Execution Time Cost Research Articles

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

Considering the uncertainty of hydrothermal wind and solar-based DG

Lately, researchers have widely centered around renewable energy assets, for example, wind control and solar units to decrease the utilization of fossil fuels as the fundamental wellspring of ecological natural contaminations. One of the fundamental difficulties for the generation of wind and solar energies is that they frequently including the uncertainty because of the stochastic natures of wind speeds and solar radiation. Along these lines, the current uncertainty to assets the wind and solar units and necessary to assess the arranging strategy of distribution systems for having a dependable execution. To display the uncertainty related along with the wind and solar power, the point estimate method (PEM) is utilized. Weibull and Beta distributions are utilized to deal with uncertain information factors. These fundamental goals present work is to minimize the generation cost of the framework is enhanced dependent on the butterfly optimization algorithm (BOA). Four case test frameworks viewed as it is discovered that the proposed strategy gives better arrangement as far as execution time and normal cost viability. The recreation results demonstrate that the entrance of sustainable energy source builds, the generation cost diminishes. The outcomes acquired along with the butterfly optimization algorithm contrasted and another one understood strategy. Also, the precise distribution of generation cost.

Optimized container scheduling for data-intensive serverless edge computing

Operating data-intensive applications on edge systems is challenging, due to the extreme workload and device heterogeneity, as well as the geographic dispersion of compute and storage infrastructure. Serverless computing has emerged as a compelling model to manage the complexity of such systems, by decoupling the underlying infrastructure and scaling mechanisms from applications. Although serverless platforms have reached a high level of maturity, we have found several limiting factors that inhibit their use in an edge setting. This paper presents a container scheduling system that enables such platforms to make efficient use of edge infrastructures. Our scheduler makes heuristic trade-offs between data and computation movement, and considers workload-specific compute requirements such as GPU acceleration. Furthermore, we present a method to automatically fine-tune the weights of scheduling constraints to optimize high-level operational objectives such as minimizing task execution time, uplink usage, or cloud execution cost. We implement a prototype that targets the container orchestration system Kubernetes, and deploy it on an edge testbed we have built. We evaluate our system with trace-driven simulations in different infrastructure scenarios, using traces generated from running representative workloads on our testbed. Our results show that (a) our scheduler significantly improves the quality of task placement compared to the state-of-the-art scheduler of Kubernetes, and (b) our method for fine-tuning scheduling parameters helps significantly in meeting operational goals.

Uso de resúmenes de evidencia para informar el tratamiento farmacológico de personas con sospecha o diagnóstico de infección respiratoria por SARS CoV-2 / COVID-19

Introduction: Due to the emergence of the SARS-CoV-2 coronavirus since December 2019, a large volume of scientific production has been generated, in some cases uncertain or controversial, especially in the pharmacological management of patients with this infection; therefore, it is considered a relevant search for methodological alternatives to carry out its rigorous, systematic and quality synthesis, but with less execution time and lower cost. Objective: To present the available evidence regarding the pharmacological management of people with suspected or diagnosed respiratory SARS-CoV-2 (COVID-19) using the method of rapid systematic reviews (RS-R) in potentially effective drugs for their management. Methodology: A systematic and structured search was conducted in Medline, Embase, Scopus, Cochrane Library, Clinical trials and Google Scholar in English. Studies included clinical practice guidelines, consensus, systematic reviews, meta-analyses, clinical trials, and other primary studies. Data search and extraction were performed by multiple reviewers, but none were paired. Results: Sixteen questions of clinical interest were resolved, related to the use in COVID-19 of lopinavir/ritonavir, nelfinavir, oseltamivir, remdesivir, ribavirin, teicoplanin, umifenovir, favipiravir, tocilizumab, ivermectin, convalescent plasma; the use of support management drugs such as dexamethasone were also evaluated, as well as the concomitant use of drugs that generated doubts, such as NSAIDs, ACEis, and ARA IIs. Conclusions: Summaries of evidence are within the pandemic scenario as a good methodological alternative to offer quality information in the short term for decision-makers.

Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera

Precision agriculture relies on the availability of accurate knowledge of crop phenotypic traits at the sub-field level. While visual inspection by human experts has been traditionally adopted for phenotyping estimations, sensors mounted on field vehicles are becoming valuable tools to increase accuracy on a narrower scale and reduce execution time and labor costs, as well. In this respect, automated processing of sensor data for accurate and reliable fruit detection and characterization is a major research challenge, especially when data consist of low-quality natural images. This paper investigates the use of deep learning frameworks for automated segmentation of grape bunches in color images from a consumer-grade RGB-D camera, placed on-board an agricultural vehicle. A comparative study, based on the estimation of two image segmentation metrics, i.e. the segmentation accuracy and the well-known Intersection over Union (IoU), is presented to estimate the performance of four pre-trained network architectures, namely the AlexNet, the GoogLeNet, the VGG16, and the VGG19. Furthermore, a novel strategy aimed at improving the segmentation of bunch pixels is proposed. It is based on an optimal threshold selection of the bunch probability maps, as an alternative to the conventional minimization of cross-entropy loss of mutually exclusive classes. Results obtained in field tests show that the proposed strategy improves the mean segmentation accuracy of the four deep neural networks in a range between 2.10 and 8.04%. Besides, the comparative study of the four networks demonstrates that the best performance is achieved by the VGG19, which reaches a mean segmentation accuracy on the bunch class of 80.58%, with IoU values for the bunch class of 45.64%.

A Recommender for Resource Allocation in Compute Clouds Using Genetic Algorithms and SVR

Resource allocation in Cloud Computing has been done reactively, hindering service guarantees and generating unnecessary charging of idle resources. In order to mitigate these problems, this work proposes and evaluates a predictive resource allocation approach, implemented as a Configuration Recommender, based on Support Vector Regression (SVR) and Genetic Algorithms (GA). This combination is used to estimate application runtime and recommends a viable and valid configuration of resources in the cloud, regarding execution time and monetary costs. As a case study, machine learning applications based on the Weka tool are chosen. The results show that predicted times were very close to actual ones, achieving an efficient estimation of time and cost and their consequent reduction.

The high frequency trade off between speed and sophistication

Central to the ability of a high frequency trader to make money is speed. In order to be first to trading opportunities, firms invest in the fastest hardware and the shortest connections between their machines and the markets. Yet this is not enough: algorithms must be short, no more than a few instructions. As a result there is a trade-off in the design of optimal high frequency trading strategies: being the fastest necessitates being less sophisticated. To understand the effect of this tension a computational model is presented that captures latency, both of code execution and information transmission. Trading algorithms are modelled through genetic programming with longer programmes allowing more sophisticated decisions at the cost of slower execution times. It is shown that, depending on the market composition, short fast strategies and slower more sophisticated strategies may both be viable and exploit different trading opportunities. The relative profits of these different approaches vary, however, slow traders benefit and social welfare increase in the presence of HFTs. A suite of regulations are tested to manage the risks associated with high frequency trading, the majority are found to be ineffective, though constraining the ratio of orders to trades may be promising.

Uso de resúmenes de evidencia para informar el tratamiento farmacológico de personas con sospecha o diagnóstico de infección respiratoria por SARS CoV-2 / COVID-19

Introduction: Due to the emergence of the SARS-CoV-2 coronavirus since December 2019, a large volume of scientific production has been generated, in some cases uncertain or controversial, especially in the pharmacological management of patients with this infection; therefore, it is considered a relevant search for methodological alternatives to carry out its rigorous, systematic and quality synthesis, but with less execution time and lower cost. Objective: To present the available evidence regarding the pharmacological management of people with suspected or diagnosed respiratory SARS-CoV-2 (COVID-19) using the method of rapid systematic reviews (RS-R) in potentially effective drugs for their management. Methodology: A systematic and structured search was conducted in Medline, Embase, Scopus, Cochrane Library, Clinical trials and Google Scholar in English. Studies included clinical practice guidelines, consensus, systematic reviews, meta-analyses, clinical trials, and other primary studies. Data search and extraction were performed by multiple reviewers, but none were paired. Results: Sixteen questions of clinical interest were resolved, related to the use in COVID-19 of lopinavir/ritonavir, nelfinavir, oseltamivir, remdesivir, ribavirin, teicoplanin, umifenovir, favipiravir, tocilizumab, ivermectin, convalescent plasma; the use of support management drugs such as dexamethasone were also evaluated, as well as the concomitant use of drugs that generated doubts, such as NSAIDs, ACEis, and ARA IIs. Conclusions: Summaries of evidence are within the pandemic scenario as a good methodological alternative to offer quality information in the short term for decision-makers.

Survey on Approximate Computing and Its Intrinsic Fault Tolerance

This work is a survey on approximate computing and its impact on fault tolerance, especially for safety-critical applications. It presents a multitude of approximation methodologies, which are typically applied at software, architecture, and circuit level. Those methodologies are discussed and compared on all their possible levels of implementations (some techniques are applied at more than one level). Approximation is also presented as a means to provide fault tolerance and high reliability: Traditional error masking techniques, such as triple modular redundancy, can be approximated and thus have their implementation and execution time costs reduced compared to the state of the art.

HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user’s tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user’s tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data- and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).

A lightweight and compromise‐resilient authentication scheme for IoTs

AbstractInternet of Things (IoTs) connects billions of devices through the Internet having billion of data points. The security and privacy of those data points are very important and considered as a major concern. Due to the resource constraints nature of IoTs, the security solutions for IoTs must be secure and lightweight in terms of processing and storage. However, many existing security solutions specifically in the field of authentication are not suitable for IoTs due to the computation involved. Alternatively, the lightweight existing solutions are vulnerable to various attack(s). In this paper, a lightweight and compromise‐resilient authentication (LCA) scheme for IoT is proposed. The proposed LCA scheme is lightweight because it uses lightweight hash and XOR operations. The security analysis manifests that the proposed scheme is compromise resilient against numerous security attacks. The proposed LCA scheme is compared with existing authentication schemes based on execution time, security, and computation cost. The outcomes show that the proposed LCA scheme is more secure and robust as compared to the existing authentication schemes proposed for IoTs.

Relay Selection Approach in Underwater Acoustic WSNs Using Bi-Partite Graph

In an underwater wireless sensor networks, it is very difficult to communicate data over the 3-D underwater acoustic signal. In acoustic communication, there is no proper transmission between a source node to sink node. In existing work, single-hop clustering protocols were used for direct communication from cluster head to base station. Since direct communication is involved, the transmission range is very large and hence the data communication becomes difficult. In proposed work, additional leverage is achieved by introducing multi-hop clustering for speedy data transmission by selecting Relay Autonomous Underwater Vehicles (Relay-AUV) without delay and maximum channel capacity in acoustic wireless communication. The proposed scheme presents Relay-AUV selection algorithm using Contextual Bandit Bi-partite Graph (CB-BG) formulated by multi-hop data transmission, which provides maximum leverage for energy saving in cluster networks. Theoretical analysis and experimental simulation results are evaluated based on performance metrics such as successive transmission rate, throughput, cost of execution time and packet delivery ratio. The results shows that the proposed CB-BG system has maximum increase in data transmission rate of 58.33%, maximum increase in throughput and network throughput of 54.95%, maximum increase in operational time cost of 27.77% and high packet delivery ratio of 66.66%. The results are encouraging and our proposed method is found to be more efficient than the weight matching algorithms and minimum distances relay policies. The proposed CB-BG mechanism performs faster and reliable communication.

A Tensor Computation and Optimization Model for Cyber-Physical-Social Big Data

With an objective to provide the proactive and personalized services for human beings, Cyber-Physical-Social Systems (CPSS), which combine the cyber space, physical space, and social space together, need to process the large scale heterogenous data first. Tensor, as an appropriate data representation tool, has been widely used for representation of heterogeneous Cyber-Physical-Social big data. When computationally processing such tensor, many necessary constraints have to be taken into account, e.g., the execution time, energy consumption, economic cost, security as well as reliability. However, the systematic integration of these constraints and then the modelling of general optimization for tensor processing become more challenging. In this paper, with such constraints being considered together, a general model for tensor computation that optimizes the execution time, energy consumption, and economic cost with acceptable security and reliability is proposed. From diverse perspectives of user requirements, a case study for the tree-based distributed High-Order Singular Value Decomposition (HOSVD) is measured. With the focus on multi-objective combination, the experimental results validate the applicability and generality of the proposed model.

Adaptive optics in the mouse eye: wavefront sensing based vs. image-guided aberration correction.

Adaptive Optics (AO) is required to achieve diffraction limited resolution in many real-life imaging applications in biology and medicine. AO is essential to guarantee high fidelity visualization of cellular structures for retinal imaging by correcting ocular aberrations. Aberration correction for mouse retinal imaging by direct wavefront measurement has been demonstrated with great success. However, for mouse eyes, the performance of the wavefront sensor (WFS) based AO can be limited by several factors including non-common path errors, wavefront reconstruction errors, and an ill-defined reference plane. Image-based AO can avoid these issues at the cost of algorithmic execution time. Furthermore, image-based approaches can provide improvements to compactness, accessibility, and even the performance of AO systems. Here, we demonstrate the ability of image-based AO to provide comparable aberration correction and image resolution to the conventional Shack-Hartmann WFS-based AO approach. The residual wavefront error of the mouse eye was monitored during a wavefront sensorless optimization to allow comparison with classical AO. This also allowed us to improve the performance of our AO system for small animal retinal imaging.

Fault tolerance for a scientific workflow system in a Cloud computing environment

High computation power is required to execute complex scientific workflows. Cloud computing resources are used viably to perform such complex workflows. Task clustering has demonstrated to be an efficient technique to decrease system overhead and to enhance the fine computational granularity tasks of a scientific workflow executed on distributed resources. However, earlier clustering methods ignore the effect of failures on the system, despite their significant impact on large-scale distributed resources, such as Clouds. In this paper, we present a new fault-tolerant task clustering method called FT-HCC that is designed by including the workflow execution time (makespan) and execution cost constraints, which are used to increase workflow performance. The proposed method is implemented and evaluated in a simulation-based approach, using a real-time workflow execution to analyze performance improvement. The results consolidate that the proposed strategies and techniques work efficiently in terms of fault tolerance and improve both workflow makespan and execution cost when compared to existing approaches.

Workflow load Balancing using soft computing base novel framework with Qos Parameters

Cloud computing represents a new era of computing network, where the resources of the system are dispersed and shared among its users in the network premises. The user of this system is able to use such resources through the technology of internet based on system of Pay-As-Per-Use. If a service is used by any type of user, it helps in production of wide variety of data. So, the cost of data transfer between two of the dependent resources will be extremely high. Additionally, an application of complex nature involves large number of tasks boosting the process of total cost of execution with respect to the used application, if the process is not scheduled in an optimized manner. In order to overcome such issues, a hybrid approach of water cycle optimization is proposed with particle swarm optimization. This method is divided into two steps of working determining under and over utilized virtual machines. In experimental analysis, the proposed approach on different scientific workflows is done where significant performance in all the workflows is based on total execution time and total execution cost.

Multi-Cloud Management Strategies for Simulating IoT Applications

The Internet of Things (IoT) paradigm is closely coupled with cloud technologies, and the support for managing sensor data is one of the primary concerns of Cloud Computing. IoT-Cloud systems are widely used to manage sensors and different smart devices connected to the cloud, hence a large amount of data is generated by these things that need to be efficiently stored and processed. Simulation platforms have the advantage of enabling the investigation of complex systems without the need of purchasing and installing physical resources. In our previous work, we chose the DISSECT-CF simulator to model IoT-Cloud systems, and we also introduced provider policies to enable cost-aware policies resource management. The aim of this paper is to further extend the simulation capabilities of this tool by enabling multi-cloud management. In this paper we introduce four cloud selection strategies aimed to reduce application execution time and utilization costs. We detail our proposed method towards multi-cloud extension, and evaluate defined strategies through a meteorological application.

The Design and Regulation of High Frequency Traders

Central to the ability of a high frequency trader to make money is speed. In order to be first to trading opportunities firms invest in the fastest hardware and the shortest connections between their machines and the markets. This, however, is not enough, algorithms must be short, no more than a few lines of code. As a result there is a trade-off in the design of optimal HFT strategies: being the fastest necessitates being less sophisticated. To understand the effect of this tension a computational model is presented that captures latency, both of code execution and information transmission. Trading algorithms are modelled through genetic programmes with longer programmes allowing more sophisticated decisions at the cost of slower execution times. It is shown that depending on the market composition short fast strategies and slower more sophisticated strategies may both be viable and exploit different trading opportunities. The relative profits of these different approaches vary, however, slow traders benefit from their presence. A suite of regulations are tested to manage the risks associated with high frequency trading, the majority are found to be ineffective, however, constraining the ratio of orders to trades may be promising.

Fairness in dataflow scheduling in the cloud

Expensive dataflow queries which may involve large-scale computations operating on significant volumes of data are typically executed on distributed platforms to improve application performance. Among these, cloud computing has emerged as an attractive option for users to execute dataflows allowing them to select proper configurations (e.g., number of machines) to achieve desired trade-offs between execution time and monetary cost. Discovering dataflow schedules that exhibit the best trade-offs within a plethora of potential solutions can be challenging, especially in a heterogeneous environment where resource characteristics like performance and price can be varied. To increase resource utilization, users may also submit multiple dataflows for execution concurrently. Traditionally, building fair schedules (schedules where the slowdown of all dataflows due to resource sharing is similar) while achieving good performance is a major concern. However, considering fairness in the cloud computing setting where monetary cost is part of the optimization objectives significantly increases the difficulty of the scheduling problem. This paper proposes an algorithm for the scheduling of multiple dataflows on heterogeneous clouds that identifies Pareto-optimal solutions (schedules) in the three-dimensional space formed from the different trade-offs between overall execution time, monetary cost and fairness. The results show that in most cases the proposed approach can provide solutions with fairer schedules without significantly impacting the quality of the execution time to monetary cost skyline compared to the state of the art where the fairness of a solution is not taken into account.

Using Diagnosis and Life Cycle Cost to Improve Reliability of an Excavator

Diagnosing of equipment before actual failure and reduced total ownership cost of excavator (TOC) is a significant issue for the mining and civil construction industries to unavoidably consider. Consequences of prolonged project execution time and extra cost due to sudden breakdowns are better minimized by studying the reliability of the equipment and having knowledge of life cycle cost model which was used in this research to ascertain the economic replacement time and cost of an excavator. Results from life cycle cost analysis carried out on an excavator show that the replacement time and total ownership cost of excavator 320C from 2010 to 2017 is 8 years and N10, 214 respectively.