Majd F. Sakr

Locality-Aware Reduce Task Scheduling for MapReduce

MapReduce offers a promising programming model for big data processing. Inspired by functional languages, MapReduce allows programmers to write functional-style code which gets automatically divided into multiple map and/or reduce tasks and scheduled over distributed data across multiple machines. Hadoop, an open source implementation of MapReduce, schedules map tasks in the vicinity of their inputs in order to diminish network traffic and improve performance. However, Hadoop schedules reduce tasks at requesting nodes without considering data locality leading to performance degradation. This paper describes Locality-Aware Reduce Task Scheduler (LARTS), a practical strategy for improving MapReduce performance. LARTS attempts to collocate reduce tasks with the maximum required data computed after recognizing input data network locations and sizes. LARTS adopts a cooperative paradigm seeking a good data locality while circumventing scheduling delay, scheduling skew, poor system utilization, and low degree of parallelism. We implemented LARTS in Hadoop-0.20.2. Evaluation results show that LARTS outperforms the native Hadoop reduce task scheduler by an average of 7%, and up to 11.6%.

BitValue Inference: Detecting and Exploiting Narrow Bitwidth Computations

We present a compiler algorithm called BitValue, which can discover both unused and constant bits in dusty-deck C programs. Bit-Value uses forward and backward dataflow analyses, generalizing constant-folding and dead-code detection at the bit-level. This algorithm enables compiler optimizations which target special processor architectures for computing on non-standard bitwidths. Using this algorithm we show that up to 31% oft he computed bytes are thrown away (for programs from SpecINT95 and Mediabench). A compiler for reconfigurable hardware uses this algorithm to achieve substantial reductions (up to 20-fold) in the size of the synthesized circuits.

Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic

MapReduce is by far one of the most successful realizations of large-scale data-intensive cloud computing platforms. MapReduce automatically parallelizes computation by running multiple map and/or reduce tasks over distributed data across multiple machines. Hadoop is an open source implementation of MapReduce. When Hadoop schedules reduce tasks, it neither exploits data locality nor addresses partitioning skew present in some MapReduce applications. This might lead to increased cluster network traffic. In this paper we investigate the problems of data locality and partitioning skew in Hadoop. We propose Center-of-Gravity Reduce Scheduler (CoGRS), a locality-aware skew-aware reduce task scheduler for saving MapReduce network traffic. In an attempt to exploit data locality, CoGRS schedules each reduce task at its center-of-gravity node, which is computed after considering partitioning skew as well. We implemented CoGRS in Hadoop-0.20.2 and tested it on a private cloud as well as on Amazon EC2. As compared to native Hadoop, our results show that CoGRS minimizes off-rack network traffic by averages of 9.6% and 38.6% on our private cloud and on an Amazon EC2 cluster, respectively. This reflects on job execution times and provides an improvement of up to 23.8%.

Optoelectronic buses for high-performance computing

Modern computer buses are typically organized by the three functions of data transfer, addressing, and arbitration/control. In this paper we present a fiber-based bus design which provides optical solutions for each of these functions. The design includes an all-optical addressing system, based on coincident pulse addressing, which eliminates the latency contribution and bandwidth limitation associated with electronic address decoding. The control system uses time-of-flight relationships between a priority chain and a feedback waveguide to implement fully distributed asynchronous and self-timed bus arbitration. >

Initial Findings for Provisioning Variation in Cloud Computing

Cloud computing offers a paradigm shift in management of computing resources for large-scale applications. Using the Infrastructure-as-a-service (IaaS) cloud computing model, users today can request dynamically provisioned, virtualized resources such as CPU, memory, disk, and network access in the form of virtualized resources. The client typically requests resources based on computational needs and pays for resource instances based on their capacity and time utilized. Mapping these virtual resource requests to physical hardware could vary for identical requests. This can potentially cause variations in the performance of applications deployed on such resources. The performance of the application can vary according to the physical layout of the provisioned hardware (the number of virtual machines (VMs), the size/configuration of the VMs and the inter-VM locality). In this paper, we study the effects of this “provisioning variation” and its impact on application performance using suitable benchmarks as well as demonstrate their effect on a few MapReduce workloads. Our initial findings indicate that provisioning variation can impact performance by a factor of 5 primarily due to I/O contention.

Believable Robot Characters

� Believability of characters has been an objective in literature, theater, film, and animation. We argue that believable robot characters are important in human-robot interaction, as well. In particular, we contend that believable characters evoke users’ social responses that, for some tasks, lead to more natural interactions and are associated with improved task performance. In a dialogue-capable robot, a key to such believability is the integration of a consistent story line, verbal and nonverbal behaviors, and sociocultural context. We describe our work in this area and present empirical results from three robot receptionist test beds that operate “in the wild.”

Marhaba, how may i help you?: effects of politeness and culture on robot acceptance and anthropomorphization

How do politeness strategies and cultural aspects affect robot acceptance and anthropomorphization across native speakers of English and Arabic? Previous work in cross-cultural HRI studies has mostly focused on Western and East Asian cultures. In contrast, Middle Eastern attitudes and perceptions of robot assistants are a barely researched topic. We investigated culture-specific determinants of robot acceptance and anthropomorphization by conducting a between-subjects study in Qatar. A total of 92 native speakers of either English or Arabic interacted with a receptionist robot in two different interaction tasks. We further manipulated the robot’s verbal behavior in experimental sub-groups to explore different politeness strategies. Our results suggest that Arab participants perceived the robot more positively and anthropomorphized it more than English speaking participants. In addition, the use of positive politeness strategies and the change of interaction task had an effect on participants’ HRI experience. Our findings complement the existing body of cross-cultural HRI research with a Middle Eastern perspective that will help to inform the design of robots intended for use in cross-cultural, multi-lingual settings.

Expressing ethnicity through behaviors of a robot character

Achieving homophily, or association based on similarity, between a human user and a robot holds a promise of improved perception and task performance. However, no previous studies that address homophily via ethnic similarity with robots exist. In this paper, we discuss the difficulties of evoking ethnic cues in a robot, as opposed to a virtual agent, and an approach to overcome those difficulties based on using ethnically salient behaviors. We outline our methodology for selecting and evaluating such behaviors, and culminate with a study that evaluates our hypotheses of the possibility of ethnic attribution of a robot character through verbal and nonverbal behaviors and of achieving the homophily effect.

Effects of Culture on the Credibility of Robot Speech: A Comparison between English and Arabic

As social robots begin to enter our lives as providers of information, assistance, companionship, and motivation, it becomes increasingly important that these robots are capableof interacting effectively with human users across different cultural settings worldwide. A key capability in establishing acceptance and usability is the way in which robots structure their speech to build credibility and express information in a meaningful and persuasive way. Previous work has established that robots can use speech to improve credibility in two ways: expressing practical knowledge and using rhetorical linguistic cues. In this paper, we present twostudies that build on prior work to explore the effects of language and cultural context on the credibility of robot speech. In the first study

Predictive control of opto-electronic reconfigurable interconnection networks using neural networks

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