Optimizing the Transition Waste in Coded Elastic Computing

Distributed computing, in which a resource-intensive task is divided into subtasks and distributed among different machines, plays a key role in solving large-scale problems, e.g., machine learning for large datasets or massive computational problems arising in genomic research. Coded computing is a recently emerging paradigm where redundancy for distributed computing is introduced to alleviate the impact of slow machines, or stragglers, on the completion time. Motivated by recently available services in the cloud computing industry, e.g., EC2 Spot or Azure Batch, where spare/low-priority virtual machines are offered at a fraction of the price of the on-demand instances but can be preempted in a short notice, we investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. Our contributions are two-fold: We first propose an efficient method to minimize the transition waste, a newly introduced concept quantifying the total number of tasks that existing machines have to abandon or take on anew when a machine joins or leaves, for the cyclic elastic task allocation scheme recently proposed in the literature (Yang et al. ISIT'19). We then proceed to generalize such a scheme and introduce new task allocation schemes based on finite geometry that achieve zero transition wastes as long as the number of active machines varies within a fixed range. The proposed solutions can be applied on top of every existing coded computing scheme tolerating stragglers.Comment: 16 page

Агрессивная форма ювенильной макромастии

ПОДРОСТКИМОЛОЧНЫЕ ЖЕЛЕЗЫ ЧЕЛОВЕКА /АНОМАЛБОЛЕЗНИ РЕДКИЕМАКРОМАСТИЯГИПЕРТРОФИЯМОЛОЧНОЙ ЖЕЛЕЗЫ ПЛАСТИКАХИРУРГИЧЕСКИЕ ОПЕРАЦИИ ВОССТАНОВИТЕЛЬНЫЕМОЛОЧНОЙ ЖЕЛЕЗЫ БОЛЕЗНИ /ТЕР /ХИ

The impact of minimising data movement on the overall performance of the simulation of complex systems applied to FLAME GPU

GPUs have been demonstrated to be highly effective at improving the performance of Multi-Agent Systems (MAS). One of the significant limitations of further performance improvements is in the memory bandwidth required to move agent data through the GPU’s memory hierarchy. This thesis investigates the impact of data dependency on the FLAME GPU framework’s overall performance as an example of Agent Based Modelling (ABM) platforms. This investigation includes discovering data dependency within FLAME GPU models. Two methods are proposed in order to minimise data movement during simulation using dependency information: (i) a functional method which is based on the concept of merging and splitting agent function; and (ii) data-aware method which uses of data dependency information to access a subset of agent and message memory at the variable level. This thesis also develops a method that allows automatic discovery of data dependencies from existing FLAME GPU models. This method is based on parsing an agent function file of a FLAME GPU model to extract all agent functions’ data dependencies. The scalability, computational complexity, internal memory requirements, and homogeneity of the agent and population of the model are examples of factors that may affect ABM applications’ overall performance. This thesis presents a standard benchmark model designed to observe the system behaviour while testing these factors. An evaluation of the performance impact of minimising data movement has been carried out by implementing the proposed FLAME GPU methods using the benchmark model and the number of existing FLAME GPU models. The comparison between the current and new system shows that reducing data movement within a simulation improves overall performance

Entrepreneurship dynamics for women entrepreneurs' inclusion in emerging markets: a case study on Saudi Arabia

Abstract not currently available

SLA-aware resource scaling for energy efficiency

Cloud data centers (CDCs) with abundant resource capacities have prevailed in the past decade. However, these CDCs often struggle to efficiently deal with resource provisioning in terms of performance and energy efficiency. In this paper, we present Energy-Based Auto Scaling (EBAS) as a new resource auto-scaling approach - that takes into account Service Level Agreement (SLA) - for CDCs. EBAS proactively scales resources at the CPU core level in terms of both the number and frequency of cores. It incorporates the dynamic voltage and frequency scaling (DVFS) technique to dynamically adjust CPU frequencies. The proactive decisions on resource scaling are enabled primarily by the CPU usage prediction model and the workload consolidation model of EBAS. The experimental results show that EBAS can save energy on average by 14% compared with the Linux governor. In particular, EBAS contributes to enhancing DVFS by making it aware of SLA conditions, which leads to savings of computing power and in turn energy.8 page(s

Teaching and Learning Computer Science at Al Baha University, Saudi Arabia : Insights from a staff development course

In this special session we meet a set of projects in computer science and engineering education at a university in Saudi Arabia. They are the product of a pedagogical development course ran in collaboration with a Swedish university during the academic year 2013/2014. The projects reflect the local situation, with its possibilities and challenges, and suggest steps to take, in the local environment, to enhance education. As such it is a unique document that brings insights from computer science and engineering education into the international literature