Dynamic resource scheduling in cloud radio access network with mobile cloud computing

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile cloud computing (MCC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile users' devices to provide better quality of service (QoS). But the power consumption has become skyrocketing for MSP as it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MCC separately while less work had considered the integration of C-RAN with MCC. In this paper, we present a unifying framework for optimizing the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MCC to minimize the power consumption of MSP while still guaranteeing the QoS for mobile users. Our objective is to maximize the profit of MSP. To achieve this objective, we first formulate the resource scheduling issue as a stochastic problem and then propose a Resource onlIne sCHeduling (RICH) algorithm using Lyapunov optimization technique to approach a time average profit that is close to the optimum with a diminishing gap (1/V) for MSP while still maintaining strong system stability and low congestion to guarantee the QoS for mobile users. With extensive simulations, we demonstrate that the profit of RICH algorithm is 3.3× (18.4×) higher than that of active (random) algorithm

Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm

Maximizing the Profit of Cloud Broker with Priority Aware Pricing

A practical problem facing Infrastructure-as-a-Service (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users' job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs' priorities to reserve resources for profit maximization? Second, how to fairly price users' job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker's profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm

The Effect of Electric Field on Nanofibers Preparation in Cylindrical-Electrode-Assisted Solution Blowing Spinning

Cylindrical-electrode-assisted solution blowing spinning (CSBS) is a novel nanofiber preparation method. The electric field of CSBS not only is one of the main innovations of this technology but also plays a key role in the preparation of nanofibers. In this article, the electric field of CSBS and the influences of electric field on the preparation of nanofibers were studied systematically for the first time by simulations, theoretical analyses, and experiments. This paper innovatively established the coaxial capacitor model for studying the CSBS electric field. The effects of electric field on the preparation and morphology of CSBS nanofibers were theoretically investigated by using this model. The theoretical formulas that can express the relationships between the various electric field variables were obtained. The electric field strength distribution, voltage distribution, and the relationships between the electric field parameters of CSBS were obtained by finite element simulations. The simulations’ results show that reducing the diameter of cylinder (DC) or increasing the voltage increase the electric field strength of the jet surface. Experimental results reveal that increasing voltage or reducing DC can reduce the diameter of nanofibers. The experimental and simulation results prove the correctness of the theoretical research conclusions. The theoretical and simulation conclusions of this paper lay a theoretical foundation for further study of CSBS electric field. The experimental conclusions can directly guide the controllable preparation of CSBS nanofibers

Identification of Natural Bamboo Fiber and Regenerated Bamboo Fiber by the Method of Modified near Infrared Spectroscopy

In this work, the applicability and effectiveness of identification of natural bamboo fiber and regenerated bamboo fiber by near infrared spectroscopy are investigated. A discrimination model based on Ward’s algorithm and Hierarchical Cluster Analysis is pretreated by the first derivative and vector normalization, which can be used to distinguish natural bamboo fiber and regenerated bamboo fiber. In addition, the near infrared spectra model exhibits a high accuracy with an effective identification of the double fibers

Identification of Natural Bamboo Fiber and Regenerated Bamboo Fiber by the Method of Modified near Infrared Spectroscopy

In this work, the applicability and effectiveness of identification of natural bamboo fiber and regenerated bamboo fiber by near infrared spectroscopy are investigated. A discrimination model based on Ward’s algorithm and Hierarchical Cluster Analysis is pretreated by the first derivative and vector normalization, which can be used to distinguish natural bamboo fiber and regenerated bamboo fiber. In addition, the near infrared spectra model exhibits a high accuracy with an effective identification of the double fibers

Cost-effective resource allocation in C-RAN with mobile cloud

Taking full advantages of two cloud-based techniques, i.e., cloud radio access network (C-RAN) and mobile cloud computing (MCC), mobile operators will be able to provide the good service to the mobile user as well as increasing their revenue. This paper aims to minimize the mobile operator's cost while at the same time, meet the task time constraints of the mobile users. In particular, we assume that the mobile cloud first completes the tasks for the mobile user and then transmits the results back to the users through C-RAN. Joint cost-effective resource allocation is proposed between MCC and C-RAN and simulation results confirm that the proposed cost minimization and resource allocation solution outperforms nonoptimal solutions

Preparation and Characterization of Textile-Grade Long Cellulose Fibers and Their Yarns from Windmill Palm

Windmill palm fiber (WPF) is an abundant source of cellulose fiber that can be used in textile manufacturing. In this study, acid-alkali palm fiber and acid-alkali-enzyme palm fiber were prepared to create blended yarns. The morphology, chemical composition, physical structural parameters, and tensile properties of the WPF samples and yarns were studied. The results indicated that both the acid-alkali and acid-alkali-enzyme treatments can be used as degumming methods to prepare windmill palm textile-grade long fibers with spinning ability. After chemical treatment, the cellulose content of WPF increased to more than 60%, up from 34%. However, the line densities of the acid-alkali and acid-alkali-enzyme textile-grade long fibers decreased to 5.29 ± 1.00 tex and 4.52 ± 0.82 tex, respectively. For the enzyme-treated fiber, a stratification phenomenon of the fiber cell walls and a decrease in the modulus were observed. The palm/cotton yarn had a high tensile strength and strip uniformity