (QoS) of communication between the different end-systems involved in the application. It is expected that the Internet will provide in the future, in addition to its traditional ``best effort'' service also some form of guaranteed service quality, probably at a cost, which is based on the reservation of appropriate system resources. Other limitations of the QoS are related to server congestion or limited network access bandwidth of the client workstation. Even though QoS negotiation and adaptation issues have been studied before, the global system management aspects related to the provisioning of negotiated QoS have not been studied extensively. In this project, we are mainly concerned with the negotiation of appropriate response time for queries in the context of electronic commerce. The management of the quality of real-time multimedia presentations was studied under a previous CITR project.

3. Objectives • To develop policies, using the principle of different classes of quality, for the management of distributed applications in servers and over the Internet. • To develop scalable models for distributed system management which are suitable for supporting a very large number of electronic commerce buyers and vendors, especially addressing QoS issues. • To develop query optimization techniques for parallel shared-nothing database servers using QoS information provided by the underlying networks

4. Major technical challenges • Formulation of policies at a high level of abstraction, close to the user's perception of quality and benefit • distinction between several classes of service (depending on user profile) • Acquiring knowledge about the system's current performance parameters in the distributed environment and ensuring that the policies are satisfied at run time • monitoring of dynamic system parameters (server workload, network congestion status) • Scalability • large user population, large number of servers, large databases • QoS considerations in distributed query processing • impact of communication QoS on query optimization • considering different optimization criteria

5. Problems addressed / novel aspects • Distributed QoS management architecture • QoS management functions: specification, mapping, negotiation, resource reservation, adaptation and monitoring • To obtain scalable QoS management, the QoS management functions may have to be distributed (user workstation, servers, networks, brokers) • Distributed QoS management protocols should be scalable • Distributed query optimization using QoS information • Depending on the user’s QoS requirements, different optimization criteria may be considered • low cost, short delay • Optimization algorithm should take available network QoS into account

6. Progress (1)Definition of several distributed algorithms for load sharing and management between brokers, users and servers • We have defined an architecture that introduces a brokerage function between clients and servers. Brokers continuously monitor the performance of affiliated servers and assign them to clients according to a previously defined server selection policy. We have defined several server selection policies to optimize the capacity of the system based on server performance characteristics and users requirements. In order to make realistic predictions about the performance of the different servers, we have developed an approach to estimate the server performance as a function of the number of concurrent requests that may share the server at a given time (report in preparation). This estimation is based on a server performance model, which is obtained by monitoring the server and its behavior under various load conditions. We have performed an analysis study of the performance of our architecture and its different load-sharing algorithm by simulation. • A prototype demonstration of this load sharing architecture is provided in the EC Major Project demonstration prototype. The selection of a server at the broker is based on performance information obtained by monitoring several Apache servers running of different computers.

7. Progress (2)Query optimization strategies integrating QoS-based cost models and translation of user requirements into query optimization criteria • We have investigated distributed query processing, particularly cost-based query optimization, and we have proposed an approach in which considers QoS (Quality of Service) both from the user's requirements perspective and from the network service availability. We have also proposed an adaptive cost model for distributed query processing. Our cost model is adaptive in the sense that first, it combines multiple optimization criteria, response time and money cost, into a simple cost model and second, it can give a more precise communication cost estimation according to the information captured by the QoS manager. This cost model is flexible because it can capture the user's willingness to pay for the query and the performance dynamics of the computer system. Accordingly, we can also consider two different optimization criteria: the user’s criteria considering the delivered response time versus the cost of the query (based on existing tariff structures), and the system’s criteria considering overall optimal resource utilization, the satisfaction of the user’s response time requirements and the net income from the usage charges. We also identified two network QoS parameters: end-to-end delay and available bandwidth, and introduced methods for measuring them. • Given the general approach described above, we have build a prototype implementation. Three data models are of interest: the user profile, the global catalog of distributed database schemas and the measured QoS information concerning the network and server load. The user profile is helpful for translating QoS requirements into query optimization criteria; it is also useful for guiding the optimizer to choose the correct cost model. The global catalog and the QoS information are mainly used by the cost models.

8. Progress (3)Algorithms for adjusting CPU priorities in order to maintain several classes of service on a single server • We specifically examined applying algorithms for service differentiation to Web servers (since it is the cornerstone of many Web applications) and implemented a version of such an algorithm in the context of an Apache server. When a user request comes into the Apache server, it gets assigned its own process. This process registers with the QoS module. Its reference handle is put into a queue and the process is put to sleep. The QoS module has a scheduling algorithm that wakes up processes based on policies. For example, assume that we have two classes of service: A and B. Assume that the "A" class is the premium class. One policy is that there can be at most "M" B class processes executing and at most "N" A class processes executing (M < N). The disadvantage of this approach is that if there are few A class processes then the CPU is not being effectively used. Another algorithm treats A and B classes equally until there are a certain number of complaints from A processes indicating that they are taking too long to process. The number of non-sleeping B class processes is reduced. We have developed and experimented with several scheduling algorithms. The QoS module was designed so that it is relatively easy to change the scheduling algorithm.

9. Milestones for 2000-2001 • Analysis of the performance of several distributed algorithms for load sharing, using experiments with a prototype implementation and performance simulations. • Performance analysis of query optimization strategies based on experimentation with the prototype and simulation studies; improved optimization strategies. • Design and prototype implementation of algorithms for adjusting multiple resource allocations in order to maintain several differentiated classes of service on a single server.

10. Some recent publications • M. Katchabaw, S. Howard, H. Lutfiyya, A. Marshall, and M. Bauer, ìMaking distributed applications manageable through instrumentationî, In Press, The Journal of Systems and Software, 1999. • H. Lutfiyya, A. Marshall, H. Bauer, P. Martin, and W. Powley, ìConfiguration Maintenance for Distributed Application Managementî, Journal of Network and Systems Management, In Press, 1999. • Bochmann, G. v., Kerhervé, B. and Mohamed-Salem, M., Service management issues in electronic commerce applications, in Electronic Commerce Technology Trends: Challenges and Opportunities, W.Kou and Y. Yesha (eds), IBM Press, 2000, pp. 227-238. • H. Lutfiyya, A. Marshall, M. Bauer, and D. Stokes, ìA Policy-Driven Approach to Availability and Performance Management in Distributed Systems, Journal of Network and Systems Management, cond. accepted, 1998. • M. Katchabaw, H. Lutfiyya, and M. Bauer, ìDriving Resource Management with Application-Level Quality of Service Specificationsî. First International Conference on Information and Computation Economies (ICE98), October, 1998. Also to be published in Journal of Decision Support Systems. • M. Katchabaw, H. Lutfiyya, and M. Bauer, ìA Model of Resource Management to Support End-to-End Application-Driven Managementî. First International Conference on Information and Computation Economies (ICE98), October, 1998. Also to be published in Journal of Decision Support Systems • H. Ye, B. Kerhervé, G. v. Bochmann, QoS-aware distributed query processing, DEXA Workshop on Query Processing in Multimedia Information Systems (QPMIDS), 10th International Workshop on Database & Expert Systems Applications, Florence, Italy, 1-3 September, 1999, Proceedings published by IEEE Computer Society, 1999. • H. Ye, G.v. Bochmann, B. Kerhervé, An adaptive cost model for distributed query processing, UQAM Technical Report, November 1999.