Emerging diseases, novel strains of reemerging diseases, and bioterrorism threats necessitate the development of computational models that can supply health care providers with tools to facilitate analysis and simulation of the progression of infectious diseases in a population. Most computational models assume homogeneous mixing within populations. However, a more realistic approach to the simulation of infectious disease outbreaks includes the stratification of populations in which the interactions between individuals are affinity-based. To examine the effects of heterogeneous populations on the outbreak dynamics, we developed a hybrid model that includes clustered individuals which represent differentiated populations. This facilitates the study of the effects of distinct behavioral properties on the dynamics of an infectious disease epidemic. Our results indicate that non-uniform interactions and affinity-driven behavior can drastically change the outbreak dynamics in the population.

Cloud spending has risen on a year-to-year basis, with the pandemic acting as the primary catalyst for its recent growth; however, “cloud waste,” referring to cloud resources that are not used to their full capacity, also follows this upward trend and causes the loss of an increasingly large amount of money. Unfortunately, present-day cloud research lacks data-driven studies that analyze why cloud users are wasting resources, or suggestions to users on how to lessen such waste. In order to prevent this over-expenditure, it is vital to choose the best-suited options when it comes to virtual machines (VM), especially for small to mid-sized businesses with limited funds and a lack of expertise. In this paper, we first analyzed the 235 GB Azure user dataset from the users’ perspective. We then implemented machine learning to determine our pricing model and the VM costs. With these statistics, we then delineated our methodology to calculate the wasted cost of each VM, and using this data, we propose an algorithm that can identify potential candidates with wasteful VMs and assist users in reducing costs. By applying our algorithm to approximately 2.7 million VMs, we demonstrate that it has the ability to help 66,721 VMs created by 1,520 users lower their monthly costs by $14.9 million. We conclude that businesses, while still reaping the benefits of cloud services, can do so at a much lighter cost and save on their VMs.

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