|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4950269||1364283||2018||15 صفحه PDF||ندارد||دانلود رایگان|
â¢We are proposing a lightweight plug-and-play elasticity service for self-organizing resource provisioning.â¢Based on the TCP (Transmission Control Protocol) congestion control, we propose an algorithm named Live Thresholding (LT).â¢The results highlight performance competitiveness in terms of application time (performance) and cost (performance Ã energy) metrics.â¢This article presented the Helpar model, which can be seen as an elasticity service for HPC applications.
Today cloud elasticity can bring benefits to parallel applications, besides the traditional targets including Web and critical-business demands. This consists in adapting the number of resources and processes at runtime, so users do not need to worry about the best choice for them beforehand. To accomplish this, the most common approaches use threshold-based reactive elasticity or time-consuming proactive elasticity. However, both present at least one problem related to the need of a previous user experience, lack on handling load peaks, completion of parameters or design for a specific infrastructure and workload setting. In this context, we developed a hybrid elasticity service for masterâslave parallel applications named Helpar. The proposal presents a closed control loop elasticity architecture that adapts at runtime the values of lower and upper thresholds. The main scientific contribution is the proposition of the Live Thresholding (LT) technique for controlling elasticity. LT is based on the TCP congestion algorithm and automatically manages the value of the elasticity bounds to enhance better reactiveness on resource provisioning. The idea is to provide a lightweight plug-and-play service at the PaaS (Platform-as-a-Service) level of a cloud, in which users are completely unaware of the elasticity feature, only needing to compile their applications with Helpar prototype. For evaluation, we used a numerical integration application and OpenNebula to compare the Helpar execution against two scenarios: a set of static thresholds and a non-elastic application. The results present the lightweight feature of Helpar, besides highlighting its performance competitiveness in terms of application time (performance) and cost (performance Ã energy) metrics.
Journal: Future Generation Computer Systems - Volume 78, Part 1, January 2018, Pages 176-190