A fair QoS-aware dynamic LTE scheduler for machine-to-machine communication

doi:10.1016/j.comcom.2016.03.013

Computer Communications

Volumes 89–90, 1 September 2016, Pages 75-86

https://doi.org/10.1016/j.comcom.2016.03.013 Get rights and content

Abstract

The long term evolution (LTE) standard plays an important role in the development of internet of things (IoT) and machine-to-machine (M2M) communication because of its high data rates, low latency, high flexibility and low cost. However, improvements are needed in the network to support the uplink-heavy traffic generated by M2M communication and also to ensure the diversity of service requirements of this communication and to control the performance reduction in the classical mobile services of human-to-human (H2H) communication. In this paper, we present a new LTE uplink scheduler that dynamically adjusts to the level of congestion of the network based on the current traffic information of each device to support the M2M traffic. Furthermore, the main goals of our approach are (i) satisfy the quality of service (QoS) requirements, (ii) ensure fair allocation of resources and (iii) control the impact of H2H traffic performance. The simulation results demonstrated that the proposed scheduling has good performance according to the three objectives aforementioned.

Introduction

In the internet of things (IoT), a great variety of smart objects (home appliances, automobiles, cell phones, etc.) will be connected to the Internet. In this new scenario, where these smart objects will be connected and interacting with each other, machine-to-machine (M2M) communication will play an important role in the deployment of IoT [1]. While the IoT is focused on the end-points and on the interconnection of physical objects with each other and with humans, the M2M communication is connectivity centric [2] and it refers to automated applications involving devices that transmit and automatically collect data (temperature, humidity, speed, position, heartbeat, etc.) from a remote source without, or with only limited, human intervention through a public network infrastructure (Wi-Fi, WiMax, UMTS, LTE, etc.) [3]. Those applications generally use a large number of low cost devices, in the same area, with various resource constraints such as limited power and limited processing and storage resources [4].

There is a very wide myriad of application areas in which M2M communication can be applied, such as healthcare, security, transportation and smart metering [3]. Furthermore, it is expected that the market for these applications increasingly grow in the next few years. According to Ericsson report, it is estimated that in 2020 there will be 50 billion devices for a global population of 8 billion [5]. Due to this vast number of devices, a large volume of traffic generated by the M2M communication is expected. Therefore, the M2M communication will be the primary source of traffic in the internet of things (IoT) and it will exceed the volume of traffic generated by human-oriented communications (VoIP, media streaming, web browsing, etc.), also called human-to-human (H2H) communication, which are responsible for most of the traffic in the Internet at present.

It is expected also that cellular network infrastructures will play a key role in the deployment of IoT, as well as for M2M communication [6]. The LTE (long term evolution) standard is one of the most promising technologies of cellular network for the development of M2M communication due to its ubiquitous coverage, seamless connectivity, high data rates, low latency, high flexibility and low cost. However, LTE was initially designed to support classical mobile services of H2H communication (VoIP, media streaming, web browsing, etc.) and compared with M2M communication, quite different characteristics are presented in M2M. Amongst these different characteristics, we can mention: (i) infrequent transmission at regular intervals of a small amount of data, (ii) delay tolerance (milliseconds to tens of minutes) and (iii) the main traffic is in uplink [7]. Because of differences between M2M and H2H communications, unexpected problems can occur when introducing M2M communication in LTE network. For instance, when a large number of M2M devices are connected in a same area, this may lead to shortage of radio resources and reduction of H2H traffic performance.

Therefore, one of the mechanisms of LTE that needs improvement is the uplink packet scheduler. Moreover, existing solutions in the literature [8] that address scheduling for the current communication (H2H) in LTE are not suitable for M2M communication, because of the assumption of few types of applications and QoS requirements of H2H communication, and also do not address the problem of shortage of resources [9]. Some solutions can be found in the literature that handles M2M communication [9], [10], [11], [12], [13], [14], [15], [16], [17]. However, they have shortcomings. One of them is that most of these solutions do not ensure the fair allocation of resources and may lead to the problem of starvation. Another weakness is related to either the lack of control over the impact of H2H performance caused by M2M communication, or this kind of impact control being poorly carried out.

Within this context, in this work, we discuss our proposal of a new dynamic uplink packet scheduler for LTE. The proposed scheduler uses the current traffic information of each device as well as allocation logs to classify the traffic and also to prioritize the allocation of radio resources. Furthermore, these data collected by the scheduler are used to measure the level of congestion of the system and hence dynamically adjust the scheduler to control the congestion. Therefore, the proposed scheduler aims to: (i) control the impact of M2M communication on H2H, (ii) ensure fairness and thus avoid the problem of starvation and (iii) meet the QoS requirements of M2M applications. Besides, the proposed approach is an extension of our previous works [18], [19]. The remainder of this paper is organized as follows. In Section 2, we classify the M2M applications and the packet schedulers for M2M, we also explain the procedure of uplink scheduling. We formulate the problem of resource allocation in the Section 3. In the Section 4, we shortly review and classify the related literature. In Section 5, we describe the proposed scheduler. We evaluate the performance of our proposed scheduler in Section 6. Finally, we present the conclusions in Section 7.

Section snippets

Overview

In this section, we present an overview of the fundamental concepts used in our work.

Problem formulation

Let $U = {1, \dots, U}$ the set of devices that require resources at TTI t and $R = {1, \dots, R}$ the set of available RBs. The problem of resource allocation can be defined as an optimization problem and can be formulated as following: $\begin{matrix} \max & \sum_{\begin{matrix} u \in U \end{matrix}} \sum_{\begin{matrix} r \in R \end{matrix}} M_{u, r} A_{u, r} \end{matrix}$ $\begin{matrix} s.t. & A_{u, r} \in {0, 1}; \forall u \in U and \forall r \in R \end{matrix}$ $\begin{matrix} \sum_{\begin{matrix} u \in U \end{matrix}} A_{u, r} \leq 1; \forall r \in R \end{matrix}$ $\begin{matrix} \sum_{\begin{matrix} r \in R \end{matrix}} P_{u, r} A_{u, r} \leq P_{u}^{M A X}; \forall u \in U \end{matrix}$ $\begin{matrix} R_{u} = {r | A_{u, r} = 1}; \forall u \in U and \forall r \in R \end{matrix}$ $\begin{matrix} r \in {i, i + 1, \dots, i + l}; \forall u \in U, \forall r \in R_{u} \\ and for some i, l | 1 \leq i \leq i + l \leq | R | \end{matrix}$

M_u,r is a prioritization function that evaluates the performance to allocate the RB r to the device u in order to achieve

Related works

In this section, we discuss some of the solutions that take into account M2M communication in the network. These solutions are compared according to their objectives and the parameters used to accomplish them.

In [10], the authors propose a scheduler whose devices are grouped into classes, dynamically created, according to their QoS requirements. The QoS requirements of the devices are placed into a class made up of two parameters: (i) the arrival rate of packets and (ii) the maximum tolerable

Proposed scheduler

In this section, we present our proposed schedulers. We describe an initial version of scheduling in Section 5.1 and, in Section 5.2, we present a dynamic version of this initial scheduler.

Performance analysis

In this section, we evaluate the performance results of our proposed schedulers against the schedulers presented in Section 4. In Section 6.1, we describe the simulation environment and the traffic models, while in Section 6.2 we show the performance metrics used for comparison. Finally, we analyze the numerical results in Section 6.3.

Conclusions and future work

In this paper, we present an LTE uplink packet scheduler for M2M communication that uses historical information about resource allocations, channel quality and QoS requirements of devices to (i) control the impact of M2M communication on H2H, (ii) avoid the problem of starvation with the fair allocation of resources and (iii) satisfy the QoS requirements. In addition, the scheduler can dynamically adjust itself based on the congestion level of the network to improve performance of the system

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View full text

A fair QoS-aware dynamic LTE scheduler for machine-to-machine communication

Abstract

Introduction

Section snippets

Overview

Problem formulation

Related works

Proposed scheduler

Performance analysis

Conclusions and future work

Internet of things: A survey on enabling technologies, protocols, and applications

IEEE Commun. Surv. Tutor.

The evolution of m2m into iot

2013 First International Black Sea Conference on Communications and Networking (BlackSeaCom)

M2m C: A Systems Approach

Congestion control in the context of machine type communications in long term evolution networks

A survey of access management techniques in machine type communications

IEEE Commun. Mag.

M2m communications in 3gpp lte/lte-a networks: Architectures, service requirements, challenges, and applications

IEEE Commun. Surv. Tutor.

Service Requirements for Machine-Type Communications

Technical Report 22.368

A survey of scheduling and interference mitigation in lte

JECE

Uplink scheduling for machine-to-machine communications in lte-based cellular systems

GLOBECOM Workshops (GC Wkshps)

Toward ubiquitous massive accesses in 3gpp machine-to-machine communications

IEEE Commun. Mag.

A qoe preserving m2m-aware hybrid scheduler for lte uplink

2013 International Conference on Selected Topics in Mobile and Wireless Networking (MoWNeT)

Performance analysis of an enhanced delay sensitive lte uplink scheduler for m2m traffic

2013 Australasian Telecommunication Networks and Applications Conference (ATNAC)

Class based dynamic priority scheduling for uplink to support m2m communications in lte

2014 IEEE World Forum on Internet of Things (WF-IoT)

On radio resource allocation in lte networks with machine-to-machine communications

2013 IEEE 77th Vehicular Technology Conference (VTC Spring)