• Journal of KIISE:Computer Systems and Theory
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• v.31 no.8
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• pp.421-431
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• 2004

This paper presents adaptive execution techniques that determine whether parallelized loops are executed in parallel or sequentially in order to maximize performance. The adaptation and performance estimation algorithms are implemented in a compiler preprocessor. The preprocessor inserts code that automatically determines at compile-time or at run-time the way the parallelized loops are executed. Using a set of standard numerical applications written in Fortran77 and running them with our techniques on a distributed shared memory multiprocessor machine (SGI Origin2000), we obtain the performance of our techniques, on average, 26%, 20%, 16%, and 10% faster than the original parallel program on 32, 16, 8, and 4 processors, respectively. One of the applications runs even more than twice faster than its original parallel version on 32 processors.

• Journal of Computing Science and Engineering
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• v.6 no.1
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• pp.26-38
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• 2012

Java has been increasingly used in programming for real-time systems. However, some of Java's features such as automatic memory management and dynamic compilation are harmful to time predictability. If these problems are not solved properly then it can fundamentally limit the usage of Java for real-time systems, especially for hard real-time systems that require very high time predictability. In this paper, we propose to exploit multicore computing in order to reduce the timing unpredictability that is caused by dynamic compilation and adaptive optimization. Our goal is to retain high performance comparable to that of traditional dynamic compilation, while at the same time, obtain better time predictability for Java virtual machine (JVM). We have studied pre-compilation techniques to utilize another core more efficiently, preoptimization on another core (PoAC) scheme to replace the adaptive optimization system (AOS) in Jikes JVM and the counter based optimization (CBO). Our evaluation reveals that the proposed approaches are able to attain high performance while greatly reducing the variation of the execution time for Java applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2294-2314
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• 2011

The key issue in providing fast and reliable access on cloud services is the effective management of resources in a cloud system. However, the high variation in cloud service access rates affects the system performance considerably when there are no default routines to handle this type of occurrence. Adaptive techniques are used in resource management to support robust systems and maintain well-balanced loads within the servers. This paper presents an adaptive resource management for cloud systems which supports the integration of intelligent methods to promote quality of service (QoS) in provisioning of cloud services. A technique of dynamically assigning cloud services to a group of cloud servers is proposed for the adaptive resource management. Initially, cloud services are collected based on the excess cloud services load and then these are deployed to the assigned cloud servers. The assignment function uses the proposed proportional ordering which efficiently assigns cloud services based on its resource consumption. The difference in resource consumption rate in all nodes is analyzed periodically which decides the execution of service assignment. Performance evaluation showed that the proposed dynamic service assignment (DSA) performed best in throughput performance compared to other resource allocation algorithms.

• Journal of the Korea Society for Simulation
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• v.12 no.4
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• pp.51-59
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• 2003

Real-Time applications on Grid environment have several problems in terms of resource management addressed as follows; (1) dynamic resource allocation to provide QoS objectives, (2) heterogeneous resources that is different scale, or capacity in same unit, and (3) resource availability, and resource needs. This paper describes the techniques of resource manager (RM) handling above problems to support QoS of dynamic real-time applications on Grid. The contributions of this paper to solve problems are as follows: unification of dynamic resource requirements among heterogeneous hosts, control of resources in heterogeneous environments, and dynamic load balancing/sharing. Our heuristic allocation scheme works not only 257% better than random, 142% better than round robin, and 36.4% better than least load in QoS sensitivity, but also 38.6% better than random, 28.5% better than round robin, and 31.6% better than least load in QoS.