Provide a set of generic keywords that define your contribution (e.g. Data Management, Workflows, High Energy Physics)
Grid scheduling, Fairness, Workflows,
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As in load balancing resources may be badly distributed at the expense of everyone. The
objective is not to starve users but on the contrary to maximize fairness between users.
How to measure the quality of a schedule with respect to fairness ? One criterion is
evaluated for all users, this leads to a vector of evaluation indexed by users. We have to
compare these vectors with an order expressing fairness. An order is fair is and only if it is
symmetrical and if it is decreasing if 2 values are changed so that their minimum decrease.
For instance [5, 3, 2, 8] is less fair than [5, 4, 2, 6] because of the second user. By
maximizing such order, it is impossible to increase an user without decreasing another
user who has less than the first one. Such order is the Leximin ordering and cannot be
represented with a scalar function. This means that we cannot always obtain fair solutions
when maximizing a scalar function in a multi-user setting.
4. Conclusions / Future plans
Multi-user scheduling is a NP-hard problem as soon as we have only one machine with 2
users and release dates. For the moment , we have studied fair on-line storage allocations
and described an algorithm able to list all possible solutions. We still have to study the online
case with unknown duration in order to propose algorithm suitable for the grid.
1. Short overview
Computing grids allow sharing of distributed resources such as CPU or storage to the
service of many scientific communities. Users submit irregular bursts of jobs. This irregular
usage of the grid allow an efficient sharing of resources because unused resources can be
collected this way. Jobs are mono-processors and arrive on-line to clusters. Jobs on a
cluster are independents, they could be run in any order. Pre-emption is not used because
of memory and communication costs.