Cloudlet Placement and Minimum-Delay Routing in Cloudlet Computing

Meng, Jiaying; Shi, Wenbin; Tan, Haisheng; Li, Xiangyang

doi:10.1109/bigcom.2017.58

Cited by 38 publications

(21 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a cloudlet's task queue may be congested, the study by Cardellini et al [30] considers that users can simultaneously transfer their tasks to the cloud and cloudlet, proposing a solution based on game theory. Due to the dynamic characteristics of the network, Meng et al [31] discussed how cloudlets are placed in wireless APs and how mobile routing jobs make requests to minimise communication delays. An approximation algorithm is used in snapshots for a specific time in different periods to select the AP as a candidate location for the cloudlet.…”

Section: Related Workmentioning

confidence: 99%

Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks

Zeng

Fang

et al. 2020

IET Communications

View full text Add to dashboard Cite

Mobile edge computing (MEC) is concerned with moving complex tasks from data sources to nearby computing resources, which can reduce computing latency and remote cloud workload. Although there has been significant research in the field of MEC, research on edge server placement in wireless metropolitan area networks (WMANs) is overlooked, and the load balancing problem of edge servers is seldom discussed. From a practical perspective, how to place edge servers efficiently in WMANs while considering load balancing between edge servers is studied. A greedy algorithm is proposed that can balance the workload of edge servers more effectively. However, the performance of the greedy algorithm as the number of servers placed increases is not ideal. Therefore, the authors combine the greedy algorithm with a genetic algorithm (GA) to minimise the number of edge servers while ensuring load balancing between edge servers and quality of service (QoS) requirements for mobile users. Finally, they conduct simulation experiments and compare the proposed algorithms with other algorithms. The improved GA proposed is superior to the greedy algorithm in terms of load balancing and the number of servers. The experimental results demonstrate that the algorithm has excellent performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks

Zeng

Fang

et al. 2020

IET Communications

View full text Add to dashboard Cite

show abstract

“…As should be obvious from this figure, when we keep up the absolute limit steady, changing the connection between the limit of single cloudlet and all out number of cloudlets will cause the normal access postponement of irregular calculation and Top-K calculation to vary. With respect to K-CPRRP calculation and DBC algorithm, if the all out number or the limit of single cloudlet is little the normal access postpone will increment [8]. Here we present virtual queuing frameworks, its essential are First In, First Out (FIFO) and Scheduled.…”

Section: An Optimal Opportunistic Scheduling Algorithmmentioning

confidence: 99%

ComputingConcept of Cloudlet in Mobile Cloud computing and Block chain

Singh*,

Marwaha

2020

IJITEE

View full text Add to dashboard Cite

Enhancing the ability to unsociable cloud helped to predict a different computing era called mobile cloud computing (MCC). Though, reserved cloud has numerous limits such as message in form of token interruption and bandwidth mark us to reflect for nearer cloud that carries the awareness of close cloudlet. Also, cloudlet has diverse benefits plus is free from numerous restrictions of various remote clouds. Only little number of resources of cloudlet destructively affect the cloudlet presentation with growing number of large users. In this article, An study of different cloud computing procedures using cloudlet and block chain methods have been defined, which supports to improve the finite reserve cloudlet recital by growing resources of cloudlets. Our purpose make rise the cloudlet presentation with this inadequate cloudlet resource also mark the improved user involvement for the person who is using cloudlet in mobile cloud computing. We investigate plus elucidate the each segment of the planned framework. In adding, we too list the significant character plus prominent benefits of way of performing of Cloudlet.

show abstract

“…Jia et al [28] presented an efficient algorithm to deploy a limited number of cloudlets in a WMAN while balancing their workload. Meng et al [29] raised an algorithm based on the snapshots of the network to place the edge servers in permanent locations and an online job routing algorithm to minimize the communication delay. Taking the cost of deploying edge servers into consideration, Fan et al [30] proposed a cost aware cloudlet placement strategy to optimize the tradeoff between the deployment cost and end-to-end delay.…”

Section: Related Workmentioning

confidence: 99%

Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks

Zeng

Ren

Deng

et al. 2018

Sensors

View full text Add to dashboard Cite

Remote clouds are gradually unable to achieve ultra-low latency to meet the requirements of mobile users because of the intolerable long distance between remote clouds and mobile users and the network congestion caused by the tremendous number of users. Mobile edge computing, a new paradigm, has been proposed to mitigate aforementioned effects. Existing studies mostly assume the edge servers have been deployed properly and they just pay attention to how to minimize the delay between edge servers and mobile users. In this paper, considering the practical environment, we investigate how to deploy edge servers effectively and economically in wireless metropolitan area networks. Thus, we address the problem of minimizing the number of edge servers while ensuring some QoS requirements. Aiming at more consistence with a generalized condition, we extend the definition of the dominating set, and transform the addressed problem into the minimum dominating set problem in graph theory. In addition, two conditions are considered for the capacities of edge servers: one is that the capacities of edge servers can be configured on demand, and the other is that all the edge servers have the same capacities. For the on-demand condition, a greedy based algorithm is proposed to find the solution, and the key idea is to iteratively choose nodes that can connect as many other nodes as possible under the delay, degree and cluster size constraints. Furthermore, a simulated annealing based approach is given for global optimization. For the second condition, a greedy based algorithm is also proposed to satisfy the capacity constraint of edge servers and minimize the number of edge servers simultaneously. The simulation results show that the proposed algorithms are feasible.

show abstract

Cloudlet Placement and Minimum-Delay Routing in Cloudlet Computing

Cited by 38 publications

References 16 publications

Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks

Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks

ComputingConcept of Cloudlet in Mobile Cloud computing and Block chain

Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks

Contact Info

Product

Resources

About