7544123
Models and Heuristics for Robust Resource Allocation in Parallel and Distributed Computing Systems
This is an overview of the robust resource allocation research efforts that have been and continue to be conducted by the CSU robustness in computer systems group. Parallel and distributed computing systems, consisting of a (usually heterogeneous) set of machines and networks, frequently operate in environments where delivered performance degrades due to unpredictable circumstances. Such unpredictability can be the result of sudden machine failures, increases in system load, or errors caused by inaccurate initial estimation. The research into developing models and heuristics for parallel and distributed computing systems that create robust resource allocations is presented.
13294756
Mapping subtasks with multiple versions on an ad hoc grid
An ad hoc grid is a heterogeneous computing system composed of mobile devices. Each computing resource is constrained in battery energy. The problem being studied is to assign statically computing resources to the subtasks of an application that has an execution time constraint, when the resources are oversubscribed. All subtasks must be executed; to accommodate this in an oversubscribed environment, each subtask has two versions: the primary or full version, and the secondary or degraded version. The secondary version utilizes only 10% of the resources that the primary version requires, and produces only 10% of the data output for the subsequent children subtasks. Thus, the degraded version (secondary version) represents a reduced capability of lesser overall value, while consuming fewer resources. The goal is to assign resources so that the application meets an execution time constraint and the battery energy constraint while minimizing the number of degraded versions used. Five resource allocation heuristics to derive near-optimal solutions to this problem are presented, evaluated, and compared.
1598637
Dynamic resource management heuristics for minimizing makespan while maintaining an acceptable level of robustness in an uncertain environment
Heterogeneous parallel and distributed computing systems may operate in an environment where certain system performance features degrade due to unpredictable circumstances. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. This paper presents a mathematical model for quantifying robustness in a dynamic environment where task execution times estimates are known to contain errors. This research proposes, evaluates, and compares ten different dynamic heuristics for their ability to maintain or maximize the proposed dynamic robustness metric in an uncertain environment. In addition, the makespan results of the proposed heuristics are compared to a lower bound
11001529
A Stochastic Approach to Measuring the Robustness of Resource Allocations in Distributed Systems
Often, parallel and distributed computing systems must operate in an environment replete with uncertainty. Determining a resource allocation that accounts for this uncertainty in a way that can provide a probabilistic guarantee that a given level of quality of service (QoS) is achieved is an important research problem. This paper defines a stochastic methodology for quantifiably determining a resource allocation's ability to satisfy QoS constraints in the midst of uncertainty in system parameters. Uncertainty in system parameters and its impact on system performance are modeled stochastically. This stochastic model is then used to derive a quantitative expression for the robustness of a resource allocation. The paper investigates the utility of the proposed stochastic robustness metric by applying the metric to resource allocations in a simulated distributed system. The simulation results are then compared with deterministically defined metrics from the literature
3042095
Measuring the Robustness of Resource Allocations in a Stochastic Dynamic Environment
Heterogeneous distributed computing systems often must operate in an environment where system parameters are subject to uncertainty. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. We present a methodology for quantifying the robustness of resource allocations in a dynamic environment where task execution times are stochastic. The methodology is evaluated through measuring the robustness of three different resource allocation heuristics within the context of a stochastic dynamic environment. A Bayesian regression model is fit to the combined results of the three heuristics to demonstrate the correlation between the stochastic robustness metric and the presented performance metric. The correlation results demonstrated the significant potential of the stochastic robustness metric to predict the relative performance of the three heuristics given a common objective function.
8219620
SPIRE: a digital library for scientific information
Abstract.In this paper we describe the architecture and implementation of a digital library framework for scientific data, particularly imagery, with a focus on support for content-based search. Content is specified by the user at one or more of the following abstraction levels: pixel, feature, and semantic. An object-definition mechanism has been developed that supports example-based and constraint-based specification of both simple and complex query targets.This framework incorporates a methodology yielding a computationally efficient implementation of image processing algorithms, thus allowing the interactive, real-time extraction and manipulation of user-specified features and content during the execution of queries. The framework is well-suited for searching scientific databases, including satellite imagery, and medical and seismic data repositories, where the richness of the information does not allow the a priori generation of exhaustive indexes.
17392905
Replacement Strategies in Steady State Genetic Algorithms: Static Environments
Recent years have seen increasing numbers of applications of Evolutionary Algorithms to non-stationary environments such as on-line process control. Studies have indicated that Genetic Algorithms using "Steady State" models demonstrate a greater ability to track moving optima than those using "Generational" models, however implementing the former requires an additional choice of which members of the current population should be replaced by new offspring. In this paper a number of selection and replacement strategies are compared for use in Steady State Genetic Algorithms working as function optimisers in dynamic environments. In addition to an algorithm with fixed mutation rates, the strategies are also compared in algorithms employing Cobb's Hypermutation method for tracking environmental changes. On-line and off-line metrics are used for comparison, which correspond to different types of real-world applications. In both cases it is shown that algorithms employing some kind of elitism outperform those that do not, which is related to previous studies on stationary environments. An investigation is made of various methods of implementing elitism, including an implicit method, "conservative" selection. It is shown that the latter, in addition to being computationally simpler, produces significantly better results on the problems used, and reasons are given for this behaviour.
37610017
Validated Cost Models for Parallel OQL Query Processing
Query cost models are widely used, both for performance analysis and for comparing execution plans during query optimisation. In essence, a cost modelp redicts where time is being spent during query evaluation. Although many cost models have been proposed, for serial, parallel and distributed database systems, surprisingly few of these have been validated against real systems. This paper presents cost models for the parallel evaluation of ODMG OQL queries, which have been compared with experimental results obtained using the Polar object database system. The paper describes the validation of the cost model for a collection of queries, using three join algorithms over the OO7 benchmark database. The results show that the cost model generally both ranks alternative plans appropriately, and gives a useful indication of the response times that can be expected from a plan. The paper also illustrates the application of the cost model by highlighting the contributions of different features and operations to query response times.
40619191
The Design, Implementation and Evaluation of an ODMG Compliant, Parallel Object Database Server
This paper describes the design, implementation and evaluation of a parallel object database server. While a number of research groups and companies now provide object database servers designed to run on uniprocessors, there has been surprisingly little work on the exploitation of parallelism to provide scalable performance in Object Database Management Systems (ODBMS). The work described in this paper takes as its starting-point the Object Database Management Group (ODMG) standard for object databases, thereby allowing the project to focus on research into parallelism, rather than on the ODBMS interfaces. The system is designed to run on a distributed memory parallel machine, and the paper describes the key issues and design decisions including: parallel query optimisation and execution, flow control, support for user-defined operations in queries, object distribution, cache management and navigational client access. The work shows that the significant differences between the object and relational database paradigms lead to significant differences in the designs of parallel servers to support these two paradigms. The paper presents an extensive performance analysis of the prototype systems which shows that good performance can be achieved on a cluster of linux PCs.
37305588
A dynamic programming algorithm for the conditional covering problem on tree graphs
In a previous article, we presented algorithms for solving the Conditional Covering Problem (CCP) on path and extended star graphs. The CCP on these graphs can be solved in O(n2) time, where n is the number of nodes in the graph. In this article, we propose a new dynamic programming procedure to solve the CCP on tree graphs. This recursion works from the leaf nodes of the tree up to the root node, using notions of protected and unprotected costs as done for the CCP path algorithm in our previous work. We introduce new preliminary routines and data structures to merge information from subpaths and subtrees, resulting in an O(n4) algorithm to optimally solve the problem. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 46(4), 186–197 2005