Estou pagando uma disciplina de Análise de Desempenho e acabei de começar umas revisões de literatura. Os artigos selecionados foram:
Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
Alexandru Iosup, Simon Ostermann, M. Nezih Yigitbasi, Radu Prodan, Thomas Fahringer, and Dick H.J. Epema
Abstract—Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining
expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds
serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their
owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel
production environments. However, the current commercial clouds have been built to support web and small database workloads,
which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may
introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the
performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing
workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to
achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing
services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the
performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific
computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be
useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer
and demand.
Index Terms—Distributed systems, distributed applications, performance evaluation, metrics/measurement, performance measures
The Impact of Virtualization on Network Performance of Amazon EC2 Data Center
Guohui Wang T. S. Eugene Ng
Abstract —Cloud computing services allow users to lease computing resources from large scale data centers operated by service
providers. Using cloud services, users can deploy a wide variety of applications dynamically and on-demand. Most cloud service
providers use machine virtualization to provide flexible and cost effective resource sharing. However, few studies have investigated
the impact of machine virtualization in the cloud on networking
performance. In this paper, we present a measurement study to characterize the impact of virtualization on the networking performance of the
Amazon Elastic Cloud Computing (EC2) data center. We measure the processor sharing, packet delay, TCP/UDP throughput and
packet loss among Amazon EC2 virtual machines. Our results show that even though the data center network is lightly utilized,
virtualization can still cause significant throughput instability and abnormal delay variations. We discuss the implications of our
findings on several classes of applications.
Index Terms —Measurement, cloud service, virtualization, networking performance
