Modeling and Evaluating the Effects of Big Data Storage Resource Allocation in Global Scale Cloud Architectures

Barbierato, Enrico; Gribaudo, Marco; Iacono, Mauro

doi:10.4018/ijdwm.2016040101

Cited by 20 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Markovian agents may be used to describe the interactions of reactions that happen in a cell in a large number of independent instances, including the effects of inhibiting factors, as well as for describing the expression of cells in tissues and organs. The technique has been applied to study biological pathways [27], cancer cells [28], whole ecosystems, such as forestry landscape [142], and other complex real-world systems [7,21,47]. The Markovian property make them suitable to describe processes that are characterized by exponentially distributed interarrival time in their evolution.…”

Section: Markovian Agentsmentioning

confidence: 99%

Towards Human Cell Simulation

Spolaor

Gribaudo

Iacono

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.

show abstract

Section: Markovian Agentsmentioning

confidence: 99%

Towards Human Cell Simulation

Spolaor

Gribaudo

Iacono

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Markov chain, evolving according its specific transition matrix. A single agent class that in a given instant is located in v is fully described Markovian agent have been used for performance evaluation in different application fields, eg, behavior of massively distributed computer architectures, 29,30 the propagation of seismic waves, 31 and the dynamics of cancer cells. 32…”

Section: Markovian Agentsmentioning

confidence: 99%

An IoT‐based monitoring approach for cultural heritage sites: The Matera case

Gribaudo

Iacono

Levis

2017

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Protection and preservation of cultural heritage is an important responsibility for policy makers, public and private institutions, and the citizens themselves. Technologists can make an important contribution by designing monitoring systems for these sites and using the data to prevent incidents. Internet-of-things technology offers, for a sustainable price and with significant flexibility, a wide range of different possibilities, fitting different circumstances: from monitoring the environmental parameters of a room in a museum to sensing structural changes in a historical building and to protecting vulnerable artifacts. In this paper, we consider the case of monitoring an extended cultural heritage area: a UNESCO protected site, the center of Matera, an Italian town that will be a European Capital of Culture in 2019. This city is a unique historical settlement, as buildings are partially carved into the rock that constitutes the geological substrate of the area, a local practice used since the prehistoric age. The extent and density of these structures makes the physical protection of the site a big challenge when the expected large crowds of tourists arrive. The objective of the proposed system is to anticipate the threats in a timely manner so that appropriate actions are taken by the authorities thus avoiding damage to the cultural heritage sites.We propose a technique for modeling the performances of the Internet-of-things-based monitoring systems that support the planning of incident management in a protected site by exploiting multiple, sparse, heterogeneous, and partially controlled sensors to monitor the behavior of the crowd. The technique is based on the use of Markovian agent models to study the parameters and the dynamics of a scenario, to understand the needs of the monitoring system.

show abstract

“…With the emergence of cloud and distributed databases, data storage has transformed quite a bit. Therefore, when we say “Data Storage,” we mean “Distributed Data Storage.”…”

Section: Data Storagementioning

confidence: 99%

“…Therefore, when we say "Data Storage," we mean "Distributed Data Storage." 18 Distributed data storage is a computer network where data or information is stored (or replicated) on more than one node or computer. 19 It commonly refers to a distributed database where information is stored on multiple nodes or a computer network in which information is stored on multiple peer network nodes.…”

Section: Data Storagementioning

confidence: 99%

Sensor data management in the cloud: Data storage, data ingestion, and data retrieval

Sangat

Indrawan-Santiago

Taniar

2017

Concurrency and Computation

View full text Add to dashboard Cite

SummarySensors are widely used in the field of manufacturing, railways, aerospace, cars, medicines, robotics, and many other aspects of our everyday life. There is an increasing need to capture, store, and analyse the dynamic semi-structured data from those sensors. A similar growth of semi-structured data in the modern web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large-scale data processing frameworks for parallel analysis. NoSQL data store such as MongoDB and data processing framework such as Apache Hadoop has been studied for scientific data analysis. However, there has been no study on MongoDB with Apache Spark, and there is a limited understanding of how sensor data management can benefit from these technologies, specifically for ingesting high-velocity sensor data and parallel retrieval of high volume data. In this paper, we evaluate the performance of MongoDB sharding and no-sharding databases with Apache Spark, to identify the right software environment for sensor data management. KEYWORDSApache Spark, data ingestion, data retrieval, data storage, MongoDB, sensor data management INTRODUCTIONSensors are becoming an integral part of many modern applications. The sensors ability to sense the environment such as temperature and humidity can provide valuable information to applications for real time or batch analytics. In some applications, a single sensor with multiple sensing capabilities is used to capture the environment data. For example, many fitness trackers employ accelerator, humidity sensor, and heart rate monitor in a single device. For some applications that require complex analytics, a single sensor with multiple sensing capabilities may not be sufficient. For example, in train safety monitoring systems, multiple groups of sensors with different capabilities may need to be deployed in different carriages to capture the movement of the train as a whole. While the data produced by the sensors are beneficial, managing collected sensor data when a large number of sensors are deployed is difficult.The volume of data collected, the velocity of ingestion of data, and the variety of data format generated by different sensor types are the three main challenges in managing data captured by large sensors systems. As the price of small sensors becomes more affordable, some sensing applications replace many specialised built and high sampling sensors system with a network of off-the-shelf sensors with lower sampling capabilities. 1,2Comparable accuracy can be achieved by the lower sampling rate by deploying more sensors. The volume of data captured by the specialised built sensor and the network of sensors may be similar. However, network of sensors usually does not have large temporary data storage unlike in a specialised built sensor where they usually include better processor and large storage capacity. In a network of sensors, data needs to be transferred to the main processing centre more often but in smaller size packets. This almost instantane...

show abstract

Modeling and Evaluating the Effects of Big Data Storage Resource Allocation in Global Scale Cloud Architectures

Cited by 20 publications

References 33 publications

Towards Human Cell Simulation

Towards Human Cell Simulation

An IoT‐based monitoring approach for cultural heritage sites: The Matera case

Sensor data management in the cloud: Data storage, data ingestion, and data retrieval

Contact Info

Product

Resources

About