100,037 research outputs found
Narrowing Frontiers of Efficiency with Evolutional Communication Rules and Cell Separation
In the framework of Membrane Computing, several efficient solutions to computationally
hard problems have been given. To find new borderlines between families of
P systems that can solve them and the ones that cannot is an important way to tackle the
P versus NP problem. Adding syntactic and/or semantic ingredients can mean passing
from non-efficiency to presumably efficiency. Here, we try to get narrow frontiers, setting
the stage to adapt efficient solutions from a family of P systems to another one. In order
to do that, a solution to the SAT problem is given by means of a family of tissue P systems
with evolutional symport/antiport rules and cell separation with the restriction that both
the left-hand side and the right-hand side of the rules have at most two objects.Ministerio de EconomĂa y Competitividad TIN2017-89842-PNational Natural Science Foundation of China No 6132010600
A semantic scheduler architecture for federated hybrid clouds
Cloud computing is one the most relevant computing paradigms available nowadays. Its adoption has increased during last years due to the large investment and research from business enterprises and academia institutions. Among all the services cloud providers usually offer, Infrastructure as a Service has reached its momentum for solving HPC problems in a more dynamic way without the need of expensive investments. The integration of a large number of providers is a major goal as it enables the improvement of the quality of the selected resources in terms of pricing, speed, redundancy, etc. In this paper, we propose a system architecture, based on semantic solutions, to build an interoperable scheduler for federated clouds that works with several IaaS (Infrastructure as a Service) providers in a uniform way. Based on this architecture we implement a proof-of-concept prototype and test it with two different cloud solutions to provide some experimental results about the viability of our approach
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Problem Theory
The Turing machine, as it was presented by Turing himself, models the
calculations done by a person. This means that we can compute whatever any
Turing machine can compute, and therefore we are Turing complete. The question
addressed here is why, Why are we Turing complete? Being Turing complete also
means that somehow our brain implements the function that a universal Turing
machine implements. The point is that evolution achieved Turing completeness,
and then the explanation should be evolutionary, but our explanation is
mathematical. The trick is to introduce a mathematical theory of problems,
under the basic assumption that solving more problems provides more survival
opportunities. So we build a problem theory by fusing set and computing
theories. Then we construct a series of resolvers, where each resolver is
defined by its computing capacity, that exhibits the following property: all
problems solved by a resolver are also solved by the next resolver in the
series if certain condition is satisfied. The last of the conditions is to be
Turing complete. This series defines a resolvers hierarchy that could be seen
as a framework for the evolution of cognition. Then the answer to our question
would be: to solve most problems. By the way, the problem theory defines
adaptation, perception, and learning, and it shows that there are just three
ways to resolve any problem: routine, trial, and analogy. And, most
importantly, this theory demonstrates how problems can be used to found
mathematics and computing on biology.Comment: 43 page
Semantic-based policy engineering for autonomic systems
This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise
Biology of Applied Digital Ecosystems
A primary motivation for our research in Digital Ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. However, the biological processes that contribute to
these properties have not been made explicit in Digital Ecosystems research.
Here, we discuss how biological properties contribute to the self-organising
features of biological ecosystems, including population dynamics, evolution, a
complex dynamic environment, and spatial distributions for generating local
interactions. The potential for exploiting these properties in artificial
systems is then considered. We suggest that several key features of biological
ecosystems have not been fully explored in existing digital ecosystems, and
discuss how mimicking these features may assist in developing robust, scalable
self-organising architectures. An example architecture, the Digital Ecosystem,
is considered in detail. The Digital Ecosystem is then measured experimentally
through simulations, with measures originating from theoretical ecology, to
confirm its likeness to a biological ecosystem. Including the responsiveness to
requests for applications from the user base, as a measure of the 'ecological
succession' (development).Comment: 9 pages, 4 figure, conferenc
Improving Strategies via SMT Solving
We consider the problem of computing numerical invariants of programs by
abstract interpretation. Our method eschews two traditional sources of
imprecision: (i) the use of widening operators for enforcing convergence within
a finite number of iterations (ii) the use of merge operations (often, convex
hulls) at the merge points of the control flow graph. It instead computes the
least inductive invariant expressible in the domain at a restricted set of
program points, and analyzes the rest of the code en bloc. We emphasize that we
compute this inductive invariant precisely. For that we extend the strategy
improvement algorithm of [Gawlitza and Seidl, 2007]. If we applied their method
directly, we would have to solve an exponentially sized system of abstract
semantic equations, resulting in memory exhaustion. Instead, we keep the system
implicit and discover strategy improvements using SAT modulo real linear
arithmetic (SMT). For evaluating strategies we use linear programming. Our
algorithm has low polynomial space complexity and performs for contrived
examples in the worst case exponentially many strategy improvement steps; this
is unsurprising, since we show that the associated abstract reachability
problem is Pi-p-2-complete
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