ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads

ARM processors have dominated the mobile device market in the last decade due to their favorable computing to energy ratio. In this age of Cloud data centers and Big Data analytics, the focus is increasingly on power efficient processing, rather than just high throughput computing. ARM's first commodity server-grade processor is the recent AMD A1100-series processor, based on a 64-bit ARM Cortex A57 architecture. In this paper, we study the performance and energy efficiency of a server based on this ARM64 CPU, relative to a comparable server running an AMD Opteron 3300-series x64 CPU, for Big Data workloads. Specifically, we study these for Intel's HiBench suite of web, query and machine learning benchmarks on Apache Hadoop v2.7 in a pseudo-distributed setup, for data sizes up to $20GB$ files, $5M$ web pages and $500M$ tuples. Our results show that the ARM64 server's runtime performance is comparable to the x64 server for integer-based workloads like Sort and Hive queries, and only lags behind for floating-point intensive benchmarks like PageRank, when they do not exploit data parallelism adequately. We also see that the ARM64 server takes $\frac{1}{3}^{rd}$ the energy, and has an Energy Delay Product (EDP) that is $50-71\%$ lower than the x64 server. These results hold promise for ARM64 data centers hosting Big Data workloads to reduce their operational costs, while opening up opportunities for further analysis.Comment: Accepted for publication in the Proceedings of the 24th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC), 201

A user perspective of quality of service in m-commerce

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2004 Springer VerlagIn an m-commerce setting, the underlying communication system will have to provide a Quality of Service (QoS) in the presence of two competing factors—network bandwidth and, as the pressure to add value to the business-to-consumer (B2C) shopping experience by integrating multimedia applications grows, increasing data sizes. In this paper, developments in the area of QoS-dependent multimedia perceptual quality are reviewed and are integrated with recent work focusing on QoS for e-commerce. Based on previously identified user perceptual tolerance to varying multimedia QoS, we show that enhancing the m-commerce B2C user experience with multimedia, far from being an idealised scenario, is in fact feasible if perceptual considerations are employed