21,270 research outputs found
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Trust Model for Optimized Cloud Services
Cloud computing with its inherent advantages draws attention for business critical applications, but concurrently expects high level of trust in cloud service providers. Reputation-based trust is emerging as a good choice to model trust of cloud service providers based on available evidence. Many existing reputation based systems either ignore or give less importance to uncertainty linked with the evidence. In this paper, we propose an uncertainty model and define our approach to compute opinion for cloud service providers. Using subjective logic operators along with the computed opinion values, we propose mechanisms to calculate the reputation of cloud service providers. We evaluate and compare our proposed model with existing reputation models
On Level Quantization for the Noncommutative Chern-Simons Theory
We show that the coefficient of the three-dimensional Chern-Simons action on
the noncommutative plane must be quantized. Similar considerations apply in
other dimensions as well.Comment: 6 pages, Latex, no figure
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Secure communication using dynamic VPN provisioning in an Inter-Cloud environment
Most of the current cloud computing platforms offer Infrastructure as a Service (IaaS) model, which aims to provision basic virtualised computing resources as on-demand and dynamic services. Nevertheless, a single cloud does not have limitless resources to offer to its users, hence the notion of an Inter-Cloud enviroment where a cloud can use the infrastructure resources of other clouds. However, there is no common framework in existence that allows the srevice owners to seamlessly provision even some basic services across multiple cloud service providers, albeit not due to any inherent incompatibility or proprietary nature of the foundation technologies on which these cloud platforms are built. In this paper we present a novel solution which aims to cover a gap in a subsection of this problem domain. Our solution offer a security architecture that enables service owners to provision a dynamic and service-oriented secure virtual private network on top of multiple cloud IaaS providers. It does this by leveraging the scalability, robustness and flexibility of peer- to-peer overlay techniques to eliminate the manual configuration, key management and peer churn problems encountered in setting up the secure communication channels dynamically, between different components of a typical service that is deployed on multiple clouds. We present the implementation details of our solution as well as experimental results carried out on two commercial clouds
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Dynamic virtual private network provisioning from multiple cloud infrastructure service providers
The Cloud infrastructure service providers currently provision basic virtualized computing resources as on demand and dynamic services but there is no common framework in existence that allows the seamless provisioning of even these basic services across multiple cloud service providers, although this is not due to any inherent incompatibility or proprietary nature of the foundation technologies on which these cloud platforms are built. We present a solution idea which aims to provide a dynamic and service oriented provisioning of secure virtual private networks on top of multiple cloud infrastructure service providers. This solution leverages the benefits of peer to peer overlay networks, i.e., the flexibility and scalability to handle the churn of nodes joining and leaving the VPNs and can adapt the topology of the VPN as per the requirements of the applications utilizing its intercloud secure communication framework
Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines
This paper presents a novel framework for designing support vector machines
(SVMs), which does not impose restriction on the SVM kernel to be
positive-definite and allows the user to define memory constraint in terms of
fixed template vectors. This makes the framework scalable and enables its
implementation for low-power, high-density and memory constrained embedded
application. An efficient hardware implementation of the same is also
discussed, which utilizes novel low power memtransistor based cross-bar
architecture, and is robust to device mismatch and randomness. We used
memtransistor measurement data, and showed that the designed SVMs can achieve
classification accuracy comparable to traditional SVMs on both synthetic and
real-world benchmark datasets. This framework would be beneficial for design of
SVM based wake-up systems for internet of things (IoTs) and edge devices where
memtransistors can be used to optimize system's energy-efficiency and perform
in-memory matrix-vector multiplication (MVM).Comment: 4 pages, 5 figures, MWSCAS 201
Hall effect in heavy-fermion metals
The heavy fermion systems present a unique platform in which strong
electronic correlations give rise to a host of novel, and often competing,
electronic and magnetic ground states. Amongst a number of potential
experimental tools at our disposal, measurements of the Hall effect have
emerged as a particularly important one in discerning the nature and evolution
of the Fermi surfaces of these enigmatic metals. In this article, we present a
comprehensive review of Hall effect measurements in the heavy-fermion
materials, and examine the success it has had in contributing to our current
understanding of strongly correlated matter. Particular emphasis is placed on
its utility in the investigation of quantum critical phenomena which are
thought to drive many of the exotic electronic ground states in these systems.
This is achieved by the description of measurements of the Hall effect across
the putative zero-temperature instability in the archetypal heavy-fermion metal
YbRhSi. Using the CeIn (with Co, Ir) family of systems as
a paradigm, the influence of (antiferro-)magnetic fluctuations on the Hall
effect is also illustrated. This is compared to prior Hall effect measurements
in the cuprates and other strongly correlated systems to emphasize on the
generality of the unusual magnetotransport in materials with non-Fermi liquid
behavior.Comment: manuscript accepted in Adv. Phy
Biexciton recombination rates in self-assembled quantum dots
The radiative recombination rates of interacting electron-hole pairs in a
quantum dot are strongly affected by quantum correlations among electrons and
holes in the dot. Recent measurements of the biexciton recombination rate in
single self-assembled quantum dots have found values spanning from two times
the single exciton recombination rate to values well below the exciton decay
rate. In this paper, a Feynman path-integral formulation is developed to
calculate recombination rates including thermal and many-body effects. Using
real-space Monte Carlo integration, the path-integral expressions for realistic
three-dimensional models of InGaAs/GaAs, CdSe/ZnSe, and InP/InGaP dots are
evaluated, including anisotropic effective masses. Depending on size, radiative
rates of typical dots lie in the regime between strong and intermediate
confinement. The results compare favorably to recent experiments and
calculations on related dot systems. Configuration interaction calculations
using uncorrelated basis sets are found to be severely limited in calculating
decay rates.Comment: 11 pages, 4 figure
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