Dynamic Parameter Allocation in Parameter Servers

To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementation of distributed parameter management---a key concern in distributed training---, but can induce severe communication overhead. To reduce communication overhead, distributed machine learning algorithms use techniques to increase parameter access locality (PAL), achieving up to linear speed-ups. We found that existing parameter servers provide only limited support for PAL techniques, however, and therefore prevent efficient training. In this paper, we explore whether and to what extent PAL techniques can be supported, and whether such support is beneficial. We propose to integrate dynamic parameter allocation into parameter servers, describe an efficient implementation of such a parameter server called Lapse, and experimentally compare its performance to existing parameter servers across a number of machine learning tasks. We found that Lapse provides near-linear scaling and can be orders of magnitude faster than existing parameter servers

Achieving Green and Healthy Homes and Communities in America

In the Fall of 2010, the National Coalition to End Childhood Lead Poisioning contracted with the National Academy to develop and execute an online dialogue that would examine ways to increase the health, safety, and energy efficiency of low- to moderate-income homes. Since 1999, the National Coalition had worked to improve low- to moderate-income housing through the support and execution of home interventions that addressed multiple issues within a home at one time; an approach that often did not align with other traditional, single-issue housing assistance programs. By 2010, the National Coalition had taken on the leadership of the Green and Healthy Homes Initiative, a public-private partnership focused on integrating funding streams to improve low- to middle-income homes across the country.With plans to expand the GHHI's operations, the National Coalition partnered with the National Academy to conduct the National Dialogue on Green and Healthy Homes, a collaborative online dailogue in which participants were asked to identify challenges to, and innovative practices for, improving the health, safety and energy-efficiency of low- to moderate- income homes. The Dialogue was live from November 4-November 22, 2010, and collected 100 hundred ideas and 362 comments from 320 registered users. Over the course of its two and a half week duration, the Dialogue received more than 2,500 visits from over 1,100 people in 48 states and territories. Key FindingsBy reviewing the feedback received in the Dialogue, the Panel was able to make a number of recommendations on how the green and healthy homes community of practice could increase the health, safety and energy efficiency of homes across the country. These recommendations included: Conduct an evaluation of current housing standards to determine if they meet the Nation's health, safety, and energy efficiency needs; Develop a tiered performance standard for healthy, safe and energy efficient homes; Group government funding streams to better align programs with the comprehensive intervention approach; Develop a long-term funding strategy to support efforts after Recovery Act funding ends; and Educate government decisionmakers and the public on the importance of developing green and healthy homes and communities, and the work that supports that development

InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape