The problem of allocating the data of a database to the sites of a communication network is investigated. This problem deviates from the well-known file allocation problem in several aspects. First, the objects to be allocated are not known a priori; second, these objects are accessed by schedules that contain transmissions between objects to produce the result. A model that makes it possible to compare the cost of allocations is presented, the cost can be computed for different cost functions and for processing schedules produced by arbitrary query processing algorithms. For minimizing the total transmission cost, a method is proposed to determine the fragments to be allocated from the relations in the conceptual schema and the queries and updates executed by the users. For the same cost function, the complexity of the data allocation problem is investigated. Methods for obtaining optimal and heuristic solutions under various ways of computing the cost of an allocation are presented and compared. Two different approaches to the allocation management problem are presented and their merits are discussed.

In this thesis, we present a new methodology for resource sharing algorithms in distributed systems. We propose that a distributed computing system should be composed of a decentralized community of microeconomic agents. We show that this approach decreases complexity and can substantially improve performance. We compare the performance, generality and complexity of our algorithms with non-economic algorithms. To validate the usefulness of our approach, we present economies that solve three distinct resource management problems encountered in large, distributed systems. The first economy performs CPU load balancing and demonstrates how our approach limits complexity and effectively allocates resources when compared to non-economic algorithms. We show that the economy achieves better performance than a representative non-economic algorithm. The load balancing economy spa...

In numerical algorithms based on adaptive mesh refinement, the computational workload changes during the execution of the algorithms. In mapping such algorithms on to distributed memory architectures, dynamic load balancing is necessary to balance the workload among the processors in order to obtain high performance. In this paper, we propose a dynamic processor allocation algorithm for a mesh architecture that reassigns the workload in an attempt to minimize both the computational and communication costs. Our algorithm is based on a heuristic for a 2D packing problem that gives provably close to optimal solutions for special cases of the problem. We also demonstrate through experiments how our algorithm provides good quality solutions in general. 1 Introduction Parallel computers with thousands of processors are becoming a commercial reality due to advanced VLSI technolgies. These machines can provide tremendous computational power needed to solve many computationally intensive pro...

Suppose you got an excellent dynamic file assignment algorithm. But, how to proceed dynamically from the current to the optimal file allocation? Imagine replication of your files and some sort of voting strategy --- the question then is, how to maintain consistency if the current and the optimal file assignment differ not only in the location of the files but also in the number of replicas? This paper tries to answer these questions and introduces the basic relocation protocols which preserve consistency during relocation, as well as a priority-driven, storage capacity-based approach to bring the basic relocation protocols in an optimal sequence in order to move quickly and as close as possible towards the optimal file assignment. 1 Introduction We consider a mathematical model of an information network of jRj nodes, some of which contain copies of our jDj data files. The degree of replicas has not to be fixed. Within this network, every node is able to communicate with every other no...

This work addresses a files allocation problem (FAP) in distributed computing systems. This FAP attempts to minimize the expected data transfer time for a specific program that must access several data files from non-perfect computer sites. We assume that communication capacity can be reserved; hence, the data transmission behavior is modeled as a many-to-one multi-commodity flow problem. A new critical-cut method is proposed to solve this reduced multi-commodity flow problem. Based on this method, two algorithms which use branch-and-bound are proposed for this FAP. The proposed algorithms are able to allocate data files having single copies or multiple replicated copies. Simulation results are presented to demonstrate the performance of the algorithms.