Provides an abstract of the keynote presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.

The paper presents a technique for performing dynamic goal-oriented buffer pool management for database management systems. To dynamically adjust the buffer pool sizes for the multiple buffer pools provided by database management systems is a complex constrained optimization problem. In the goal-oriented approach, the user specifies each buffer pool's random access response time goal and the total available number of buffers for all buffer pools. The problem is to dynamically expand or contract the buffer pool sizes based on the database workload to achieve these pre-defined response time goals for each buffer pool while maintaining the same total number of buffers in the database system. Our goal satisfaction algorithm monitors goal satisfaction of each buffer pool and periodically changes buffer pool sizes to improve goal satisfaction. The expansion and contraction process does not allocate new or free up existing virtual storage. We demonstrate that dynamic tuning can greatly improve buffer pool goal satisfaction through trace driven simulations.

A new approach to performing resource allocation in autonomous distributed computer systems is explored. As opposed to previous work which is based on interprocessor cooperation, the distributed system is modeled as a competitive society of microeconomic agents. The model is applied to the problem of managing distributed, replicated data objects. In this economy, jobs and transactions submitting read/write operations on data objects pay the processors to perform the operations. The processors use the revenue they earn to lease copies of data objects on which they can make a profit. The data objects themselves are active agents and set the prices for leasing copies. The economy is evaluated by a simulation study. The experiments show that the economy can substantially improve performance by varying the placement and number of copies of each data object.>

No abstract available.

A flow control mechanism based on an economic model of optimum decentralized decision making is presented. The virtual circuits (VCs) of the network are considered as economic agents competing for link capacity resources. Each VC is equipped with a preference relation defining its individual throughput-delay goals. The work makes three contributions to the study of flow control protocols. First, it presents fully decentralized flow control algorithms that allocate capacity to reflect both, varying priorities among VCs as well as their preferences and needs. Second, it establishes that the flow control algorithms result in Pareto-optimal allocation of capacities. This implies that the algorithms accomplish optimally fair allocation of resources given the set priorities and preferences. Third, experimental evidence is provided that the algorithms converge rapidly.>

A novel approach to allocating and sharing communication and computational resources in a distributed system is described. The approach, which is based on concepts drawn from microeconomics, uses algorithms that are competitive rather than cooperative. The effectiveness of these concepts is demonstrated by describing an economy that improves the performance of a distributed system by implementing load balancing. In this economy, competition sets prices for the resources in the system. Jobs complete for the resources by issuing bids, and the resource allocation decisions are made through auctions held by the processors. The benefits of the method include limited complexity and algorithms that are intrinsically decentralized and modular. Simulation studies show that these economies achieve substantial performance benefits.>