Advanced Reservation (AR) is used to guarantee resource provisioning for many different types of
applications including workflows. This technique is still under a huge controversy in both Business and
Research communities because of its potentialit
y of reducing resource utilization. Most of the works
proposed in this domain suggest reservation for the whole workflow schedule, and on all available
resources at the same time, which worsen the problem of resource utilization.
Many solutions are introduced to improve resource utilization under advanced reservation through
generating relaxed and elastic reservation plans that local scheduling systems could modify to improve
utilization and decrease internal fragmentation. These solutions depend mainly on changing rigid AR,
which considered to be the most difficult kind of reservation, into relaxed and elastic ones through adding
extra time on the resulted schedule and then distributing it on all tasks of the workflow.
This paper presents a new autonomic algorithm (EWARP) for producing elastic reservation plans for
workflow applications which doesn’t add extra times. Instead, it depends on exploiting the timing gaps
produced by the different scheduling algorithms. The new algorithm use the technique of discovering
timing gaps, but modifies it, and adds to it to be used for producing an elastic reservation plan for
workflows. The results presented in this paper demonstrate how the proposed algorithm outperforms
existing works in the fields by a lower bound approximating 25%.This shows that (EWARP) algorithm
offer efficient and practical solutions for the problem of scheduling workflow applications under QoS
constrains.
Many researches showed the ability of advance reservation to improve the predictability of the
system; that allows it to deliver the applications required time constrains. Applications with
many tasks require the system to ensure a number of reserv
ations on many different distributed
resources, which usually carried out through multi-level negotiation adding by that additional
overhead on the application total response time. The extra overhead depends on many
parameters including system workload and contention. Workflows add more complexity due to
their tasks’ dependencies; thus, any rejection of or delay for a task reservation would increase
application complete time.
This paper suggests the use of elastic advanced reservation plans that depend on time gaps
presented in the sub-optimal schedules, in order to improve the reservation acceptance rate. It
presents an elastic co-reservation agent which provides the needed reservations using First Fit
allocation strategy.
The results show the ability of the proposed agent to always improve the acceptance rate with an
average of (22.25%). The more important came out result is the agent ability to increase the
reservation acceptance rate with the increasing of system competence, reaching (48.4%) for
simultaneous 90 users at the system.
The work aims to make benefit from existence
multi-CPU and multi-GPU, exploiting the calculation processes
which do multi-GPU, which aims to form mechanism to
scheduling a directed acyclic graph(DAG), it aims to reduce
communication between resources and inter linked task
scheduling in the best form.
In this paper, we compare the performance of sporadic tasks scheduler algorithms on
a multi-core platform in order to determine the best algorithm in terms of a set of
parameters adopted by researchers in this field, which in turn gives us accurate
details
about the quality of such algorithms when applied to a set of sporadic tasks generated
according to uniformed Logarithmic probability distribution.
The simulation is done using Simso simulator, which proved the reliability of high
performance by the testimony of many researchers in this field, as it provides the
possibility of generating tasks according to specific probability distributions, and simulates
accurate details related to the characteristics of random tasks.
