121 research outputs found

    Remote Performance Monitor (RPM)

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    Mobile, resource-constrained, battery-powered devices have emerged as key access points to the world\u27s digital infrastructure. To enable our understanding of the performance of these devices, we must be able to efficiently collect accurate profile data from these devices after they are deployed in the field. Moreover, understanding the full-system power and energy behavior of these systems for real programs is vital if users are to accurately characterize, model, and develop effective techniques for extending battery life. Unfortunately, extant approaches to measuring and characterizing power and energy consumption focus on high-end processors, do not consider the complete device, employ inaccurate (program-only) simulation, rely on inaccurate, course-grained battery level data from the device, or employ expensive power measurement tools that are difficult to share across research groups and students. To address these issues, we developed remote performance monitor (RPM). The first component of RPM is an efficient technique for collecting accurate sample-based program profiles. The key to the efficacy of this technique is that we identify when to sample using the repeating patterns in program execution, phases. To enable fine-grained, full-system characterization of embedded computers, we couple and unify phase-aware profiling, hardware performance monitoring, and power and energy measurement within RPM. RPM consists of a tightly coupled set of components which (1) control lab equipment for power measurements and analysis, (2) configure target system characteristics at run-time (such as CPU and memory bus speed), (3) collect target system data using on-board hardware performance monitors (HPMs) and (4) provide a remote access interface. Users of RPM can submit and configure experiments that execute programs on the RPM target device (currently a Stargate sensor platform that is very similar to an HP iPAQ) to collect very accurate power, energy, and CPU performance data with high resolution

    Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and Systems - Report on the Workshop ICOOOLPS'2006 at ECOOP'06

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    ICOOOLPS'2006 was the first edition of ECOOP-ICOOOLPS workshop. It intended to bring researchers and practitioners both from academia and industry together, with a spirit of openness, to try and identify and begin to address the numerous and very varied issues of optimization. This succeeded, as can be seen from the papers, the attendance and the liveliness of the discussions that took place during and after the workshop, not to mention a few new cooperations or postdoctoral contracts. The 22 talented people from different groups who participated were unanimous to appreciate this first edition and recommend that ICOOOLPS be continued next year. A community is thus beginning to form, and should be reinforced by a second edition next year, with all the improvements this first edition made emerge.Comment: The original publication is available at http://www.springerlink.co

    Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and Systems - Report on the Workshop ICOOOLPS'2007 at ECOOP'07

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    ICOOOLPS'2007 was the second edition of the ECOOP-ICOOOLPS workshop. ICOOOLPS intends to bring researchers and practitioners both from academia and industry together, with a spirit of openness, to try and identify and begin to address the numerous and very varied issues of optimization. After a first successful edition, this second one put a stronger emphasis on exchanges and discussions amongst the participants, progressing on the bases set last year in Nantes. The workshop attendance was a success, since the 30-people limit we had set was reached about 2 weeks before the workshop itself. Some of the discussions (e.g. annotations) were so successful that they would required even more time than we were able to dedicate to them. That's one area we plan to further improve for the next edition

    On the Future of Cloud Engineering

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    Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many different types of service offerings, developer productivity enhancement tools, and new application classes as well as the manifestation of cloud functionality closer to the user at the edge. The notion of utility computing, however, has remained constant throughout its evolution, which means that cloud users always seek to save costs of leasing cloud resources while maximizing their use. On the other hand, cloud providers try to maximize their profits while assuring service-level objectives of the cloud-hosted applications and keeping operational costs low. All these outcomes require systematic and sound cloud engineering principles. The aim of this paper is to highlight the importance of cloud engineering, survey the landscape of best practices in cloud engineering and its evolution, discuss many of the existing cloud engineering advances, and identify both the inherent technical challenges and research opportunities for the future of cloud computing in general and cloud engineering in particular

    International Workshop on Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and Systems - Report on the Workshop ICOOOLPS'2007 at ECOOP'07

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    ICOOOLPS'2007 was the second edition of the ECOOP-ICOOOLPS workshop. ICOOOLPS intends to bring researchers and practitioners both from academia and industry together, with a spirit of openness, to try and identify and begin to address the numerous and very varied issues of optimization. After a first successful edition, this second one put a stronger emphasis on exchanges and discussions amongst the participants, progressing on the bases set last year in Nantes. The workshop attendance was a success, since the 30-people limit we had set was reached about 2 weeks before the workshop itself. Some of the discussions (e.g .annotations) were so successful that they would required even more time than we were able to dedicate to them. That's one area we plan to further improve for the next edition

    Site fidelity and range size of wintering Barnacle Geese Branta leucopsis

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    Barnacle Geese restrict their movements to relatively few key sites and exhibit considerable variation in ranging behaviour. To examine individual and seasonal variation in site fidelity, habitat use, range size and foraging strategies of Barnacle Geese Branta leucopsis, the movements of 18 male Barnacle Geese tagged in two discrete areas were tracked for 3–6 months from late autumn until departure on the spring migration. Tagged geese concentrated their feeding in a relatively small proportion of apparently suitable habitat. Geese moved increasingly further afield in midwinter, and there was a clear predeparture shift to the largest area of relatively undisturbed, and possibly more nitrogen-rich, saltmarsh on the Solway. Birds from one of the two capture sites tended to be more sedentary and have smaller home ranges

    Reducing Load Delay to Improve Performance of Internet-Computing Programs

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    xvii I Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 II Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 A. Implementation of the Java Language Specification . . . . . . . . . . . . . . . . 8 1. Access Rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2. Class File Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 B. The Java Virtual Machine (JVM) . . . . . . . . . . . . . . . . . . . . . . . . . . 10 C. Applets v/s Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 D. The Java Execution Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 III Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 A. Transfer Delay Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1. Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2. Startup Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 B. Compilation Delay Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1. Continuous Compilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2. Adaptive Compilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 C. Other Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 IV Experimental Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 A. Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 B. Transfer Delay Optimization Methodology . . . . . . . . . . . ..
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