A constraints-based resource discovery model for multi-provider cloud environments

Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user's point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services, such as network configuration or data replication, and operating costs, such as hosting cost and data throughput. Providers' cost models often change and new commodity cost models, such as spot pricing, have been introduced to offer significant savings. In this paper, a software abstraction layer is used to discover infrastructure resources for a particular application, across multiple providers, by using a two-phase constraints-based approach. In the first phase, a set of possible infrastructure resources are identified for a given application. In the second phase, a heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic is most appropriate; for others a performance-based heuristic may be used. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental result shows the proposed model could dynamically select an appropriate set of resouces that match the application's requirements.

Organised Criminals as 'Agents of Obligation': The Case of Ireland

Relying on Brown’s (2005a, b) thesis that contemporary shifts in penal policy are best understood as a reprisal of colonial rationality, so that offenders become ‘non-citizens’ or ‘agents of obligation’, this article argues that this framework finds support in developments in Irish criminal justice policy. Recent legislation aimed at offenders suspected of involvement in ‘organised crime’ is examined through this lens. These offenders have found themselves reconstituted as ‘agents of obligation’ with duties to furnish information about their property and movements, report to the police concerning their location and, importantly, refrain from criminal activity or face extraordinary sanctions. It is therefore argued that this paradigm is a useful heuristic device through which to understand recent developments in Irish criminal justice and elsewhere. In light of the trends observed in Ireland, certain refinements and extensions to Brown’s argument are put forward for consideration.

On the Qualitative Comparison of Decisions Having Positive and Negative Features

Making a decision is often a matter of listing and comparing positive and negative arguments. In such cases, the evaluation scale for decisions should be considered bipolar, that is, negative and positive values should be explicitly distinguished. That is what is done, for example, in Cumulative Prospect Theory. However, contrary to the latter framework that presupposes genuine numerical assessments, human agents often decide on the basis of an ordinal ranking of the pros and the cons, and by focusing on the most salient arguments. In other terms, the decision process is qualitative as well as bipolar. In this article, based on a bipolar extension of possibility theory, we define and axiomatically characterize several decision rules tailored for the joint handling of positive and negative arguments in an ordinal setting. The simplest rules can be viewed as extensions of the maximin and maximax criteria to the bipolar case, and consequently suffer from poor decisive power. More decisive rules that refine the former are also proposed. These refinements agree both with principles of efficiency and with the spirit of order-of-magnitude reasoning, that prevails in qualitative decision theory. The most refined decision rule uses leximin rankings of the pros and the cons, and the ideas of counting arguments of equal strength and cancelling pros by cons. It is shown to come down to a special case of Cumulative Prospect Theory, and to subsume the “Take the Best” heuristic studied by cognitive psychologists.

Granular anchors under vertical loading - axial pull

Granular anchors are a relatively new concept in ground engineering with relatively little known regarding their load–displacement behaviour, failure modes, ultimate pullout capacity and also potential applications. A granular anchor consists of three main components: a base plate; tendon and compacted granular backfill. The tendon is used to transmit the applied load to the base plate which compresses the granular material to form the anchor. A study of the load–displacement response and ultimate pullout capacity of granular anchors constructed in intact lodgement till and made ground deposits is reported in this paper. Parallel tests were also performed on cast insitu concrete anchors which are traditionally used for anchoring purposes. A new method of analysis for the determination of the ultimate pullout capacity of granular anchors is presented and verified experimentally, with the dominant mode of failure controlled by the column length to diameter ratio. Granular anchors with L/D > 7 principally failed on bulging whereas short granular anchors failed on shaft resistance, with the latter mobilising similar pullout capacities as conventional concrete anchors.

DESIGNING VLSI SIGNAL PROCESSING CHIPS USING DATA DEPENDENCE GRAPHS.

The highly structured nature of many digital signal processing operations allows these to be directly implemented as regular VLSI circuits. This feature has been successfully exploited in the design of a number of commercial chips, some examples of which are described. While many of the architectures on which such chips are based were originally derived on heuristic basis, there is an increasing interest in the development of systematic design techniques for the direct mapping of computations onto regular VLSI arrays. The purpose of this paper is to show how the the technique proposed by Kung can be readily extended to the design of VLSI signal processing chips where the organisation of computations at the level of individual data bits is of paramount importance. The technique in question allows architectures to be derived using the projection and retiming of data dependence graphs.

Algorithms and architectures for high performance recursive filtering

Recently, a number of most significant digit (msd) first bit parallel multipliers for recursive filtering have been reported. However, the design approach which has been used has, in general, been heuristic and consequently, optimality has not always been assured. In this paper, msd first multiply accumulate algorithms are described and important relationships governing the dependencies between latency, number representations, etc are derived. A more systematic approach to designing recursive filters is illustrated by applying the algorithms and associated relationships to the design of cascadable modules for high sample rate IIR filtering and wave digital filtering.

Systematic methodology for the design of high performance recursive digital filters

A systematic design methodology is described for the rapid derivation of VLSI architectures for implementing high performance recursive digital filters, particularly ones based on most significant digit (msd) first arithmetic. The method has been derived by undertaking theoretical investigations of msd first multiply-accumulate algorithms and by deriving important relationships governing the dependencies between circuit latency, levels of pipe-lining and the range and number representations of filter operands. The techniques described are general and can be applied to both bit parallel and bit serial circuits, including those based on on-line arithmetic. The method is illustrated by applying it to the design of a number of highly pipelined bit parallel IIR and wave digital filter circuits. It is shown that established architectures, which were previously designed using heuristic techniques, can be derived directly from the equations described.

The effects and side-effects of statistics education: Psychology students’ (mis-)conceptions of probability

In three studies we looked at two typical misconceptions of probability: the representativeness heuristic, and the equiprobability bias. The literature on statistics education predicts that some typical errors and biases (e.g., the equiprobability bias) increase with education, whereas others decrease. This is in contrast with reasoning theorists’ prediction who propose that education reduces misconceptions in general. They also predict that students with higher cognitive ability and higher need for cognition are less susceptible to biases. In Experiments 1 and 2 we found that the equiprobability bias increased with statistics education, and it was negatively correlated with students’ cognitive abilities. The representativeness heuristic was mostly unaffected by education, and it was also unrelated to cognitive abilities. In Experiment 3 we demonstrated through an instruction manipulation (by asking participants to think logically vs. rely on their intuitions) that the reason for these differences was that these biases originated in different cognitive processes.

Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

An automatic COLREGs-compliant obstacle avoidance system for an unmanned surface vehicle

Unmanned surface vehicles are becoming increasingly vital tools in a variety of maritime applications. Unfortunately, their usability is severely constrained by the lack of a reliable obstacle detection and avoidance system. In this article, one such experimental platform is proposed, which performs obstacle detection, risk assessment and path planning (avoidance) tasks autonomously in an integrated manner. The detection system is based on a vision-LIDAR (light detection and ranging) system, whereas a heuristic path planner is utilised. A unique property of the path planner is its compliance with the marine collision regulations. It is demonstrated through hardware-in-the-loop simulations that the proposed system can be useful for both uninhabited and manned vessels.

An extended framework for evidential reasoning systems

Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems

Identity, Unity, and the Limits of Democracy

Can the demos be uncoupled from the ethnos? Can there be a democratic politics of state‐boundaries, or are borders a condition of the possibility of democratic politics rather than a possible subject for those politics? The author argues for the decoupling strategy and affirms the possibility of a democratic politics about borders, anchoring the discussion in the politics of Northern Ireland. The argument turns on the analysis of public reasoning. It is argued first that culturalist accounts of self‐determination are misconceived and that political institutions, and not cultural identity, make collective self‐determination possible. Second, that the demos is constituted by acts of mutual recognition required by the practice of public reasoning, and that this practice cannot be confined with state‐boundaries. Taken together this allows us to conceive of the unity of a people as constituted by practices of public reason, given effect by institutions whose configuration is never finally fixed.

Masks as methodology and the phenomenological turn: issues of interpretation

The purposes of this chapter are to argue for (i) the heuristic value of the concept of mask and masking in research which has its basis in psychodynamic theory but relating it to socio-cultural theory as means to understanding self-experience (ii) the value of creating and performing masks as one valuable methodological ‘embodied’ form in social and educational research that represent individuals’ richly textured self-other constructions and allow for the interrogation of any simplistic dichotomies associated with notions of ‘inside’ ‘outside’ categories (iii) exploring possibilities and dilemmas of interpretation within this frame

Non-convex dynamic economic/environmental dispatch with plug-in electric vehicle loads

Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.