SaTScan on a Cloud: On-Demand Large Scale Spatial Analysis of Epidemics

et al. 2021

Animals

The spread of disease in livestock is an important research topic of veterinary epidemiology because it provides warnings or advice to organizations responsible for the protection of animal health in particular and public health in general. Disease transmission simulation programs are often deployed with different species, disease types, or epidemiological models, and each research team manages its own set of parameters relevant to their target diseases and concerns, resulting in limited cooperation and reuse of research results. Furthermore, these simulation and decision support tools often require a large amount of computational power, especially for models involving tens of thousands of herds with millions of individuals spread over a large geographical area such as a region or a country. It is a matter of fact that epidemic simulation programs are often heterogeneous, but they often share some common workflows including processing of input data and execution of simulation, as well as storage, analysis, and visualization of results. In this article, we propose a novel architectural framework for simultaneously deploying any epidemic simulation program both on premises and on the cloud to improve performance and scalability. We also conduct some experiments to evaluate the proposed architectural framework on some aspects when applying it to simulate the spread of African swine fever in Vietnam.

Section: Related Workmentioning

confidence: 99%

Towards a Framework for High-Performance Simulation of Livestock Disease Outbreak: A Case Study of Spread of African Swine Fever in Vietnam

Pham¹,

et al. 2021

Animals

“…This framework is restricted to a specific epidemic model, namely, a modified version of the Susceptible, Exposed, Infectious, and Recovered (SEIR) model and does not support elasticity. Price et al presented an approach for executing a compute‐intensive application for epidemic analysis on cloud resources. The approach uses the Nimbus cloud to obtain resources on demand.…”

Section: Related Workmentioning

confidence: 99%

“…To summarize, some of the related work for executing epidemic simulations does not exploit clouds and their associated parallelization, cost, and agility benefits . Other related work exploits clouds but lacks support for elasticity, multi‐cloud deployment, fault tolerance, or required facilities beyond bag‐of‐tasks execution . To the best of our knowledge, our work is the first to propose a complete practical solution for building and executing simulation applications, structured as bags of tasks, across multiple clouds with support for elasticity and fault‐tolerance.…”

Section: Related Workmentioning

confidence: 99%

A service‐based framework for building and executing epidemic simulation applications in the cloud

Concurrency and Computation

Pham

Morin

et al. 2019

The cloud has emerged as an attractive platform for resource-intensive scientific applications, such as epidemic simulators. However, building and executing such applications in the cloud presents multiple challenges, including exploiting elasticity, handling failures, and simplifying multi-cloud deployment. To address these challenges, this paper proposes a novel service-based framework called DiFFuSE that enables simulation applications with a bag-of-tasks structure to fully exploit cloud platforms. This paper describes how the framework is applied to restructure two legacy applications, simulating the spread of bovine viral diarrhea virus and Mycobacterium avium subspecies paratuberculosis, into elastic cloud-native applications. Experimental results show that the framework enhances application performance and allows exploring different cost-performance trade-offs while supporting automatic failure handling and elastic resource acquisition from multiple clouds. KEYWORDScloud computing, epidemic simulation, high performance computing, simulation models INTRODUCTIONAs global transport networks continue to expand, the risk of infectious disease transmission grows, making research in pathogen spread on humans and animals increasingly important. An essential tool for studying pathogen spread is computer simulation based on mechanistic modeling. However, running epidemic simulations at large scale is time consuming for multiple reasons. First, the simulation models can capture fine-grained dynamics, considering different population scales (individual, population, and meta-population) and accounting for multiple layouts (eg, combining population and infection dynamics with farm management decisions). 1 Second, some processes can be data-driven and exploit large amounts of real-life observed data, mostly concerning the population dynamics of hosts. Third, understanding the underlying biological system requires performing and comparing many simulation scenarios, eg, through a model sensitivity analysis. Finally, producing reliable results demands large numbers of simulation runs when models are stochastic.Speeding up the execution of large-scale simulations requires parallelism. Parallelism can be implemented at the single-computer level using multiple cores and processors as well as the distributed level using multiple computers interconnected through a network. There are currently three main options for obtaining the necessary computing resources for running simulations in parallel. The first option is using dedicated high-end multicore systems or clusters. The limitation is that such infrastructures are typically shared among many local users, resulting in long waiting times. At the same time, extending such infrastructures to increase user satisfaction is time consuming and costly. The second option is grid computing, which allows sharing computers from multiple clusters in multiple sites. The main limitation of grids is the heterogeneity of the software stacks in each cluster, which makes application deployment di...

DiFFuSE, a Distributed Framework for Cloud-Based Epidemic Simulations: A Case Study in Modelling the Spread of Bovine Viral Diarrhea Virus

Pham

2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

Morin

et al. 2017

Performing large-scale epidemic simulations is time-consuming and requires massive amounts of computing resources. An attractive option for providing such resources is cloud computing. However, fully exploiting cloud resources requires converting existing simulation applications into elastic, cloudenabled applications. To this end, this paper proposes a novel service-based framework for building and executing epidemic simulations in the cloud and describes how the framework was applied to build a cloudenabled simulation of the spread of the bovine viral diarrhea virus. The report provides experimental evidence that the framework increases performance and allows exploring cost-performance trade-offs while automatically handling failures and supporting elastic allocation of resources from multiple clouds.