Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

Abstract

Edge cloud is a cloud computing system built on edge infrastructure. Task scheduling optimization is the key technology to ensure the quality of service in edge cloud. However, the openness of the edge cloud environment challenges the load balancing and profit optimization of task scheduling. In this paper, we analyze the business process and optimization factors of task scheduling in edge cloud. First, we propose a resource constrained task scheduling profit optimization algorithm (RCTSPO), which consists of clustering preprocessing, classification, profit matrix construction and optimal scheduling strategy calculation. Clustering preprocessing gathers similar tasks into one class and perform a classification on the clustered tasks. Then construct the profit matrix for resource constrained task scheduling, and the optimal task scheduling strategy is obtained based on the constructed profit matrix. Second, Petri nets are used to construct the different components of edge cloud, such as resource, task, user request and virtual machine, thus forming the task scheduling model of edge cloud. Third, the properties of task scheduling model are verified by using the related theory and tools of Petri nets. Finally, several experiments are done to evaluate the proposed method, the simulation results show that the algorithm not only achieves the maximum profit, but also performs well in terms of time, reliability and load balancing of task scheduling.

A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing

“…Therefore, several works concerned the optimization of the resource cost or the profit for service providers. For example, Chen et al (2020) presented a task scheduling method to optimize the profit, where the value of a task was proportional to the resource amounts and the time it took, and resources were provided in the form of VM. Their proposed method first classified tasks based on the amount of its required resources by K-means.…”

“…Task scheduling or offloading is an effective way for optimizing the task performance and the resource efficiency for DE3C, which decides the location (the corresponding device, an edge or a cloud) where each task to be processed (offloading decision) and the computing resources which each task performs on in a specified order (task assignment and ordering) ( Wang et al, 2020b ; Islam et al, 2021 ). Therefore, several works have proposed various task scheduling methods trying to optimize the response time ( Han et al, 2019 ; Meng et al, 2019 ; Meng et al, 2020 ; Apat et al, 2019 ; Ren et al, 2019 ; Liu et al, 2019a ; Wang et al, 2021 ), the resource cost ( Mahmud et al, 2020 ; Gao et al, 2019 ; Chen et al, 2019 ) or the profit ( Chen et al, 2020 ; Yuan & Zhou, in press ) for providing services in DE3C. These works were concerned on addressing only one or two sub-problems of task scheduling, e.g.…”

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

“…A very few works concern the QoE as a metric directly. energy Gao et al [49] independent full cost Chen et al [50] independent full cost Chen et al [51] independent full profit Yuan et al [52] independent full profit Lin et al [53] independent full performance, energy Du et al [54] independent full performance, energy Duan et al [55] independent full performance, energy Mahmud et al [56] independent full performance, profit Li et al [57] independent full Performance, cost Sun et al [58] independent full performance, cost Adhikari et al [59] independent full performance, utilization Ma et al [60] independent full QoE, cost Miao et al [61] independent partial performance Kai et al [62] independent partial performance Guo et al [63] independent partial performance Meng et al [64], [65] independent partial performance hop-e Cui et al [66], [67] independent partial performance hop-d, hop-e Sarkar et al [68] independent partial performance hop-e Ouyang et al [69] independent partial performance Y Cheng et al [70] independent partial energy Xia et al [71] independent partial energy Zhang et al [72] independent partial cost Chabbouh et al [73] independent partial performance, balance Y Wang et al [74] independent partial performance, cost Zhao et al [75] independent partial performance, cost Khayyat et al [76] independent partial performance, energy Alshahrani et al [77] independent partial performance, energy Chen et al [78] independent partial performance, cost, energy Hong et al [16] independent partial performance, energy hop-d Sun et al [79] independent partial performance, energy Long et al [80] independent partial performance, energy Nguyen et al…”

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

“…Despite of the efficacy, the aforesaid VM migration or load‐balancing process undergoes uncertainty such as iterative hotspot creation, 10,11 higher SLAV, and more downtime that imposes service reliability 11 . In sync with the exceedingly rising demands and aforesaid challenges, there is the need of a robust and highly efficient load‐management strategies 10–16 where the multiple nodes or VMs could be provided sufficient resource without imposing any SLAV and/or downtime. Moreover, scheduling VM migration while guaranteeing QoS/SLA sensitive and energy efficiency can be of great significance 13–16 …”

“…Although the use of prediction concept can help avoiding false‐positive scheduling, not significant efforts are made so far. Furthermore, for SLA‐centric cloud service provision, there is the need of network‐sensitive allocation even under constrained SLA conditions 5,12,17,18 . Unlike classical bin packing‐oriented VM consolidation concepts, to ensure QoS demands, the migration controller requires considering other performance aspects as well, especially under dynamic cloud condition 19–24 .…”