A Community-based Cloud Computing Caching Service

Caching has become an important technology in the development of cloud computing-based high-performance web services. Caches reduce the request to response latency experienced by users, and reduce workload on backend databases. They need a high cache-hit rate to be fit for purpose, and this rate is dependent on the cache management policy used. Existing cache management policies are not designed to prevent cache pollution or cache monopoly problems, which impacts negatively on the cache-hit rate. This paper proposes a community-based caching approach (CC) to address these two problems. CC was evaluated for performance against thirteen commercially available cache management policies, and results demonstrate that the cache-hit rate achieved by CC was between 0.7% and 55% better than the alternate cache management policies

Memristor-based Random Access Memory: The delayed switching effect could revolutionize memory design

Memristor’s on/off resistance can naturally store binary bits for non-volatile memories. In this work, we found that memristor’s another peculiar feature that the switching takes place with a time delay (we name it “the delayed switching”) can be used to selectively address any desired memory cell in a crossbar array. The analysis shows this is a must-be in a memristor with a piecewise-linear ?-q curve. A “circuit model”-based experiment has verified the delayed switching feature. It is demonstrated that memristors can be packed at least twice as densely as semiconductors, achieving a significant breakthrough in storage density

Multi-dimensional key generation of ICMetrics for cloud computing

Despite the rapid expansion and uptake of cloud based services, lack of trust in the provenance of such services represents a significant inhibiting factor in the further expansion of such service. This paper explores an approach to assure trust and provenance in cloud based services via the generation of digital signatures using properties or features derived from their own construction and software behaviour. The resulting system removes the need for a server to store a private key in a typical Public/Private-Key Infrastructure for data sources. Rather, keys are generated at run-time by features obtained as service execution proceeds. In this paper we investigate several potential software features for suitability during the employment of a cloud service identification system. The generation of stable and unique digital identity from features in Cloud computing is challenging because of the unstable operation environments that implies the features employed are likely to vary under normal operating conditions. To address this, we introduce a multi-dimensional key generation technology which maps from multi-dimensional feature space directly to a key space. Subsequently, a smooth entropy algorithm is developed to evaluate the entropy of key space

Spark on Entropy: A Reliable & Efficient Scheduler for Low-latency Parallel Jobs in Heterogeneous Cloud

In heterogeneous cloud, the provision of quality of service (QoS) guarantees for on-line parallel analysis jobs is much more challenging than off-line ones, mainly due to the many involved parameters, unstable resource performance, various job pattern and dynamic query workload. In this paper we propose an entropy-based scheduling strategy for running the on-line parallel analysis as a service more reliable and efficient, and implement the proposed idea in Spark. Entropy, as a measure of the degree of disorder in a system, is an indicator of a system’s tendency to progress out of order and into a chaotic condition, and it can thus serve to measure a cloud resource’s reliability for jobs scheduling. The key idea of our Entropy Scheduler is to construct the new resource entropy metric and schedule tasks according to the resources ranking with the help of the new metric so as to provide QoS guarantees for on-line Spark analysis jobs. Experiments demonstrate that our approach significantly reduces the average query response time by 15% - 20% and standard deviation by 30% - 45% compare with the native Fair Scheduler in Spark

Long-term, low-dose lead exposure alters the gonadotropin-releasing hormone system in the male rat.

Lead is a male reproductive toxicant. Data suggest that rats dosed with relatively high levels of lead acetate for short periods of time induced changes in the hypothalamic gonadotropin-releasing hormone (GnRH) at the molecular level, but these changes were attenuated with increased concentration of exposure. The current study evaluated whether exposure to low levels of lead acetate over longer periods of time would produce a similar pattern of adaptation to toxicity at the molecular and biologic levels. Adult 100-day-old Sprague-Dawley male rats were dosed with 0, 0.025, 0.05, 0.1, and 0.3% lead acetate in water. Animals were killed after 1, 4, 8, and 16 weeks of treatment. Luteinzing hormone (LH) and GnRH levels were measured in serum, and lead levels were quantified in whole blood. Hypothalamic GnRH mRNA levels were also quantified. We found no significant differences in serum LH and GnRH among the groups of animals treated within each time period. A significant dose-related increase of GnRH mRNA concentrations with lead dosing occurred in animals treated for 1 week. Animals treated for more than 1 week also exhibited a significant increase in GnRH mRNA, but with an attenuation of the increase at the higher concentrations of lead with increased duration of exposure. We conclude that the signals within and between the hypothalamus and pituitary gland appear to be disrupted by long-term, low-dose lead exposure

Imaging atom-clusters by hard x-ray free electron lasers

The ingenious idea of single molecule imaging by hard x-ray Free Electron Laser (X-FEL) pulses was recently proposed by Neutze et al. [Nature,406,752(2000)]. However, in their numerical modelling of the Coulomb explosion several interactions were neglected and no reconstruction of the atomic structure was given. In this work we carried out improved molecular dynamics calculations including all quantum processes which affect the explosion. Based on this time evolution we generated composite elastic scattering patterns, and by using Fienup's algorithm successfully reconstructed the original atomic structure. The critical evaluation of these results gives guidelines and sets important conditions for future experiments aiming single molecule structure solution.Comment: 8 pages, 4 figures, submitted to Europhysics Letter

Spin-encoded quantum computer near ultimate physical limits

Landauer’s bound is applicable to irreversible quantum operations. In this study, we showcased that the Doppler temperature manifests the existence of Landauer’s bound, which does not block a spin from (irreversibly) flipping with a tiny amount of energy via quantum tunneling. Verified by a spin–spin magnetic interaction experiment, this (energy) amount was determined to be only 1.25 times the theoretical value of Landauer’s bound. Based on Heisenberg’s principle, we defined information from a measuring perspective: one bit of information corresponds to the smallest error when quantifying the product of the measured energy uncertainty (delta E) and the measured time duration (delta t). We then illustrate an optically manipulated, spin-encoded, near-Landauer-bound, near-Heisenberg-limit quantum computer that encompasses this new definition of information. This study may represent the last piece of the puzzle in understanding both quantum Landauer erasure and Heisenberg’s quantum limit since a single spin is the smallest information carrier

DIANA: Data Interface All-iN-A-place for Big Data

“Variety” in Big Data means we have a wide range of data types and sources: e.g. file systems and database systems co-exist for decades as two popular data-accessing interfaces. This work is to unify these two interfaces by presenting a Data Interface All-iN-A-place (DIANA). The first challenge lies in distinguishing structured and un-structured data and diverting them to different underlying platforms. It is demonstrated that a speedup of 5000 in indexing has been achieved at the expense of a slowdown of 100 in extracting attributes. A DIANA-based cloud storage system is constructed for versatile, long distance and large volume big data accessing operations to address “Volume” and “Velocity” in Big Data. It encapsulates a dynamic multi-stream/multi-path engine at the socket level, which conforms to Portable Operating System Interface (POSIX)

Breaking Landauer’s bound in a spin-encoded quantum computer

It is commonly recognized that Landauer's bound holds in (irreversible) quantum measurement. In this study, we overturned this common sense by extracting a single spin from a spin–spin magnetic interaction experiment to demonstrate that Landauer’s bound can be broken quantitatively by a factor of 10^4∼10^10 via quantum spin tunneling. It is the quantum limit (ℏ/2≈10^−34J⋅s), rather than Landauer’s bound, that governs the performance of a spin qubit. An optically-manipulated spin-encoded quantum computer is designed, in which energy bound well below kBT to erase a spin qubit at the expense of a long spin relaxation time is theoretically sensible and experimentally verified. This work may represent the last piece of the puzzle in quantum Landauer erasure in terms of a single spin being the smallest and the closest to the quantum limit