How do native English speakers and native Korean speakers politely make a request to a robot? Previous human-robot interaction studies on English have demonstrated that humans use indirect speech acts (ISAs) frequently to robots to make their requests polite. However, it is unknown whether humans considerably used ISAs to robots in other languages. In addition to ISAs, Korean has other politeness expressions called honorifics, which indicate different politeness from that of ISAs. This study aimed to investigate the cultural differences in humans' politeness expressions and politeness when they make requests to robots and to re-examine the effect of conventionality of context on the use of politeness expressions. We conducted a replication experiment of Williams et al. (2018) on native Korean speakers and analyzed their use of ISAs and honorifics. Our results showed that ISAs are rarely used in task-based human-robot interaction in Korean. Instead, honorifics are more frequently used than ISAs and are more common in conventionalized contexts than in unconventionalized contexts. These results suggest that the difference in politeness expressions and politeness between English and Korean exist in both human-robot interaction and human-human interaction. Furthermore, the conventionality of context has a strong constraint on making humans follow social norms in both languages.

In a heterogeneous distributed system composed of various types of computing platforms such as supercomputers, grids, and clouds, a two-level scheduling approach can be used to effectively distribute resources of the platforms to users in the first-level, and map tasks of the users in nodes for each platform in the second-level for executing many-task applications. When scheduling heterogeneous resources, service providers of the system should consider the fairness among multiple users as well as the system efficiency. However, the fairness cannot be achieved by simply distributing an equal amount of resources from each platform to every user. In this paper, we investigate how to address the fairness issue among multiple users in a heterogeneous distributed system. We present three first-level resource allocation policies of a provider affinity first policy, an application affinity first policy, and a platform affinity based round-robin policy, and two second-level task mapping policies of a most affected first policy and a co-runner affinity based round-robin policy. Using trace-based simulations, we evaluate the performance of various combinations of the first and second level scheduling policies. Our extensive simulation results demonstrate that the first-level policy plays a crucial role to achieve relatively good fairness.

Running big data analytics frameworks in the cloud is becoming increasingly important, but their resource managers in the current form are not designed to consider virtualized environments. In this work, we investigate various levels of data locality in a virtualized environment, ranging from rack locality to memory locality. Exploiting extra fine-grained levels of data locality in a virtualized environment, our memory locality-aware scheduling algorithm effectively increases the cache hit ratio and thereby reduces network traffic and disk I/O. However, a high cache hit ratio does not necessarily imply a shorter job execution time in MapReduce applications. To resolve this issue, we develop the Cache-Affinity and Virtualization-Aware (CAVA) resource manager, which measures the cache affinity of MapReduce applications at runtime and efficiently manages distributed in-memory caches of a limited size by assigning high priority to applications that have high cache affinity. The proposed memory locality-aware scheduling algorithm is also integrated into the CAVA resource manager. Our extensive experimental study shows that CAVA exhibits overall good performance over various workloads composed of multiple big data analytics applications by considering the fine-grained data locality levels in virtualized clusters and by efficiently using scarce memory resources.

In this work, we investigate techniques to improve the performance of big data analytics in virtualized clusters by effectively increasing the utilization of cached data and efficiently using scarce memory resources.

In this paper, we investigate techniques to effectively orchestrate HDFS in-memory caching for Hadoop. We first evaluate a degree of benefit which each of various MapReduce applications can get from in-memory caching, i.e. cache affinity. We then propose an adaptive cache local scheduling algorithm that adaptively adjusts the waiting time of a MapReduce job in a queue for a cache local node. We set the waiting time to be proportional to the percentage of cached input data for the job. We also develop a cache affinity cache replacement algorithm that determines which block is cached and evicted based on the cache affinity of applications. Using various workloads consisting of multiple MapReduce applications, we conduct experimental study to demonstrate the effects of the proposed in-memory orchestration techniques. Our experimental results show that our enhanced Hadoop in-memory caching scheme improves the performance of the MapReduce workloads up to 18% and 10% against Hadoop that disables and enables HDFS in-memory caching, respectively.

High-Throughput Computing (HTC) and Many-Task Computing (MTC) paradigms employ loosely coupled applications which consist of a large number, from tens of thousands to even billions, of independent tasks. To support such large-scale applications, a heterogeneous computing system composed of multiple computing platforms with different types such as supercomputers, grids, and clouds can be used. On allocating heterogeneous resources of the system to multiple users, there are three important aspects to consider: fairness among users, efficiency for maximizing the system throughput, and user satisfaction for reducing the average user response time. In this paper, we present three resource allocation policies for multi-user and multi-application workloads in a heterogeneous computing system. These three policies are a fairness policy, a greedy efficiency policy, and a fair efficiency policy. We evaluate and compare the performance of the three resource allocation policies over various settings of a heterogeneous computing system and loosely coupled applications, using simulation based on the trace from real experiments. Our simulation results show that the fair efficiency policy can provide competitive efficiency, with a balanced level of fairness and user satisfaction, compared to the other two resource allocation policies.

Recent emerging applications from a wide range of scientific domains often require a very large number of loosely coupled tasks to be efficiently processed. To support such applications effectively, all the available resources from different types of computing platforms such as supercomputers, grids, and clouds need to be utilized. However, exploiting heterogeneous resources from the platforms for multiple loosely coupled many-task applications is challenging, since the performance of an application can vary significantly depending on which platform is used to run it, and which applications co-run in the same node with it. In this paper, we analyze the platform and co-runner affinities of many-task applications in distributed computing platforms. We perform a comprehensive experimental study using four different platforms, and five many-task applications. We then present a two-level scheduling algorithm, which distributes the resources of different platforms to each application based on the platform affinity in the first level, and maps tasks of the applications to computing nodes based on the co-runner affinity for each platform in the second level. Finally, we evaluate the performance of our scheduling algorithm, using a trace-based simulator. Our simulation results demonstrate that our scheduling algorithm can improve the performance up to 30.0%, compared to a baseline scheduling algorithm.