Container-based virtualization offers advantages such as high performance, resource efficiency, and agile environment. ese advantages make Internet of ings (IoT) device management easy. Although container-based virtualization has already been introduced to IoT devices, the different network modes of containers and their performance issues have not been addressed. Since the network performance is an important factor in IoT, the analysis of the container network performance is essential. In this study, we analyze the network performance of containers on an IoT device, Raspberry Pi 3. e results show that the network performance of containers is lower than that of the native Linux, with an average performance difference of 6% and 18% for TCP and UDP, respectively. In addition, the network performance of containers varies depending on the network mode. When a single container runs, bridge mode achieves higher performance than host mode by 25% while host mode shows better performance than bridge mode by 45% in the multicontainer environment.

SUMMARYFacing practical limits to increasing processor frequencies, manufacturers have resorted to multi-core designs in their commercial products. In multi-core implementations, cores in a physical package share the last-level caches to improve inter-core communication. To efficiently exploit this facility, operating systems must employ cache-aware schedulers. Unfortunately, virtualization software, which is a foundation technology of cloud computing, is not yet cache-aware or does not fully exploit the locality of the last-level caches. In this paper, we propose a cache-aware virtual machine scheduler for multi-core architectures. The proposed scheduler exploits the locality of the last-level caches to improve the performance of concurrent applications running on virtual machines. For this purpose, we provide a space-partitioning algorithm that migrates and clusters communicating virtual CPUs (VCPUs) in the same cache domain. Second, we provide a time-partitioning algorithm that co-schedules or schedules in sequence clustered VCPUs. Finally, we present a theoretical analysis that proves our scheduling algorithm is more efficient in supporting concurrent applications than the default credit scheduler in Xen. We implemented our virtual machine scheduler in the recent Xen hypervisor with para-virtualized Linux-based operating systems. We show that our approach can improve performance of concurrent virtual machines by up to 19% compared to the credit scheduler.

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SUMMARYIn this paper, we analyze the performance impact of JobTracker failure in Hadoop. A JobTracker failure is a serious problem that affects the overall job processing performance. We describe the cause of failure and the system behaviors because of failed job processing in the Hadoop. On the basis of the analysis, we build a job completion time model that reflects failure effects. Our model is based on a stochastic process with a node crash probability. With our model, we run simulation of performance impact with very credible failure data available from USENIX called computer failure data repository that have been collected for past 9 years. The results show that the performance impact is very severe in that the job completion time increases about four times typically, and in a worst case, it increases up to 68 times.