477,484 research outputs found

    Complexity Reduction: Local Activity Ranking By Resource Entropy For QoS-aware Cloud Scheduling

    Get PDF
    The principle of local activity originated from electronic circuits, but can easily translate into other non-electrical homogeneous/heterogeneousmedia.Cloudresourceisanexample of a locally-active device, which is the origin of complexity in cloud scheduling system. However, most of the researchers implicitly assume the cloud resource to be locally passive when constructing new scheduling strategies. As a result, their research solutions perform poorly in the complex cloud environment. In this paper, we ?rst study several complexity factors caused by the locally-active cloud resource. And then we extended the ”Local Activity Principle” concept with a quantitative measurement based on Entropy Theory. Furthermore, we classify the scheduling system into ”Order” or ”Chaos” state with simulating complexity in the cloud. Finally, we propose a new approach to controlling the chaos based on resource’s Local Activity Ranking for QoS-aware cloud scheduling and implement such idea in Spark. Experiments demonstrate that our approach outperforms thenativeSparkFairSchedulerwithservercostreducedby23%, average response time improved by 15% - 20% and standard deviation of response time minimized by 30% - 45%

    Entropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management

    Get PDF
    In cloud service resource management system, complexity limits the system’s ability to better satisfy the application’s QoS requirements, e.g. cost budget, average response time and reliability. Numerousness, diversity, variety, uncertainty, etc. are some of the complexity factors which lead to the variation between expected plan and actual running performance of cloud applications. In this paper, after defining the complexity clearly, we identify the origin of complexity in cloud service resource management system through the study of ”Local Activity Principle”. In order to manage complexity, an Entropy-based methodology is presented to use which covers identifying, measuring, analysing and controlling (avoid and reduce) of complexity. Finally, we implement such idea in a popular cloud engine, Apache Spark, for running Analysis as a Service (AaaS). Experiments demonstrate that the new, Entropy-based resource management approach can significantly improve the performance of Spark applications. Compare with the Fair Scheduler in Apache Spark, our proposed Entropy Scheduler is able to reduce overall cost by 23%, improve the average service response time by 15% - 20% and minimized the standard deviation of service response time by 30% - 45%

    Emerging Consciousness as a Result of Complex-Dynamical Interaction Process

    Get PDF
    A quite general interaction process within a multi-component system is analysed by the extended effective potential method, liberated from usual limitations of perturbation theory or integrable model. The obtained causally complete solution of the many-body problem reveals the phenomenon of dynamic multivaluedness, or redundance, of emerging, incompatible system realisations and dynamic entanglement of system components within each realisation. The ensuing concept of dynamic complexity (and related intrinsic chaoticity) is absolutely universal and can be applied to the problem of consciousness that emerges now as a high enough, properly specified level of unreduced complexity of a suitable interaction process. This complexity level can be identified with the appearance of bound, permanently localised states in the multivalued brain dynamics from strongly chaotic states of unconscious intelligence, by analogy with classical behaviour emergence from quantum states at much lower levels of world dynamics. We show that the main properties of this dynamically emerging consciousness (and intelligence, at the preceding complexity level) correspond to empirically derived properties of natural versions and obtain causally substantiated conclusions about their artificial realisation, including the fundamentally justified paradigm of genuine machine consciousness. This rigorously defined machine consciousness is different from both natural consciousness and any mechanistic, dynamically single-valued imitation of the latter. We use then the same, truly universal concept of complexity to derive equally rigorous conclusions about mental and social implications of the machine consciousness paradigm, demonstrating its indispensable role in the next stage of civilisation development

    The Transitivity of Trust Problem in the Interaction of Android Applications

    Full text link
    Mobile phones have developed into complex platforms with large numbers of installed applications and a wide range of sensitive data. Application security policies limit the permissions of each installed application. As applications may interact, restricting single applications may create a false sense of security for the end users while data may still leave the mobile phone through other applications. Instead, the information flow needs to be policed for the composite system of applications in a transparent and usable manner. In this paper, we propose to employ static analysis based on the software architecture and focused data flow analysis to scalably detect information flows between components. Specifically, we aim to reveal transitivity of trust problems in multi-component mobile platforms. We demonstrate the feasibility of our approach with Android applications, although the generalization of the analysis to similar composition-based architectures, such as Service-oriented Architecture, can also be explored in the future

    The city as a socio-technical system a spatial reformulation

    Get PDF

    Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy

    Get PDF
    The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer’s disease (AD) patients using the Multiscale Entropy (MSE). The MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant difference

    Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

    Get PDF
    This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

    Dislocation avalanche correlations

    Full text link
    Recently, mechanical tests on ice as well as dislocation dynamics simulations have revealed that plastic flow displays a scale-free intermittent dynamics characterized by dislocation avalanches with a power law distribution of amplitudes. To further explore the complexity of dislocation dynamics during plastic flow, we present a statistical analysis of dislocation avalanche correlations and avalanche triggering. It is shown that the rate of avalanche triggering immediately after any avalanche is larger than the background activity due to uncorrelated events. This self-induced triggering increases in intensity, and remains over the background rate for longer times, as the amplitude of the mainshock increases. This analysis suggests that stress redistributions and the associated collective dislocation rearrangements may be responsible for aftershock triggering in the complex process of plastic deformation.Comment: 8 pages, 3 figures, presented at ICSMA-13, August 2003, Budapes
    • 

    corecore