Towards Edge Slicing: VNF Placement Algorithms for a Dynamic &amp; Realistic Edge Cloud Environment

2018 IEEE Global Communications Conference (GLOBECOM)

Naas

2018

Self Cite

5G, as the next phase of mobile communications standards, intends to offer connectivity with greater throughput, higher capacity, lower latency and higher mobility range. 5G is promised to meet the demands of emerging applications, such as Internet of things (IoT) (e.g., wearables, connected cars, mobile phones, robots, and smart home appliances) to access to the Internet. However, the devices on IoT are expected to grow exponentially in the following years, resulting a dramatic increase in bandwidth requirements. Thus, more efficient management and planning of the network's bandwidth resources are essential in the evolution of mobile systems. In this vein, we propose an Intelligent Scheme (IS) to schedule the communications in the mobile systems for enabling emerging applications, such as connected cars. In our scheme, a core component controller is added to the network, which consists of two parts: the controller database and the controller server. The resource availability status of each cell is recorded in the database. The controller server cooperates with the Intelligent Start Algorithm (ISA) on the end-user controlling the traffic of the whole network. The uploading of time-non-sensitive content is delayed if there are not enough resources at the located cell. We evaluate the performance of this Intelligent Scheme (IS) based on NS-3. The obtained results indicate that our Intelligent Scheme (IS) manages to reduce the packet loss and improve the Quality of Experience (QoE) for users. As most of the added functions can run in the format of software, no dedicated hardware is needed and the overall system cost is expected to be minimal.

Section: B Results Analysismentioning

confidence: 99%

Scheduled Communications in Next Generationmobile Networks

Taleb

2018 IEEE Global Communications Conference (GLOBECOM)

Naas

2018

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“…Although in several other deployments, UE-related parameters (e.g., delay and mobility) were neglected, they were considered in the recent work of the authors [14], [19], [23]- [25], [28], [29]. In [14], a new simulator dubbed Network Slice Planner (NSP) was introduced. NSP defines a tool to simulate mobile service usage over a particular geographical area and in real time 1 .…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Logic-based Mechanism for An Efficient Cloud Resource Planning

Laghrissi

Taleb

2019 IEEE Wireless Communications and Networking Conference (WCNC)

et al. 2019

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The key concept beneath Multi-Access Edge Computing (MECs) is to place cloud resources in closer proximity to end-users, through the installation of small-scale cloud infrastructures at the network edge. In MEC environments, we identify two issues: 1) data about users' activities are not always available, and 2) the available virtual resource planning mechanisms (i.e., algorithms for the placement of Virtual Network Functions-VNFs) are not efficient enough to fulfill the QoS requirements and deployment costs. In this vein, we design a layered framework to define the presence of Mobile BroadBand User Equipments (UEs) and automate the underlying virtual resource placement and management based on the Fuzzy Logic Controller paradigm (FLC). Experimentation results show that our framework, compared to baseline solutions, achieves good performance results; the end-to-end delay is enhanced by 25%, the resource consumption is reduced by 30%, and the environmental impact, reflected by the carbon footprint that depends on the amount of deployed Virtual Machines (VMs), is reduced by 50%.

“…To validate the proper operation of our solution, we employed two software tools: i) the "Network Slice Planner" NSP [13], and ii) a system-level simulator of an LTE network.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…NSP is a simulation tool that implements accurate models for the users' behavior and mobility, and a compound traffic model for cellular networks. We used the NSP [13] to generate synthetic signaling workload in an LTE network. We extended the compound traffic model of this tool by including the traffic models employed in [7].…”

Section: A Experimental Setupmentioning

confidence: 99%

A Queuing Based Dynamic Auto Scaling Algorithm for the LTE EPC Control Plane

Prados-Garzon

Laghrissi

2018 IEEE Global Communications Conference (GLOBECOM)

et al. 2018

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This is the accepted version of the original article published by IEEE.Abstract-Network softwarization paradigm, whose main enabler is Network Functions Virtualization (NFV), facilitates the automation of the management operations and orchestration of the future networks, thus reducing the operational expenditures of the network. The envisioned management practices include the introduction of automation in the scaling of network services. This may enable operators to handle workload fluctuations to keep the desired performance with great agility and reduced costs. This procedure introduces a non-negligible delay in allocating or releasing virtual resources. Therefore, waiting until the system is overloaded or underutilized so as to scale resources up or down could negatively impact user Quality of Experience, or lead to inefficient resource utilization. In this vein, this paper proposes a novel and agile Dynamic Auto Scaling Algorithm for the Long Term Evolution (LTE) virtualized Evolved Packet Core (vEPC) Control Plane (CP). The resources dimensioning stage of the algorithm is based on an original queuing model for the LTE CP. To model the LTE CP, we use an open network of G/G/m queues. We also provide expressions to derive the steady state transition probabilities of the queuing network. Finally, we validate the proper operation of our solution using accurate simulation tools.