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COLLECTIVE SERVICING OF HETEROGENOUS TRAFFIC STREAMS OVER 3GPP LTE NETWORK AND APPLICATION OF ACCESS CONTROL
DOI: 10.36724/2072-8735-2022-16-3-43-49
The publication has been supported by the Russian Foundation for Basic Research, project No. 20-37-90048 postgraduates.
Umer Mukhtar Andrabi,
Moscow Institute of Physics and Technology (State University) Moscow, Russia, umer.andrabi@phystech.edu
Mikhail S. Stepanov,
Moscow University of Communication and Informatics, Moscow, Russia, mihstep@yandex.ru
Manuscript received l5 February 2022; Accepted l4 March 2022
Keywords: Radio Resource Management (RRM), Video traffic flows, loT, Network Slicing, Operator surveillance system, Access Control
Due to the progressions in the cellular technologies in recent times, communicating networks have seen vast expansion in the volumes and multiplicities of the data transmitted over the enhanced smart network especially IoT. The regulation and organization of such enormous amounts of data is posing substantial challenges across the various network industries. Because majority of data traffic transferred over such complex networks is comprised of "Big Data" That is continuously growing and evolving. 3GPP have acknowledged such scenarios and have given several recommendations and procedures to manage resources in such situations. But regrettably there are no concrete explanations and techniques on how these resources should be distributed in heterogenous networks. The mathematical modeling that considers the characteristics of traffic flows originating and accepting for servicing is deemed as one of the critical answers to such problems. To address this issue, we have designed a model of resource allocation and sharing strategy for conjoint servicing of real time video traffic referred as "Heavy traffic" of surveillance cameras and low-quality video traffic, referred as "Light traffic" which are transmitted as files over LTE cell facilities in a surveillance operator system. In the model, the access control is incorporated to create the conditions for differentiated servicing of heterogenous traffic incoming sessions. The mathematical model can be utilized to study reservation-based resource allocation scenarios over a shared heterogenous network.
Information about authors:
Umer Mukhtar Andrabi, Department of Infocommunication Systems and Networks, Moscow Institute of Physics and Technology (State University), Moscow, Russia Mikhail S. Stepanov, Department of Communication Networks and Switching Systems, Moscow University of Communication and Informatics, Moscow, Russia
Для цитирования:
Андраби Умэр Мукхтар, Степанов М.С. Совместное обслуживание разнородных потоков трафика по сети 3GPP LTE и применение контроля доступа // T-Comm: Телекоммуникации и транспорт. 2022. Том 16. №3. С. 43-49.
For citation:
Umer Mukhtar Andrabi, Stepanov M.S. (2022). Collective servicing of heterogenous traffic streams over 3GPP LTE network and application of access control. T-Comm, vol. 16, no.3, pр. 43-49. (in Russian)
Introduction
Mobile cellular networks have revolutionized global communication systems and changed the means of social interaction, information dissemination and media dissemination. These networks have advanced from a simple wireless technology to an indispensable asset today. Thanks to the continuous development of mobile technologies, modern ubiquitous systems are used in all spheres of everyday life. Cellular networks are rapidly developing and expanding every day, and five generations of mobile technologies have been deployed to date. These networks have evolved from voice communication (1G and 2G) to data packet service (3G and 4G) and are now moving to smart networks (5G).
Human-centered communication design was one of the common trends in previous generations (from 1G to 4G), and most services were provided to humans [1]. However, with the introduction of 5 G cellular technology and the Internet of Things (IoT), this trend is changing, and intelligent networks are being deployed. The basic concept of the Internet of Things is "Everything is connected", where services will be provided to both animate and inanimate (people and devices) objects, and the idea is to create a "Smart Ecosystem" with procedures for intelligent monitoring (Video Cameras) [2, 3].
These intelligent networks are decision-making systems that have evolved from human intervention systems (for example, communication systems between people), and dominate human activities in various sectors such as healthcare, finance and security and surveillance [4]. This intelligent ecosystem is aimed at accumulating the payload of the machine, then analyzing the collected data using an intelligent system and making intelligent decisions to automatically control our environment. These intelligent and decision-making objects form the so-called machine-to-machine communication systems (M2M). It is assumed that by 2023, M2M connections will account for half of the total global traffic and will account for 50% of the total number of connections and devices [5], as shown in Figure 1.
Fig. 1. Global Growth of Devices and Connections, Cisco Annual Report for 2018-2023 [5]
In order to cope with the new restrictions on connecting to machines and increase the level of the Internet of Things, it is necessary to revise the previous generations of cellular networks, moving from a human-oriented approach to a machine-oriented approach.
The demand for M2M applications is increasing rapidly due to low cost and low-power capabilities of M2M devices and these applications are intended to automate our daily affairs without human interference [6]. M2M applications are used across all the spheres and the most prominent ones are Healthcare, Smart transportation, Security and surveillance, Smart-Ecosystem etc.
To stand this evolution of IoT communication traffics and to handle the multiplicity of IoT applications and services, 5G network is considered an appropriate solution to influence emerging technologies such as Human-to-Human (H2H) and machine-to-machine (M2M) communications [7, 8]. The notion that M2M is outdated and is replaced by It is irrelevant, as M2M is the precursor of the IoT Revolution. It's M2M that has laid the foundation for network system upon which the overall IoT universe is raised. Additionally, most of the functionalities of IoT where taken from the machines interacting with each other in the background, which is core idea of M2M.
Over the past few years with the introduction of 5G wireless technology, the internet of tings (IoT) has grown immensely, especially under the banner of Machine-to-Machine (M2M) communication. More and more smart devices like sensors, actuators, smart meters etc. are being connected to the IoT network to establish a smart ecosystem. As the number of devices is growing tremendously, their connectivity is a matter of concern. For small coverage area IoT application, various short-range wireless technologies such as Bluetooth, Zigbee, Wi-Fi, or other optical wireless communication (OWC), are used for the connectivity.
However, deployment of MTC is one of the key challenges i.e. implementation of IoT use cases (which are mainly MTC devices) within the existing cellular networks is an uphill task. This is due the fact that the prevailing cellular networks are mainly designed to support human centric (H2H) communication. As the number of machine type devices (MDT) is constantly growing and this has resulted in expansion of IoT and overloading present cellular networks. According to recent survives the share of MTDs has increased by almost 51 % and nearly 200 times upsurge in global mobile data traffic by 2020 [9].
This massive increase in MTDs and the data is posing a serious risk to the cellular network. Though the main issue is not bandwidth, as MTC utilizes very low bandwidth as compared to cellular networks, the bulk of devices may create a major congestion at the base station (BS). The processing of tremendous of number of requests from both MTC and HTC (Human-type communication) devices, may prove a difficult job for a BS, thus creating congestion and signaling problems within access and core network.
This trend has led to development of telecommunications and increase in the volumes and the diversity of Internet of Things (IoT) applications. The IoT is typically a M2M network of numerous physical smart objects (vehicles, actuators, sensors, etc.) which have capacity to generate, manage and exchange data without human intervention [1-6]. Digital devices used in such type of networks usually have low-storage capabilities and processing capacities. These devices are dedicated to deliver information reliably to the data centers for collecting and proceeding. Simultaneous use of smart objects and low-traffic smart meters we are witnessing a huge impact of multimedia traffic, especially in domain of smart surveillance and monitoring where data is collected by video surveillance [10].
T
The potential and dimensions of LTE, 4G and now 5G networks are a noteworthy improvement over earlier generation radio access technologies, but the dimensions resources are still expected to be limited, especially during emergency and crises due to higher demand. The resources required to support video over wireless networks are significant. The demand for such services is expected to be high and prioritized. As such, research is needed as to the efficient utilization of available radio resources for the transmission of video content. Hence there is a requirement of a framework for considering the wireless transmission of video in a holistic fashion especially in surveillance and monitoring systems.
This situation has been acknowledged and supported by 3GPP especially transmission of critical video data over an existing LTE network. Although 3GPP has offered procedures and technical tools that can be employed to share and mange radio resources between in heterogenous traffic flows over a specific network and have provided recommendations and procedures for such scenarios but unfortunately there are no solid explanations on how these resources should be shared. The mathematical modeling that considers the features of traffic streams forming and accepting for servicing is considered one of the key solutions to this kind of problems [7-10].
The model implements access control to create conditions for differentiated servicing of coming sessions. In the model, for all random variables used have an exponential distribution with corresponding mean value but the acquired results are acceptable for models with arbitrary distribution of service times. The model takes into consideration three different states, State 1: here all the available radio resources are strictly distributed among "Heavy" (real-time high-quality video traffic) and "Light" (video data from low-quality video camera) devices traffic flows and is referred as Slicing State. State 2: here all available radio resources are fully shared among the given traffic flows and this state is referred as Fully shared state. State 3: Access controlled state, here the access to a resource is constrained depending on the number of resources engaged by corresponding traffic flow.
The rest of the paper is organized as follows. In Section 2 the general functional model is discussed to given an over view of structure and functionalities of the system. In Section 3 the mathematical model is given and characteristics of various calculations is formulated. Section 4 is devoted to system of state equations that relates the model's stationary probabilities and main performance measures are defined numerical assessment of the suggested scenarios of resource sharing is performed in Section 5. A brief summary of results is presented in section 6 and finally conclusions are drawn in the last section.
General Functional Model
In this paper we address the above-mentioned challenges by constructing an analytical framework for modeling the process of resource sharing for an operator planning to create and exploit surveillance system. Considering surveillance and security of prime importance in smart based environments and massive indispensable share of IoT. Smart or intelligent surveillance associates sensing (Smart meters, actuators, and sensors) and video analysis, equates or cross-verifies the inputs and produces precise and accurate results based on heterogeneous data streams.
For instance, let's consider a scenario where smart meters or sensors deployed on railway tracks detect that a train is over speeding, which can be immediately equated or cross-verified with the video input. In our model, the system consists of numerous video cameras to perform video monitoring. The collected data is transmitted as heterogenous data streams to analytical centers over existent infrastructure of LTE network, as shown in Figure 2.
The video collected by the video cameras either can be 720p High Definition (HD) with a resolution of 1280 x 720 pixels (px), which can be used for real-time surveillance or low definition (low quality) videos which can be used as Video on Demand (VoD). The HD videos run at 30 frames / sec., supported by H.264 / AVC codec and encoded with a Main profile encoder at 3.1 Level. The bitrate range for these type of videos range from 500 kb/s to 1 Mb/s, this allows best compromises on quality and bandwidth usages. The total bandwidth required is equal to total data transmitted by each individual installed camera.
Considering a separate LTE cell with a base station placed right in its center that handle the process of resources sharing. The volume of available radio resources of LTE cell in uplink (UL) direction is given by network slicing (NS) for serving traffic streams produced by HD surveillance cameras and Low-quality cameras is measured in units of its smallest granularity. NS techniques makes it possible to divide physical network infrastructure virtually into virtual segments. These virtual segments can be joined with each other and can be operated independently, this scheme of virtual division. This slicing is implemented on a common shared underlying infrastructure to achieve independent dedicated network elements for different types of services and requirements [11],[12]. Although NS is considered one of the critical policies of 5G for resource distribution and management, it should be noted that this procedure is also applicable for LTE and LTE-A networks.
It is evident for the considered model, that the smallest requirement has "Light" device session and it is referred as resource unit (RU). Supposing the total amount of given resource units is a function of the number of resources blocks (RB) available.
Fig. 2. The functional model of resource sharing between "Heavy" and "Light" video traffic streams
Description Of Mathematical Model
Let's denote by v, the total number of resource units and c denotes the transmission speed provided by one unit. Supposing that the surveillance cameras are varying in quality, that means corresponding traffic sessions originating from cameras are varying by volume. To consider this property we suppose n types of traffic sessions. Assuming that LTE devices traffic sessions of type k are arriving after random time intervals having exponential distribution with parameter Xk, each session needs bk resource units for servicing and engages this resource for random time having exponential distribution with parameter k = 1,..., n. It is proposed that blocked LTE-devices sessions are terminated without resumption. Let's assume traffic sessions originating from low-quality video camera devices are arriving after random time having exponential distribution with parameter Xd, each session requires bd resource units to transmit files having exponential distribution with mean F. The time duration for which low-quality video camera sessions are served has exponential distribution with value F / bd and parameter bd / F. It is proposed that blocked LTE-devices sessions are terminated without resumption.
Let us characterize different states of resource sharing by incoming traffic streams. The simplest state cab be described as a scenario where total available v resource units are precisely distributed among LTE devices sessions and low-quality video camera device sessions. Denoting by vi the number of resource units that is allotted exclusively to LTE device sessions. Let's denote by vb = v - vt the number of resource units allotted for exclusive usage to low-quality video camera sessions. By adjusting the values of vi and vb we can prioritize the resource usage with respect to the chosen traffic type. But this sort of resource sharing strategy greatly reduces the usage of resource unit, we will prove it later in the coming sections.
Next state is related with implementation of access control. Let us denote for k-th flow of LTE devices sessions by ck the maximum allowed number of traffic sessions that can be served concurrently.
Similarly, denoting by cd maximum allowed number of traffic sessions that can be served concurrently for low-quality video camera sessions. For this type of resource usage the traffic session of the flow can be blocked for two reasons: (1) if vk = ckbk resource units have already been engaged by incoming sessions of k-th flow or (2) if total number of engaged resource units is greater than v - bk. The same conditions are true for low-quality video camera devices sessions where incoming session of this type can be blocked for two reasons: (1) if vd = cdbd resource units have already been engaged by low-quality video camera devices sessions or (2) if total number of engaged resource units is greater than v - bd. We will show later that by implementing the access control (i.e.by choosing the appropriate values for vk, k = 1, ... , n and vd) we can grant priorities in resource usage to the selected traffic type and enhance the usage of resource unit compare to static state scenario.
The last state deals with the case where resources are fully shared without giving priority to some traffic streams. Here we usually enhance the usage of resource unit in contrast to earlier formulated states but we are not able to reach the same level of sessions losses for all type of traffic streams considered in the model. All three devised states can be modeled by choosing v
appropriately and access boundaries vk, k = 1, ... , n and vd. Hence our further study will be based on the model employing access control for resource sharing.
Denoting by ik(t) the number of LTE devices sessions of the flow being serviced at time t, and d(t) represents the number of sessions of low-quality video camera devices being served at time t. The dynamic of a model states changing is described by Markov process
r (t) = (i,(t),..., in (t), d (t)),
defined on the finite set of model's states S. Let us denote by (ii, ... , in, d) the state of r(t). The vector (i1v.., in, d) belongs to S when ik, k = 1, ... , n, d varies as follows
0 < ik < Ck,k = 1,...,n;
0 < d < c,;ib +... + i b + db, < v.
d ' 11 n n d
Let us denote by i for state (i1v.., in, gaged resource units i = i1 b1 + ... + in b,
(1)
d) £ S the number of en, + dbd.
Denoting by p(i1,..., in, d) the value of stationary probability of state (i1, ..., in, d) £ S. It can be referred as the fraction of time the model stays in the state (i1, ..., in, d) . This interpretation gives the possibility to use the values of p(i1, ... , in, d) to evaluate the model's main performance measures. Defining for k-th flow of LTE devices traffic by nk which is the portion of lost sessions and mk defines the mean number of engaged resource units. Their formal definitions are given as
n,
= Z p(i1'-'in'd); mk = Z p(i1'-'in'd)iA-
,d )e Uk ,d )e S
Here parameter Uk is defined as subset of S having property (i1v.., in, d) £ Uk, if ik +1 > ck or i + bk > v. Similarly, we define the performance measures of low-quality video camera devices traffic servicing. Where nd signifies the portion of lost sessions, while the mean number of engaged resource units are represented by md.
n.
= Z p(i1'-'in'd); md = Z p(i1'-'zn'd)dbd-
(i1,...,in ,i)e Ud (1,-,in,d)e S
Here parameter Ud is defined as subset of S having property
(i1, ... , in, d) £ Ud , if d+1 > cd or i + bd > v.
System Of State Equations
After equating the intensity of transitions r(t) from the model's arbitrary state (i1v.., in, d) £ S to the intensity of transition r(t) into the state (i1v.., in, d) , state of state are acquired, which are given below:
PQi,-,in,d)x( (i +bk <vi +1 <ck) +1M +
k=1
+21 a+bd < v d+1< cd)+dMj ) =
=Jp(/i,...,ik -1,...,",d)V(ik >0) +
k=1
+ P(i ,...,in, d-1)ÂdI(d > 0) +
n
+ZP(Î,...,ik +U,in,d)(ik + W(i + b <v,ik +1 <Ck) + k=1
+P(i1,..., in, d+1)(d+1) /udI (i+bd < v, d+1 < cd ).
(2)
1 flj
fl" a.,
»(i ,...,i ,d)=——d-.
" ; # i,! i ! d,!
1 n 1
(3)
Here ak = ^ / ßk and ad = / ßd are offered traffic load expressed in Erlangs, while N is normalizing constant.
N = ^ ^ fl" fld
(4,...,i„ ,d)e S i1 ! in ! d1!
Numerical assessment
The developed model and the algorithm on such scenarios will be exercised for analysis of various conditions for differentiated servicing of heterogenous traffic on a common pool of resource units.
The intensity of traffic load can be described by p, it is load offered per one resource unit. To define p it's important to find out the load offered to each traffic stream considered in the model. Denoting by Ak , the offered load expressed in terms of resource
A.b
units for k-th flow of LTE devices traffic Ak = ,, k = akbk.
r-k
Similarly, Ad is denoted for low-quality video camera devices
sessions, Ad =
-b„ =
d = flcbd = b • Hence the value of p can
A +...+a" + Ad
be defined by the relation p
It means that the first flow forms traffic stream with "heavy" (LTE traffic flow) requests, the second traffic stream with "light" (low-quality video camera) requests. Conjoint servicing of "light" and "heavy" requests leads to the uncontrolled allocation of r.u. in favor of "light" requests. Let us implement the Network Slicing concept for creation of conditions for differentiated servicing.
Let us consider the following values for the model , the input parameters: v = 200 resource units (r.u.); transmission rate that is provided by one resource unit is c = 100 kbit/c; n = 1; b1 = 20 r.u.; bd = 1 r.u.; F = 100 kbit; 1/uj = 10 c; 1/ p.d = 1 c.
Also, ax = 10 Erl. For Slicing scenario vj = v2 = 100 r.u. For Access controlled scenario vi = 200 r.u., vd = 100 r.u. Supposing that both traffic flows in the model produce the same offered load A1 = Ad = vp / 2 . Lets us to find the intensities A1, Xd of incoming session for each flow considered in the model for known
values of p. During this analysis we found out despite equality of offered traffic "light" (low-quality video camera) obtain priority in engaging the transmission resource, when p > 1 as shown in Figure 3.
Here /(•) - is an indicator function. Values P(/'i, ... , in) should be normalized. It can be proved that r(t) is reversible Markov process. From relations of detailed balance follows that values of P(i1, ... , in,d) can be found as a unique solution of the system of state equation that has a product form [11-13].
Fig. 3. The portion of the lost sessions for "light" and "heavy" traffic flows
So to overcome this sort of difficulties it is necessary to create the conditions for differentiated servicing of incoming sessions. The Three states of resource sharing are compared: Slicing when resources are strictly divided among LTE devices and low-quality video camera devices traffic streams, Fully shared, when resources are fully shared and Access controlled, when the access to resource is restricted depending of the amount of resource occupied by corresponding traffic stream.
The properties are compared for these their resource allocation states based on the above given parameters are shown in with Figure 4, that presents the portion of the lost LTE devices sessions vs intensity of offered low-quality video camera devices sessions and Figure 5 that presents the mean value of resource unit usage vs intensity of offered low-quality video camera devices sessions.
Fig. 4. The portion of the lost "heavy" traffic sessions vs intensity of offered "light" traffic sessions
d
d
1
S, 0.95
r3 it
0.5 -I-1-1-1-
50 75 100 125 150
Intensity of offered light traffic sessions, ad
Fig. 5. The mean value of resource unit usage vs intensity of offered "light" traffic sessions
Results
I. The simplest of the resource allocation states is Slicing state: when resources are strictly distributed among LTE devices and low-quality video camera devices traffic streams can be exploited to attain proposed values of performance indicators but have two shortcomings. (a). The high degree of sensitivity towards the values of offered load that demands a prior information of the traffic intensity. (b) Reduced values of resource unit usage in contrast to the other two states (Access controlled and Fully shared scenarios).
II. Fully sh d state has the best values of resource unit usage but incurs the degradation of losses for heavy traffic particularly in overload conditions.
III. ccess controlled state outperforms Slicing state and is free from undesirable characteristics of Fully shared state. But the suggested technique of resource allocation state is recommended for implementation over 5G mobile networks.
Conclusion
The model for radio resource distribution and sharing for conjoint servicing of real time video traffic of surveillance cameras and low-quality video camera data traffic over LTE cell facilities is constructed. In the model the access control is implemented to construct the provisions for differentiated servicing of incoming traffic sessions. All random variables utilized in the model have exponential distribution with corresponding mean values but the obtained results are valid for models with arbitrary distribution of service times. By means of this model, the key performance measures of interest are drawn by exploiting the values of probabilities of model's stationary conditions.
The model and obtained algorithms can be utilized to study the states of resource sharing and distribution between heterogenous data streams over 3GPP LTE. The constructed model furthermore provides the options to derive the volume of resource units and access control parameters needed for serving incoming traffic with given values of performance indicators. The analytical framework can be further modified to incorporate the possibility of reservation and the usage of processor sharing discipline for serving low-quality video camera sessions traffic in a more disciplined manner [13-16].
References
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T-Comm Tom 16. #3-2022
СОВМЕСТНОЕ ОБСЛУЖИВАНИЕ РАЗНОРОДНЫХ ПОТОКОВ ТРАФИКА ПО СЕТИ 3GPP LTE
И ПРИМЕНЕНИЕ КОНТРОЛЯ ДОСТУПА
Андраби Умэр Мукхтар, Московский физико-технический институт (государственный университет), Москва, Россия,
umer.andrabi@phystech.edu
Степанов Михаил Сергеевич, Московский Технический Университет Связи и Информатики (МТУСИ), Москва, Россия,
mihstep@yandex.ru
Аннотация
В связи с развитием сотовых технологий в последнее время в сетях связи наблюдается значительное увеличение объемов и количества данных, передаваемых по усовершенствованной интеллектуальной сети, особенно IoT. Регулирование и организация таких огромных объемов данных создает серьезные проблемы в различных сетевых отраслях. Потому что большая часть трафика данных, передаваемого по таким сложным сетям, состоит из "Big data", которые постоянно растут и развиваются. 3GPP признал такие сценарии и дал несколько рекомендаций и процедур для управления ресурсами в таких ситуациях. Но, к сожалению, нет конкретных объяснений и методов того, как эти ресурсы должны распределяться в гетерогенных сетях. Математическое моделирование, учитывающее характеристики транспортных потоков, возникающих и принимаемых для обслуживания, считается одним из важнейших ответов на такие проблемы. Для решения этой проблемы мы разработали модель распределения ресурсов и стратегии совместного использования для совместного обслуживания видеотрафика в реальном времени, называемого " Тяжелым трафиком" камер наблюдения, и видеотрафика низкого качества, называемого "Легким трафиком", которые передаются в виде файлов по сотовым сетям LTE в системе оператора видеонаблюдения. В модель встроен контроль доступа для создания условий для дифференцированного обслуживания сеансов входящего разнородного трафика. Математическая модель может быть использована для изучения сценариев распределения ресурсов на основе резервирования в общей гетерогенной сети.
Ключевые слова: управление радиоресурсами (RRM), Потоки видеотрафика, IoT, сегментирование сети (NS), Система наблюдения за оператором, Контроль доступа.
Литература
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15. Stepanov S. N., Korobkina A. V., Volkov A. O., Malikova E. E., Panov A. E. The Analysis of Traffic Balancing for Data Centers Serving Requests of LEO Mobile Satellite Systems // 202I Systems of Signals Generating and Processing in the Field of on Board Communications, 202I, pp. I-6.
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T-Comm Vol.16. #3-2022
