Double Auction-based Scheduling of Scientific Applications in Distributed Grid and Cloud Environments

Economy models have long been considered as a promising complement to the classical distributed resource management not only due of their dynamic and decentralized nature, but also because the concept of financial valuation of resources and services is an inherent part of any such model. In its broadest sense, scheduling of scientific applications in distributed Grid and Cloud environments can be regarded as a market-based negotiation between a scheduling service optimizing user-centric objectives (execution time, budget), and a resource manager optimizing provider-centric metrics (resource utilization, income, job throughput). In this paper, we propose a new instantiation of the negotiation protocol between the scheduler and resource manager using a market-based Continuous Double Auction (CDA) model. We analyze different scheduling strategies that can be applied and identify general strategic patterns that can lead to a fast and cheap work ow execution. In the experimental study, we demonstrate that under certain circumstances one can benefit by applying an aggressive scheduling strategy.     

Evaluating the impact of AND/OR search on 0-1 integer linear programming

AND/OR search spaces accommodate advanced algorithmic schemes for graphical models which can exploit the structure of the model. We extend and evaluate the depth-first and best-first AND/OR search algorithms to solving 0-1 Integer Linear Programs (0-1 ILP) within this framework. We also include a class of dynamic variable ordering heuristics while exploring an AND/OR search tree for 0-1 ILPs. We demonstrate the effectiveness of these search algorithms on a variety of benchmarks, including real-world combinatorial auctions, random uncapacitated warehouse location problems and MAX-SAT instances.     

Conjugate mixture models for clustering multimodal data

The problem of multimodal clustering arises whenever the data are gathered with several physically different sensors. Observations from different modalities are not necessarily aligned in the sense there there is no obvious way to associate or compare them in some common space. A solution may consist in considering multiple clustering tasks independently for each modality. The main difficulty with such an approach is to guarantee that the unimodal clusterings are mutually consistent. In this letter, we show that multimodal clustering can be addressed within a novel framework: conjugate mixture models. These models exploit the explicit transformations that are often available between an unobserved parameter space (objects) and each of the observation spaces (sensors). We formulate the problem as a likelihood maximization task and derive the associated conjugate expectation-maximization algorithm. The convergence properties of the proposed algorithm are thoroughly investigated. Several local and global optimization techniques are proposed in order to increase its convergence speed. Two initialization strategies are proposed and compared. A consistent model selection criterion is proposed. The algorithm and its variants are tested and evaluated within the task of 3D localization of several speakers using both auditory and visual data.     

A differential model of the complex cell

The receptive fields of simple cells in the visual cortex can be understood as linear filters. These filters can be modeled by Gabor functions or gaussian derivatives. Gabor functions can also be combined in an energy model of the complex cell response. This letter proposes an alternative model of the complex cell, based on gaussian derivatives. It is most important to account for the insensitivity of the complex response to small shifts of the image. The new model uses a linear combination of the first few derivative filters, at a single position, to approximate the first derivative filter, at a series of adjacent positions. The maximum response, over all positions, gives a signal that is insensitive to small shifts of the image. This model, unlike previous approaches, is based on the scale space theory of visual processing. In particular, the complex cell is built from filters that respond to the 2D differential structure of the image. The computational aspects of the new model are studied in one and two dimensions, using the steerability of the gaussian derivatives. The response of the model to basic images, such as edges and gratings, is derived formally. The response to natural images is also evaluated, using statistical measures of shift insensitivity. The neural implementation and predictions of the model are discussed.     

An overview of depth cameras and range scanners based on time-of-flight technologies

Time-of-flight (TOF) cameras are sensors that can measure the depths of scene points, by illuminating the scene with a controlled laser or LED source and then analyzing the reflected light. In this paper, we will first describe the underlying measurement principles of time-of-flight cameras, including: (1) pulsed-light cameras, which measure directly the time taken for a light pulse to travel from the device to the object and back again, and (2) continuous-wave-modulated light cameras, which measure the phase difference between the emitted and received signals, and hence obtain the travel time indirectly. We review the main existing designs, including prototypes as well as commercially available devices. We also review the relevant camera calibration principles, and how they are applied to TOF devices. Finally, we discuss the benefits and challenges of combined TOF and color camera systems.     

Dielectric characterization and conduction modelling of a water tree degraded LDPE

Distribution of electric energy by extruded polymer insulated cables continues to be a subject of outstanding relevance in modern industrialized countries all over the world. Dielectric characterization, conduction modelling and finally diagnostics of polymeric insulations are necessary steps towards the development of reliable and less expensive robust technologies of electric power distribution. This paper is devoted to a detailed experimental/theoretical study of the conductive properties of LDPE affected by different levels of degradation by water trees. Water tree layers of different lengths were grown in accelerated conditions and were characterized by water tree kinetics, time-dependent permittivity and polarization current. The polarization current was found to obey a Curie-von Schweidler law whose parameters were used to characterize the effect of ageing time. A new conduction model that takes into account dipole interactions and was obtained from a two-wells Debye model is presented which allows us to give an interpretation of the effect of ageing. This laboratory study was intended to improve the characterization of service power cables aged by water trees     

Secure Sharing of Tuple Spaces in Ad Hoc Settings

Security is emerging as a growing concern throughout the distributed computing community. Typical solutions entail specialized infrastructure support for authentication, encryption and access control. Mobile applications executing over ad hoc wireless networks present designers with a rather distinct set of security requirements. A totally open setting and limited resources call for lightweight and highly decentralized security solutions. In this paper we propose an approach that relies on extending an existing coordination middleware for mobility (Lime). The need to continue to offer a very simple model of coordination that assures rapid software development led to limiting extensions solely to password protected tuple spaces and per tuple access control. Password distribution and security are relegated to the application realm. Host level security is ensured by the middleware design and relies on standard support provided by the Java system. Secure interactions among agents across hosts are accomplished by careful exploitation of the interceptor pattern and the use of standard encryption. The paper explains the design strategy used to add security support in Lime and its implications for the development of mobile applications over ad hoc networks.     

Synthesis of probabilistic models for quality-of-service software engineering

An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability.