A collaborative mobile edge computing and user solution for service composition in 5G systems

Ridhawi, Ismaeel Al; Aloqaily, Moayad; Kotb, Yehia; Ridhawi, Yousif Al; Jararweh, Yaser

doi:10.1002/ett.3446

Cited by 78 publications

(52 citation statements)

References 58 publications

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“…In the future, we will extend from the control of traffic signals at adjacent intersections to the control of signals at regional intersections, to realize the coordinates of vehicles, roads and people in the region. In addition, we can improve cloud computing capability through the emerging automobile network cloud technology, such as mobile edge computing [39]. The rapid development of communication technology provides a strong guarantee for the calculation and sharing of big data in the vehicular cloud environment.…”

Section: Discussionmentioning

confidence: 99%

Queue Spillover Management in a Connected Vehicle Environment

et al. 2018

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To alleviate the queue spillovers at intersections of urban roads during rush hours, a solution to the cross-spill problem based on vehicle networking technologies is proposed. This involves using connected vehicle technology, to realize the interactive information on vehicle and intersection signal control. The maximum control distance between intersections is determined by how vehicles are controlled and would travel in that connected environment. A method of calculating overflow tendency towards intersection queuing is also proposed, based on the maximum phase control distance. By this method, the intersection overflow is identified, and then the signal phases are re-optimized according to the requirements of different phases. Finally, overflow prevention control was also performed in this study. The VISSIM simulation results show that the method can better prevent the overflow of queues at intersections.

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Section: Discussionmentioning

confidence: 99%

Queue Spillover Management in a Connected Vehicle Environment

et al. 2018

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“…We use packets of eight different sizes including (0.5, 1, 1. 5,4,8,16,32, and 64 kB). The simulation experiment with each packet size is carried out five times.…”

Section: Impact Of Packet Size On Switch Energy Consumption Without Mmentioning

confidence: 97%

Integrated network and hosts energy management for cloud data centers

Al-Jarrah

Al-Zoubi

Jararweh

2019

Trans Emerging Tel Tech

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Cloud computing provides online services to users from anywhere in a pay‐as‐you‐go basis. This leads to the construction of different forms of data centers around the world, which usually consumes huge amount of energy and produce pollution (heat and carbon dioxide footprint). Many efforts have focused on managing cloud resources to decrease their energy consumption. Since servers are the highest energy consumers in data centers, most researchers have focused on optimizing their energy consumption while other resources such as network devices and cooling systems have received less attention. In this paper, we propose a network‐aware virtual machine (VM) relocation algorithm, which optimizes the distribution of the VMs running on the data center in order to conserve energy in both servers and network devices (switches). The proposed technique targets the communicated VMs and places them closer to each other. This would reduce the traffic overhead between any communicating VMs so that fewer network devices are involved in the communication process. This also increases the VMs consolidation to some servers while leaving other servers idle. Switching off the idle servers and network devices would significantly reduce the total energy consumed by the data center. The proposed algorithm is evaluated using simulation and the performance of the proposed algorithm is compared against different previously proposed algorithms in the literature. Performance metrics used are the energy consumed by the host and by the network switches, the service level agreement violations, and the number of VM migrations. The impact of different packet sizes, the efficiency of switching the network elements to sleep mode, and the impact of resource homogeneity and heterogeneity are also studied and discussed. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of all evaluation metrics.

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“…However, if the mobile user moves frequently, they should select stable MECS at the new location each time. Al Ridhawi et al proposed the solution to decompose the data in the cloud into a set of files and services [28]. They performed caching on the user mobile device to provide frequently requested files and services quickly.…”

Section: Related Workmentioning

confidence: 99%

ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management

2019

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Recently, new mobile applications and services have appeared thanks to the rapid development of mobile devices and mobile network technology. Cloud computing has played an important role over the past decades, providing powerful computing capabilities and high-capacity storage space to efficiently deliver these mobile services to mobile users. Nevertheless, existing cloud computing delegates computing to a cloud server located at a relatively long distance, resulting in significant delays due to additional time to return processing results from a cloud server. These unnecessary delays are inconvenient for mobile users because they are not suitable for applications that require a real-time service environment. To cope with these problems, a new computing concept called Multi-Access Edge Computing (MEC) has emerged. Instead of sending all requests to the central cloud to handle mobile users' requests, the MEC brings computing power and storage resources to the edge of the mobile network. It enables the mobile user device to run the real-time applications that are sensitive to latency to meet the strict requirements. However, there is a lack of research on the efficient utilization of computing resources and mobility support when mobile users move in the MEC environment. In this paper, we propose the MEC-based mobility management scheme that arranges MEC server (MECS) as the concept of Zone so that mobile users can continue to receive content and use server resources efficiently even when they move. The results show that the proposed scheme reduce the average service delay compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because mobile users can continuously receive services, even when they move frequently.

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A collaborative mobile edge computing and user solution for service composition in 5G systems

Cited by 78 publications

References 58 publications

Queue Spillover Management in a Connected Vehicle Environment

Queue Spillover Management in a Connected Vehicle Environment

Integrated network and hosts energy management for cloud data centers

ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management

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