For example, the vehicular cloud computing (VCC) has been introduced in [4] as a .... schemes a typical traffic volume model for HP services as well as two traffic ...
Resource Management in Fog-Enhanced Radio Access Network to Support Real-Time Vehicular Services Jun Li, Carlos Natalino, Dung Pham Van, Lena Wosinska, and Jiajia Chen Department of Communication Systems KTH Royal Institute of Technology Stockholm, Sweden {jun5, carlosns, dungpham, wosinska, jiajiac}@kth.se Abstract—With advances in the information and communication technology (ICT), connected vehicles are one of the key enablers to unleash intelligent transportation systems (ITS). On the other hand, the envisioned massive number of connected vehicles raises the need for powerful communication and computation capabilities. As an emerging technique, fog computing is expected to be integrated with existing communication infrastructures, giving rise to a concept of fogenhanced radio access networks (FeRANs). Such architecture brings computation capabilities closer to vehicular users, thereby reducing communication latency to access services, while making users capable of sharing local environment information for advanced vehicular services. In the FeRANs service migration, where the service is migrated from a source fog node to a target fog node following the vehicle's moving trace, it is necessary for users to access service as close as possible in order to maintain the service continuity and satisfy stringent latency requirements of real-time services. Fog servers, however, need to have sufficient computational resources available to support such migration. Indeed, a fog node typically has limited resources and hence can easily become overloaded when a large number of user requests arrive, e.g., during peak traffic, resulting in degraded performance. This paper addresses resource management in FeRANs with a focus on management strategies at each individual fog node to improve quality of service (QoS), particularly for real-time vehicular services. To this end, the paper proposes two resource management schemes, namely fog resource reservation and fog resource reallocation. In both schemes, real-time vehicular services are prioritized over other services so that their respective vehicular users can access the services with only one hop. Simulation results show that the proposed schemes can effectively improve one-hop access probability for real-time vehicular services implying low delay performance, even when the fog resource is under heavy load. Keywords—Fog computing; resource management; connected vehicle; real-time service
I.
INTRODUCTION
Connectivity is becoming an expected feature in vehicles [1-2], fostering “connected vehicles”, to enhance the situational awareness and provide an information-rich travel environment. On the other hand, it poses a challenge on collecting, storing, and processing a large amount of data
generated by the vehicles. To effectively support the vehicular services, it is commonly agreed that vehicular communication systems should be enhanced by integrating wireless/cellular network infrastructure with cloud computing technologies [3]. For example, the vehicular cloud computing (VCC) has been introduced in [4] as a variant of mobile cloud computing and further investigated in [5, 6]. In the VCC, the centralized cloud (which is typically realized by large-scale data centers) offers network access, computation and storage capabilities for vehicular devices. However, the latency caused by the wide area network (WAN) in the VCC can be a barrier for the deployment of real-time vehicular services, such as criticalevent warnings (e.g., collision warning, emergency stop warning) for traffic safety, which require extremely low latency. In addition, most of information and contents processed by vehicular services have local relevance only and therefore may not be handled by the centralized cloud because of the potentially long distance from the vehicles. In this regard, fog computing, which brings the computation resources as close as possible to the end users, has a great potential to overcome the latency problem in the VCC and satisfy the delay requirement of real-time vehicular services. Typically, fog computing can also be referred to as edge computing based on the idea where computation resources are deployed at the edge of the network in close proximity to the end users [7], enabling distributed cloud computing paradigm to improve not only latency but also the availability and reachability of services compared to the conventional centralized cloud computing paradigm [8, 9]. Besides, fog servers (referred to as cloudlets) can be colocated with wireless access points or cellular base station (BS), e.g., long term evolution LTE’ evolved Node B (eNB), forming BS-Fog node and giving rise to the concept of fogenhanced radio access networks (FeRANs), as illustrated in Fig. 1 [7,10]. Due to ubiquitous access to RAN infrastructure, such FeRANs can be a promising candidate to support vehicle-to-everything (V2X) connectivity, i.e., to facilitate information exchange not only among vehicles, but also
BS-Fog
BS-Fog BS-Fog
Fig. 1. A high-level view of FeRAN for connected vehicles.
between the vehicles and the other entities, such as vulnerable road users (pedestrians, cyclists), local sensors, cameras and roadside units, thereby supporting various vehicular services for higher safety, efficiency, and convenience of transportation systems. Although the concept of FeRAN has many potential advantages in supporting connected vehicles, a number of challenges have to be tackled to make it become a reality. In this regard, how to efficiently handle the mobility of vehicles is among the most critical challenges in fulfilling the V2X service requirements. High speed of the vehicles may introduce frequent handovers in cellular systems, resulting in connection interruptions. In FeRAN-based V2X networks, when a vehicle moves from one area to another, not only handover but also service migration needs to be performed in order to maintain the service continuity and high performance, which are particularly critical for safety-related vehicular services [1]. In addition, vehicular users make decision based mainly on the information from local environment (such as sensors, cameras or roadside units, see also Fig. 1). Thus, service migration, referring to relocating services from a source fog node to another (target) fog node, should follow the mobility of the respective vehicles and maintain a one-hop service access delay (e.g., below 5 milliseconds) [10-11]. Such one-hop access means that the service can be accessed directly at the BS with which the user equipment (UE) is associated, where only the last wireless segment between the UE and BS is involved. The one-hop access delay represents the minimum communication latency that the vehicle has to experience in the FeRAN. Meanwhile, the target fog node should have enough resources for the migrated services. Otherwise, the services have to stay at the source fog node, implying an increased access delay, which may be larger than the stringent latency requirement for real-time vehicular services. In the FeRAN-based V2X networks, each BS-Fog usually has a limited amount of computation resources and its workload is highly volatile, mainly due to the mobility of
vehicles. It is therefore important to achieve high reliability, low latency, and scalability by employing efficient resource management mechanisms even for the scenarios with high load and fast mobility. However, recent studies on resource management in fog computing focus on optimized resource sharing among a cluster of neighboring fog or cloudlet nodes [3, 12-13]. For example, in [3], R. Yu et al. proposed a resource sharing scheme, in which resource utilization is improved through the inter-cooperation between cloudlets. M. Jia et al. [12] proposed a load balancing algorithm by dynamically relocating tasks running in the heavy-loaded cloudlets to the light-loaded ones. L. Gu [13] investigated cost-efficient resource management by jointly considering base station association and task distribution. These schemes usually require complex computation and high communication overhead, especially in a large network. Furthermore, in some schemes, e.g., in [3], one-hop access delay cannot be maintained due to the fact that several nodes may need to cooperate with each other to offer a certain service. To date, existing study in the literature do not consider local strategies for managing resources at the individual nodes. In FeRAN-based V2X networks, service migration is of key importance to maintain one-hop access delay and hence low latency for vehicular services, particularly for high priority (HP) traffic, such as the one generated by traffic safety-related services. In contrast to the existing schemes, which cannot eliminate the high communication and processing overhead for multi-node scheduling, a local resource management strategy prioritizing the selected services (e.g., the HP services) at each individual fog node could be more realistic. Therefore, this paper addresses the local fog resource management in the FeRAN-based V2X environment. To support real-time vehicular services, two schemes, namely fog resource reservation (FRR) and fog resource reallocation (FRL), are proposed and evaluated. In the first scheme some fog resources at each BS-Fog are reserved based on traffic load. The amount of the reserved resources can be estimated by employing the existing traffic flow predication methods. The second scheme is based on the strategy to release part of fog resources used for low-priority (LP) services and reallocate it to HP services. For the evaluation of the proposed schemes a typical traffic volume model for HP services as well as two traffic models for LP services are considered to understand how the traffic pattern affects the performance. The simulation results show that the proposed schemes can effectively increase one-hop access probability for the HP services. Especially, when the workload is heavy, the one-hop access probability for both the FRR and FRL can still be obviously higher than in the case where no any special resource management is implemented.
II.
SERVICE MIGRATION IN FOG ENHANCED-RAN FOR CONNECTED VEHICLES
In the FeRAN, fog servers are co-located with the base station, forming a BS-Fog node (see Fig. 1), which integrates the communication resource for RAN and computation resource for fog computing. The architecture of a BS-Fog is shown in Fig. 2, where the BS is responsible for network functions (e.g., handovers), whereas the fog servers provide computational resources (e.g., computing and storage capability) locally. When a vehicle moves from one BS-Fog’s coverage area to another, a handover is triggered. At the same time, ongoing services hosted at the source BS-Fog also need to be handled. As shown in Fig. 3, once the handover is triggered, the source BS-Fog checks the resources used for the ongoing services (e.g., CPU, memory), and sends Migration Request message which includes the information of the required resources to the target BS-Fog. After receiving Migration Request message, the target BS-Fog makes a decision whether to accept the service migration request or not, according to the resource management strategy. If the request is approved, the target BSFog sends Migration Request ACK message to the source BSFog (see Decision ① in Fig. 3). After receiving the Migration Request ACK message, the source BS-Fog starts to perform the service migration and service can still be accessed with one hop after the vehicle moves to the area covered by the target BS-Fog. Otherwise, the target BS-Fog sends Migration Request NACK message to the source BS-Fog (see Decision ② in Fig. 3). In this case, the service still needs to run at the source BS-Fog and the vehicle has to access the service with more than one hop (i.e., the hop(s) between two neighboring BS-Fogs has to be counted for access), which results in an obviously higher access delay. The decision whether the service migration request is accepted or not is made by the target BS-Fog based on the status of its own resources, for which the resource management strategy plays a vital role. Thus, an efficient resource management strategy is desired to maximize the resource utilization and improve the probability of a successful service migration, resulting in the increase of one-hop access probability for services, particularly for delay-sensitive services, i.e., the HP services, which is discussed in the following sections.
vehicle
Source BS-Fog
Target BS-Fog
Triggering handover Checking ongoing service Migration Request Resource management strategy Decision ① Migration Request ACK Migrating service Access the target BS-Fog with one hop
Decision ②
Migration Request NACK Access the target BS-Fog with two hops
Fig. 3. Service migration procedure.
III. FOG RESOURCE MANAGEMENT SCHEMES In this section, we investigate local resource management strategies at an individual fog node. We take the computing resources (e.g., CPU) as an example and illustrate the proposed schemes. The storage resources can be treated similarly, which is not the focus in this paper. Two schemes are proposed, which aim at prioritizing the HP services in order to increase its one-hop access probability while reducing impacts on the LP services. A. Scheme1: Fog Resource Reservation (FRR) In this scheme, a certain amount of fog computing resource is reserved for the HP services for a period when only the HP services are allowed to use these reserved resources. If the resources reserved for the HP services are not sufficient, the common (non-reserved) computing resources can be allocated for the HP services upon a migration request, if available. In this case, both the HP and LP services can access the nonreserved computing resources with an equal opportunity according to the principle of first-come-first-served. If neither the reserved computing resources nor the common computing resources can satisfy the requirement of HP services, service migration will not be issued. The flow chart of the proposed FRR scheme is shown in Fig. 4, whereas the symbols used in TABLE I. EXPLANATION OF SYMBOLS.
BS-Fog Management module Fog server
Symbol
Rea-time vehicular service
...
... Radio resource (BS)
Environmental sensors
Vehicles
Fig. 2. Architecture of BS-Fog.
Humans
Description
𝑅𝑎𝑟
The amount of available reserved computing resource
𝑅𝑎𝑐
The amount of available common (non-reserved) computing resource
𝑅𝑎𝑙
The amount of available released computing resource from the running LP services
𝑅𝑎𝑡
The amount of total available computing resource in one fog
𝑅𝑟𝑒𝑞
The amount of required computing resource for each service
ACK
Request is accepted
NACK
Request is rejected
Start
Start
Caculate Rat
Check service priority Rat>Rreq ?
Yes
HP service?
No
Yes
No
ACK
Check service priority
Caculate Rar
Caculate Rac HP service?
Yes
Rar>Rreq ?
No
Rac>Rreq ?
No
Yes
NACK
Yes
No ACK
NACK
Caculate Ral
ACK
No Rar+Rac>Rreq ?
Rat+Ral>Rreq ?
No
Yes NACK
Yes
ACK
NACK
ACK
Waiting next request
Waiting next request
Fig. 4. The flow chart of FRR.
Fig. 5. The flow chart of FRL.
the flow chart are explained in Table I. The key of this scheme is how to estimate the amount of computing resources reserved for the HP service properly. Overestimating leads to low resource utilization, whereas underestimating can significantly decrease the one-hop access probability for the HP services. In our paper, safety-related vehicular services are considered as HP services [3, 14]. The total amount of required computing resources for such services is proportional to the number of vehicles in the coverage area of one fog. Traffic volume prediction methods have been widely investigated to improve the traffic efficiency. For instance deep-learning-based traffic volume prediction method is proposed in [15], based on which the traffic estimation error can be kept below 10% of the exact traffic volume for the 60minutes traffic flow prediction), and the required computing resources for the HP services in the proposed FRR scheme can be reserved accordingly. Whether the amount of the reserved computing resources is set statically or dynamically can be determined by the level of traffic fluctuation. In our proposed FRR, the amount of reserved computing resource for a certain period is calculated by 𝑅 = 𝜆ℎ × 𝑇 ℎ × 𝑅𝑟𝑒𝑞 , where 𝑇 ℎ is the average service time and 𝜆ℎ is the average request arrival rate of the HP service. B. Scheme 2: Fog Resource Reallocation (FRL) In this scheme, a certain amount of computing resources used for the LP services can be released and reallocated to newly migrated HP services when there are no enough available resources. Releasing a certain amount of computing resources degrades the performance of LP service, (e.g., processing time). The degradation on computing capability can
be defined as the ratio of the total released resources over the total requested resource, which can be expressed as the following equation [14]: 𝑡+𝑇
𝐷=
∫𝑡
𝑡+𝑇
𝐶𝑟𝑒𝑞 (𝑡)−∫𝑡
𝐶𝑝𝑟𝑜 (𝑡)
𝑡+𝑇 𝐶𝑟𝑒𝑞 (𝑡) ∫𝑡
,
(1)
where 𝐶𝑟𝑒𝑞 (𝑡) is the maximum requested computing resource for one service, and 𝐶𝑝𝑟𝑜 (𝑡) is the provided computing resource at time t. Each LP service requires the amount of resources above a certain threshold in order to satisfy a certain level of performance, and the computing resources cannot be released below this threshold. For example, the threshold for the computing resource requirement of the LP service is set to M. If the amount of the current allocated resources for a running LP service is equal to M, the resource for this on-going service is not allowed to be released. Otherwise, this running service has to be teared down, which is not fair from the operation point of view. If no resources can be released, the service migration request will not be acknowledged. In the proposed FRL scheme, M is set according to the quality of service (QoS) requirement for the LP services. The flow chart of the proposed FRL is shown in Fig. 5, where the symbols used in the flow chart are explained in Table I. IV.
PERFORMANCE EVALUATION
In this section, the performance of the proposed schemes is evaluated by numerical simulations with MATLAB.
Fig. 6(a) The daily profiles of the arrival rate for the HP (with two peaks) and the LP services (with the constant average arrival rate), (b) one-hop access probability for the HP service, (c) one-hop access probability for the LP service, (d) resource utilization, (e) degradation degree for the LP service in the FRL scheme, (f) percentage of resource reserved and used for the HP service in the FRR scheme.
A. Simulation Setup In our simulations we consider an urban area of 400 km2 with 400 distributed BS-Fogs, and the diameter of each cell equal to 1 km [16, 17]. We assume that each vehicle only performs one HP service (safety-related). The required computing resources in units (one unit of computing resource is referred to as 4000 million instructions per second (MIPS) for CPU) are presented in Table II [3, 18]. We assume the Poisson arrivals of vehicles [19, 20]. Correspondingly, the arrivals of HP service requests are also according to the Poisson process. The time that vehicles are served by one BSFog is dependent on several factors such as the route, speed of the vehicles, and coverage area of one BS-Fog. The speed of the vehicles is assumed to be Gaussian distributed and vary from 30 km/h to 60 km/h [17, 18]. For simplicity, we assume that the length of the route of vehicles passing the cells is equal to 1km per cell. Thus, the service time can be considered to be TABLE II. VALUES OF PARAMETERS IN SIMULATION. Term
Value
Total number of computing units in one fog
500
Number of computing units for each HP service
3
Number of computing units for each LP service
[2,6]
Arrival rate for HP service (Request /second)
(0,1)
Arrival rate for LP service (Request /second)
(0,2)
Average serving time in one fog for HP service (second)
90
Standard deviation of the serving time in one fog for HP service (second)
10
Average serving time in one fog for LP service (second)
120
Gaussian distributed. The average and standard deviation of service time are shown in Table II. Besides, we also assume that the LP service requests arrive according to the Poisson process, whereas their average service time is exponentially distributed [12]. In the urban scenario, the typical daily profile of vehicular traffic volume has two peaks, which are in the morning and afternoon, respectively [16]. The variation of the arrival rate of the HP services (safety-related vehicular service) is assumed to follow the vehicular traffic daily profile. Without a loss of generality, we use the daily profile of the traffic volume modeled for the Cologne metropolitan area [16]. The HP service request arrival rate variation follows such a daily profile. The value of the peak at 8:00 is normalized to 1 request per second (Req/s) in the coverage area of one BS-Fog, while the afternoon peak at 17:30 is 0.8 Req/s (see blue curve in Fig. 6(a) or Fig. 7(a)) [16, 21]. For the LP services, two scenarios are considered. In Scenario 1, the average arrival rate of the LP services is constant in the entire period of 24 hours, which can be assumed as background traffic (see red curve in Fig. 6(a)). The arrival rate of the LP services for Scenario 2 is based on the data traffic model in a European city (see red curve in Fig. 7(a)) [22]. Connected vehicle is one of fog application scenarios, among others mentioned in Fig. 2. In this scenario, we assume that the maximum arrival rate for the LP service requests is twice as high as the HP service request arrival rate, i.e., the peak value of the arrival rate for the LP service requests is set to 2 Req/s. In our simulation, the arrival rate for both HP and LP service requests are sampled per hour. All results are obtained by averaging 103 repeated simulations.
B. Results Discussion In this subsection, the performance of two proposed schemes is evaluated. We compare our results with the benchmark scheme where no resource management is applied, i.e., where the HP and LP services get resources with an equal opportunity. 1) Scenario 1 In Fig. 6(a), the blue curve illustrates the arrival rate of the HP service requests with two peaks at 8:00 and 17:30, while the red curve corresponds to the variation in time of the LP services arrival, which follows a uniform distribution between 0 to 2 Req/s during 24 hours. Fig. 6(b) shows the one-hop access probability for the HP services in all three considered schemes. It can be seen that the proposed resource management schemes have an obviously higher one-hop access probability than that in the benchmark, especially when the workload is high. For example, when the resource utilization is above 90% at 8:00 (as shown in Fig. 6(d)) the one-hop access probability for FRR is 35% higher than the benchmark, whereas that for FRL with M=4 is about 33% higher than benchmark. For the FRR, at a high load more computing resources are reserved for the HP services, and more service migration requests for the HP services can be acknowledged. As shown in Fig. 6(f) in the FRR scheme the used resources for the HP service are always slightly lower than the reserved ones. It is because the required amount of resources cannot be defragmented. It happens that the coming migrated HP service requires 3 units of resources, but the remaining resources are less than 3 units, which is not sufficient and the resources remain unutilized. In the FRL scheme, the HP services can get resources from the ongoing LP services when the load is heavy. On the other hand, releasing the ongoing LP services’ computing resources in the FRL degrades its performance. When M decreases from 4 to 2, during the peak time at 8:00 the one-hop access probability increases from 0.91 to 1, but the degradation degree increases from 16% to 21%, as shown in Fig. 6(e). In this scenario, more resources are released, which will degrade the QoS of the LP services. Thus, there is a clear tradeoff between high
one-hop access probability for the HP service and low degradation degree for the LP service. M can be set to reach a good balance between these two performance metrics. Fig. 6(c) shows the daily one-hop access probability for the LP services. The one-hop access probability for the benchmark is higher than that in the proposed schemes in the whole period of 24 hours, which is up to 23% and 6.7% compared to the FRR and the FRL, respectively. Obviously, the one-hop access probability for the LP services suffers more in the FRR than in the FRL, due to relatively low utilization in the FRR. 2) Scenario 2 In this scenario, the arrival rate of LP services is based on a typical data traffic model from European city [22] which is used to further investigate the performance of the proposed schemes. In Fig. 7(a), the blue curve corresponds to the arrival rate of the HP service requests with two peaks at 8:00 and 17:30, which is the same as in Scenario 1. The red curve is the arrival rate of the LP service requests, which first decreases to its valley at 6:00 and increases gradually to its peak at 23:00. Fig. 7(b) shows the daily one-hop access probability for the HP services. The proposed schemes have a better performance than the benchmark in terms of one-hop access probability in the afternoon (when the total load of the traffic is high). When the resource utilization is close to 95% at 17:30 as shown in Fig. 7(d), the one-hop access probability for the FRR is 65% higher than the benchmark, whereas for the FRL with M=4 it is about 60% higher than the benchmark. It can be seen that the higher the -traffic load, the better the performance of the proposed schemes in terms of one-access probability for the HP services. Besides, at 17:30, the one-hop access probability for the FRL with M=4 is slightly lower than that for the FRR. That is because the computing resources that are allowed to be released from the LP services fail to meet the threshold required for the HP services. As discussed in Scenario 1, to further increase the one-hop access probability, M needs to be kept as low as possible. For example, when M decreases from 4 to 2, the one-hop access probability for the HP services increases from 0.9 to 1. On the other hand, as shown in Fig.
Fig. 7(a) The daily profiles of the arrival rate for the HP (with two peaks) and the LP services (following data traffic model in the European city), (b) one-hop access probability for the HP service, (c) one-hop access probability for the LP service, (d) resource utilization, (e) degradation degree for the LP service in the FRL scheme, (f ) percentage of resource reserved and used for the HP service in the FRR scheme.
7(e), degradation degree for the LP services increases from 15% to 19%. As shown in Fig. 7(c), the one-hop access probabilities for the LP services in the FRR and FRL are lower than that in the benchmark under high loads. For example, at 17:30, the onehop access probability in case of using the FRR is 18% lower, whereas that for FRL is about 2% lower compared to the benchmark. V.
CONCLUSIONS
In this paper we study the benefits of implementing local resource management to improve quality of service, particularly for HP vehicular services. Two local resource management schemes (FRR and FRL) for the FeRAN have been proposed to increase the one-hop access probability by prioritizing computing resources for HP services. In the FRR, fog computing resources are reserved according to number of vehicles, which can be estimated by using traffic flow prediction methods with historical traffic-related data. The FRL is performed when there are not enough available computing resources for the HP services, and part of the computing resources used for the LP services can be reallocated to the HP services. A typical traffic volume model for the HP services as well as two traffic models for LP services have been considered for the performance evaluation of the proposed schemes. The simulation results show that the proposed schemes can effectively increase one-hop access probability for the HP services. Especially when the workload is heavy (e.g., the resource utilization is more than 90% in Scenario 1), the one-hop access probability for both FRR and FRL is 30% higher than that of the benchmark, where no local resource management is employed. The improvement increases to more than 60% when the resource utilization increases to 95% (e.g., at 17:30 in Scenario 2). However, the FRR and FRL also have different impact on the performance of the LP services. The FRR has a lower one-hop access probability for the LP services, whereas the computing resources for LP services may have to be released resulting in degraded performance in the FRL. ACKNOWLEDGMENT This work was supported by the Göran Gustafssons Stiftelse, Natural Science Foundation of Guangdong Province (Grant No. 508206351021) and National Natural Science Foundation of China (Grant No. 61550110240, 61671212). REFERENCES [1]
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