This paper describes an information system designed to support the large volume of monitoring information generated by a distributed testbed. This monitoring information is produced by several subsystems and consists of status and performance data that needs to be federated, distributed, and stored in a timely and easy to use manner. Our approach differs from existing approaches because it federates and distributes information at a low architectural level via messaging; a natural match to many of the producers and consumers of information. In addition, a database is easily layered atop the messaging layer for consumers that want to query and search the information. Finally, a common language to represent information in all layers of the information system makes it significantly easier for users to consume information. Performance data shows that this approach meets the significant needs of FutureGrid and would meet the needs of an experimental infrastructure twice the size of FutureGrid. In addition, this design also meets the needs of existing distributed scientific infrastructures.
The Condor system contains a Collector service that contains classads that describe hosts. These descriptions contain standard information, but can also include custom information. This service is uses as part of Condor matchmaking @cite_17 .
@cite_17
1809943808 2140332639 57737232 2143649597
Conventional resource management systems use a system model to describe resources and a centralized scheduler to control their allocation. We argue that this paradigm does not adapt well to distributed systems, particularly those built to support high throughput computing. Obstacles include heterogeneity of resources, which make uniform allocation algorithms difficult to formulate, and distributed ownership, leading to widely varying allocation policies. Faced with these problems, we developed and implemented the classified advertisement (classad) matchmaking framework, a flexible and general approach to resource management in distributed environment with decentralized ownership of resources. Novel aspects of the framework include a semi structured data model that combines schema, data, and query in a simple but powerful specification language, and a clean separation of the matching and claiming phases of resource allocation. The representation and protocols result in a robust, scalable and flexible framework that can evolve with changing resources. The framework was designed to solve real problems encountered in the deployment of Condor, a high throughput computing system developed at the University of Wisconsin-Madison. Condor is heavily used by scientists at numerous sites around the world. It derives much of its robustness and efficiency from the matchmaking architecture. The design, implementation, and performance of the Condor scheduling system, which operates in a workstation environment, are presented. The system aims to maximize the utilization of workstations with as little interference as possible between the jobs it schedules and the activities of the people who own workstations. It identifies idle workstations and schedules background jobs on them. When the owner of a workstation resumes activity at a station, Condor checkpoints the remote job running on the station and transfers it to another workstation. The system guarantees that the job will eventually complete, and that very little, if any, work will be performed more than once. A performance profile of the system is presented that is based on data accumulated from 23 stations during one month. > Many web services aim to track clients as a basis for analyzing their behavior and providing personalized services. Despite much debate regarding the collection of client information, there have been few quantitative studies that analyze the effectiveness of host-tracking and the associated privacy risks. In this paper, we perform a large-scale study to quantify the amount of information revealed by common host identifiers. We analyze month-long anonymized datasets collected by the Hotmail web-mail service and the Bing search engine, which include millions of hosts across the global IP address space. In this setting, we compare the use of multiple identifiers, including browser information, IP addresses, cookies, and user login IDs. We further demonstrate the privacy and security implications of host-tracking in two contexts. In the first, we study the causes of cookie churn in web services, and show that many returning users can still be tracked even if they clear cookies or utilize private browsing. In the second, we show that host-tracking can be leveraged to improve security. Specifically, by aggregating information across hosts, we uncover a stealthy malicious attack associated with over 75,000 bot accounts that forward cookies to distributed locations. This work was done while Ting-Fang was an intern at Microsoft Research. Martin Abadi is also affiliated with the University of California, Santa Cruz. We present and evaluate an implementation of a prototype scalable web server consisting of a load-balanced cluster of hosts that collectively accept and service TCP connections. The host IP addresses are advertised using round robin DNS (RR-DNS) technique, allowing any host to receive requests from any client. Once a client attempts to establish a TCP connection with one of the hosts, a decision is made as to whether or not the connection should be redirected to a different host-namely, the host with the lowest number of established connections. We use the low-overhead Distributed Packet Rewriting (DPR) technique to redirect TCP connections. In our prototype, each host keeps information about the remaining hosts in the system. Load information is maintained using periodic multicast amongst the cluster hosts. Performance measurements suggest that our prototype outperforms both pure RR-DNS and the stateless DPR solutions.