Algorithm for CPU resource allocator case study and compering between ordinary and ML Algorithms

The importance of Power Transmission System PTS fault detection and classification is increasing day by day as because consumption of electricity is increasing. Short circuit fault in Power Transmission Line Network PTLN can cause severe damage to the power transmission system as well as economic loss. Power Transmission System requires new methods to detect and classify fault behaviour to prevent it from heavy damage. Machine Learning ML algorithms can be very effective to classify and detect various types of faults within the PTLN. There are variety of ML algorithms to recognise and classify the faults but as complexity of PTS is increasing day by day, reliability of these algorithms is decreasing. This study uses various types of ML algorithms to generate predictive models to evaluate what kind of algorithm is more appropriate to recognise and classify faults within the PTLN. Faults investigated in this research work include (L-L) double line fault, (L-L-L) three phase fault, (L-G) line to ground fault, (L-L-G) double line to ground fault, and (L-L-L-G) three phase fault with the involvement of the ground. The data was evaluated using six (06) ML algorithms that are Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (Knn), Random Forest, XGBoost (XGB) and Naive Bayes (NB) for recognise of fault and classification within the PTLN. The performance of ML algorithms obtained by comparing the results and determine which algorithm is fast and more accurate. These results can be used to create more effective ML algorithms for PTS. The results indicate that the application of ML algorithms for PTS task could improve the PTLN yield and save time for technical teams.

Resource allocation in pastoral dairy production systems: Evaluating exact and genetic algorithms approaches

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

PurposeResource allocation is challenged by dynamic environments where changes are frequent. The purpose of this paper is to identify resource allocation challenges and practices in service units that perform both project and non-project activities in dynamic environments. Its goal is to show that top-down mechanisms of project resource allocation need to be replaced by or supplemented with mechanisms that are more flexible.Design/methodology/approachA qualitative comparative case study was conducted in two service units of two project-based firms. The main source of data consisted of semi-structured interviews with 17 service managers and staff members.FindingsThis study shows that resource allocation is not necessarily a top-down process at all, and the practices are context-dependent. Two more flexible approaches are revealed – hybrid resource allocation and bottom-up resource allocation – as examples of managing resource allocation in service units that engage in projects under uncertain conditions. The results of the analysis highlight prioritisation and adapting to change and delay as the main issues that managers face in allocating resources to different types of projects and service activities in dynamic environments.Research limitations/implicationsThe two target companies chosen for the qualitative research design limit the analysis to project-based firms in a business-to-business context. Further, the viewpoint of the service unit is central to the study. Studying project resource allocation in different organisational contexts and uncovering the perspectives of product development and delivery units would offer promising directions for future research.Practical implicationsThe study reveals that in dynamic project settings such as service organisations, top-down mechanisms of resource allocation need to be accompanied by other, more flexible approaches to ensure the sufficient resourcing of projects and related services in dynamic environments. Companies need to establish practices for resource allocation changes that are caused by re-prioritising tasks and accommodating changes and delays in their project and service activities.Originality/valueCompared to a top-down perspective taken in previous research, the study proposes a more flexible approach for resource allocation in constantly changing environments with different project and service activities. Previous studies have focussed on resource competition between projects, placing project managers in the central role for resource allocation. By contrast, this study discusses hybrid and bottom-up resource allocation, both of which involve broader personnel engagement in resource allocation tasks, drawing on the experience of all employees.

We develop an optimal control model for optimally allocating effort between detection and correction processes, during testing phase of a software development life cycle (SDLC). The objective is to propose a resource allocation plan to minimise the total testing cost with the reliability criteria using flexible software reliability growth model considering testing effort under dynamic environment. Furthermore, we have incorporated the learning curve effect in correction cost function due to its importance in cost estimation, and resource allocation decisions. A technique based on optimal control theory is proposed for the software managers to optimise the allocation of the limited resources, while genetic algorithm is used to find the approximate value of detection and correction effort. An illustrative example is presented. The optimal release policy under reliability criteria is also discussed.

Random unitary beamforming is one of the schemes that can reduce the amount of feedback information in multiuser diversity techniques with multiple-antenna downlink transmission. In Multiple-Input Multiple-Output (MIMO) systems, throughput performance is greatly improved using AMC (Adaptive Modulation and Coding). Throughput performance is also improved by allocating power among streams appropriately. In random unitary beamforming, the transmitter has only partial channel state information (CSI) of each receiver. Thus, it is difficult for random unitary beamforming to use conventional power allocation methods that assumes that all receivers has full CSI. In this paper, we propose a new scheduling algorithm with power allocation for downlink random unitary beamforming that improves throughput performance without full CSI. We provide numerical results of the proposed scheduling algorithm and compare them to those of the conventional random unitary beamforming scheduling algorithm. We show that random unitary beamforming achieves the best system throughput performance with two transmit antennas. We also show that the proposed algorithm attains higher throughput with the small increase of feedback than the random unitary beamforming scheduling algorithm.

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service (QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm (GA), ant colony optimization with genetic algorithm (ACO-GA), and quantum genetic algorithm (QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACO-GA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.

Mobile Edge Computing (MEC) is a promising computing paradigm, which can provide mobile devices with powerful computing capability and low delay by offloading resource-intensive tasks to the MEC Server. However, due to the varying wireless channel conditions and different task delay requirements, the resource allocation decision made by MEC Server cannot always achieve the lowest system energy consumption. In this paper, we consider resource allocation problem in a multi-user MEC system. The objective is to achieve minimum system energy consumption under delay constraint by suitably allocating communication and computing resources. We solve the communication resources (subcarrier) allocation problem through regression algorithm, make use of three regression algorithms to predict subcarrier allocation. And then calculate the computing resources (CPU frequency) allocation and system energy consumption. Simulation results show that machine learning can achieve near-optimal system energy consumption, where the system energy consumption of the ANN(Artificial Neural Network), Xgboost (Extreme Gradient Boosting), and RF(Random Forest) algorithms is 1.30%, 7.49%, and 23.29% higher than the optimal system energy consumption respectively. In the view of prediction loss, ANN and Xgboost perform better than RF.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

Spectrum access system (SAS) is a spectrum sharing framework proposed to share the spectrum between the incumbent users and the citizen broadband radio service devices, i.e. Priority access users and general authorized access (GAA) users. In this paper, we propose an interfering angle based method for the joint resource (channel and transmit power) allocation problem to the mobile and fixed GAA users. With mobile GAA users, the set of GAA users that can hear each other will change at different time instants making the resource allocation problem more challenging. The resource allocation of fixed and mobile GAA users is done considering coexistence with priority users, as well as coexistence between mobile and fixed GAA users. For the conflict-free resource allocation to fixed and mobile GAA users, we propose to use the maximum allowed transmit power for the beams of fixed GAA users that lie within the interference range of mobile GAA users. The simulation results show improved capacity from our proposed method while satisfying a predetermined interference constraint.

Considering the proliferation in the number of cloud users on an everyday basis, the task of resource provisioning in order to support all these users becomes a challenging problem. When resource allocation is non-optimal, users may face high costs or performance issues. So, in order to maximize profit and resource utilization while satisfying all client requests, it is essential for Cloud Service Providers to come up with ways to allocate resources adaptively for diverse conditions. This is a constrained optimization problem. Each client that submits a request to the cloud has its own best interests in mind. But each of these clients competes with other clients in the quest to obtain required quantum of resources. Hence, every client is a participant in this competition. So, a preliminary analysis of the problem reveals that it can be modelled as a game between clients. A game theoretic modelling of this problem provides us an ability to find an optimal resource allocation by employing game theoretic concepts. Resource allocation problems are NP-Hard, involving VM allocation and migration within and possibly, among data centres. Owing to the dynamic nature and number of requests, static methods fail to surmount race conditions. Using a Min-Max Game approach, we propose an algorithm that can overcome the problems mentioned. We propose to employ a utility maximization approach to solve the resource provisioning and allocation problem. We implement a new factor into the game called the utility factor which considers the time and budget constraints of every user. Resources are provisioned for tasks having the highest utility for the corresponding resource.

Cloud computing has emerged as a prevailing platform for internet service hosting. To best utilize Cloud resources for profit making, Cloud providers rely on intelligent resource allocation algorithms when provisioning the virtualized environments for tenant service hosting. Conventional resource allocation proposals mainly focus on efficient allocation of the computing and storage resources, with little effort on ensuring the network performance of tenant services. To address this issue, a number of recent efforts abstract tenant services in the form of virtual infrastructures for resource allocation. A virtual infrastructure specifies the tenant's demand of both the computing resources for hosting virtual servers, and the network bandwidth for inter-virtual server communications. With the problem of resource allocation for virtual infrastructures being NP-hard in general networks, heuristic algorithms have been proposed for this problem. In this paper, we propose a novel optimization technique, named sequential rounding, to tackle the resource allocation problem for virtual infrastructures. The proposed technique extends the rounding technique used for the traditional virtual network embedding problem, while minimizing mapping conflicts introduced by the virtual infrastructure embed- ding problem. Experiments show that our proposed algorithm outperforms existing algorithms regarding both the acceptance ratio and average embedding cost of virtual requests.

Antenna modeling based on meta-heuristic intelligent algorithms and neural networks

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.

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.