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The running mode and existing issues of current resource scheduling system operation model of TT＆amp;C network were discussed firstly.Then the development trends of future resource scheduling and management models were summarized, included integration of data transmission, automation of system operation, intelligence of algorithm allocation, coexistence of random access and pre-allocation.In the end, accorded to the management demands of integration, automation and intelligentization, the operating framework of the future resource scheduling model was put forward.After that, the key technologies which need to be broken through of the framework were researched deeply.And the further construction object of resource scheduling research was proposed in order to provided a reference for subsequent engineering applications.

In a cloud context, merging complimentary numerous virtual machines (VMs) on an existing physical machine (PM) is the primary method for optimizing physical resources. One well-known area of research concentrates on making better use of VM migration resources when taking into account the dynamically changing resource demands of VMs. Finding the ideal balance between the complexity and performance of the VM migration optimization is the problem here. On the one hand, effective resource reuse is achieved through VM migration planning, and on the other, VM migration frequency is decreased to improve migration efficiency. On the other hand, a cloud data centre's enormous PM and VM population typically makes migration planning more challenging, which impedes the VM migration decision-making process. By reducing the number of VM migration options to make VM migration planning easier and address these issues, this study recommend a hybrid Ant Colony and Genetic Algorithm (AGO) resource pool architecture. Then, establishing this model as a base, we develop the hybrid resource-reuse optimization method, which maximizes resource utilization with a minimal number of VM migrations. Finally, we evaluate hybrid AGO using simulation testing and real-world trials on a working cloud platform. Compared to similar methods, the findings show that hybrid AGO increases average resource utilization by 15%, reduces the use of PMs by 15%, and decreases the average number of migrations by 30%. [ABSTRACT FROM AUTHOR]

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The survival soil on which the traditional martial arts culture depends is becoming thinner and thinner. A large number of superb martial arts skills and martial arts experience disappear with the passing of the older generation of martial artists. In order to realize the information preservation of traditional Wushu, this paper puts forward the establishment and application of traditional Wushu intelligent learning resource database under the background of big data. In the context of big data, data mining technology is used to realize personalized recommended learning services, obtain the feature structure of intelligent learning system through operation, extract the average dynamic features of specific data in the resource database, obtain fuzzy constraints according to the value, determine the membership function, and adjust the membership function parameters through training fuzzy neural network, so as to gradually improve the reasoning accuracy. Finally, the matching rule set is used to filter the resource data packets to achieve the purpose of communication. In order to verify the effect of the model, compared with the traditional model, the results show that when the number of nodes is 500, the average transmission rate is as high as 90%, the average delay is 12 seconds, and the throughput performance is 91%. It can be verified that the model designed in this paper can effectively improve the propagation rate, reduce the average delay and strengthen the throughput performance.

IntroductionAmid the rising interest in sustainable urban development, Positive Energy Districts (PEDs) have become a focus of research. This study examines the dynamic processes that influence the development and scalability of PEDs from a co-evolutionary business ecosystem perspective.MethodsTo delve into the dynamics of Positive Energy Districts, we applied the business ecosystem framework to a real-world case study, namely the Hunziker Areal. Our research methodology involved the development and validation of a high-level conceptual model. This was achieved through workshops and guided interviews with experts engaged in pilot and research projects related to PEDs.ResultsThe study highlights the significance of employing a systemic approach to evaluate the potential of PEDs in enhancing housing sustainability while creating value for diverse stakeholders. Through the utilization of causal loop diagrams, key feedback loops explaining the diffusion of PEDs are identified. Moreover, the study reveals varying perceptions of PED utility among stakeholders, who assess the impact using different Key Performance Indicators (KPIs) such as CO2 target achievement and well-being. Key factors influencing technology adoption, such as low prosumer electricity unit cost, are also identified.DiscussionOur findings further shed light on crucial aspects affecting value capture and the attractiveness of the ecosystem to investors. Additionally, the study underscores the critical role of supportive policies and regulations in facilitating the diffusion and scalability of Positive Energy Districts.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Working memory can be subdivided into two relatively independent subordinate systems, the maintenance subsystem and the manipulation subsystem. Although the two subsystems are quite heterogeneous, research thus far has not adequately distinguished the resource pools of the two subsystems. Additionally, previous research on the relationship between working memory and unconscious priming is paradoxical. Different subsystems leading to different effects on unconscious priming might be the reason for the paradoxical research. Therefore, the current article aimed to distinguish the resource pools among two working-memory subsystems and to investigate the relationship between the two subsystems and unconscious priming. Methods: To address these issues, a maintenance dual-task and a manipulation dual-task program were developed. Each participant had to separately perform the two dual tasks in a balanced order. In each dual task, participants first completed a masked priming task accompanied by working-memory load. As a control, participants completed a prime identification test to confirm that the processing of the masked prime was at the unconscious level. The maintenance dual task comprised sandwich masking trials accompanied by Sternberg trials, while the manipulation dual task comprised sandwich masking trials accompanied by N-back trials. Results: The results of the prime identification test indicated that the participants could not consciously perceive the masked prime of both dual tasks. The results of the working-memory task of both dual tasks indicated that the load manipulation was successful for both dual tasks. Most importantly, the results of the masking task of both dual tasks showed that an increase in working-memory load decreased the magnitude of unconscious priming in the manipulation dual task, whereas an increase in working-memory load did not decrease unconscious priming in the maintenance dual task. These observations demonstrate that the manipulation subsystem, rather than the maintenance subsystem, interferes with unconscious priming. Together with previous research, we propose a two-pool attention resource model to explain the modulation of working memory on unconscious priming by dissociating the executive resource pool of the manipulation system from the retention resource pool of the maintenance system. Thus, the current work confirms and extends the extant literature about the dependence of unconscious processing on attention resources by suggesting that unconscious priming shares a common resource pool with the manipulation subsystem. [ABSTRACT FROM AUTHOR]

The greenhouse vegetable industry play an important strategic role in the adjustment of agricultural transformation mode and the reform of supply side in Heilongjiang Province. Facility horticulture in Heilongjiang Province develops rapidly in recent years, technical support is in great demand, but the experts' technology support for facility horticulture is far from enough. Experts' on-site guidance costs much time and money in the countryside, while the service efficiency is very low. To solve this urgent problem, the architecture of "greenhouse vegetable intelligent terminal system based on cloud service" and the key technologies of implementation (low-cost IoT, distributed real-time operating architecture, virtual expert service, neural network image recognition and mobile terminal service) were put forward. Based on expert services, supplemented by data mining technology, IoT devices were used as expert's remote perception means, smart phones as user terminals, cloud service for integrating knowledge, resources and Internet of Things data to provide vegetable experts and greenhouse vegetable users with high information acquisition, storage, analysis,decision-making capabilities and effective solutions. Experts could view vegetable production status in greenhouses remotely through the Internet, get image and growth environment data, then provide remote guidance to vegetable farmers through the system, expert knowledge would be stored, mined and reused by the system. The Internet of Things system could automatically send out early warning information by judging the air temperature, humidity, illumination intensity and soil moisture in greenhouse. The application of knowledge map and neural network technology would reduce the workload of experts and increase concurrent processing capability of services at the same time. At present, part of this research has been applied in different user groups such as agricultural research departments, enterprises, vegetable cooperatives and farmers in Heilongjiang Province. The system can provide experts with remote inquiry means of greenhouse vegetable production environment, and has the characteristics of simple deployment and low cost. It is suitable for various greenhouse vegetable scenarios, including fruit and edible fungi. In order to popularize this technology in greenhouse vegetable production in China, and achieve an efficient experts' technical support, this research also proposed technical solutions of a large-scale application scenario through cloud computing in future.

Vehicular cloud computing (VCC) is a promising technology for the intelligent transport system. It provides a better quality of transport and vehicular services that will increase the safety and comfort of drivers and passengers. A group of vehicles creates VCC and offers vehicular services for its users in the absence of infrastructure to support the system. The vehicles having computing, storage, communication, and sensing devices can share these resources with other vehicles. VCC creates a resource pool by aggregating all the shared resources of nearby vehicles. The major challenge for VCC is to manage the resources from different vehicles in a resource pool and to allocate necessary resources to its user on‐demand. The authors propose a semi‐Markov decision process based resource allocation method for the VCC system called flexible resource allocation for vehicular cloud system to manage and allocate the resources. The proposed method finds optimal resource allocation strategies for different states of the VCC system and maximises the long‐term expected reward under different parameter settings. [ABSTRACT FROM AUTHOR]

The storage pool is an important part of the resource pool and plays an important role in data storage and data backup.The storage pool has the characteristics of large scale,prominent isomerism,and complex functions.The storage pool management system of the storage pool guarantees the normal work of the storage pool.Based on the design of the storage pool architecture,the functional framework of the storage pool was clarified,and the deployment structure of the storage pool management system and the overall architecture of the storage pool management system was proposed and designed,and the implementation functions and display form of the management system were elaborated in detail.The research and implementation of the storage pool management system not only promoted the construction of the power cloud resource pool,but also provided a good reference for other companies’ cloud computing technology.

Cloud computing promotes the information construction and becomes an inevitable choice for electric enterprise.How to make cloud computing land is a huge problem for electric enterprise.The concept of electric cloud was proposed and it was confirmed that resource pool was one of the technologies to make cloud computing land in electric enterprise. According to the characters of electric enterprise, the designing principle about the capacity and the base schema of resource pool was proposed.And the implementation plans of the resource pool were given.The key factors of resource pool and their assembling methods can be known clearly form the base schema of resource pool,there is great practical importance on the planning and strategy of resource pool.

A monitoring system solution of supporting multi-datacenter and multiple private cloud environments were proposed.In order to achieve load balancing and easy scale-out，the unified monitoring system adopted a distributed framework and encapsulated Zabbix API and OpenStack API.This solution was significant for the study of similar distributed cluster scheduling system.

In cloud computing,mining anomaly pattern of nodes plays an important role in promptly diagnosing node states of resource pool. According to the data characters of computing resource,storage resource and network resource,it preprocessed the information of node status. And it used association rule algorithm to mine the rules among the nodes' parameter status of resource pool,e. g. "high level-high level" and"low level-high level". This paper proposed an improved Eclat algorithm .called i-Eclat, which based on vertical data format of restrictive attribute connection,i-Eclat at reduced the number of connections by transforming the format of state data and constructing a non-frequent 2-itemsets, as well as it constructed the storage structure to limit the redundant attribute connections. Extensive experiments have been conducted to demonstrate that this method can find the hiding rules among resource pool's nodes. And it also shows that i-Eclat at outperforms traditional methods on computing efficiency, especially on processing big data sets. [ABSTRACT FROM AUTHOR]

DBA scheme based on Load balance algorithm (LBA) and wavelength recycle mechanism (WRM) for multi-wavelength upstream transmission is proposed in this paper. According to 1 Gbps and 10 Gbps line rates, ONUs are grouped into different VPONs. To facilitate wavelength management, resource pool is proposed to record wavelength state. To realize quantitative analysis, a mathematical model describing metro-access network (MAN) environment is presented. To 10G-EPON upstream, load balance algorithm is designed to ensure load distribution fairness for 10G-OLTs. To 1G-EPON upstream, wavelength recycle mechanism is designed to share remained wavelengths. Finally, the effectiveness of the proposed scheme is demonstrated by simulation and analysis. [ABSTRACT FROM AUTHOR]

A Selected Differential Shuffled Frog Leaping Algorithm (SDSFLA) is proposed. Compared with the classic Shuffled Frog Leaping Algorithm (SFLA)，SDSFLA improves the efficiency of populationupdatingby increasingthe number of updated vectors, uses thecrossover operator and mutation operator of Differential Evolution(DE) to strengthen information exchanges between vectors，uses multiple updating strategies to improve the success rate of the trial vectors, and increases the diversity of the population viarandomly-selected control parameters. Based on 16 benchmark functions, SDSFLA is compared with an improved frog leaping algorithm and two improved differential evolution algorithms. The test results confirm the validity and stability of the SDSFLA algorithm. [ABSTRACT FROM AUTHOR]