مختصر البحث:
The unpredictable and huge data generation nowadays by smart devices from IoT and mobile crowd sensing
(MCS) applications like sensors, and smartphones requires processing power and rapid responses in a specific time
frame (deadline), and user cos…
The unpredictable and huge data generation nowadays by smart devices from IoT and mobile crowd sensing
(MCS) applications like sensors, and smartphones requires processing power and rapid responses in a specific time
frame (deadline), and user cost, i.e., budget. Cloud provides these capabilities to serve organizations and customers,
but when using cloud appear some limitations, the most important is task scheduling. Nevertheless, the most of earlier
scheduling algorithms have focused on only one evaluation parameter such as makespan or cost and so on, as the
scheduling process goal. Optimizing a single metric may not ensure an improvement in the cloud performance in
general, so the need may arise for algorithms that concentrate more broadly on enhancing more than one parameter.
Therefore, this paper presents a deadline-budget multi-objective dynamic scheduling scheme (DB-MODS) to execute
user tasks on VMs within QoS limits in order to finish the task within the deadline and budget if possible. DB-MODS
using K-Means based on task length and deadline for clustering incoming tasks into groups and categories VMs based
on capacity thresholds. In addition, the task belonging to every cluster is assigned to a suitable VM in VMs groups.
based on objective functions, which can be one for user request depending on (deadline and budget) and the second is
for system depending on (execution time and cost). This paper used CloudSim plus to simulate and evaluate our
approach. The DB-MODS approach performance was compared to scheduling algorithms from the previous literature.
Both random and Google cloud jobs (GOCJ) workloads are used to evaluate the efficiency of DB-MODS. The results
show that DB-MODS outperforms the other algorithms by minimizing makespan by 41%, energy by 46%, and
increasing success ratio by 35%, in comparison to existing load balancing and scheduling methods: EEVS, LAS,
Greedy-R, DTS, Random, and Greedy-P in the first scenario. Minimizing makespan by 46%, cost by 44%, and
maximize throughput by 80% when compared with MOCS, Max-Min, HABC-LJF, FCFS, MOPSO, Q-learning,
MOABCQ-FCFS, and MOABCQ-LJF in a second scenario, and minimizing makespan by 46%, and increase
throughput by 39% when compared with HESGA, GA, and ACO in a third scenario in average. In addition to the
possibility of applying and employing DB-MODS for scheduling deadline-critical tasks in a heterogeneous cloud
system.
