Anthropomorphic design in intelligent vehicle agents (IVAs) is crucial for driving safety and user experience. Cultural background may shape user preferences, as evidenced by Chinese car manufacturers offering more anthropomorphic IVAs (e.g., physical robots, human-like virtual agents) than their Western counterparts. While prior research has examined cross-cultural differences in visual anthropomorphism, behavioral anthropomorphism remains understudied. Here we evaluated the performance of eight IVAs (five from Chinese brands, three from Western brands) in responding to user requests and their social repair behaviors (e.g., apology and promise) following request failures. Overall, Chinese and Western IVAs did not differ in their corrective responses or likelihood of employing repair behaviors. However, Chinese IVAs were more likely to use combined behaviors rather than single ones and to incorporate intimacy expressions in their repair behaviors. Our findings highlight cultural design nuances in behavioral anthropomorphism, with implications for the culturally adaptive design of IVAs.

The goal of the proposed benchmarking and capacity planning framework is to evaluate the amount of resources needed for processing a given workload while meeting the specified performance requirements. There are two essential components in our capacity planning framework: (i) the capacity measurements of different hardware and software solutions using a specially designed set of media benchmarks; and (ii) a media service workload profiler, called MediaProf, which extracts a set of quantitative and qualitative parameters characterizing the service demand. The capacity planning tool matches the requirements of the media service workload profile, SLA and configuration constraints to produce the best available cost/performance solution. In case of a multi-node configuration, the capacity planner performs a cluster sizing evaluation by taking into account the choice of load balancing solution.

This paper addresses the problem of mapping the requirements of a known media service workload into the corresponding system resource requirements and accurately sizing a media server cluster to handle the workload. In this paper, we propose a new capacity planning framework for evaluating the resources needed for processing a given streaming media workload with specified performance requirements. The performance requirements are specified in a service level agreement (SLA) containing: i) basic capacity requirements that define the percentage of time the configuration is capable of processing the workload without performance degradation while satisfying bounds on system utilization; and ii) performability requirements that define the acceptable degradation of service performance during the remaining, non-compliant time and in case of node failures. Using a set of specially benchmarked media server configurations, the capacity planning tool matches the overall capacity requirements of the media service workload profile with the specified SLAs to identify the number of nodes necessary to support the required service performance.

The goal of the proposed capacity planning tool is to provide the best cost/performance configuration for support of a known media service workload. There are two essential components in our capacity planning tool: i) the capacity measurements of different h/w and s/w solutions using a specially designed set of media benchmarks and ii) a media service workload profiler, called MediaProf, which extracts a set of quantitative and qualitative parameters characterizing the service demand. The capacity planning tool matches the requirements of the media service workload profile, SLAs and configuration constraints to produce the best available cost/performance solution.

Currently, Internet hosting centers and content distribution networks leverage statistical multiplexing to meet the performance requirements of a number of competing hosted network services. Developing efficient resource allocation mechanisms for such services requires an understanding of both the short-term and long-term behavior of client access patterns to these competing services. At the same time, streaming media services are becoming increasingly popular, presenting new challenges for designers of shared hosting services. These new challenges result from fundamentally new characteristics of streaming media relative to traditional web objects, principally different client access patterns and significantly larger computational and bandwidth overhead associated with a streaming request. To understand the characteristics of these new workloads we use two long-term traces of streaming media services to develop MediSyn, a publicly available streaming media workload generator. In summary, this paper makes the following contributions: i) we model the long-term behavior of network services capturing the process of file introduction and changing file popularity, ii) we present a novel generalized Zipf-like distribution that captures recently-observed popularity of both web objects and streaming media not captured by existing Zipf-like distributions, and iii) we capture a number of characteristics unique to streaming media services, including file duration, encoding bit rate, session duration and non-stationary popularity of media accesses.

Fundamental to the design of reliable, high-performance network services is an understanding of the performance characteristics of the service as perceived by the client population as a whole. Understanding and measuring such end-to-end service performance is a challenging task. Current techniques include periodic sampling of service characteristics from strategic locations in the network and instrumenting Web pages with code that reports client-perceived latency back to a performance server. Limitations to these approaches include potentially nonrepresentative access patterns in the first case and determining the location of a performance bottleneck in the second.This paper presents EtE monitor, a novel approach to measuring Web site performance. Our system passively collects packet traces from a server site to determine service performance characteristics. We introduce a two-pass heuristic and a statistical filtering mechanism to accurately reconstruct different client page accesses and to measure performance characteristics integrated across all client accesses. Relative to existing approaches, EtE monitor offers the following benefits: i) a latency breakdown between the network and server overhead of retrieving a Web page, ii) longitudinal information for all client accesses, not just the subset probed by a third party, iii) characteristics of accesses that are aborted by clients, iv) an understanding of the performance breakdown of accesses to dynamic, multitiered services, and v) quantification of the benefits of network and browser caches on server performance. Our initial implementation and performance analysis across three different commercial Web sites confirm the utility of our approach.