[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

Data Mining Practical Machine Learning Tools and Techniques

http://cloud.pubs.dbs.uni-leipzig.de/sites/cloud.pubs.dbs.uni-leipzig.de/files/Brandic2008CloudcomputingandemergingITplatformsVisionhypeand.pdf

Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.

https://beloglazov.info/papers/2010-cloudsim-spe.pdf

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

The edge computing paradigm has recently attracted research efforts coming from different application domains. However, evaluating an edge platform or algorithm is impeded by the lack of suitable benchmarks. We propose a methodology for characterizing edge workloads from different application domains. It is a first step towards defining workloads to be included in a future edge benchmarking suite. We evaluate the methodology on three use cases and find that defining a common and standard set of workloads is plausible.

Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads

It is our honor to serve as guest editors of this special issue of the Scientific Programming journal on Science-Driven Cloud Computing. We are pleased to present 7 high-quality contributions that focus on scientific cloud computing. Scientific computing involves a broad range of technologies, from highperformance computing (HPC) which is heavily focused on compute-intensive applications, high-throughput computing (HTC) which focuses on using many computing resources over long periods of time to accomplish its computational tasks, many-task computing (MTC) which aims to bridge the gap between HPC and HTC by focusing on using many resources over short periods of time, to data-intensive computing which is heavily focused on data distribution and harnessing data locality by scheduling of computations close to the data. This special issue on Science-Driven Cloud Computing provides the scientific community a dedicated forum for discussing new research, development, and deployment efforts in running these kinds of scientific computing workloads on Cloud Computing infrastructures. This special issue will focus on the use of cloud-based technologies to meet new compute intensive and data intensive scientific challenges that are not well served by the current supercomputers, grids or commercial clouds. What architectural changes to the current cloud frameworks (hardware, operating systems, networking and/or programming

/pdf/special-issue-on-science-driven-cloud-computing-20bkd3kb1t.pdf

Special issue on Science-Driven Cloud Computing

In a joint effort between computer scientists and theoretical chemists new tools have been developed for Grid applications leading to the efficient management of large computational campaigns in the field of quantum chemical calculations. For that purpose, the Vienna Grid Environment (VGE) software has been successfully extended allowing efficient job submission, status control and data retrieval. In addition, the services of the Compchem Virtual Organization of Enabling Grids for E-science (EGEE) Grid environment have been used. Extensive photodynamical simulation runs using the software packages COLUMBUS and NEWTON-X have been performed on the cis-trans isomerization of a model retinal system, aiming at a detailed picture of the primary processes of vision.

Nonadiabatic Ab initio surface-hopping dynamics calculation in a grid environment - first experiences

Service Mediation and Negotiation Bootstrapping as First Achievements Towards Self-adaptable Cloud Services

A critical challenge for data stream processing at the edge of the network is the consistency of the machine learning models in distributed worker nodes. Especially in the case of non-stationary streams, which exhibit high degree of data set shift, mismanagement of models poses the risks of suboptimal accuracy due to staleness and ignored data. In this work, we analyze model consistency challenges of distributed online machine learning scenario and present preliminary solutions for synchronizing model updates. Additionally, we propose metrics for measuring the level and speed of data set shift.

Ivona Brandic

Papers

Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads

Special issue on Science-Driven Cloud Computing

Nonadiabatic Ab initio surface-hopping dynamics calculation in a grid environment - first experiences

Service Mediation and Negotiation Bootstrapping as First Achievements Towards Self-adaptable Cloud Services

Consistency of the Fittest: Towards Dynamic Staleness Control for Edge Data Analytics