The paper "Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing" by Iosup et al. (2011) evaluates the performance of cloud computing services for scientific computing, particularly focusing on Many-Task Computing (MTC) workloads. The authors analyze the presence of MTC users in scientific computing environments and compare the performance of four commercial cloud services—Amazon EC2, GoGrid, ElasticHosts, and Mosso—using microbenchmarks and application kernels. They also simulate the performance characteristics and cost models of clouds and other scientific computing platforms, including grids and parallel production infrastructures. The results indicate that current clouds need an order of magnitude improvement in performance to be useful for the scientific community, highlighting the need for improvements in resource allocation and scheduling techniques. The study concludes that while clouds offer potential benefits such as scalability and cost savings, they currently fall short in meeting the performance demands of MTC-based scientific computing.The paper "Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing" by Iosup et al. (2011) evaluates the performance of cloud computing services for scientific computing, particularly focusing on Many-Task Computing (MTC) workloads. The authors analyze the presence of MTC users in scientific computing environments and compare the performance of four commercial cloud services—Amazon EC2, GoGrid, ElasticHosts, and Mosso—using microbenchmarks and application kernels. They also simulate the performance characteristics and cost models of clouds and other scientific computing platforms, including grids and parallel production infrastructures. The results indicate that current clouds need an order of magnitude improvement in performance to be useful for the scientific community, highlighting the need for improvements in resource allocation and scheduling techniques. The study concludes that while clouds offer potential benefits such as scalability and cost savings, they currently fall short in meeting the performance demands of MTC-based scientific computing.