Green Energy Model for Grid Resource Allocation

Sun, G.; Chen, H., and Mao, Y., 2020. The efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 691–695. Coconut Creek (Florida), ISSN 0749-0208.With the continuous development of marine technology, the tasks of marine survey, exploration, navigation, transportation and development are becoming more and more serious. It is necessary to pay attention to the problem of planning resource allocation in coastal areas, so as to design a more reasonable resource allocation scheme. The traditional analysis model can only converge locally, resulting in the deviation of the analysis results of resource allocation efficiency. Therefore, the construction method of the efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm is proposed. According to the relationship between fuzzy operator and fuzzy scale, this method calculates the carrying capacity of coastal environmental resources. In light of the prediction results of the dynamic demand forecast function of emergency resources, it sets the evaluation weight of the efficiency of technological innovation resource allocation of coastal enterprises. Based on the global convergence characteristics of genetic algorithm, it realizes the construction of the efficiency analysis model of technological innovation resource allocation of coastal enterprises. The experimental results show that compared with the traditional method, the model based on genetic algorithm has stronger convergence and can achieve global data convergence. It can be seen that the analysis results of this model accord with the reality and can be applied to the analysis of resource allocation efficiency at this stage.

Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.

A topological model of mesh-network of LTE technology presented using hypergraphs and Koenig graphs has been offered. The capabilities of hypergraphs were used to describe the distribution of frequency resources and to assess the characteristics of the initial configurations of the mesh network. Koenig graphs were used for a comparative analysis of the results. This made it possible to describe all possible configurations of the mesh-network, as well as its elements represented in the form of vertices and edges of the hypergraph. A mathematical model of the frequency resource allocation in the LTE technology mesh-network. Its novelty lies in the description of the problem of the frequency resource allocation as subchannel allocation problem in taking into account the interference between the network stations.

The system of streaming data and complex events distributed processing is considered. A mathematical model for computational resource consumption and allocation in edge computing is offered. On the basis of mathematical model there was developed a multi-agent algorithm as a part of agent-based software architecture for distributed processing of streaming data, which had been developed by the authors previously. The efficiency of the developed algorithm has been investigated by means of simulation in AnyLogic. The resource allocation problem in edge computing was simulated and the algorithm demonstrated satisfying results both at static and dynamic regimes. Also, the perspectives of intellectual technologies using in resource allocation for edge computing are considered.

SummaryIn today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such asMakespan, is treated as first‐class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi‐objective global minimization problem withMakespanandenergy consumptionas the main criteria. We apply thedynamic voltage and frequency scalingmodel for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy‐aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm‐based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is sufficient to maintain the desired quality level(s). Copyright © 2012 John Wiley & Sons, Ltd.

In order to improve the statistical ability of online education allocation resources, a statistical model of online education allocation resources for the coordinated development of regional economy is proposed. Based on the principle of comparability and practicability in the selection of statistical indicators of online education allocation resources, this paper constructs a statistical indicator system of online education allocation resources, assigns the weight of statistical indicator system of online education allocation resources by using the specific steps of analytic hierarchy process, and calculates the allocation level of educational resources of each school in six administrative regions by using the weighted index and model, According to the results of the cluster analysis of the types of school resource allocation, by introducing the difference coefficient, the balance of the allocation of various types of school education resources in the region is determined. Through the establishment of the statistical model of online education resource allocation, the statistics of online education resource allocation is realized. The experimental results show that the statistical model of online education resource allocation for the coordinated development of regional economy has relatively high statistical ability in terms of statistical efficiency and student satisfaction.

Despite the importance of intrahousehold decision-making, economists have not always done well to understand what happens within the boundaries of the home. This is because mainstream economists have focused on the market economy, often ignoring the unpaid work done within the home. Economic models of intrahousehold resource allocation have extended beyond initial challenges to the unitary model of the household, demonstrating that many households do not obtain cooperative or efficient outcomes, at least in strictly economic terms. Models of household decision-making and resource allocation are silent on the processes. They consider how proxies for bargaining power affect the outcomes, but were not designed to tell us about the processes of how these decisions and allocations were reached. The advances in the intrahousehold allocation and decision-making literature have frequently occurred due to the relationships between the theory and the empirical work. Feminist theory and realities on the ground challenged early empirical work that treated households as though they were individuals.

The results of development of the mathematical model for time-frequency resource allocation in the downlink of the LTE technology using the Resource Allocation Type 1 are given. The proposed model is aimed at provision of the guaranteed quality of service for wireless network users by allocating the required downlink bandwidth to user equipment. The analysis of the proposed model for time-frequency resource allocation in the LTE technology from the viewpoint of providing the required bandwidths for user equipment in the downlink was conducted.

The results of development of a mathematical model for time-frequency resource allocation in the LTE technology downlink using Resource Allocation Type 1 are given. Using the proposed model allows taking into account the technological features of the LTE downlink (splitting the resource blocks into subsets, allocation of RB of only one subset to user equipment), as well as territorial remoteness of user equipment (type of modulation system and coding). During the analysis it was found that use of the proposed model is aimed at providing each user equipment with quality of service according to the bandwidth in the downlink with the possibility of access to additional (unguaranteed) bandwidth. The analysis of the proposed model for time-frequency resource allocation in the LTE technology from the viewpoint of providing the required bandwidths for user equipment in the downlink was conducted.

Manufacturing Grid (MG), a complex system, is a kind of collaboration network with scale-free characteristic structure, and system robustness is a critical statistic indicator for its sustainable operation. Resource allocation, a component in MG, plays an important function to satisfy customers’ various requirements, and it is also a key step in MG evolutionary process with the expansion of tasks and factories (resource providers). Firstly, a particle swarm optimization-based resource allocation model is proposed with both task economics and system robustness as the optimization objectives. Then, the collaboration network and evolution models for MG are constructed by integrating the resource allocation model into its evolutionary process. Later on, the influence and impact of the allocation model to MG network structure are analyzed with the evolution of tasks, resources, and factories. Comparison with production–collaboration model for MG (MPC) and single-objective optimization model (SOP-MPC) reveal that the proposed recourse allocation model has much better agreement with our objective. In addition, the MG collaboration network can keep its scale-free structure during the evolution. Finally, a case study is used to illustrate with machine tool products and resources as the empirical analysis data to validate the results and comparisons.

The performance of computational grids mainly depends on the resource allocation service of a resource management system. Efficient resource allocation is essential for better resource utilisation which could be for both providers and grid users. Resource allocation includes the scheduling of gridlets to the available resources. However, the biggest challenges for grid users are to select the best resources from the available grid resources and to allocate these resources for scheduling of the gridlets. To address these issues and enhance the resource utilisation process, we propose a semantic and proximity-aware fuzzy rule-based model that improves the resource utilisation in a grid environment. The model uses fuzzy techniques with four parameters such as semantic similarity, proximity, number of total machines and number of total processors of each machine. The experimental results provide promising results. Overall, the proposed semantic and proximity-aware fuzzy rule-based decentralised resource discovery model improves the resource utilisation by 23% as compared to non-fuzzy first come first serve (FCFS) technique in a computational grid environment.

<p class="MsoNormal" style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;"><span style="color: black; font-size: 9pt; mso-bidi-font-weight: bold;"><span style="font-family: Times New Roman;">Selecting the appropriate resources for the particular task is one of major challenging work in the computational grid. The major objective of resource allocation in grid is effective scheduling of tasks and in turn the reduction in execution time. Hence the resource allocation must consider some specific characteristics of the resources, tasks and then decide the metrics to be used accordingly. Ant algorithm, which is one of the heuristic algorithm suits well for the allocation and scheduling in grid environment. In this paper, a Grid Resource Allocation based on Ant Algorithm (GRAAA) is proposed. The simulation result shows that the proposed algorithm is capable of producing high quality allocation of grid resources to tasks. </span></span>

The purpose of Part 2 is to develop a model for resource allocation of state prevention funds to be distributed to its substate jurisdictions based on the relative need for prevention services measured in terms of composite risk-factor index (COMRISK) scores computed for each county. The risk factors are extracted from an extensive review of risk and protective factors addressed in the prevention literature. Based on twenty-two risk and protective factors identified, we were able to explain 71.3 percent of the total variation in student drug using behavior observed at the individual level. By aggregating individual COMRISK scores to the county level, we were able to determine aggregated COMRISK index scores at the county level. By determining the proportion of each county's share of the total statewide COMRISK and by weighting the latter proportion by the population size of each county, we have devised Prevention Needs Index (PNI) score based on the risks for each county. Finally, the county's share of PNI score as a proportion of the total statewide PNI score is computed. The latter proportion is then multiplied by the total amount of prevention resources available at the state. In this way, we were able to develop an alternative resource allocation model solely based on risk and protective factors for determining prevention needs of each county, independent of composite index score of drug use (COMDRUG) presented in Part 1. A comparison of three models for resource allocation has shown a significant amount of similarity of the total funds computed for each county. Accordingly, no preference is made among the resource allocation models suggested, although it is emphasized that the final decision concerning the level of funding must be made on the selection of the resource allocation algorithms rather than the suggested amount of funding computed for each county.

A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.