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Emerging Internet-of-Everything (IoE) services require connected devices to respond instantly and operate for long durations. As smart mobile devices (SMDs) are often powered by batteries of limited capacity, offloading some computational tasks to nearby edge servers is a promising solution to reduce the latency and energy consumption of SMDs in operation. However, a challenge of computation offloading in the IoE system is the large number of SMDs that need to be handled. To address this problem, in this paper, we propose an intelligent two-stage computation offloading scheme with multiple optimization objectives. In the first stage, we categorize the computation tasks into classes (e.g., computation-intensive, data-intensive) and make early offloading decisions on some classes of tasks with offloading preferences. In the second stage, we make offloading decisions on the remaining tasks by solving a multi-objective optimization problem using the powerful Non-dominated Sorting Genetic Algorithm (NSGA-II). This two-stage design can help reduce the size of task instances in the second optimization stage, thus accelerating the convergence of the NSGA-II algorithm. The extensive simulation results show that, compared to the existing NSGA-II algorithm-based optimization methods, the proposed offloading scheme improves the performance by 10% in terms of latency and energy consumption. • The data transmission and task execution processes are modeled as queueing systems. • A model is proposed to balance the data-intensive and computation-intensive tasks. • An algorithm is proposed to make offloading decisions in advance for heavy tasks using preferences. • The modified NSGA-II algorithm is applied to the remaining computation tasks to obtain the optimal offloading decisions. • Comprehensive experiments are conducted for comparisons of the proposed method with some benchmark methods. [ABSTRACT FROM AUTHOR]

Network-accessible task offloading is for low latency; offline task offloading is for low cost. Offline devices cannot directly access service nodes due to lack of resources. Accordingly, the latter involves more steps and optimization variables such as: where to offload tasks, how to allocate computation resources, how to adjust offloading ratio and transmit power, and such optimization variables and hybrid combination features are highly coupled with each other. In this paper, we first formulate a Mixed Integer Nonlinear Programming Problem (MINLP) for such task offloading under energy and delay constraints. Furthermore, we decompose it into two subproblems so as to efficiently solve the formulated MINLP, and design a low-cost and low-complexity Joint Optimization for Energy Consumption and Task Processing Delay (JOET) algorithm to optimize selection decisions, resource allocation, offloading ratio and transmit power adjustment. We carry out extensive simulation experiments to validate JOET. Simulation results demonstrate that JOET outperforms many representative existing schemes in quickly converge and effective reduction of energy consumption and delay. Specifically, the average energy consumption and the average delay have been reduced by 15.93% and 13.70%, respectively, and the load balancing efficiency has increased by 10.20%. [ABSTRACT FROM AUTHOR]