Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

IEEE Trans. Emerg. Top. Comput. Intell.

et al. 2020

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

Section: B Sias In CC and Ecmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

IEEE Trans. Emerg. Top. Comput. Intell.

et al. 2020

“…They subsume the obtained insights into an intelligent architecture including Ant Colony Optimization and Self Organizing Map. In [20], a resource allocation method (LET-ACO) is proposed and about 40% energy consumption can be avoided in LET-ACO.…”

Section: B Related Work Applying Aco To Tsp and Mtspmentioning

confidence: 99%

Ant Colony Optimization With an Improved Pheromone Model for Solving MTSP With Capacity and Time Window Constraint

Ma²,

et al. 2020

IEEE Access

Self Cite

The optimization of logistics distribution can be defined as the multiple traveling salesman problem (MTSP). The purpose of existing heuristic algorithms, such as Genetic Algorithm (GA), Ant Colony Algorithm (ACO), etc., is to find the optimal path in a short time. However, two important factors of logistics distribution optimization, including work time window and the carrying capacity of the vehicle in distribution system, have been ignored. In this paper, we consider the influences of time limitation of modern commercial logistics and carrying capacity of the vehicle on the logistics optimization, and then propose a MTSP with constraints of time window and capacity of each salesman. We design a novel hybrid algorithm by combining the minimum spanning 1-tree with ACO to find the optimal solution. In addition, we improve the pheromone update rules to increase the search efficiency of ACO algorithm. The experiments show that the novel hybrid algorithm achieves a shorter path than the other algorithms. INDEX TERMS multiple traveling salesman problem, ant colony optimization, time window, capacity

“…For the multi-task scenarios with sequential constraints, sun et al [15] proposed an energy-aware mobility management algorithm, and the cost of switching between tasks is considered. In addition, the literatures [16]- [23] also investigated the problem of task offloading, but the environment is cloud system.…”

Section: Related Workmentioning

confidence: 99%

“…The fifth constraint (15) indicates that the proportion of computing resource x c i,j allocated to offloaded tasks does not exceed the MEC total computing resource. The sixth constraint (16) indicates that the upload power p i of the offloaded user does not exceed the value of the maximum power p max i of the UE. The seventh constraint (17) indicates that the total execution delay of the UE does not exceed the value of maximum delay T max i .…”

Section: Optimization Model Of Multi-user Offloading Benefitsmentioning

confidence: 99%

Joint Wireless Source Management and Task Offloading in Ultra-Dense Network

Pang

2020

IEEE Access

Self Cite

The ultra-dense network (UDN) based on mobile edge computing (MEC) is an important technology, which can achieve the low-latency of 5G communications and enhance the quality of user experience. However, how to improve the task offloading efficiency is a hot topic of UDN under the constraint on the limited wireless resources. In this article, we propose a heuristic task offloading algorithm HTOA to optimize the delay and energy consumption of offloading tasks in UDN. Firstly, a convex programming model for MEC resource allocation is established, which aims to obtain the optimal allocation set of resources for offloading tasks, and optimize the execution delay of offloading tasks. Followed by, the problem of joint channel allocation and user upload power control is solved by the greedy strategy and golden section method, which aims to optimization the delay and energy consumption of task upload data. Compared with the random task offloading algorithm, numerical simulations show that the algorithm HTOA can effectively reduce the delay and energy consumption of task offloading, and perform better as the number of users increases. INDEX TERMS Ultra-dense network (UDN), mobile edge computing (MEC), task offloading.