Enabling Low Latency Services on LTE Networks

García-Pérez, Cesar A.; Merino, Pedro

doi:10.1109/fas-w.2016.59

Cited by 17 publications

(23 citation statements)

References 11 publications

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“…The most time‐consuming procedure of the control plane is the Attach procedure that is used to register the UE in the network. The measurements obtained in our previous work show that the procedure can take up to 1 second. This procedure will normally have a low impact on the data plane, as it is done once at the beginning of the connection, but it is important as it includes the negotiation of the default data plane tunnels.…”

Section: Lte and Sources Of Latencymentioning

confidence: 99%

“…The research possibilities around the technologies are wide and span many different areas; in the work of Mao et al, a detailed survey on the different aspects of MEC is provided. To position our paper regarding related work, we first summarize complementary approaches that were presented with the paper that originated this work, then we provide a comparison of our work with the solutions related to latency reduction.…”

Section: Related Workmentioning

confidence: 99%

“…This paper presents 2 approaches to implement fog computing in mobile networks to reduce latency for critical services. The first proposal, called Fog Gateway, was presented in our previous work . More details about this approach are discussed in this paper, including possible evolutions toward 5G networks and alternative designs to improve the behavior and experimental validation of the proposals.…”

Section: Introductionmentioning

confidence: 99%

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Experimental evaluation of fog computing techniques to reduce latency in LTE networks

García-Pérez

Merino

2017

Trans Emerging Tel Tech

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The changes in new mobile networks toward a full Internet protocol-based architecture have led to opportunities for service-oriented optimizations based on emergent technologies like fog computing, software-defined networking, or network function virtualization. This paper explores 2 ways of using these new technologies to reduce the latency in Long-Term Evolution (LTE) networks. Both solutions reduce the path that the data packets should follow from the base station (evolved Node B [eNB]) to the network components that connect to the servers. The first solution, called Fog Gateway, is based on the interception of the packets in the tunnel at the eNB and their redirection to local servers running the fog services. This solution is fully compliant with the current LTE architecture and only requires new components. The second solution, called General Packet Radio Service Tunneling Protocol Gateway (GTP), is based on splitting the eNB's functionality to avoid unnecessary GTP encapsulation of the packets geared toward the fog services. This paper includes an analysis of the latency split in LTE networks, the evaluation of both solutions with experiments in an end-to-end LTE network testbed, and a discussion around their applicability in future fifth-generation networks. The results confirm that they are feasible to provide low-latency services and that they are compatible with some of the emergent paradigms (software-defined networking and network function virtualization) as well as with the studies on fifth-generation networks from the standardization bodies.

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Section: Lte and Sources Of Latencymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experimental evaluation of fog computing techniques to reduce latency in LTE networks

García-Pérez

Merino

2017

Trans Emerging Tel Tech

Self Cite

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“…The RTT of existing high speed is still high for real-time migration of VM workloads. [22][23][24] Lee et al 25 proposed an architecture-aware native code-offloading framework for mobile devices. The framework does not rely on code annotation and virtualization for offloading.…”

Section: Related Workmentioning

confidence: 99%

SIMDOM: A framework for SIMD instruction translation and offloading in heterogeneous mobile architectures

Shuja

Gani

et al. 2017

Trans Emerging Tel Tech

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Fog and mobile edge computing is a paradigm that augments resource‐scarce mobile devices with resource‐rich network servers to enable ubiquitous computing. Smartphone applications rely on code offloading techniques to leverage high‐performance computing opportunities available on edge and cloud servers for compute‐intensive applications. Mobile (ARM) and edge/cloud (x86) architectures are heterogeneous and necessitate dynamic binary translation for compiled code migration that increases the application execution time. The application execution time and energy consumption should be lesser on the edge/cloud server as compared with the local mobile execution for optimal offload. Multimedia‐based applications contain a large set of single instruction multiple data (SIMD) instructions that are compute and resource intensive. However, dynamic binary translation techniques of SIMD instructions lose the parallelism and optimization because of inefficient vector‐to‐scalar translation. We present a framework for SIMD instruction translation and offloading for mobile devices (SIMDOM) in cloud and edge environments. The SIMDOM framework reduces the execution overhead of migrated vectorized multimedia application by using vector‐to‐vector instruction mappings. The framework maps and translates ARM SIMD intrinsic instructions to x86 SIMD intrinsic instructions such that an application programmed for the mobile platform can be executed on the cloud server without any modification. The offload decision is based on inputs from the device energy, network, and application profilers. Experiments show that SIMDOM framework provides 84.78%, 3.41%, and 79.93% energy, time, and performance efficiency, respectively, compared with local offload‐disabled execution. Compared with compiled code offloading, the SIMDOM framework provides 55.99%, 57.50%, and 96.23% energy, time, and performance efficiency, respectively.

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“…In [33], the authors examined the Fog Gateway (FG) as an intermediate component in the Long Term Evolution (LTE) to reduce the communication time in mobile networks among users in the same area. FG can prevent all traffic of specific services from reaching the core network by analyzing the inner destination IP address of the traffic using tunneling protocol (GTP) and then determining whether to route the packet to the fog network or to forward it to the destination Service Gateway (SGW).…”

Section: Related Workmentioning

confidence: 99%

Delay-aware power optimization model for mobile edge computing systems

Jararweh

Al‐Ayyoub

Al-Quraan

et al. 2017

Pers Ubiquit Comput

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Reducing the total power consumption and network delay are among the most interesting issues facing large-scale Mobile Cloud Computing (MCC) systems and their ability to satisfy the Service Level Agreement (SLA). Such systems utilize cloud computing infrastructure to support offloading some of user's computationally heavy tasks to the cloud's datacenters. However, the delay incurred by such offloading process lead the use of servers (called cloudlets) placed in the physical proximity of the users, creating what is known as Mobile Edge Computing (MEC). The cloudlet-based infrastructure has its challenges such as the limited capabilities of the cloudlet system (in terms of the ability to serve different request types from users in vast geographical regions). To cover the users demand for different types of services and in vast geographical regions, cloudlets cooperate among each other by passing user requests from one cloudlet to another. This cooperation affects both power consumption and delay. In this work, we present a mixed integer linear programming (MILP) optimization model for MEC systems with these two issues in mind. Specifically, we consider two types of cloudlets: local cloudlets and global cloudlets, which have higher capabilities. A user connects to a local cloudlet and sends all of its traffics to it. If the local cloudlet cannot serve the desired request, then the request is moved to another local cloudlet. If no local cloudlet can serve the request, then it is moved to a global cloudlet which can serve all service types. The process of routing requests through the hierarchical network of cloudlets increases power consumption and delay. Our model minimizes power consumption while incurring an acceptable amount of delay. We evaluate it under several realistic scenarios to show that it can indeed be used for power optimization of large-scale MEC systems without violating delay constraints.

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Enabling Low Latency Services on LTE Networks

Cited by 17 publications

References 11 publications

Experimental evaluation of fog computing techniques to reduce latency in LTE networks

Experimental evaluation of fog computing techniques to reduce latency in LTE networks

SIMDOM: A framework for SIMD instruction translation and offloading in heterogeneous mobile architectures

Delay-aware power optimization model for mobile edge computing systems

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