Task deployment has become a hot topic for load balancing in edge computing. In view of the problem that most of the hosts are overloaded in the edge computing, the central load is unbalanced. Much work focuses on the load balancing of the cloud data center or the short-term load balancing of edge data centers. In order to solve the host selection problem of task deployment in joint cloud data centers with edge computing while achieving the overall long-term load balancing, this paper utilizes that the deployment mode of joint cloud model, on this basis, proposes a deployment strategy HEELS based on the analysis of heuristic task clustering method and glowworm swarm optimization algorithm. Its main idea consists of two parts. First, the task with large resources in the current task set is filtered out by the clustering analysis, and the task offloading technology is exploited to upload the result to the cloud computing center for deployment and calculation. Then, the optimized GSO algorithm is exploited in the edge computing center, and the idea of SCA is combined into the optimization of step size so that the optimized GSO algorithm has an adaptive step size, achieving better global search ability in the early stage and better local convergence ability in the later stage. The experimental results show that compared with the existing research, HEELS realizes better load balancing effect and makes the joint datacenter more green and efficient.

The field of live VM (virtual machine) migration has been a hotspot problem in green cloud computing. Live VM migration problem is divided into two research aspects: live VM migration mechanism and live VM migration policy. In the meanwhile, with the development of energy-aware computing, we have focused on the VM placement selection of live migration, namely live VM migration policy for energy saving. In this paper, a novel heuristic approach PS-ES is presented. Its main idea includes two parts. One is that it combines the PSO (particle swarm optimization) idea with the SA (simulated annealing) idea to achieve an improved PSO-based approach with the better global search's ability. The other one is that it uses the Probability Theory and Mathematical Statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization for energy saving as it has considered not only the optimization of the current problem scenario but also that of the future problem. The experimental results demonstrate that PS-ES evidently reduces the total incremental energy consumption and better protects the performance of VM running and migrating compared with randomly migrating and optimally migrating. As a result, the proposed PS-ES approach has capabilities to make the result of live VM migration events more high-effective and valuable.

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA‐LS, which is a heuristic and self‐adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA‐LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA‐LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long‐term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA‐LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high‐effective and meaningful.

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS‐ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC‐based approach with better global exploration’s ability and local exploitation’s ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC‐based process to faster get the final optimal solution. As a result, the whole approach achieves a longer‐term efficient optimization for power saving. The experimental results demonstrate that PS‐ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high‐effective and meaningful.

Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people’s future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user’s social relations in real world and variances in trajectory and then utilize this association to model the user’s mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers’ trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity–based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity–based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches.