Server clustering is a common design principle employed by many organisations who require high availability, scalability and easier management of their infrastructure. Servers are typically clustered according to the service they provide whether it be the application(s) installed, the role of the server or server accessibility for example. In order to optimize performance, manage load and maintain availability, servers may migrate from one cluster group to another making it difficult for server monitoring tools to continuously monitor these dynamically changing groups. Server monitoring tools are usually statically configured and with any change of group membership requires manual reconfiguration, an unreasonable task to undertake on large-scale cloud infrastructures. In this paper we present the Cloudlet Control and Management System (C2MS), a system for monitoring and controlling dynamic groups of physical or virtual servers within cloud infrastructures. The C2MS extends Ganglia - an open source scalable system performance monitoring tool - by allowing system administrators to define, monitor and modify server groups without the need for server reconfiguration. In turn administrators can easily monitor group and individual server metrics on large-scale dynamic cloud infrastructures where roles of servers may change frequently. Furthermore, we complement group monitoring with a control element allowing administrator-specified actions to be performed over servers within service groups as well as introduce further customized monitoring metrics. This paper outlines the design, implementation and evaluation of the C2MS.
The C2MS uses Ganglia as its foundation for infrastructure monitoring. Ganglia is a popular open source scalable system performance monitoring tool @cite_13 @cite_5 @cite_11 used widely in the High Performance Computing (HPC) community. Its popularity, easy installation process, easy to use web interface and its extensibility were the main factors why we chose Ganglia to build the C2MS upon.
@cite_5 @cite_13 @cite_11
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Abstract Ganglia is a scalable distributed monitoring system for high performance computing systems such as clusters and Grids. It is based on a hierarchical design targeted at federations of clusters. It relies on a multicast-based listen announce protocol to monitor state within clusters and uses a tree of point-to-point connections amongst representative cluster nodes to federate clusters and aggregate their state. It leverages widely used technologies such as XML for data representation, XDR for compact, portable data transport, and RRDtool for data storage and visualization. It uses carefully engineered data structures and algorithms to achieve very low per-node overheads and high concurrency. The implementation is robust, has been ported to an extensive set of operating systems and processor architectures, and is currently in use on over 500 clusters around the world. This paper presents the design, implementation, and evaluation of Ganglia along with experience gained through real world deployments on systems of widely varying scale, configurations, and target application domains over the last two and a half years. Written by Ganglia designers and maintainers, this book shows you how to collect and visualize metrics from clusters, grids, and cloud infrastructures at any scale. Want to track CPU utilization from 20,000 hosts every ten seconds? Ganglia is just the tool you need, once you know how its main components work together. This hands-on book helps experienced system administrators take advantage of Ganglia 3.x. Learn how to extend the base set of metrics you collect, fetch current values, see aggregate views of metrics, and observe time-series trends in your data. Youll also examine real-world case studies of Ganglia installs that feature challenging monitoring requirements. Determine whether Ganglia is a good fit for your environment Learn how Ganglias gmond and gmetad daemons build a metric collection overlay Plan for scalability early in your Ganglia deployment, with valuable tips and advice Take data visualization to a new level with gweb, Ganglias web frontend Write plugins to extend gmonds metric-collection capability Troubleshoot issues you may encounter with a Ganglia installation Integrate Ganglia with the sFlow and Nagios monitoring systems Contributors include: Robert Alexander, Jeff Buchbinder, Frederiko Costa, Alex Dean, Dave Josephsen, Peter Phaal, and Daniel Pocock. In this paper, we report on the experimental results of running a large, tightly coupled, distributed multiscale computation over a hybrid High Performance Computing (HPC) infrastructures. We connected EC2 based cloud clusters located in USA to university clusters located in Switzerland. We ran a concurrent multiscale MPI based application on this infrastructure and measured the overhead induced by extending our HPC clusters with EC2 resources. Our results indicate that accommodating some parts of the multiscale computation on cloud resources can lead to low performance without a proper adjustment of CPUs power and workload. However, by enforcing a load-balancing strategy one can benefit from the extra Cloud resources. We connect an EC2-cloud cluster to a university cluster located in Switzerland.We run a distributed multiscale CFD computation over this extended infrastructure.We evaluate and compare the distributed execution to a local execution.We describe our experience of running parallel CFD application on hybrid platforms.Multiscale computation on cloud requires an adjustment of CPUs power and workload. Performance analysis is a crucial step in HPC architectures including clouds. Traditional performance analysis methodologies that were proposed, implemented, and enacted are functional with the objective of identifying bottlenecks or issues related to memory, programming languages, hardware, and virtualization aspects. However, the need for energy efficient architectures in highly scalable computing environments, such as, Grid or Cloud, has widened the research thrust on developing performance analysis methodologies that analyze the energy inefficiency of HPC applications or their associated hardware. This paper surveys the performance analysis methodologies that investigates into the available energy monitoring and energy awareness mechanisms for HPC architectures. In addition, the paper validates the existing tools in terms of overhead, portability, and user-friendly parameters by conducting experiments at HPCCLoud Research Laboratory at our premise. This research work will promote HPC application developers to select an apt monitoring mechanism and HPC tool developers to augment required energy monitoring mechanisms which fit well with their basic monitoring infrastructures.