Fast algorithms for capacitated cloudlet placements

Ma, Longjie; Wu, Jigang; Chen, Long; Liu, Zhusong

doi:10.1109/cscwd.2017.8066734

Cited by 30 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective is to minimize the average access delay between the mobile users and the activated cloudlets serving the users. Ma et al [13] propose a New Heuristic Algorithm (NHA) and a Particle Swarm Optimization (PSO) algorithm for the delaying problem. In the above approaches, cloudlets are fixed in the placement, which cannot maintain efficiency in response to the requirements of applications for frequently moving devices.…”

Section: Related Workmentioning

confidence: 99%

“…Although there is an increasing number of researches in mobile cloudlet offloading technology [9][10][11], fewer researches about how to place cloudlets in a given network region are conducted [3,7,[12][13][14]. It is necessary to pay attention to how cloudlets should be placed in a given network since the suitable cloudlets placements contribute to enhancing the cloud service for dynamic context-aware mobile applications.…”

Section: Introductionmentioning

confidence: 99%

“…The price of the cloudlets is so expensive that the number of the cloudlets is limited, so we should maximize the utilization of cloudlets by a suitable cloudlets placement. Some current researches pay attention to the cloudlets placement to alleviate the low utilities of cloudlets [3,[12][13][14], the cloudlets placement in their studies are fixed, but, in practice, users are constantly moving. Fixed-placed cloudlets cannot maintain efficiency in responding to the application requirements for the constant mobile devices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Adaptive Cloudlet Placement Approach for Mobile Application on Spark

Zhang

Wang

Zhou

et al. 2018

Security and Communication Networks

View full text Add to dashboard Cite

The applications of mobile devices are increasingly becoming computationally intensive while the computing capability of the user’s mobile device is limited. Traditional approaches offload the tasks of mobile applications to the remote cloud. However, the rapid growth of mobile devices has made it a challenge for the remote cloud to provide computing and storage capacities with low communication delays due to the fact that the remote cloud is geographically far away from mobile devices. Reducing the completion time of applications in mobile devices through the technical expending mobile cloudlets which are moving collocated with Access Points (APs) is necessary. To address the above issues, this paper proposes EACP-CA (Enhanced Adaptive Cloudlets Placement approach based on Covering Algorithm), an enhanced adaptive cloudlet placement approach for mobile applications in a given network area. We apply the CA (Covering Algorithm) to adaptively cluster the mobile devices based on their geographical locations, the aggregation regions of the mobile devices are identified, and the cloudlet destination locations are also confirmed according to the clustering centers. In addition, we can also obtain the traces between the original and destination locations of these mobile cloudlets. To increase the efficiency, we parallelize CA on Spark. Extensive experiments show that the proposed approach outperforms the existing approach in both effectiveness and efficiency.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Adaptive Cloudlet Placement Approach for Mobile Application on Spark

Zhang

Wang

Zhou

et al. 2018

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Ma et al [24] realized the placement of cloudlets in WMAN is important and proposed a New Heuristic Algorithm (NHA) to reduce the sorting process of APs running time to solve the delay problem. Xiang et al [25] proposed an adaptive cloud placement method for GPS big data mobile applications that adjusts the placement of the cloud by identifying the aggregated area of mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Cloudlet Management for Privacy Preservation in Wireless Metropolitan Area Networks

Huang

Dou

et al. 2018

Security and Communication Networks

View full text Add to dashboard Cite

Currently, cloudlet based wireless metropolitan area network (WMAN) is emerging as an effective paradigm to extend the performance of mobile devices, which enables the execution of computational intensive mobile applications. But the normal operation of cloudlets consumes a large amount of energy, which brings about carbon dioxide emissions, aggravation of the greenhouse effect, etc. Meanwhile the data transmission of various mobile users among cloudlets would cause leakage of personal privacy. In view of this challenge, we propose an energy-efficient cloudlet management method, named ECM, for privacy preservation in WMAN. Technically, an optimization model is designed to formalize our problem. Then, based on live virtual machine (VM) migration technique, a corresponding privacy-aware VM scheduling method for energy saving is designed to determine which VMs should be migrated and where they should be migrated. Finally, experimental data demonstrate that the proposed method is both efficient and effective.

show abstract

“…is work is also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), and introduced to calculate the response delay of o oading tasks and utilized two algorithms to solve the optimization problem which could minimize the maximum response time [39]. Ma et al [40] presented a new heuristic algorithm (NHA) and particle swarm optimization (PSO) algorithm for reducing delay which are more e ective than existing methods. Rodrigues et.al.…”

mentioning

confidence: 99%

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Xue

Cui

et al. 2019

Complexity

View full text Add to dashboard Cite

By means of the complex systems, multiple renewable energy sources are integrated to provide energy supply for users. Considering that there are massive services needed to process in complex systems, the mobile services are offloaded from mobile devices to edge servers for efficient implementation. In spite of the benefits of complex systems and edge servers, massive resource requirements for implementing the increasing resource requests decrease the execution efficiency and affect the whole resource usage of edge servers. Therefore, it remains an issue to achieve dynamic scheduling of the computing resources across edge servers. With the consideration of this issue, a Balanced Resource Scheduling Method, named BRSM, for trade-offs between virtual machine (VM) migration cost and energy consumption of VM migrations for edge server management, named BRSM, is designed in this paper. Technically, we analyze the load conditions of edge servers and formulate the energy consumption of VM migrations and VM migration cost as a multi-objective optimization problem. Then, we propose a dynamic resource scheduling method for WMAN to deal with the multi-objective optimization problem. In addition, nondominated sorting genetic algorithm III (NSGA-III) is adopted to generate optimal resource scheduling strategies. Finally, we conduct experiment simulations to testify the efficiency of the proposed method BRSM.

show abstract

Fast algorithms for capacitated cloudlet placements

Cited by 30 publications

References 11 publications

Enhanced Adaptive Cloudlet Placement Approach for Mobile Application on Spark

Enhanced Adaptive Cloudlet Placement Approach for Mobile Application on Spark

Energy-Efficient Cloudlet Management for Privacy Preservation in Wireless Metropolitan Area Networks

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Contact Info

Product

Resources

About