Abstract: Cloud computing (CC) has already gained overall
appreciation in research and practice. Whereas the willingness to
integrate cloud services in various IT environments is still unbroken,
the previous CC procurement processes run mostly in an unorganized
and non-standardized way. In practice, a sufficiently specific, yet
applicable business process for the important acquisition phase is
often lacking. And research does not appropriately remedy this
deficiency yet. Therefore, this paper introduces a field-tested
approach for CC procurement. Based on an extensive literature
review and augmented by expert interviews, we designed a model
that is validated and further refined through an in-depth real-life case
study. For the detailed process description, we apply the event-driven
process chain notation (EPC). The gained valuable insights into the
case study may help CC research to shift to a more socio-technical
area. For practice, next to giving useful organizational instructions
we will provide extended checklists and lessons learned.
Abstract: This paper is concerned with the design and implementation of MICOSim, an event-driven simulator written in Java for evaluating the performance of Grid entities (users, brokers and resources) under different scenarios such as varying the numbers of users, resources and brokers and varying their specifications and employed strategies.
Abstract: Fair share objective has been included into the goaloriented
parallel computer job scheduling policy recently. However,
the previous work only presented the overall scheduling performance.
Thus, the per-user performance of the policy is still lacking. In this
work, the details of per-user fair share performance under the
Tradeoff-fs(Tx:avgX) policy will be further evaluated. A basic fair
share priority backfill policy namely RelShare(1d) is also studied.
The performance of all policies is collected using an event-driven
simulator with three real job traces as input. The experimental results
show that the high demand users are usually benefited under most
policies because their jobs are large or they have a lot of jobs. In the
large job case, one job executed may result in over-share during that
period. In the other case, the jobs may be backfilled for
performances. However, the users with a mixture of jobs may suffer
because if the smaller jobs are executing the priority of the remaining
jobs from the same user will be lower. Further analysis does not show
any significant impact of users with a lot of jobs or users with a large
runtime approximation error.