Evolutionary Multi-Objective Optimization for Computation Offloading in Collaborative Edge-Cloud Computing

Abstract

Many real-world problems involve multiple and conflicting objectives to be optimized simultaneously, which are formulated as multi-objective optimization problems (MOPs). In general, problems with more than three objectives or a large number of decision variables are referred to as many-objective or large-scale optimization problems (MaOPs or LSMOPs), respectively. Recently, a variety of multi-objective evolutionary algorithms (MOEAs) inspired by nature have been developed to solve MOPs. However, the performance of MOEAs often deteriorates when faced with many-objective or large-scale optimization. In addition, these MOEAs using the classical Pareto dominance principle face no strong selection pressure towards the Pareto front with regard to many-objective optimization. Furthermore, it is difficult for a limited population size to explore the high-dimensional search space of large-scale optimization.

With the development of the Internet of things (IoT) and mobile networks, more and more computation-intensive and latency-critical applications are deployed to mobile devices. Due to some limitations inherent to mobile devices including limited computing capability, storage space, and battery lifetime, these applications cannot run efficiently on mobile devices. To address this issue, computation offloading in mobile cloud and edge computing (MCC and MEC) provides a promising paradigm to migrate computation-heavy parts of mobile applications to the cloud and edge servers. Hence, MCC and MEC computation offloading may involve different objectives such as reducing time and saving energy. Offloading decision making can be described as multi-criteria optimization and we develop efficient MOEAs to solve different computation offloading models.

This work covers evolutionary multi-objective optimization for computation offloading in collaborative MCC and MEC. Its main content includes two parts: (i) evolutionary multi-objective optimization and (ii) optimization in offloading. Specifically, the contributions of this thesis can be summarized as follows:

Proposing a multi-objective artificial bee colony algorithm based on decomposition to improve convergence and diversity for solving normalized and scaled MOPs.

Developing a many-objective evolutionary algorithm with adaptive weight vectors for dealing with normalized and scaled MaOPs.

Designing a novel archive maintenance for adapting weight vectors in decomposition-based evolutionary algorithms for handling MOPs and MaOPs with irregular Pareto fronts.

Proposing three constrained MOEAs to deal with constrained MOPs as well as offloading problems in IoT-edge-cloud computing networks.

Exploring and comparing two evolutionary large-scale sparse multi-objective optimization algorithms for tackling collaborative edge-cloud offloading problems.

Studying a novel multi-objective computation offloading algorithm to solve offloading problems with the consideration of compression, security and mobility.