Interdependent Multi-layer Spatial Temporal-based Caching in

Heterogeneous Mobile Edge and Fog Networks

Vu San Ha Huynh

and Milena Radenkovic

School of Computer Science, University of Nottingham, Nottingham, U.K.

Keywords: Content Caching, Mobile Edge and Fog Networks, Network Multilayer Interplay, Spatial-temporal Locality.

Abstract: Applications and services hosted in the mobile edge/fog networks today (e.g., augmented reality, self-driving,

and various cognitive applications) may suffer from limited network coverage and localized congestion due

to dynamic mobility of users and surge of traffic demand. Mobile opportunistic caching at the edges is

expected to be an effective solution for bringing content closer and improve the quality of service for mobile

users. To fully exploit the edge/fog resources, the most popular contents should be identified and cached.

Emerging research has shown significant importance of predicting content traffic patterns related to users’

mobility over time and locations which is a complex question and still not well-understood. This paper tackles

this challenge by proposing K-order Markov chain-based fully-distributed multi-layer complex analytics and

heuristics to predict the future trends of content traffic. More specifically, we propose the multilayer real-time

predictive analytics based on historical temporal information (frequency, recency, betweenness) and spatial

information (dynamic clustering, similarity, tie-strength) of the contents and the mobility patterns of contents’

subscribers. This enables better responsiveness to the rising of newly high popular contents and fading out of

older contents over time and locations. We extensively evaluate our proposal against benchmark (TLRU) and

competitive protocols (SocialCache, OCPCP, LocationCache) across a range of metrics over two vastly

different complex temporal network topologies: random networks and scale-free networks (i.e. real

connectivity Infocom traces) and use Foursquare dataset as a realistic content request patterns. We show that

our caching framework consistently outperforms the state-of-the-art algorithms in the face of dynamically

changing topologies and content workloads as well as dynamic resource availability.

1 INTRODUCTION

User mobile devices today are increasingly intelligent

which leads to the explosive development of new

applications involving distributed real-time mobile

processing and increasing traffic demands (e.g. HD

video streaming, remote health care, critical

applications for public safety communications,

augmented/virtual reality apps and automatic

driving/traffic control). Varying mobility patterns,

network topology changes, potential disconnections

and resource restrictions in mobile environments pose

many challenges for the design and implementation

of future mobile network algorithms, particularly

content caching with an aim to bring contents

proactively as close as possible to the users and

improve the reliability and efficiency of mobile

edge/fog networks and users’ services. Typical

https://orcid.org/0000-0002-5472-5328

https://orcid.org/0000-0003-4000-6143

edge/fog networks consist of heterogeneous nodes

which can include end users and edge devices with

different computing resources and communication

capabilities (Liu et al., 2018). Our architecture design

assumes that the communication between edge/fog

nodes is handled in mobile opportunistic multi-hop

manner.

Existing opportunistic caching policies in mobile

edge/fog networks such as (Wang et al., 2014; Fricker

et al., 2012) typically cannot capture, predict and

adapt to the spatial-temporal locality of content

requests needed for more accurate content popularity-

based caching decisions because they rely on

assumptions that content interests occur

independently of users’ mobility and resources. More

specifically, when content caching predictions are not

sufficiently fine-grained, they result in increased

cache miss (i.e. the content request needs to traverse

Huynh, V. and Radenkovic, M.

Interdependent Multi-layer Spatial Temporal-based Caching in Heterogeneous Mobile Edge and Fog Networks.

DOI: 10.5220/0008167900340045

In Proceedings of the 9th International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS 2019), pages 34-45

ISBN: 978-989-758-385-8

from subscribe to publisher), delay and resource

consumption. In order to tackle this complex

challenge, we propose a next-generation content

caching approach that is able to capture more

accurately the dynamic spatial-temporal correlation

of mobility and traffic patterns as well as the

mobility-content traffic interplay needed to support

more reliable, adaptive caching algorithms. Previous

research (Wang et al., 2017; Radenkovic et al., 2018)

has shown that the centralised solution is not scalable

in mobile complex heterogeneous network topologies

due to its high complexity and single point of failure

problem. Moreover, distributed solution technique

(Wang et al., 2017) may still cause high connectivity

overheads although it provides a cheaper computable

lower bound compared to the centralised solution. In

addition, previous research has also shown that

collaborative caching usually outperforms both

locally and centrally optimized algorithms (Saha et

al., 2013). Therefore, we propose a fully-distributed

predictive analytic and heuristic-based caching

approach which comprises of multi-layer

complementary real-time distributed predictive

heuristics to maintain the best possible trade-off

between caching performance and resource

consumption. Note that our focus is not to build a

protocol that forces nodes to cooperate to achieve

mutual benefit, but rather to design an underlying

algorithm that ensures no node attains lower utility by

collaborating with others, similarly to (Radenkovic

and Huynh, 2017; Wang et al., 2017; Radenkovic et

al., 2018). Existing research utilizes different

approaches to deal with trend prediction such as

reinforcement learning, Bayesian learning, Markov

chain (Ruan et al., 2019), or Exponential Moving

Average (Radenkovic and Huynh, 2017; Radenkovic

et al., 2018; Huynh and Radenkovic, 2018). Due to

the continuous nature of users’ mobility and content

requests, in this paper, we propose to apply the

concept of high-order Markov chain to our complex

real-time analytics to more accurately predict the

content traffic trends based on the historical

information of content traffics related users’ mobility.

This paper extends the multilayer

multidimensional predictive heuristics integrated in

CafRepCache (Radenkovic and Huynh, 2017;

Radenkovic et al., 2018) which is an predictive

adaptive collaborative cognitive forwarding and

caching framework in heterogeneous opportunistic

mobile networks. We propose a novel prediction

model that allows improved congruency with both the

underlying network and users demands, more

accurately capture the temporal and spatial locality of

mobility and content requests as well as mobility-

content traffic patterns interplay. We build on two

integral complementary multidimensional predictive

analytics and heuristics: i) temporal predictive

analytics and heuristics that captures the temporal

locality of content requests upon request access time,

enables more responsiveness to the rising trend of

newly high popular contents and fading out of older

contents over time as well as avoid one-timer contents

and mitigate flash crowd effect; ii) spatial predictive

analytics and heuristics that capture the spatial

locality of user mobility and content requests as well

as balance the trade-off between serving different

regions of contents’ subscribers. We exploit mobility-

content traffic patterns interplay and do not focus on

resource analytics (Radenkovic and Huynh, 2017;

Radenkovic et al., 2018) in this paper.

The paper begins by providing an overview of the

related work in section 2, section 3 describes and

discusses our models and multilayer novel predictive

heuristics, section 4 evaluates the effect of each

complement heuristic on caching performance in

mobile DTN across a range of metrics over two

different network topologies and a real-world

location-based service dataset for content workloads.

Section 5 gives a conclusion.

2 RELATED WORK

Authors in (Le et al., 2015) propose a forwarding and

cache replacement policy for SocialCache based on

content popularity driven by frequency and freshness

of content requests. As part of its replacement policy,

SocialCache may remove a cached content from the

network, thus reduce the cache hit ratio and increase

delays. This problem is exacerbated when the

resource is limited and the replacement rate is high as

(Le et al., 2015) is not resource and congestion aware.

Authors in (Zhang et al., 2014) propose Optimal

Cache Placement Based on Content Popularity

(OCPCP) that takes a caching decision based on the

frequency of content requests at a caching node. That

is, the more frequent requests for content, the higher

chance that content will be requested again. Time

Aware Least Recent Used (TLRU) (Bilal and Kang,

2014) is an extension of the simple LRU in which the

time stamp of an arriving content is recorded locally

by a single node. The arriving content is cached if the

average request time is smaller than the time stamps

of the stored contents. Authors in (Mardham et al.,

2018) propose Location-based Caching that uses

decay function measured by the function of distances

between subscribers and caching points and some

varying attributes, such as time or number of requests

Interdependent Multi-layer Spatial Temporal-based Caching in Heterogeneous Mobile Edge and Fog Networks

to classify contents and replace them when cache

memory is full. (Mardham et al., 2018) considers the

fairness problem that some contents belonging to

some specific location may be more important and is

cached often across the network, even if it is not very

popular. Authors in (Flores et al., 2017) proposed a

social-aware hybrid offloading strategy for load

balancing and computation sharing based on node's

stability which is measured by contact frequency and

duration in order to improve the availability of

offloading support for mobile users. However,

(Flores et al., 2017) does not support high topology

dynamics with intermittent disconnections and

dynamically changing publishers and subscribers

workload patterns.

Existing opportunistic caching policies such as

(Wang et al., 2014; Fricker et al., 2012) rely on an

assumption that the content distribution in the

networks approximately follows Zipf's law (Yoneki

et al., 2008; Breslau et al., 1999) or the

characterisation of content requests have been based

on Independent Reference Model (IRM) which

assumes that content requests occur independently.

However, authors in (Dabirmoghaddam et al., 2014;

Dán and Carlsson, 2010) have questioned the validity

of the IRM model and Zipf’s law model. According

to the proposal in (Dabirmoghaddam et al., 2014),

content requests often exhibit both temporal locality

and spatial locality. Researches on today’s social

networks (Dabirmoghaddam et al., 2014; D’Silva et

al., 2018) suggest that if content is requested at a

certain period in time, more likely it will be requested

again in the near future. In fact, the content is not

requested scattered randomly and independently over

time; but rather particular contents are interested at a

certain time interval, while its popularity gradually

fades out. Spatial locality of content requests is based

on the fact that content requests of the same content

are more likely to be issued by geographically close

areas. More precisely, the requests coming from a

specific region in space are more likely to be similar

than those collected over regions far apart (D’Silva et

al., 2018).

3 FULLY-DISTRIBUTED

PREDICTIVE MOBILE EDGE

AND FOG CACHING

In this section, we briefly describe CafRepCache

(Radenkovic and Huynh, 2017; Radenkovic et al.,

2018) framework, then we propose to extend its

integral multilayer fully-distributed predictive

heuristics.

CafRepCache (Radenkovic and Huynh, 2017;

Radenkovic et al., 2018) is a multi-path content and

interest forwarding and replication with adaptive

collaborative caching framework in heterogeneous

opportunistic mobile networks. It utilises fully-

localised and ego networks multi-layer predictive

heuristics about dynamically changing topology,

resources and content popularity to manage dynamic

trade-offs between minimizing the end-to-end latency

and maximising content delivery while enabling

resource efficiency and congestion avoidance.

CafRepCache relies on three theory foundation:

network science, social science and information

science as shown in Fig. 1.

Figure 1: CafRepCache theory foundation.

CafRepCache system is modelled as a network G

that consists of a set N of nodes 



(



 ) and a set

E of edges, G = (N, E). As the connectivity of the

network and the state of the nodes change over time,

each of these sets is modelled as time series, thus N =

{



: t  T} and E = {



: t  T}. We denote with 

a set of content files that can be requested by the

network. Each content 





  (or 



for simplicity)

is published at time t. Node 



  may act as either

a subscriber of content 



, denoted by 





or a

publisher 





or a caching point 





at any time t

from any location while being mobile. Thus for any

content 



, a set of subscribers who are interested in





is denoted as 





 









  and so on. Each

node in the network is able to perform predictive

analytics of multivariate mixed data (e.g. content and

mobility) as well as collaborate and exchange its local

observations with other neighbour nodes when two

nodes are in contact in order to capture and detect

PECCS 2019 - 9th International Conference on Pervasive and Embedded Computing and Communication Systems

various events (e.g. user connectivity patterns,

request patterns) in more accurate and responsive

manner without the need of global knowledge. We

define “ego network” of each node 



: 



as a

network consisting of 



together with the nodes they

are connected most recently (i.e. k-hops neighbours)

or nodes it meets at regular interval (most frequently)

over the time duration ∆T and all the links among

those nodes. In this way, ego network allows each

node to give its own regional or temporal perspective

of the network (or both are included).

3.1 Dynamic Complex

Temporal-Graph Heterogeneous

Network Topologies

In order to provide insights of users’ content traffic-

related mobility, connectivity patterns, this section

analyses CafRepCache in different underlying

network topologies with different degree of mobility,

connectivity patterns which helps us to better design

novel modelling analytics to capture and predict the

spatial-temporal correlations of content request

patterns with underlying network topologies.

Empirical evidence shows fixed and wired networks

are mostly scale-free, whereas mobile and

opportunistic networks can be either random or scale-

free. Thus, we analyse theoretically CafRepCache in

extremely heterogeneous random topologies as well

as scale-free topologies to understand fundamental

performance limitations of CafRepCache in different

realistic networks.

3.1.1 Complex Temporal Random Network

Topologies

In random networks, the majority of previous studies

in mobile networks assume Poisson contact processes

and model user encounters as independent Poisson

processes with rate λ due to the time between two

consecutive contacts of a pair of nodes follows

exponential distribution (Bornholdt and Schuster,

2006). The probability of some node 



 

connecting with some other node 



(Bornholdt and

Schuster, 2006) is: 

























The probability that node 



  connects with

exactly n other nodes (Bornholdt and Schuster, 2006)

within time T: P(n encounters) 















. Let 



be the probability that content 



is stored in the

cache of an arbitrary nodes, then the probability of the

cache miss in random network is:

Prob(content 



is not in the cache) *

Prob(interest request never reaches correct caching

points within time T)

=   



 *



 







= (  



*





















  







3.1.2 Complex Temporal Scale-free Network

Topologies

Complex temporal scale-free networks are

characterized by a highly heterogeneous degree

distribution, which follow a power-law distribution

(Yoneki et al., 2008). Although the network may

change significantly over time, the degrees of its

nodes obey the power-law model at any time (Yoneki

et al., 2008).

The probability P(n encounters) of a node in the

network goes for large values of n as: 











where  is the shape parameter of the power-law

distribution and represents the degree of the power-

tail.

Then the probability of a cache miss of content 



in scale-free networks is (   



 *









 









3.2 Spatial-temporal Analytics and

Heuristics for Content Caching

In this section, we describe two non-trivial predictive

analytics and heuristics (i.e. K-order Markov

chained-based temporal heuristics and spatial

clustering heuristics) that cover different dimensions

of the spatial-temporal dynamics and mobility-

content traffic interdependence problem. When

combined together, they allow forming dynamic

transient interest and data dissemination topologies

based on predictive analysis and commonalities

between their interests, caches and retrieval histories

as well as connectivity histories.

3.2.1 Dynamic K-order Markov

Chained-based Temporal Heuristics

Each node in the network resolves the request

frequency, recency and betweenness in fully-

localised distributed manner. When two nodes are in

contact, they exchange their local observations and

continuously resolve the value of dynamically

changing predictive heuristic based on both its local

observation and the collaborative observations it gets

from others. We apply the concept of K-order Markov

chain on our complex analytics to predict the content

traffic trends based on the historical information of

Interdependent Multi-layer Spatial Temporal-based Caching in Heterogeneous Mobile Edge and Fog Networks

content traffics related users’ mobility. When K = 1,

a lot of historical state information is ignored and only

the information of the current moment is used. Such

limitations make the practical application of 1-order

Markov chain prediction method lack of prediction

accuracy. In line with (Ruan et al., 2019), compared

with other existing time-order-based prediction

methods, the K-order Markov chain performs much

better when the order number K = 2. We apply K-

order Markov chain to leverage efficiently historical

information of content requests and subscribers’

mobility to predict the future trends of content

traffics. The K-order is defined as:

































 



































We then introduce our method to measure the

temporal heuristics based on request frequency,

recency and betweenness as below.

Request frequency counters are additively

increased upon the arrival of a content request and

decreased through time. This is in order to ensure that

if the number of requests for a content has been

reduced, its popularity counter will be reduced

accordingly and the content will be subject to eviction

or offload to other nodes. Given a caching point

observes average f interests of content k during the

interval , the request frequency is measured as:

 



where  is a

control parameter. Request frequency implies that the

more content requests have been observed by a

caching point during a short interval time , the

more likely that content will be requested in the same

interval, thus capture the temporal locality of content

requests

Request recency enables our caching design to

capture the content popularity trend in responsive

manner based on the recorded time stamp of recent

requests in different locations for each caching point,

thus allow to predict adaptively the emerging contents

that may become highly popular in near future and

contents that are currently considered as high popular

but will be less popular soon. Given a caching point n

observes the most recent interest of content k at





, then we denote 



, 







, etc. as the time that previous interests

have been recorded by the caching point. The request

recency is measured as how likely a recent content

request will trigger a subsequent request at the current

time.

 

































Request recency analytic shows that the smaller

gap between current time and recent requests

observed in the past of content k, the higher chance

that content k will be requested soon in the future.

Request betweenness provides the trade-off

between current observed content popularity versus

long terms interest in it in order to balance between

potentially one-timer contents or fake news and long-

term useful content. The time gap between

continuous requests in the period of time  is

measured as: 



 













, 



 



,…

Then the request betweenness heuristic is

measured as:

 















 



 











The novel temporal heuristics are the combination

of request frequency, recency and betweenness which

allow CafRepCache to choose the best suitable

contents to cache and when to cache by predicting in

real time the locality trend of content request patterns

over time in different locations and avoid losing

valuable contents by reducing the caches for one-

timers contents and fake news.

  









 





















3.2.2 Dynamic Spatial Clustering Heuristics

As the requests are more likely to be similar in a

specific region, we propose the fully-localised

distributed spatial heuristics that aim to classify,

recognize and predict content interests coming from

dynamic changing clusters of subscribers. The spatial

heuristic shows that the higher request rate coming

from the same localised region or dynamic cluster of

different subscribers, the higher likely that content

will be requested again by other subscribers within

that location. Given a caching point observes interest

requests of content k from a set of subscribers   



within a time interval  , the spatial heuristic is

measured by the clustering coefficient (Nicosia et al.,

2013), similarity, closeness and tie strength

(Radenkovic and Huynh, 2017; Radenkovic and

Grundy, 2011; Daly and Haahr, 2007) between

different subscribers of the same content as below:

 

























 













 





















 



in which the similarity value (Daly and Haahr,

2007; Nicosia et al., 2013) between 







 



within

a time interval ∆t is:

















where 









is the similarity in contacts between two subscriber









of the content k. The closeness centrality (Daly

PECCS 2019 - 9th International Conference on Pervasive and Embedded Computing and Communication Systems

and Haahr, 2007; Nicosia et al., 2013) of 



is a

measure of how close 



is to any other node in 



. It

is measured as the inverse of the average distance

from 



to any other node in 











where 



the distance between 



and 



in the set of subscribers

 



The node tie strength value (Radenkovic and

Huynh, 2017; Radenkovic and Grundy, 2011; Daly

and Haahr, 2007) between of subscribers 







 



within a time interval ∆t is:











































































where























measures the frequency of

contacts between 







;























measures the

recency of contacts between 







and























indicate the relative distance by hops between 







The complex temporal graph metrics of contact

frequency, recency and topology distance allow

congruency with the underlying dynamic changing

network topology and connectivity. In order to

balance the trade-off between serving contents

requested from a specific local region of highly

connected subscribers and from multiple less-

connected subscribers, we favours the weak tie

strength which helps to give a wider and broader

long-term predictive content popularity instead of

only favouring and serving the contents requested

from a local region of highly connected subscribers.

Spatial heuristics allow CafRepCache to choose

the most suitable caching points to cache popular

contents based on its relative location with the

subscribers and between the subscribers themselves.

3.3 CafRepCache’s Combined Heuristics

In section 3.2, we described our novel approach for

opportunistic caching protocol that, we argue, is

essential to be able to capture the spatial-temporal

locality of mobility and content requests as well as the

mobility-content traffic interplay. The above two

non-trivial heuristics 







 cover different

dimensions of the mobility-content traffic pattern

interdependences problem and when combined they

allow managing a number of trade-offs we identified.

Each caching point 



in the network resolves and

combines the two heuristics to measure 















denoted as the popularity of 



during the interval

time . 







 implies the probability of how

likely the content 



will be requested in a period of

time:

















  





























where 



is the weighting factor of each heuristic,























 is the respective utilities of each

heuristic as measurements of their relative gain, loss

or equality, calculated as pair-wise comparison

between the node’s own heuristics and that of the

encountered contacts:























 



































    

The predictive analytics are resolved by caching

nodes’ local observations and collaborative

observations from neighbours within its ego network

in order to allow each caching point have a more

regional converged perspective of the network

without the need of global network knowledge.

The total content popularity heuristic is measured

as:















































4 EXPERIMENT SETUP AND

EVALUATION

This section presents an evaluation of our caching

algorithm, first introducing a set of state-of-the-art

caching policies as competitive caching algorithms

and metrics for comparing the experimental results.

For the underlying network topology and mobility

patterns, we use a simulation-driven data trace with

two very different network topologies: random

network and real-world mobility traces Infocom

(Scott et al., 2006) in ONE simulator (Keränen et al.,

2009) as scale-free network in order to give a deeper

and more accurate performance overview of

CafRepCache. We use Foursquare New York Dataset

(Yang et al., 2014) as a real trace for content requests.

This dataset is collected through location-based

service Foursquare API

(https://developer.foursquare.com/) describing the

spatial-temporal locality of content requests in terms

of user interests at public venues, it contains 227,428

subscriptions of 18,201 users in different locations of

New York city during the period of 10 months. Each

check-in is associated with its time stamp, its GPS

coordinates and its semantic meaning (represented by

fine-grained venue-categories).

We compare and evaluate CafRepCache on the

overall caching performance measured by different

criteria (e.g. cache hit ratio, latency, eviction rate,

etc.) against multiple state-of-the-art and benchmark

proposals: SocialCache (Le et al., 2015), Optimal

Cache Placement Based Content Popularity (OCPCP)

(Zhang et al., 2014), Time Aware Least Recent Used

Interdependent Multi-layer Spatial Temporal-based Caching in Heterogeneous Mobile Edge and Fog Networks

Table 1: Values of the simulation parameters.

Parameter

Value

Complex temporal network

topologies

Random network, Scale-free

network (Infocom)

Content request pattern

Foursquare New York

Number of nodes

50-100

Simulation duration

1 - 3 hours

Request rate

1-25 request/min

Number of contents





- 



File size

1 MB - 8.4 MB

Interest packet size

8 kB - 128 kB





0.1 - 0.6%

(TLRU) (Bilal and Kang, 2014) and LocationCache

(Mardham et al., 2018). We have run six increments

of the number of subscribers and publisher ranging

from 10% to 60% of the total number of nodes. Due

to limited space, we report on experiments with

increasing number of subscribers, but note that the

results for increasing number of publishers are similar

to the ones presented here. Without losing generality,

we assume that 25% of node population are

publishers and varying the number of subscribers. All

experiments are repeated ten times and averaged with

different random subscribers and publishers. For each

experiment, either the cache hit ratio or average

latency (in hops) or eviction ratio will be shown. The

general simulation parameters details are shown in

Table 1.

Fig. 2 shows the spatial and temporal correlation

of content traffic (i.e. temporal requests pattern of

mobile subscribers) in Foursquare dataset for a

content in different locations over time.

Figure 2: Spatial-temporal correlation of content requests.

It shows the temporal patterns (similarity) of

content traffic during weekdays and weekend: if a

content is requested at a certain point in time, more

likely it will be requested again in near future. In fact,

nor are the content references scattered randomly and

independently over time; rather, a content might be of

particular interest at a certain time interval, while its

popularity gradually fades out. The locations of

mobile subscribers feature different degrees of

similarity in content request such that the location 1

and 2 which are relatively close to each other have

similar request patterns compared to that of location

3 which are far away from others. This captures the

impact that the geographical diversity of the users has

on the observed trace of requested contents by them.

More precisely, the requests coming from a specific

region in space are more likely to be similar than

those collected over regions far apart.

In order to understand the scalability of caching

points with regarding to the increasing number of

subscribers, we vary the number of subscribers to

evaluate the number of caching points in Fig.3,

average latency (measured by the number of hops) in

Fig. 4 and cache hit ratio (which refers to how many

interest packets are matched with the contents in

caching points without being forwarded to

publishers) in Fig.5 that indicate the efficiency of

caching decisions and locations in random topology

and scale-free topology.

Figure 3: Number of caching points vs. number of

subscribers.

Fig. 3 shows that the relative number of

CafRepCache caching points increases from 12% to

23% in random networks and from 2 to 11% in scale-

free networks regarding the growth of subscribers.

We argue that random networks with short average

paths and low clustering require more number of

caching points to serve the dynamic mobile

PECCS 2019 - 9th International Conference on Pervasive and Embedded Computing and Communication Systems

subscribers while scale-free networks with high

social character need less number of caching points

which converge to 11% regarding the increasing

number of subscribers from 40% to 60%. Fig. 4 and

5 shows that CafRepCache achieves 79.4% and

92.4% cache hit ratio while the hop count average

from the caching points to subscribers are 3.01 and

2.7 in random network and scale-free network

respectively.

We argue that in random network, CafRepCache

benefits from its cache redundancy mechanism that

select highly suitable locations for caching and

replication when needed as adaptive replication and

caching are both necessary to address multi-user data

communications in dynamic fragmented and sparse

topologies. In scale-free network, CafRepCache

utilities its multidimensional predictive analytics and

complex temporal graph metrics to make caching

decisions in a predictive manner and congruent with

the underlying network mobility, connectivity and

content interest.

Figure 4: Average hops count between caching points and

subscribers.

We show graphs of random network topologies as

a worst case scenario in Fig.3-5 and we will focus on

scale-free network topologies for the rest of our

experiments as it allows our caching decision

makings to leverage the spatial-temporal correlations

of mobility and traffic patterns as well as mobility-

traffic interdependence.

To evaluate the effectiveness of the content

request frequency heuristic, we vary the content

request patterns which follows different content

popularity skewness  (0.6-1.1) utilized in Hawkess

process (Dabirmoghaddam et al., 2014) and measure

Figure 5: Success ratio vs. number of subscribers.

the cache hit ratio. In line with (Dabirmoghaddam et

al., 2014), when α becomes larger, there is small

number of contents that account for the majority of

total requests, or being requested more frequently

compared to the others.

Fig. 6 shows that our cognitive caching achieves

the highest cache hit ratio (ranging from 31% to

86.4%) compared to other competitive algorithms

(OCPCP, TLRU, SocialCache, LocationCache)

regarding the increase of popularity skewness α. We

observe that higher α leads to bigger gap between

CafRepCache and others. This is because the request

frequency heuristic takes advantage of highly skewed

content popularity and content request temporal

locality to predict efficiently the incoming content

requests and adapts strongly with the content requests

while others neglect or could not adapt with the

temporal locality of content. CafRepCache is

followed by LocationCache and SocialCache that

manage up to 76.6% cache hit ratio regarding the

increase of content popularity skewness. We observe

that LTRU and OCPCP achieve the lowest cache hit

ratio (ranging from 24,3% to 60%) as it relies only on

simple request frequency or recency metric, thus

could not be able to predict adaptively the temporal

locality of content request patterns.

As the content popularity changes in real-time

regarding the variation in user interests, we further

study the impact of varying content popularity

fluctuation on the caching performance in order to

evaluate the effectiveness of the content request

recency heuristic. The Fig.7 shows the popularity

variation range from 20 to 120 popularity rank for

each round of the experiment. As caching

performance of slight variation on content popularity

has advantage over sharp variation, it is

Interdependent Multi-layer Spatial Temporal-based Caching in Heterogeneous Mobile Edge and Fog Networks

Figure 6: Cache hit ratio vs Popularity zipf alpha.

challenging for the caching algorithms to adapt

quickly to the severe and quick popularity alteration.

As shown in Fig.7, CafRepCache’s request

recency metric keeps good stability regarding the

popularity change (typically above 83.1% cache hit

ratio), predict adaptively the emerging contents that

may become highly popular in near future and

contents that are currently considered as high popular

but will be less popular soon. The sensitiveness of our

proposed algorithm provides better adaptation to

rapidly changing content popularity and different

network environment while all other competitive

caching algorithms could not be able to adapt with

sharp variation in popularity.

Figure 7: Cache hit ratio vs Popularity variation.

We vary the cache size and measure the cache

eviction ratio to evaluate the effectiveness of the

content request betweeness heuristic. Eviction is one

of the popular metrics for the performance evaluation

of content caching. When the cache space is full and

caching point makes a decision to cache a new arrived

content then one of the cached contents is evicted or

offloaded to free up the buffer. When the resource is

limited and the eviction rate is potentially high due to

one-timer contents, the overall network throughput is

affected in terms of cache hit ratio and content

retrieval latency. In other words, if a stored content is

evicted incorrectly and a new request arrives for it,

then there is a cache miss and the content has to be

retrieved from other nodes or publishers directly. As

a result, this increases the content retrieval latency

and decreases the cache hit ratio of the caching

services. Smaller cache buffer size offers more

selective cached contents, thus requires more accurate

content popularity prediction.

Fig. 8 shows that CafRepCache achieves the

lowest eviction ratio regarding the dynamic changing

in size of the caching points (decreasing from 0.62 to

0.34 eviction ratio when the cache size is increased)

compared to the state-of-the-arts caching algorithms.

CafRepCache is followed by SocialCache and

LocationCache (ranging from 0.75 to 0.46 eviction

ratio) while TLRU and OCPCP have the worst

performance, especially when the cache space is

relatively small. This is due to the request betweeness

heuristic allows CafRepCache balance the trade-off

between current observed content popularity versus

long terms interest in order to avoid caching quickly

potentially one-timer contents or fake news and

losing the long-term useful contents.

Figure 8: Eviction rate vs Relative cache size.

In order to understand the request spatial-based

heuristics integrated in our caching algorithm, we

vary the content request patterns which follows

PECCS 2019 - 9th International Conference on Pervasive and Embedded Computing and Communication Systems

different spatial localisation factor β (0.1-0.85)

utilized in Hawkess process (Dabirmoghaddam et al.,

2014) and measure the cache hit ratio. In line with

(Dabirmoghaddam et al., 2014), with low localization

factor β, content requests are generated

independently. The generated trace, therefore,

conforms to the IRM assumption and hence, one

single time content is considered. With a localization

factor of 0.85, other near nodes in a region more likely

request the same content after a node requests it.

Fig. 9 shows that the request spatial clustering

heuristic enables CafRepCache to adapt with the

spatial locality of content requests. CafRepCache

achieves the best performance (around 87% cache hit

ratio) followed by LocationCache (80%) and

SocialCache (72%). TLRU and OCPCP have no (or

little) improvement to the accuracy of predicting

content popularity, ranging from 52% to 63% cache

hit ratio regarding the dynamically changing of

spatial localization factor.

We observe that higher β even leads to bigger gap

between CafRepCache and other state-of-the-art

content solutions. It is due to the spatial locality

heuristic helps to classify and recognise content

interests coming from localised group of subscribers,

then adaptively predict that the contents will be

requested again by other subscribers within that

location.

Figure 9: Cache hit ratio vs Spatial localisation factor.

We evaluate the effect of subscriber-caching point

connectivity on the performance of different state-of-

the-art caching protocols in order to understand the

nature of caching points regarding the dynamic

connectivity of mobile subscribers.

Fig. 10 shows that CafRepCache outperforms

other competitive caching protocols in terms of

average delay measured by the number of hops.

Figure 10: Average latency (hops) vs. Subscriber-Caching

point connectivity.

CafRepCache is able to bring the cached contents to

only a few hops away from subscribers (2.7 hops)

regarding the dynamic centrality of caching points.

This is due to CafRepCache benefits from

multidimensional multilayer analytics that allow it to

place the most suitable set of contents in the most

suitable set of caching points which are not only

highly central but also have similarity in contacts and

interest requests with the subscribers.

5 CONCLUSIONS

We proposed multilayer adaptive predictive

distributed collaborative analytics and heuristics for

enabling spatial-temporal locality awareness of

mobility and traffic patterns as well as mobility-

content traffic interplay for opportunistic caching in

mobile edge/fog networks. Our combined heuristics

allow the caching protocol to be more flexible and

responsive to the dynamic mobility and complex

content request patterns in the unreliable scenarios

imposed by varying publishers and subscribers as

well as dynamic resource availability. We performed

extensive real trace-driven experiments in ONE

simulation (Keränen et al., 2009) and showed that the

proposed predictive heuristics help CafRepCache

caching framework to perform better than the state-

of-the-art caching solutions in terms of success ratio,

cache hit ratio, average delay and eviction rate.

We aim to explore our ego-network analytics

and heuristics in greater detail and propose adaptive

context-aware weighting of the complementary

analytics and utilities. We plan to deploy our

approach in different application scenarios which

Interdependent Multi-layer Spatial Temporal-based Caching in Heterogeneous Mobile Edge and Fog Networks

have complex temporal network topologies such as

smart agriculture (Wietrzyk and Radenkovic, 2010;

Brun-Laguna et al., 2016), urban emergency (Huynh

and Radenkovic, 2017), intelligent transport system

(Loscri et al., 2019), and smart manufacturing

(Radenkovic et al., 2015) using software-defined

networking (SDN) or network function virtualization

(NFV) as in (Radenkovic, 2016; Radenkovic and

Huynh, 2016).

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