Joint resource allocation and cross-regional scheduling for major epidemic outbreaks

With the explosive growth of Mobile Internet applications, the massive amount of data traffic produced by the data applications, such as mobile multimedia services, will bring big challenge to the next generation (5G) mobile communication networks. Recently, hierarchical cloud service networks have been proposed to address this big challenge. By introducing distributed hierarchical cloud network architecture, mobile users can enjoy efficient high-quality and low-delay local services. However, with the limited resources in the hierarchical cloud networks, it is essential to develop efficient resource scheduling and content delivery mechanisms. In this paper, we investigate the resource allocation for hierarchical cloud networks. We propose a Joint Resource Allocation (JRA) scheme, aiming at improving resource utilization, as well as reducing the content access delay to improve user Quality of Experience (QoE). Our fundamental idea is to jointly allocate spectrum and energy resources in wireless networks, caching and processing resources in Cloudlets, to maximize overall system resource utilization. We conduct simulation experiments to validate the effectiveness of our proposed JRA scheme. Numerical results show that the performance of JRA in terms of content access delay and resource utilization can be significantly improved compared with traditional schemes.

The requirements for large-scale computing, storage, and network capabilities by the business and scientific communities have led to the development of the grid/cloud network. Grid network users can access a shared set of resources for scientific computing tasks. Cloud tenants are offered IT infrastructure through infrastructure as a service. An efficient resource scheduling mechanism across the network, as a result, will improve the resource utilization and also reduce the capital cost of scheduling in the cloud significantly. In this paper, we focus on the joint resource (processor, storage, and network) allocation in the grid/cloud environment. The multilayer optical network architecture is introduced to guarantee the reservation of the network bandwidth resource. We investigate the bandwidth guaranteed joint resource scheduling from the cloud provider's point of view, which is completing the resource scheduling with minimal capital expenditure. The mixed integer linear programming (MILP) formulations and heuristics (best-fit and tabu search) are developed to solve our problems. The results show that both MILP and heuristics work well to solve the problem, and the heuristics are much more time-efficient. In addition, the tabu search method achieves the optimal resource allocation and also reaches a lower blocking rate compared to the best-fit method.

The article presents a methodological approach to assessing and regulating the imbalance of demand and supply of professional personnel in regional labor markets. The author's methodology for assessing imbalances in professional personnel consists of four stages. In the first stage, the degree of regional differentiation is assessed according to the criteria of sectoral structural changes in employment and production based on indicators of regional asymmetry of structural changes, the quadratic coefficient of absolute structural changes, the integral coefficients of structural differences by K. Gatev, A. Salai, the V.M. Ryabtseva. In addition, the paper determines the dominant type of imbalance in the supply and demand of professional personnel (in the context of higher vocational training, secondary vocational training, nongovernmental education) in regional labor markets, it also classifies the subjects of the Russian Federation according to this criterion and identifies the differences in regional situations. The second stage is to assess the dynamic characteristics of the imbalance of professional personnel based on indicators of demand and supply volatility. The volatility of the types of imbalances in demand and supply of professional personnel is determined (in the context of higher vocational education, secondary vocational education, nongovernmental education), and an assessment and differentiation of regional situations is made based on the variability of imbalances. The third stage consists of assessing the determinants of the imbalance of professional personnel in regional labor markets. Based on groups of indicators that induce spatial differences in situations of imbalance in demand and supply of professional personnel in the constituent entities of the Russian Federation, these differences are identified, grouped by the dominant type of imbalance, according to the most significant factors (based on the tools of regression models). The contribution of each factor is determined. The fourth stage is to develop monitoring and tools for regulating regional labor markets in accordance with the type, dynamic characteristics and determinants of the imbalance of professional personnel.

GPARS: Graph predictive algorithm for efficient resource scheduling in heterogeneous GPU clusters

Enabling device-to-device (D2D) communication in cellular network is shown to be a promising candidate in the future fifth generation (5G) wireless network. Specifically, the hybrid architecture leads to flexible and efficient resource allocation for cellular users and D2D users, thereby enhancing utilization of spectrum resources. However, if not properly handled, mutual interference will be significantly raised with the proliferation of devices, which bottlenecks the performance of the hybrid system. In this paper, joint mode selection and resource allocation is considered for a sparse code multiple access (SCMA) enabled uplink cellular network. Targeting at maximizing system sum rate, we formulate a joint optimization problem, which, however, is shown to be a mixed-integer nonlinear programming (MINLP) problem. To solve the problem, we propose a graph-based joint mode selection and resource allocation (GMR) algorithm. GMR is an interference-aware algorithm. In particular, when GMR is applied, D2D users reuse SCMA resources only when the interference level to cellular users is below a predefined threshold. Otherwise, dedicated resources are allocated to D2D users to avoid the overwhelming mutual interference. Simulation results have confirmed the efficacy of GMR in enhancing spectrum efficiency and system sum rate in the hybrid system.

Many of the available researches have been concentrating on the profits maximization of cloud providers, while the actual necessities of cloud users have been ignored. Here, a Clustering based User preference (CUP) resource scheduling technique is proposed that can be used by a cloud provider for meeting the resource needs of a user in a better way. The novel CUP scheduling mechanism consists of four stages: resource matching, resource selection, clustering and resource scheduling. The user must be given a consideration if the same user puts forward multiple requirements. Updating of user demands and preferences are done at the resource scheduling stage. This method chooses the “best” VM which improves resourcefulness of CC and thereby minimizes the average response time of tasks. The results show that the CUP algorithm proposed efficiently satisfies the diverse requirements of the users and assists in the better resource utilization.

A hierarchical adaptive federated reinforcement learning for efficient resource allocation and task scheduling in hierarchical IoT network

As one of the most important components in IoT system, machine type communication (MTC) will definitely play an increasingly important role in the academia and industry in the near future for 5G application. Currently, most of the access control work concentrates on avoiding preamble collision without considering SINR requirement. In order to further improve the number of effective access devices (EADs), we propose a joint access control and resource allocation. Due to the fact that the main characteristics of MTC, distinct from the conventional cellular network, are massive access and small amount of data, we formulate an indicator function optimization problem, rather than a continuous rate function considered in cellular network, to maximize the number of EADs. This optimization problem is NP-hard and highly complex to solve, so we decouple the problem into two sub-problems, then we utilize convex optimization tool (CVX) and Hungarian algorithm to perform the joint access control and resource allocation under multi-cell scenario. Moreover, we derive the optimal closed-form power allocation under 2-cell scenario based on Lagrange KKT method. Simulation results demonstrate that the proposed joint access control and resource allocation scheme can achieve a significant improvement performance on EADs with fewer iterations, compared to some classic access methods.

Cognitive vehicular ad-hoc networks (CVANETs) are expected to improve spectrum utilization efficiently and offer both infotainment and safety services for vehicles. In this paper, the joint route selection and resource allocation problem is considered for CVANETs. Taking into account the lifetime of transmission links, we first propose a candidate link selection method which selects the transmission links satisfying the link lifetime constraint. Then stressing the importance of transmission delay, we formulate the joint route selection and resource allocation problem as an end-to-end transmission delay minimization problem. As the formulated optimization problem is a complicated integer nonlinear problem, which cannot be solved conveniently, we equivalently transform the original problem into two subproblems, i.e., resource allocation subproblem for candidate links and route selection subproblem. Solving the two optimization subproblems by applying the K shortest path algorithm and the Dijkstra algorithm, respectively, we can obtain the joint route selection and resource allocation strategy. Simulation results demonstrate the effectiveness of the proposed algorithm.

Optimal resource allocation in cloud systems is NP-hard due to the involvement of several conflicting objectives and unpredictable cloud traffic. To improve user satisfaction and resource utilization while minimizing end-user cost, the joint allocation of cloud resources is inevitable. In this work, we model end-user cost in cloud as the optimization objective using bandwidth and compute allocation as the decision variables. To solve the joint Virtual Machine Placement (VMP) problem we propose a single point, greedy, software-defined network (SDN)-based solution that minimizes end-user cost by making certain changes to the fat-tree datacenter architecture. Mathematically, we show that the overall objective function is convex hence solving it using a weighted-sum greedy approach will induce solutions that are Pareto-optimal. Experimental evaluations confirm up to 15% reduction in the response time and up to 14% increase in the efficiency of resources. To analyse the risks involved in deploying delay-sensitive applications over cloud and to show the effects of resource allocation approaches, we perform a risk analysis of delay-sensitivity in cloud using real-time CVD detection. The results confirm the reduced response time due to the proposed approach while maintaining the efficiency of detection.

The orthogonal frequency division multiple access (OFDMA) scheme has been selected as a potential candidate for many emerging broadband wireless access standards. In this paper, a new joint scheduling and resource allocation scheme is proposed for the OFDMA systems using contiguous subcarrier permutation. The proposed resource allocation algorithm provides contiguous sets of frequency-time resource units following a rectangular shape yielding a reduction on the required burst signalling. The joint scheduling and resource allocation process is divided into two phases: the QoS requirements fulfilment and the input buffers emptying status. For each phase, a specific prioritization function is defined in order to obtain a trade-off between the fairness and the spectral efficiency maximization. The new prioritization scheme provides a reduction of 50% of the 99th percentile from the delivered packets delay in case of non real-time services, and 30% of the packet loss rate in case of real-time services compared to the proportional fair scheduling function. On the other hand, it is also demonstrated that using the rectangular data packing algorithm, the number of required bursts per frame can be reduced up to a few tenths without compromising the performance.

The business paradigms are changing amidst changing business environment. There are newer technologies at disposal, rising customer awareness and their expectations from the need to attain efficiency and effectiveness in business processes for survival and competitive advantage. This paper provides insights on the significance of business decision making and present state of organizations that are striving to achieve optimal utilization of limited business resources. The paper highlights the nature of linear programming model and its importance in effective business decision making. The business impact of model is illustrated in the case using the linear programming model and transportation method through excel solver in computer manufacturing firms to help in deciding optimum quantity to produce within limited resources and how the computers manufactured can be distributed to market places at minimal cost. The paper elicits the effectiveness of the linear programming model to realize good decision making in business by meeting the business objectives through optimal utilization of resources. It concludes that model driven decision support system enhances the productivity and profitability of the firm in a constrained environment and is a highly effective model for solving business problems.

Abstract: The population of South Jakarta reached 2.235.606 people as of 2023 (BPS, 2024), with a percentage of occupying owned residential buildings of 51,36% (BPS, 2022). Estimates indicate that only approximately 1,148,207 people currently reside in private assets. Rental prices need to be considered based on the strategic location to minimize the risk of rental house business owners suffering losses. The imbalance in the supply and demand of the residential market will cause the availability of land in productive areas to be increasingly limited. The existence of an imbalance in market supply and demand can be an opportunity for prospective property entrepreneurs in the field of rental houses. The analysis was carried out by calculating income predictions using multiple linear regression methods and spatial analysis between rental houses and the accessibility of public facilities in South Jakarta City. The results obtained indicate that the accessibility of public facilities in Setia Budi Sub-district provides an opportunity for high income. Keywords: Boarding House, Income Rediction, Multiple Linear Regression, Spatial Analysis

The purpose. The purpose of the article is to substantiate the need to improve the modern state employment policy under the intensification of external migration of the population of Ukraine. Effective institutional-administrative, organizational-economic, and information tools of the state employment policy should be used to create and improve the quality of existing jobs, intensify employment in rural areas, eliminate shadow employment and wages, equalize imbalances in supply and demand in the labor market. Design/Methodology/Approach. To study the need to develop modern state employment policy in Ukraine, the authors used the following research methods - theoretical and logical explanation, statistical analysis, systematization and explanation, semantic analysis. The study was conducted within the framework of institutional economic theory, migration, and state regulation of the economy. Conclusions. The studies have shown that the containment of external migration processes by increasing the number and improving the quality of jobs, in connection with which the task of developing the state employment policy against the background of high migration activity of the population of Ukraine is relevant. It is substantiated that the main challenges of the modern sphere of employment in Ukraine lead to an increase in external migration, namely: formation of labor shortage in the national labor market, intensification of destructive changes in employment; low pay, especially for budget sphere workers, the spread of shadow employment schemes, which leads to a decrease in social protection of employees; strengthening of professional and qualification imbalance in demand and proposals in the labor market due to the mismatch in training the specialists for the needs of the economy. Other challenges are the unformed labor market in rural areas, preservation trends of open and hidden unemployment among the rural population, reducing the demand for labor in rural areas; lack of appropriate conditions for the development of inclusive labor market, the insufficient realization of opportunities and potential of the state employment services in ensuring the innovative development of employment. The objective necessity of realization of effective institutional-administrative, organizational-economic, and information tools of the state employment policy is proved, which largely depends on the natural containment of migration processes in Ukraine. The implementation of the presented tools is focused on economic stabilization of the situation in the country and regions by stimulating the de-shadowing of business and income, increase the welfare of the population, increase of opportunities for young people in employment, and the growth of aspirations of migrant workers to remigration and realization of own business goals on the territory of Ukraine. Originality/value. The value of the research lies in the development of the mechanisms to ensure the competitiveness of national and regional labor markets as a tool for regulating external labor migration, as well as for improvement of the state employment policy, especially the development of innovative and creative types of work.

As a new networking paradigm adapted to the shift of Internet usage, named data networking (NDN) shows a promising prospect. The enhanced NDN data plane calls for a flexible management of the incoming requests. In this paper, we tackle the problem of designing a forwarding strategy of content requests for NDN from a single-node point of view. Based on the Semi-Markov Decision Process (SMDP) theory, we develop a Joint Adaptive Forwarding and Resource Allocation (JAFRA) strategy to route incoming requests to the best interface. Load balance among interfaces is also taking into account in our SMDP model to strike a trade-off between network profit and user satisfaction. We provide performance comparisons based on various scenarios, and the simulation results demonstrate that the JAFRA strategy resulted from the proposed analytical model leads to a significant enhancement in quality of service (QoS) provisioning for the requests and better resources utilization.