Performance optimization problem in speculative prefetching

Speculative prefetching has been proposed to improve the response time of network access. Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which has been largely ignored, that of performance modeling. We analyze the performance of a prefetcher that has uncertain knowledge about future accesses. Our performance metric is the improvement in access time, for which we derive a formula in terms of resource parameters (time available and time required for prefetehing) and speculative parameters (probabilities for next access). We develop a prefetch algorithm to maximize the improvement in access time. The algorithm is based on finding the best solution to a stretch knapsack problem, using theoretically proven apparatus to reduce the search space. An integration between speculative prefetching and caching is also investigated.

A novel non-enzymatic glucose sensor based on nickel (II) oxide electrospun nanofibers

A new enzymeless glucose sensor has been fabricated via electrospinning technology and subsequent calcination. The morphology and structure of the as-prepared nanofibers have been characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The electrocatalytic oxidation of glucose in alkaline medium at nickel oxide modified glassy carbon electrodes has been investigated. The modified electrodes offer excellent electrocatalytic activity toward the glucose oxidation at low positive potential (0.3 V). Glucose has been determined chronoamperometrically at the surface of NiO nanofibers modified electrode in 0.5 mM NaOH. Under the optimized condition, the calibration curve is linear in the concentration range of 2 × 10−3 mM∼1 mM, and 1 mM∼9.5 mM. The detection limit (signal-to-noise 3) and response time are 3.394 × 10−6 M and 2 s, respectively. The NiO electrospun nanofibers is easy to prepare and feasible in economy. The modified electrode is steady and can be used repeatedly, so it is reasonable to expect its broad use in non-enzymatic glucose sensor.

A clusterized firewall framework for cloud computing

Cloud computing is becoming popular as the next infrastructure of computing platform. However, with data and business applications outsourced to a third party, how to protect cloud data centers from numerous attacks has become a critical concern. In this paper, we propose a clusterized framework of cloud firewall, which characters performance and cost evaluation. To provide quantitative performance analysis of the cloud firewall, a novel M/Geo/1 analytical model is established. The model allows cloud defenders to extract key system measures such as request response time, and determine how many resources are needed to guarantee quality of service (QoS). Moreover, we give an insight into financial cost of the proposed cloud firewall. Finally, our analytical results are verified by simulation experiments.

Data replication approach with consistency guarantee for data grid

Data grids have been adopted by many scientific communities that need to share, access, transport, process, and manage geographically distributed large data collections. Data replication is one of the main mechanisms used in data grids whereby identical copies of data are generated and stored at various distributed sites to either improve data access performance or reliability or both. However, when data updates are allowed, it is a great challenge to simultaneously improve performance and reliability while ensuring data consistency of such huge and widely distributed data. In this paper, we address this problem. We propose a new quorum-based data replication protocol with the objectives of minimizing the data update cost, providing high availability and data consistency. We compare the proposed approach with two existing approaches using response time, data consistency, data availability, and communication costs. The results show that the proposed approach performs substantially better than the benchmark approaches.

Type-aware task placement in geo-distributed data centers with low OPEX using data center resizing

With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the type-aware task placement problem over geo-distributed data centers. With the consideration of the diversity of user requests and server clusters in modern data centers, we formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an efficient solution is designed for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the greedy algorithm and much approaches the optimal results. © 2014 IEEE.

Glucose administration and cognitive function: differential effects of age and effort during a dual task paradigm in younger and older adults

RATIONALE: Current research suggests that glucose facilitates performance on cognitive tasks which possess an episodic memory component and a relatively high level of cognitive demand. However, the extent to which this glucose facilitation effect is uniform across the lifespan is uncertain. METHODS: This study was a repeated measures, randomised, placebo-controlled, cross-over trial designed to assess the cognitive effects of glucose in younger and older adults under single and dual task conditions. Participants were 24 healthy younger (average age 20.6 years) and 24 healthy older adults (average age 72.5 years). They completed a recognition memory task after consuming drinks containing 25 g glucose and a placebo drink, both in the presence and absence of a secondary tracking task. RESULTS AND CONCLUSIONS: Glucose enhanced recognition memory response time and tracking precision during the secondary task, in older adults only. These findings do not support preferential targeting of hippocampal function by glucose, rather they suggest that glucose administration differentially increases the availability of attentional resources in older individuals.

Performance analysis using subsuming methods: an industrial case study

Large-scale object-oriented applications consist of tens of thousands of methods and exhibit highly complex runtime behaviour that is difficult to analyse for performance. Typical performance analysis approaches that aggregate performance measures in a method-centric manner result in thinly distributed costs and few easily identifiable optimisation opportunities. Subsuming methods analysis is a new approach that aggregates performance costs across repeated patterns of method calls that occur in the application's runtime behaviour. This allows automatic identification of patterns that are expensive and represent practical optimisation opportunities. To evaluate the practicality of this analysis with a real world large-scale object-oriented application we completed a case study with the developers of letterboxd.com - a social network website for movie goers. Using the results of the analysis we were able to rapidly implement changes resulting in a 54.8% reduction in CPU load and an 49.6% reduction in average response time.

A decentralized cloud firewall framework with resources provisioning cost optimization

Cloud computing is becoming popular as the next infrastructure of computing platform. Despite the promising model and hype surrounding, security has become the major concern that people hesitate to transfer their applications to clouds. Concretely, cloud platform is under numerous attacks. As a result, it is definitely expected to establish a firewall to protect cloud from these attacks. However, setting up a centralized firewall for a whole cloud data center is infeasible from both performance and financial aspects. In this paper, we propose a decentralized cloud firewall framework for individual cloud customers. We investigate how to dynamically allocate resources to optimize resources provisioning cost, while satisfying QoS requirement specified by individual customers simultaneously. Moreover, we establish novel queuing theory based model M/Geo/1 and M/Geo/m for quantitative system analysis, where the service times follow a geometric distribution. By employing Z-transform and embedded Markov chain techniques, we obtain a closed-form expression of mean packet response time. Through extensive simulations and experiments, we conclude that an M/Geo/1 model reflects the cloud firewall real system much better than a traditional M/M/1 model. Our numerical results also indicate that we are able to set up cloud firewall with affordable cost to cloud customers.

Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.

Optimal task placement with QoS constraints in geo-distributed data centers using DVFS

 With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the task placement problem over geo-distributed data centers. We exploit the dynamic frequency scaling technique and formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an optimal solution is discovered for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the traditional resizing scheme, i.e., by activating/deactivating certain servers in data centers.

Necessary and sufficient checkpoint selection for temporal verification of high-confidence cloud workflow systems

On-time completion is an important temporal QoS (Quality of Service) dimension and one of the fundamental requirements for high-confidence workflow systems. In recent years, a workflow temporal verification framework, which generally consists of temporal constraint setting, temporal checkpoint selection, temporal verification, and temporal violation handling, has been the major approach for the high temporal QoS assurance of workflow systems. Among them, effective temporal checkpoint selection, which aims to timely detect intermediate temporal violations along workflow execution plays a critical role. Therefore, temporal checkpoint selection has been a major topic and has attracted significant efforts. In this paper, we will present an overview of work-flow temporal checkpoint selection for temporal verification. Specifically, we will first introduce the throughput based and response-time based temporal consistency models for business and scientific cloud workflow systems, respectively. Then the corresponding benchmarking checkpoint selection strategies that satisfy the property of “necessity and sufficiency” are presented. We also provide experimental results to demonstrate the effectiveness of our checkpoint selection strategies, and finally points out some possible future issues in this research area.

Workflow temporal verification for monitoring parallel business processes

Workflow temporal verification is conducted to guarantee on-time completion, which is one of the most important QoS (Quality of Service) dimensions for business processes running in the cloud. However, as today's business systems often need to handle a large number of concurrent customer requests, conventional response-time based process monitoring strategies conducted in a one-by-one fashion cannot be applied efficiently to a large batch of parallel processes because of significant time overhead. Similar situations may also exist in software companies where multiple software projects are carried out at the same time by software developers. To address such a problem, based on a novel runtime throughput consistency model, this paper proposes a QoS-aware throughput based checkpoint selection strategy, which can dynamically select a small number of checkpoints along the system timeline to facilitate the temporal verification of throughput constraints and achieve the target on-time completion rate. Experimental results demonstrate that our strategy can achieve the best efficiency and effectiveness compared with the state-of-the-art as and other representative response-time based checkpoint selection strategies.

Evolution of 3D printed soft actuators

Developing soft actuators and sensors by means of 3D printing has become an exciting research area. Compared to conventional methods, 3D printing enables rapid prototyping, custom design, and single-step fabrication of actuators and sensors that have complex structure and high resolution. While 3D printed sensors have been widely reviewed in the literature, 3D printed actuators, on the other hand, have not been adequately reviewed thus far. This paper presents a comprehensive review of the existing 3D printed actuators. First, the common processes used in 3D printing of actuators are reviewed. Next, the existing mechanisms used for stimulating the printed actuators are described. In addition, the materials used to print the actuators are compared. Then, the applications of the printed actuators including soft-manipulation of tissues and organs in biomedicine and fragile agricultural products, regenerative design, smart valves, microfluidic systems, electromechanical switches, smart textiles, and minimally invasive surgical instruments are explained. After that, the reviewed 3D printed actuators are discussed in terms of their advantages and disadvantages considering power density, elasticity, strain, stress, operation voltage, weight, size, response time, controllability, and biocompatibility. Finally, the future directions of 3D printed actuators are discussed.

Dose-response associations between screen time and overweight among youth

Objective. To examine dose-response associations between screen time and overweight, independent of physical activity and dietary intake.

Methods. Participants were 580 Dutch youth (13 years; 48% boys). Body mass index, waist circumference and skinfold thickness at four sites determined weight status. Questionnaires examined television viewing and computer use, participation in organized sport and high caloric snack and sugar-containing beverage consumption.

Results. There were no significant associations among boys. Compared with girls spending less than two hours/day in screen time, those who spent three to four hours/day were more likely to be classified as overweight by waist circumference (odds ratio [OR]=3.4; 95% confidence intervals [CI]=1.1-10.7; p=0.03), and this likelihood increased substantially among those spending more than four hours/day (OR=5.5; 95% CI=2.1-14.1; p≤0.0001).

Conclusions. Girls who spend three or more hours/day in screen time are at increased risk of being classified as overweight by waist circumference.

Reconstruction of in vivo time-evolving neuroendocrine dose - response properties unveils admixed deterministic and stochastic elements

Homeostasis in the intact organism is achieved implicitly by repeated incremental feedback (inhibitory) and feedforward (stimulatory) adjustments enforced via intermittent signal exchange. In separated systems, neurohormone signals act deterministically on target cells via quantifiable effector-response functions. On the other hand, in vivo interglandular signaling dynamics have not been estimable to date. Indeed, experimentally isolating components of an interactive network definitionally disrupts time-sensitive linkages. We implement and validate analytical reconstruction of endogenous effector-response properties via a composite model comprising (i) a deterministic basic feedback and feedforward ensemble structure; (ii) judicious statistical allowance for possible stochastic variability in individual biologically interpretable dose–response properties; and (iii) the sole data requirement of serially observed concentrations of a paired signal (input) and response (output). Application of this analytical strategy to a prototypical neuroendocrine axis in the conscious uninjected horse, sheep, and human (i) illustrates probabilistic estimation of endogenous effector dose–response properties; and (ii) unmasks statistically vivid (2- to 5-fold) random fluctuations in inferred target-gland responsivity within any given pulse train. In conclusion, balanced mathematical formalism allows one to (i) reconstruct deterministic properties of interglandular signaling in the intact mammal and (ii) quantify apparent signal-response variability over short time scales in vivo. The present proof-of-principle experiments introduce a previously undescribed means to estimate time-evolving signal-response relationships without isotope infusion or pathway disruption.