Last updated on December 15, 2014. This conference program is tentative and subject to change
However, existing load-balancing strategies interfere with brownout self adaptivity. Load-balancers are often based on response times, that are already controlled by the self-adaptive features of the application, hence they are not a good indicator of how well a replica is performing.
In this paper, we present novel load-balancing strategies, specifically designed to support brownout applications. They base their decision not on response time, but on user experience degradation. We implemented our strategies in a self-adaptive application simulator, together with some state-of-the-art solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones.
This paper develops one data representation that is scalable in dimension and efficiently stores/retrieves multi-attribute time series in the presence of noise. Here the proposed data representation is a multi-input multi-output autoregressive model (MIMO ARX) with an exogenous input. MIMO ARX models are an advantageous data representation because they are a dimension-reducing representation that inherently describes the inter-dependencies in the data while enabling the creation of efficient noise mitigation approaches. Tests using real-life vehicle data show the effectiveness of these data representations in the application of passenger vehicle localization.
In this article we introduce and discuss preliminary results this so-called return trip of the contrast problem. The tools of geometric optimal control theory have been effectively applied to the contrast problem, and they are similarly employed to this problem which shares many characteristics. The time-minimal transfer in the single-spin case is presented, and preliminary results in the two-spin case are given.
Risk is modeled and quantified
(i) The average performance is not an adequate measure of success. It is found empirically that a histogram of QoS is approximately Gaussian, and consequently each load will eventually receive poor service.
(ii) The variance can be estimated from a refinement of the LTI model that includes a white-noise disturbance; variance is a function of the randomized policy, as well as the power spectral density of the reference signal.
Additional local control can eliminate risk
(iii) The histogram of QoS is truncated through this local control, so that strict bounds on service quality are guaranteed.
(iv) This has insignificant impact on the grid-level performance, beyond a modest reduction in capacity of ancillary service.
In recent work, a low-complexity optimal solution was developed for this problem under a long-term time-average resource constraint. However, in real systems with instantaneous resource constraints, how to optimally exploit the temporal correlation and satisfy realistic stringent constraint on the instantaneous service remains elusive. In this work, we incorporate a stringent constraint on the simultaneously scheduled users and propose a low-complexity scheduling algorithm that dynamically implements user scheduling and dummy packet broadcasting. We show that the throughput region of the optimal policy under the long-term average resource constraint can be asymptotically achieved in the stringent constrained scenario by the proposed algorithm, in the many users limiting regime.
In this paper, non-fragile control via output feedback control for uncertain fuzzy systems is considered and a control design of stabilizing controllers with robustness against uncertainties of system parameters and control gains is proposed. The robust stability analysis of the closed-loop system and controller design are given in terms of LMI conditions, which are less conservative than the existing results.