Opportunity cost algorithms for reduction of I/O and interprocess communication overhead in a computing cluster

Computing clusters (CC) consisting of several connected machines, could provide a high-performance, multiuser, timesharing environment for executing parallel and sequential jobs. In order to achieve good performance in such an environment, it is necessary to assign processes to machines in a manner that ensures efficient allocation of resources among the jobs. The paper presents opportunity cost algorithms for online assignment of jobs to machines in a CC. These algorithms are designed to improve the overall CPU utilization of the cluster and to reduce the I/O and the interprocess communication (IPC) overhead. Our approach is based on known theoretical results on competitive algorithms. The main contribution of the paper is how to adapt this theory into working algorithms that can assign jobs to machines in a manner that guarantees near-optimal utilization of the CPU resource for jobs that perform I/O and IPC operations. The developed algorithms are easy to implement. We tested the algorithms by means of simulations and executions in a real system and show that they outperform existing methods for process allocation that are based on ad hoc heuristics.     

Randomization in individual choice behavior.

There is ample evidence that people cannot generate random series when instructed to do so. Rather, they produce sequences with too few symmetries and long runs and too many alternations among events. The authors propose a psychological theory to account for these findings, which assumes that subjects generate nonrandom sequences that locally represent theoretical random series subject to a constraint on their short-term memory. Closed-form expressions are then derived for the major statistics that have been used to test for deviations from randomness. Results from 3 experiments with 2 and 3 equiprobable alternatives support the model on both the individual and group levels. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Provision of step-level public goods: Effects of inequality in resources.

Twelve groups of 5 players each participated in a multiple-trial social dilemma game in which each player, i, receives a possibly different monetary endowment, ei, and then chooses independently and anonymously whether to contribute it to a monetary public good. The collective good, r, is supplied to all of the group members if the sum of contributions is equal to or larger than a prespecified and known threshold; it is not supplied otherwise. It was found (a) that the proportion of contributions decreased with experience, (b) that players contributed more often, the higher their endowment, but (c) that players with lower endowments were perceived to be more likely to contribute than were players with higher endowments. Group data provided strong support to a model in which the decision to contribute or not depends on ei and r, altruism, group size, and the probability of being critical to the group outcome. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Distributed envy minimization for resource allocation

Minimizing envy in distributed discrete resource or task allocation, is an unusual distributed optimization challenge, since the quality of the allocation for each of the agents is dependent, not only on its own allocation, but on the allocation of others as well. Thus, in order to perform distributed search for allocations with minimal envy there is a need to design innovative algorithms that can cope with the challenging constraint structure of an envy minimization problem. Distributed methods for minimizing envy among agents in indivisible resource allocation problems are presented. First, Distributed Envy Minimization Problems (DEMP) are formulated as Distributed Constraint Reasoning problems. When the DEMPs are large, and cannot be solved by a complete search an incomplete local search algorithm is presented. Each transfer of a good from one agent to another involves the change of state of more than one agent. Thus, a minimizing envy local search algorithm must build upon actions (transfers) that include multiple agents. Since DEMPs are particularly susceptible to local minima during local search, the paper proposes an algorithm that alternates between two different hill climbing search phases. The first phase uses one-transfer steps while the other exploits envy cycle elimination steps. An algorithm that minimizes envy while preserving efficiency, is proposed. The proposed algorithm finds a Pareto optimal allocation with low envy. In the context of resource allocation problems, a Pareto optimal solution is particularly desirable since it presents a stable solution. The proposed algorithm first finds a divisible Pareto optimal envy-free allocation using a Fisher market equilibrium. This allocation is transferred into an indivisible allocation of goods while maintaining the Pareto optimal characteristic of the allocation and a low envy level among agents.     

Learning science via animated movies: Its effect on students’ thinking and motivation

Some researchers claim that animations may hinder students’ meaningful learning or evoke misunderstandings. In order to examine these assertions, our study investigated the effect of animated movies on students’ learning outcomes and motivation to learn. Applying the quantitative methodology, two pre- and post-questionnaires were administered: Science thinking skills and Motivation to learn science. Students’ overall achievement in science was examined by their report card scores. The research population (N = 1335) was divided into experimental (N = 926) and control (N = 409) groups from 11 elementary schools. Findings indicated that the use of animated movies promoted students’ explanation ability and their understanding of scientific concepts. Findings also indicated that students who studied science with the use of animated movies developed higher motivation to learn science, in terms of: self-efficacy, interest and enjoyment, connection to daily life, and importance to their future, compared to the control students. Following the definition of multimedia, the students who study with the use of animated movies, applied all three learning styles: visual, auditory and kinesthetic. The use of multimedia and the fact that the students were engaged in exploring new concepts, that were relevant to their daily life experiences, can explain the positive results.     

Real-time 2D to 3D video conversion

We present a real-time implementation of 2D to 3D video conversion using compressed video. In our method, compressed 2D video is analyzed by extracting motion vectors. Using the motion vector maps, depth maps are built for each frame and the frames are segmented to provide object-wise depth ordering. These data are then used to synthesize stereo pairs. 3D video synthesized in this fashion can be viewed using any stereoscopic display. In our implementation, anaglyph projection was selected as the 3D visualization method, because it is mostly suited to standard displays.

Modelling and Solving Employee Timetabling Problems

Employee timetabling is the operation of assigning employees to tasks in a set of shifts during a fixed period of time, typically a week. We present a general definition of employee timetabling problems (ETPs) that captures many real-world problem formulations and includes complex constraints. The proposed model of ETPs can be represented in a tabular form that is both intuitive and efficient for constraint representation and processing. The constraint networks of ETPs include non-binary constraints and are difficult to formulate in terms of simple constraint solvers. We investigate the use of local search techniques for solving ETPs. In particular, we propose several versions of hill-climbing that make use of a novel search space that includes also partial assignments. We show that, on large and difficult instances of real world ETPs, where systematic search fails, local search methods perform well and solve the hardest instances. According to our experimental results on various techniques, a simple version of hill climbing based on random moves is the best method for solving large ETP instances.