Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe EU DataGrid Workload Management System: towards the second major release
59
0
0.0
(
0
)
Added by
Massimo Sgaravatto
Publication date
2003
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
In the first phase of the European DataGrid project, the workload management package (WP1) implemented a working prototype, providing users with an environment allowing to define and submit jobs to the Grid, and able to find and use the ``best resources for these jobs. Application users have now been experiencing for about a year now with this first release of the workload management system. The experiences acquired, the feedback received by the user and the need to plug new components implementing new functionalities, triggered an update of the existing architecture. A description of this revised and complemented workload management system is given.
rate research
Read More
Application users have now been experiencing for about a year with the standardized resource brokering services provided by the workload management package of the EU DataGrid project (WP1). Understanding, shaping and pushing the limits of the system has provided valuable feedback on both its design and implementation. A digest of the lessons, and better practices, that were learned, and that were applied towards the second major release of the software, is given.
Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution. With increasing data volumes in emerging cloud workloads, it is critical to factor in the network costs for transferring workloads across data centers. For geo-distributed data centers, many researchers have been exploring strategies for energy cost minimization and intelligent inter-data-center workload distribution separately. However, prior work does not comprehensively and simultaneously consider data center energy costs, data transfer costs, and data center queueing delay. In this paper, we propose a novel game theory-based workload management framework that takes a holistic approach to the cloud operating cost minimization problem by making intelligent scheduling decisions aware of data transfer costs and the data center queueing delay. Our framework performs intelligent workload management that considers heterogeneity in data center compute capability, cooling power, interference effects from task co-location in servers, time-of-use electricity pricing, renewable energy, net metering, peak demand pricing distribution, and network pricing. Our simulations show that the proposed game-theoretic technique can minimize the cloud operating cost more effectively than existing approaches.
Adult content constitutes a major source of Internet traffic. As with many other platforms, these sites are incentivized to engage users and maintain them on the site. This engagement (e.g., through recommendations) shapes the journeys taken through such sites. Using data from a large content delivery network, we explore session journeys within an adult website. We take two perspectives. We first inspect the corpus available on these platforms. Following this, we investigate the session access patterns. We make a number of observations that could be exploited for optimizing delivery, e.g., that users often skip within video streams.
The International Lattice Datagrid (ILDG) is a federation of several regional grids. Since most of these grids have reached production level, an increasing number of lattice scientists start to benefit from this new research infrastructure. The ILDG Middleware Working Group has the task of specifying the ILDG middleware such that interoperability among the different grids is achieved. In this paper we will present the architecture of the ILDG middleware and describe what has actually been achieved in recent years. Particular focus is given to interoperability and security issues. We will conclude with a short overview on issues which we plan to address in the near future.
Blue Waters is a Petascale-level supercomputer whose mission is to enable the national scientific and research community to solve grand challenge problems that are orders of magnitude more complex than can be carried out on other high performance computing systems. Given the important and unique role that Blue Waters plays in the U.S. research portfolio, it is important to have a detailed understanding of its workload in order to guide performance optimization both at the software and system configuration level as well as inform architectural balance tradeoffs. Furthermore, understanding the computing requirements of the Blue Waters workload (memory access, IO, communication, etc.), which is comprised of some of the most computationally demanding scientific problems, will help drive changes in future computing architectures, especially at the leading edge. With this objective in mind, the project team carried out a detailed workload analysis of Blue Waters.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
