Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Muraña, Jonathan; Nesmachnow, Sergio

doi:10.1177/00375497211020083

Cited by 9 publications

(2 citation statements)

References 42 publications

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“…Indeed, there has been an ongoing series of works to improve the cloud platform and architecture in order to keep up with rising data volumes and application requirements for cloud services. In this regard, Muran ˜a and Nesmachnow 26 proposed a multicriteria planning heuristics for efficient cloud application response time.…”

Section: Work Related To Extending Cloudsimmentioning

confidence: 99%

Extending CloudSim to simulate sensor networks

et al. 2022

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With the enormous growth of sensing devices tending to the use of Internet of everything, data aggregated by these devices are the biggest data streams generated in the history of IT. Thus, aggregating such data in the cloud for leveraging powerful cloud computing processing and storage is essential, and it eventually led to the emergence of Sensor-Cloud concept. This has allowed aggregation of the sensors’ data to the cloud for further processing, storage, and visualization. Furthermore, virtualization makes the sensors accessible to other end-user applications that require such data. All of these features are expected to be provided by the Sensor-Cloud invisibly, without the end-user application developer being aware of the sensor location or hardware specifications. For these reasons, a simulation platform where Sensor-Cloud infrastructure agents and components may be modeled, scheduling policies defined, and execution time assessed is essential to assure performance and quality of service. The aim of this study is to develop such a platform by enhancing CloudSim, the most well-known and powerful simulation tool for cloud computing. A user-friendly Java Script Swing-based graphical user interface (GUI) has been carefully designed and implemented for this purpose. The user can then utilize the specific interface to define the Cloudlet type as well as the scheduling on a single virtual machine. Finally, a simulation study is carried out on the platform to demonstrate its efficiency and accuracy. We were able to fully model the needed scenarios and acquire real-time results, displaying good accuracy in terms of application response time with a mean absolute percentage error (MAPE) of 3.37%, demonstrating the increased proposed platform’s proper operation.

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Section: Work Related To Extending Cloudsimmentioning

confidence: 99%

Extending CloudSim to simulate sensor networks

et al. 2022

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“…Although supercomputers have been considered as the de facto computer architectures for the execution of HPC applications, their operation and maintenance cost increase at higher rates 4 when compared with other forms of cluster architectures. These increasing rates have led the scientific community into the development of emerging and low-cost non-HPC clusters.…”

Section: Introductionmentioning

confidence: 99%

Estimating the overhead and coupling of scientific computing clusters

Vivas

Castro

2021

SIMULATION

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Since simulation became the third pillar of scientific research, several forms of computers have become available to drive computer aided simulations, and nowadays, clusters are the most popular type of computers supporting these tasks. For instance, cluster settings, such as the so-called supercomputers, cluster of workstations (COW), cluster of desktops (COD), and cluster of virtual machines (COV) have been considered in literature to embrace a variety of scientific applications. However, those scientific applications categorized as high-performance computing (HPC) are conceptually restricted to be addressed only by supercomputers. In this aspect, we introduce the notions of cluster overhead and cluster coupling to assess the capacity of non-HPC systems to handle HPC applications. We also compare the cluster overhead with an existing measure of overhead in computing systems, the total parallel overhead, to explain the correctness of our methodology. The evaluation of capacity considers the seven dwarfs of scientific computing, which are well-known, scientific computing building blocks considered in the development of HPC applications. The evaluation of these building blocks provides insights regarding the strengths and weaknesses of non-HPC systems to deal with future HPC applications developed with one or a combination of these algorithmic building blocks.

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Quantitative Characterization of Scientific Computing Clusters

Vivas

Castro

2022

Communications in Computer and Information Science

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Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters

Cited by 9 publications

References 42 publications

Extending CloudSim to simulate sensor networks

Extending CloudSim to simulate sensor networks

Estimating the overhead and coupling of scientific computing clusters

Quantitative Characterization of Scientific Computing Clusters

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