Abstract
Mobile users typically have a series of complex tasks consisting of time-constrained workflows and concurrent workflows that need to be processed. However, these tasks cannot be performed directly locally due to resource limitations of the mobile terminal, especially for battery life. Fortunately, mobile edge computing (MEC) has been recognized as a promising technology which brings abundant resource at the edge of mobile network enabling mobile devices to overcome the resource and capacity constraints. However, edge servers, such as cloudlets, are heterogeneous and have limited resources. Thus, it is important to make an appropriate offloading strategy to maximize the utility of each cloudlet. In view of this, the time consumption and energy consumption of mobile devices and resource utilization of cloudlets have been taken into consideration in this study. Firstly, a multiconstraint workflow mode has been established, and then a multiobjective optimization mode is formulated. Technically, an improved optimization algorithm is proposed to address this mode based on Nondominated Sorting Genetic Algorithm II. Both extensive experimental evaluations and detailed theoretical analysis are conducted to show that the proposed method is effective and efficiency.
Highlights
With the development of Internet core technologies such as Wireless Sensor Network (WSN), Near Field Communication (NFC), and Radio Frequency Identification (RFID), the mobile devices (MDs), such as mobile phones, are in full swing in recent years [1,2,3,4]
Compared with traditional devices, such as the computer, an MD still has some limitations in resources and computing capacity, which leads to mass energy and time consumption, especially for the complex tasks generated by the multiconstraint applications [6,7,8,9]
Both the energy consumption and time consumption of MDs as well as the resource utilization of cloudlets are taken into account. e main contributions of this paper can be summarized as follows: (1) To solve the computation offloading problem for complex tasks in multicloudlet environment, we propose a multiobjective optimization algorithm for complex tasks in multicloudlet environment (MOHWE)
Summary
With the development of Internet core technologies such as Wireless Sensor Network (WSN), Near Field Communication (NFC), and Radio Frequency Identification (RFID), the mobile devices (MDs), such as mobile phones, are in full swing in recent years [1,2,3,4]. Compared with traditional devices, such as the computer, an MD still has some limitations in resources and computing capacity, which leads to mass energy and time consumption, especially for the complex tasks generated by the multiconstraint applications [6,7,8,9]. Due to the long distance between MDs and cloud, transmission latency, which is caused by computation offloading and data transmission, is inevitable and has even been considered as the bottleneck of MCC [16,17,18]. High latency may even lead to huge energy consumption, which is intolerable for MDs [19,20,21], namely, computation offloading in MCC may not be able to meet our demand for the quality of service in some complicated environment
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