Analysis of Cumulative Distribution Function of the Response Time in Cloud Computing Systems with Dynamic Scaling

The advent of practical rateless codes enables implementation of highly efficient packet-level forward error correction (FEC) strategies for reliable data broadcasting in loss-prone wireless networks. Yet, the critical question of accurately quantifying the proper amount of redundancy has remained largely unsolved. In this paper, we exploit advances in extreme value theory to rigorously address this problem. Under the asymptotic regime of a large number of receivers, we derive a closed-form expression for the cumulative distribution function (CDF) of the completion time of file distribution. We show the existence of a phase transition associated with this CDF and accurately locate the transition point. We derive tight convergence bounds demonstrating the accuracy of the asymptotic estimate for the practical case of a finite number of receivers. We also provide an asymptotic closed-form expression on the expected completion time under heterogeneous packet loss. We demonstrate the benefits of our approach through simulation and through real experiments on a testbed of 20 Tmote Sky sensors. Specifically, we augment the existing Rateless Deluge software dissemination protocol with an extreme value FEC strategy. The experimental results reveal reduction by a factor of five in retransmission request messages and by a factor of two in total dissemination time, at the cost of a marginally higher number of data packet transmissions in the order of 5%.

Cloud computing is promising technology to manage and improve utilization of computing center resources to deliver various computing and IT services. For the purpose of energy saving there is no need to unnecessarily operate many servers under light loads, and they are switched off. On the other hand, some servers should be switched on in heavy load cases to prevent very long delays. Thus, waiting times and system operating cost can be maintained on acceptable level by dynamically adding or removing servers. One more fact that should be taken into account is significant server setup costs and activation times. For better energy efficiency, cloud computing system should not react on instantaneous increase or instantaneous decrease of load. That is the main motivation for using queuing systems with hysteresis for cloud computing system modelling. In the paper, we provide a model of cloud computing system in terms of multiple server threshold-based infinite capacity queuing system with hysteresis and noninstantanuous server activation. For proposed model, we develop a method for computing steady-state probabilities that allow to estimate a number of performance measures.

With the widespread application of cloud computing technology, the energy consumption problem of data centers is becoming increasingly severe. This research is dedicated to presenting a highly efficient and smart solution for optimizing energy usage in cloud computing systems, especially since effectively managing energy consumption through resource allocation and task scheduling has now become a critical challenge in the field. In view of this, the study first constructs a resource allocation model based on redundant resource recovery to dynamically identify and recover redundant resources. Second, a task scheduling optimization method based on dual deep reinforcement learning is established, which utilizes directed acyclic graphs to model task dependencies and optimizes task execution order and resource allocation strategies through reinforcement learning agents. In the testing of the resource allocation optimization model, when the number of tasks is 500, the redundant resource recovery rate is 87.0%, and the computational complexity is 16.7 s. The response time in the regional resource environment is 2.28 s, and the load adaptability is 95.8%. In the task scheduling method test, the proposed method has a memory occupancy of 85 MB and a scheduling delay of 0.13 s in a high-bandwidth environment of 300 Mbps. The results show that the proposed method outperforms traditional scheduling strategies in terms of resource utilization and energy consumption, and contributes to achieving the goal of green cloud computing. The research aims to provide an efficient and intelligent solution for energy optimization in cloud computing systems.

ANNOUNCEMENT: Award for the Best Paper in the Journal of Mathematical Psychology

The predominant definition of extinction risk in conservation biology involves evaluating the cumulative distribution function (CDF) of extinction time at a particular point (the "time horizon"). Using the principles of decision theory, this article develops an alternative definition of extinction risk as the expected loss (EL) to society resulting from eventual extinction of a species. Distinct roles are identified for time preference and risk aversion. Ranges of tentative values for the parameters of the two approaches are proposed, and the performances of the two approaches are compared and contrasted for a small set of real-world species with published extinction time distributions and a large set of hypothetical extinction time distributions. Potential issues with each approach are evaluated, and the EL approach is recommended as the better of the two. The CDF approach suffers from the fact that extinctions that occur at any time before the specified time horizon are weighted equally, while extinctions that occur beyond the specified time horizon receive no weight at all. It also suffers from the fact that the time horizon does not correspond to any natural phenomenon, and so is impossible to specify nonarbitrarily; yet the results can depend critically on the specified value. In contrast, the EL approach has the advantage of weighting extinction time continuously, with no artificial time horizon, and the parameters of the approach (the rates of time preference and risk aversion) do correspond to natural phenomena, and so can be specified nonarbitrarily.

Cloud Computing System (CCS) aims to power the next generation data centers and enables application service providers to lease data center capabilities for deploying applications depending on user Quality of Service (QoS) requirements. Huge investments and complex managements are shifted from users to providers. To improve efficiency and simplify the management, in this paper, intelligent cloud computing system (ICCS) is presented and discussed. Data warehouse is introduced to record the inside and outside data of the CCS for data analysis and data mining, Some tactics of the performance optimization of the CCS can be found and the management problems such as resource allocation policies, infrastructure development plan, capabilities management and so on can be quantified through analyzing all this data, Based on these, building a cloud computing management information system(CCMIS) which is different from the general management information systems(MIS) is the major task in this paper.

In this work, the Cumulative Distribution Function (CDF) and the Probability Density Function (PDF) are examined for a data set of finite elements. The CDF and the PDF are valid only for the theoretical asymptotes when the number of elements in the set approaches infinity. The equivalent functions defined for a finite set are currently unknown. In various fields, especially in signal processing, data size is usually statistically limited and more accurate analysis is often required for the validation of new algorithms. In this work, discontinuous CDF (DCDF) is defined and proposed for measuring the ‘statistical distance’ and the ‘statistical error’. These new definitions enable comparisons of different data sets with each other and with the theoretical asymptotic function CDF. The proposed statistical functions are illustrated on Gaussian distributed data.

Nonlinear evolution of the cosmological background density field as diagnostic of the cosmological reionization

Response time is the key performance measure in on-line transaction processing systems and other client-server architectures. Not only is it important to achieve a low average value and correspondingly high throughput, but response time should also be fairly consistent in order to provide a good quality of service. We develop a new algorithm for computing the probability density function of response times in Markovian models of client-server systems. We model the clients and servers by central server queueing networks and obtain response time densities as simple functions of time under independence assumptions that are shown to hold asymptotically as network size increases. The communication network is modelled as a single server queue with mean service time determined by its operational characteristics. We consider an Ethernet and construct a new model, of interest in its own right, that captures details not modelled hitherto. This model is validated against simulation and shows good agreement up to moderate utilisations, the normal operating environment for Ethernets. The whole client-server model is implemented in the Metron Athene Client-Server capacity planning tool and sample runs are examined.

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.

A generalized distribution function of fiber orientation for injection molded composites

ABSTRACTRecently, a hybrid ranked set sampling (HRSS) scheme has been proposed in the literature. The HRSS scheme encompasses several existing ranked set sampling (RSS) schemes, and it is a cost-effective alternative to the classical RSS and double RSS schemes. In this paper, we propose an improved estimator for estimating the cumulative distribution function (CDF) using HRSS. It is shown, both theoretically and numerically, that the CDF estimator under HRSS scheme is unbiased and its variance is always less than the variance of the CDF estimator with simple random sampling (SRS). An unbiased estimator of the variance of CDF estimator using HRSS is also derived. Using Monte Carlo simulations, we also study the performances of the proposed and existing CDF estimators under both perfect and imperfect rankings. It turns out that the proposed CDF estimator is by far a superior alternative to the existing CDF estimators with SRS, RSS and L-RSS schemes. For a practical application, a real data set is considered on the bilirubin level of babies in neonatal intensive care.

A generalized ranked set sampling (RSS) plan has recently been provided in the literature called varied L RSS (VLRSS). It is shown that VLRSS encompasses several existing RSS variations and also it efficiently estimates the population mean. In this article, we extend the work and consider estimating the cumulative distribution function (CDF) using VLRSS. Three new CDF estimators are proposed and their asymptotic properties are also theoretically investigated. Taking into account the information supported by the unmeasured sampling units, we also propose a general class of CDF estimators. Using small Monte Carlo experiments, we study the behavior of the proposed CDF estimators with respect to the conventional CDF estimator under RSS. It is found that the conventional RSS-based CDF is outperformed by at least one of VLRSS-based CDF estimators in most of the considered cases. Finally, an empirical example is utilized to illustrate the potential application of the proposed estimators.

This paper proposes a dynamic global backlight dimming algorithm based on the analysis of the cumulative distribution function (CDF) of the image histogram. For each intensity level, the convexity of the CDF is calculated using the distance to the uniform histogram curve. Using these values, the proposed algorithm characterizes the shape of the CDF to find the proper clipping ratio. This ratio enables to get the maximum level data beyond which the intensity levels are clipped, as the results of backlight dimming but image distortion. The experimental results show that the proposed algorithm gives a good compromise between backlight power reduction and image quality preservation, quantified by the PSNR metric.

The California HIgh school Cosmic ray ObServatory (CHICOS) is a ground-based scintillator array designed to measure the extended air showers of ultra-high energy cosmic rays. The goal of the project is to gain insight into the origin of ultra-high energy cosmic rays by measuring the energy spectrum and the distribution of arrival directions. The CHICOS array has been in operation since 2003. It consists of 77 pairs of scintillator dectectors deployed at schools in the San Fernando and San Gabriel valleys near Los Angeles, and is designed to observe cosmic ray air showers at energies of 10^18 eV and above. In addition, the Chiquita subarray is designed to observe smaller showers in the energy range of 1016 - 1019 eV. We present new descriptions of the air shower lateral distribution function and time distribution function, which have been derived from AIRES-generated simulated air showers. The new functions are specific to the CHICOS altitude and allow for a maximum likelihood shower reconstruction method, which is more appropriate to the CHICOS data than the χ2 minimization method. We present several analyses of the accuracy of the reconstruction software in the energy ranges available to the Chiquita and CHICOS arrays. The energy spectrum between 1017 eV and 1019 eV has been measured by the Chiquita subarray. At the lowest energy range, it is found to agree with previous measurements, while the measured flux falls below previous experiments for energies greater than approximately 1017.5 eV. The CHICOS energy spectrum above 1018.4 eV is found to agree with previous results published by AGASA. However, we do not observe the cutoff in the spectrum at 1020 eV reported more recently by the Auger and HiRes Collaborations. A correlation analysis between CHICOS data and nearby active galactic nuclei (AGN) was performed. No excess of cosmic rays was observed in the vicinity of nearby AGN. The maximum correlation was observed for cosmic ray events with E > 1020 eV and for AGN with z