Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
Economical and Robust Provisioning of N-Tier Cloud Workloads: A Multi-level Control Approach
IEEE International Conference On Distributed Computing Systems (ICDCS), Minneapolis, Minnesota, USA, Jun 21-24, 2011.
Pengcheng Xiong*, Zhikui Wang†, Simon Malkowski*, Qingyang Wang*,
Deepal Jayasinghe*, Calton Pu*
*Georgia Institute of Technology
†Hewlett Packard Laboratories
Resource provisioning for N-tier web applications in Clouds is non-trivial due to at least two reasons. First, there is an inherent optimization conflict between cost of resources and Service Level Agreement (SLA) compliance. Second, the resource demands of the multiple tiers can be different from each other, and varying along with the time. Resources have to be allocated to multiple (virtual) containers to minimize the total amount of resources while meeting the end-to-end performance requirements for the application. In this paper we address these two challenges through the combination of the resource controllers on both application and container levels. On the application level, a decision maker (i.e., an adaptive feedback controller) determines the total budget of the resources that are required for the application to meet SLA requirements as the workload varies. On the container level, a second controller partitions the total resource budget among the components of the applications to optimize the application performance (i.e., to minimize the round trip time). We evaluated our method with three different workload models—open, closed, and semiopen— that were implemented in the RUBiS web application benchmark. Our evaluation indicates two major advantages of our method in comparison to previous approaches. First, fewer resources are provisioned to the applications to achieve the same performance. Second, our approach is robust enough to address various types of workloads with time-varying resource demand without reconfiguration.
FULL PAPER: pdf