Resource Allocation

Resource allocation, as the main objective of managerial science, requires analyzing the long- and short-term effects of a policy, although this analysis would be more difficult in dynamic and volatile industries such as financial technology. Moreover, the integration of industries leads to more diverse product categories for a single company and makes it difficult for the implementation of decision making about resource allocation. In this regard, systemic PPM (PPM) models can be applied to balance long- and short-term generated values of the company by adopting policies about resource allocation for different products with respect to risk management concepts. The proposed systemic model should include interrelationships between different products, time relevant, and most importantly the potentials of dynamic analysis of product strategies, which is the main purpose of this research. The research strategy is to conduct a case study on the Iranian financial technology industry, by using systemic PPM modeling. In this research, a dynamic model was used in the payment industry, due to its competitive forces. Thus, system dynamics methodology was the research tool for analyzing data. Further, four cycles of risk management, resource allocation, innovation, and development were identified and then, analyzed in a dynamic approach to evaluating their efficiency for business development. Based on the results, the system dynamics methodology provided great outcomes for this problem. Finally, scenario analysis, focus, deep understandings of the decision-making process with respects to mental models, and stock and flow diagrams were among the most significant findings of this article.

This paper aims to show the asymmetric effect of oil shocks on Iran’s economy. It uses nonlinear time series models to investigate the asymmetric effect of oil shocks on resource allocation in Iran’s economy. The results show that adverse oil shocks have been more persistent during the last decades and severely negatively affect resource allocation in Iran’s economy. Different oil shocks have different implications for importing and exporting countries, and the rigidity of state fiscal systems in exporting countries causes adverse oil shocks to be more persistent. The oil economy’s response to positive and negative oil shocks depends on the structure of the economy. The government budget and trade balance have significant implications for the effects of oil shocks on oil-exporting economies. The government budget is highly dependent on oil revenues, so in the case of adverse oil shocks, the pass-through exchange rate will cause high inflation because of foreign exchange shortage and overshoot in the exchange rate. 

Cloud computing has emerged as a pivotal technology for managing and processing data, with a primary objective to offer efficient resource access while minimizing expenses. The allocation of resources is a critical aspect that can significantly reduce costs. This process necessitates the continuous assessment of the current status of each resource to design algorithms that optimize allocation and enhance overall system performance. Numerous algorithms have been developed to address the challenge of resource allocation, yet many fail to satisfy requirements of time efficiency and load balancing in cloud computing environments. This paper introduces a novel approach that classifies tasks according to their resource demands, employs a modified particle swarm optimization (PSO) algorithm, and incorporates load balancing strategies. The proposed method initially clusters tasks based on their resource requirements, subsequently utilizes the PSO algorithm to determine the best task-to-resource assignments, and finally implements a load balancing algorithm to reduce costs through balanced load distribution. The validity of the proposed method is tested and simulated using the Cloudsim tool. The simulation results indicate that the proposed method achieves lower average response time, waiting times, and energy consumption than existing baseline methods.

The term “cloud computing (CC)” has been extensively studied and utilized by major corporations since its inception. Within the realm of cloud computing, various research topics and perspectives have been explored, including resource management, cloud security, and energy efficiency. This paper explores the intersection of data quality and business process management within the context of cloud computing. Specifically, it examines how Quality of Service (QoS)-driven task scheduling algorithms in cloud environments can enhance data quality and optimize business processes. Cloud computing still faces the significant challenge of determining the most effective way to schedule tasks and manage available resources. We need effective scheduling strategies to manage these resources because of the scale and dynamic resource provisioning in modern data centers. The purpose of this work is to provide an overview of the various task scheduling methods that have been utilized in the cloud computing environment to date. An attempt has been made to categorize current methods, investigate issues, and identify important challenges present in this area. Our data reveals that 34% of researchers are focusing on makespan for QoS (Quality of Service) metrics, 17% on cost, 15% on load balancing, 10% on deadline, and 9% on energy usage. Other criteria for the Quality of Service (QoS) parameter contribute far less than the ones mentioned above. According to this study, scheduling algorithms commonly used by researchers include the genetic algorithm in bio-inspired systems and particle swarm optimization in swarm intelligence 80% of the time. According to the available literature, 70% of the studies have utilized CloudSim as their simulation tool of choice. Our findings suggest that current methodologies mainly employ genetic algorithms and particle swarm optimization, with CloudSim being a popular simulation tool. Ongoing work emphasizes refining scheduling strategies to enhance resource management in dynamic data center environments, providing crucial insights into future quality-of-service (QoS)-driven scheduling algorithms for cloud computing.