18935022
Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment
In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.
12505197
Dynamic mapping in a heterogeneous environment with tasks having priorities and multiple deadlines
In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign resources to tasks (match) and order the execution of tasks on each resource (schedule in a manner that exploits the heterogeneity of the resources and tasks. The mapping (defined as matching and scheduling) of tasks onto machines with varied computational capabilities has been shown, in general, to be an NP-complete problem. Therefore, heuristic techniques to find a near-optimal solution to this mapping problem are required. Dynamic mapping is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no communication among tasks), and tasks have priorities and multiple deadlines. This research proposes, evaluates, and compares eight dynamic heuristics. The performance of the best heuristics is 83% of an upper bound.
15504838
Utilization-based heuristics for statically mapping real-time applications onto the HiPer-D heterogeneous computing system
Real-time applications continue to increase in importance as they are employed in various critical areas, such as command and control systems. These applications have traditionally required custom-made systems to execute them. Recently, with the widespread use of increasingly powerful commercial off-the-shelf (COTS) products, some real-time system designers have started a shift from custom development to COTS-based systems to achieve lower costs and more flexible systems. This research investigates the problem of allocating real-time applications to a set of COTS heterogeneous machines connected together by a COTS high-speed network. The heuristics were implemented on the High Performance Distributed Computing Program's (HiPer-D) Naval Surface Warfare Center (NSWC) testbed. At the specification of NSWC, the goal of this study is to design static resource allocation heuristics that balance the utilization of the computation and network resources in the HiPer-D system based on the system information provided. The broader goal is to maximize the time before dynamic reallocation is required for managing an increased workload at runtime.
2562587
Creating a Robust Desktop Grid using Peer-to-Peer Services
The goal of the work described in this paper is to design and build a scalable infrastructure for executing grid applications on a widely distributed set of resources. Such grid infrastructure must be decentralized, robust, highly available, and scalable, while efficiently mapping application instances to available resources in the system. However, current desktop grid computing platforms are typically based on a client-server architecture, which has inherent shortcomings with respect to robustness, reliability and scalability. Fortunately, these problems can be addressed through the capabilities promised by new techniques and approaches in peer-to-peer (P2P) systems. By employing P2P services, our system allows users to submit jobs to be run in the system and to run jobs submitted by other users on any resources available in the system, essentially allowing a group of users to form an ad-hoc set of shared resources. The initial target application areas for the desktop grid system are in astronomy and space science simulation and data analysis.
34981478
An approach to feature based modelling by dependency alignment for the maintenance of the trustworthy system
Functionalities in a software system can be categorized as accidental or essential, where accidental functionalities are dependent on essential ones, but not vice versa. Distinguishing essential functionalities from accidental ones in the development can lead to the reduction of maintenance effort and to the trustworthy system. In this paper, we propose feature based modelling (FBM) in which accidental functionalities are aligned with essential functionalities. The feature in this approach, as a logical unit, provides traceability from a functionality in the requirements to an implemented module with effective dependency management, thus the accidental features can be modified with minimal disruption to the essential features. FBM enables us to limit working boundary of maintenance activities, thus helps to keep the trustworthiness of the system against unskillful modification.
2382558
Convex Hull Ensemble Machine for Regression and Classification
We propose a new ensemble algorithm called Convex Hull Ensemble Machine (CHEM). CHEM in Hilbert space is first developed and modified for regression and classification problems. We prove that the ensemble model converges to the optimal model in Hilbert space under regularity conditions. Empirical studies reveal that, for classification problems, CHEM has a prediction accuracy similar to that of boosting, but CHEM is much more robust with respect to output noise and never overfits datasets even when boosting does. For regression problems, CHEM is competitive with other ensemble methods such as gradient boosting and bagging.
12515507
Using content-addressable networks for load balancing in desktop grids
Desktop grids have evolved to combine Peer-to-Peer and Grid computing techniques to improve the robustness, reliability and scalability of job execution infrastructures. However, efficiently matching incoming jobs to available system resources and achieving good load balance in a fully decentralized and heterogeneous computing environment is a challenging problem. In this paper, we extend our prior work with a new decentralized algorithm for maintaining approximate global load information, and a job pushing mechanism that uses the global information to push jobs towards underutilized portions of the system. The resulting system more effectively balances load and improves overall system throughput. Through a comparative analysis of experimental results across different system configurations and job profiles, performed via simulation, we show that our system can reliably execute Grid applications on a distributed set of resources both with low cost and with good load balance.
20202426
Integration of a Structural Index with a Structural Join for Accelerating Path Queries
Current techniques for processing XML queries can be divided into two groups. One is structural index techniques that reduce the search space by traversing a structural summary instead of a data graph. And the other is structural join techniques that can efficiently compute ancestor-descendant pairs from two lists of nodes without traversing the data graph. In this paper, we propose a structural index technique and a novel structural join technique and we prove that these two proposed techniques are integrated to accelerate processing of a path query in the experiments.