Resource and instance hour minimization for deadline constrained dag applications using computer clouds

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Resource and Instance Hour Minimization for Deadline Constrained DAG
Applications Using Computer Clouds
Abstract:
In this paper, we address the resource and virtual machine instance hour
minimization problem for directed-acyclic-graph based deadline constrained
applications deployed on computer clouds. The allocated resources and instance
hours on computer clouds must: (1) guarantee the satisfaction of a deadline
constrained application's end-to-end deadline; (2) ensure that the number of
virtual machine (VM) instances allocated to the application is minimized; (3)
under the allocated number of VM instances, determine application execution
schedule that minimizes the application's makespan; and (4) under the decided
application execution schedule, determine a VM operation schedule, i.e., when a
VM should be turned on or off, that minimizes total VM instance hours needed to
execute the application. We first give lower and upper bounds for the number of
VM instances needed to guarantee the satisfaction of a deadline constrained
application's end-to-end deadline. Based on the bounds, we develop a heuristic
algorithm called minimal slack time and minimal distance (MSMD) algorithm that
finds the minimum number of VM instances needed to guarantee the
application's deadline and schedules tasks on the allocated VM instances so that
the application's makespan is minimized. Once the application execution schedule
and the number of VM instances needed are determined, the proposed VM
instance hour minimization (IHM) algorithm is applied to further reduce the
instance hours needed by VMs to complete the application's execution. Our

experimental results show that the MSMD algorithm can guarantee applications'
end-to-end deadlines with less resources than the HEFT [32], MOHEFT [16], DBUS
[9], QoS-base [40] and Auto-Scaling [25] heuristic scheduling algorithms in the
literature. Furthermore, under allocated resources, the MSMD algorithm can, on
average, reduce an application's makespan by 3.4 percent of its deadline. In
addition, with the IHM algorithm we can effectively reduce the ap- lication's
execution instance hours compared with when IHM is not applied.

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Abstract— Cloud computing is a rising high performance computing environment with a huge scale, heterogeneous collection of self-sufficient systems and elastic computational design. To develop the overall performance of cloud computing, through the deadline constraint, a task scheduling replica is traditional for falling the system power utilization of cloud computing and recovering the yield of service providers. To improve the overall act of cloud environment, with the deadline constraint, a task scheduling model is conventional for reducing the system performance time of cloud computing and improving the profit of service providers. In favor of scheduling replica, a solving technique based on multi-objective genetic algorithm (MO-GA) is considered and the study is determined on programming rules, intersect operators, mixture operators and the scheme of arrangement of Pareto solutions. The model is designed based on open source cloud computing simulation platform CloudSim, to obtainable scheduling algorithms, the result shows that the proposed algorithm can obtain an enhanced solution, thus balancing the load for the concert of multiple objects.

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