A Machine Learning based Context-aware Prediction Framework for
Edge Computing Environments
Abdullah Fawaz Aljulayfi
1,2 a
and Karim Djemame
School of Computing, University of Leeds, Leeds, U.K.
Prince Sattam Bin Abdulaziz University, K.S.A.
Keywords: Edge Computing, Self-adaptive Systems, Machine Learning, Prediction Framework, Linear Regression,
Support Vector Regression, Neural Networks, Sliding Window.
Abstract: A Context-aware Prediction Framework (CAPF) can be provided through a Self-adaptive System (SAS) re-
source manager to support the autoscaling decision in Edge Computing (EC) environments. However, EC
dynamicity and workload fluctuation represent the main challenges to design a robust prediction framework.
Machine Learning (ML) algorithms show a promising accuracy in workload forecasting problems which may
vary according to the workload pattern. Therefore, the accuracy of such algorithms needs to be evaluated and
compared in order to select the most suitable algorithm for EC workload prediction. In this paper, a thorough
comparison is conducted focusing on the most popular ML algorithms which are Linear Regression (LR),
Support Vector Regression (SVR), and Neural Networks (NN) using real EC dataset. The experimental results
show that a robust prediction framework can be supported by more than one algorithm considering the EC
contextual behavior. The results also reveal that the NN outperforms LR and SVR in most cases.
The EC paradigm has emerged to support the Internet
of Things (IoT) applications by pushing the computa-
tional capabilities towards the edge of the network
(Dolui and Datta, 2017; Shi and Dustdar, 2016). Such
support requires the efficient management of edge re-
sources to fulfill the IoT applications’ requirements
such as mobility and low latency. However, the EC
resource management process is not a trivial task be-
cause of the nature of EC and the rapid increase in the
number IoT devices which is estimated to be 41.6 bil-
lion devices (Framingham, 2019).
The SASs have seen a significant level of interest
in different research areas like autonomic computing
and pervasive computing and provide self-manage-
ment properties and exhibit system properties such as
self-awareness to achieve adaptation (Kavanagh et
al., 2019). They can monitor resources, state and be-
havior. Therefore, a SAS is a promising solution to
efficiently support the resource management automa-
tion in EC as it can adjust itself according to the op-
eration environment (Arcaini et al., 2015; D’Angelo,
2018; Kavanagh et al., 2019; Kramer and Magee,
2007; Krupitzer et al., 2015; Singh and Chana, 2015;
Xu and Buyya, 2019). Such adaptation can be either
proactive whereby the SAS uses the historical data to
forecast the future system behavior or changes in the
environment (Al-Dhuraibi et al., 2018; Galante and
De Bona, 2012; Moreno-vozmediano et al., 2019), re-
active whereby the system is adjusted in real-time by
continually monitoring the system behavior and oper-
ational environment, or hybrid whereby the system
uses both reactive and proactive approaches.
In a proactive adaptation, designing a robust pre-
diction framework for forecasting EC workload and
supporting auto-scaling is challenging (Delicato et
al., 2017; Gupta et al., 2017; Kaur et al., 2017; B. Liu
et al., 2020; Toczé and Nadjm-Tehrani, 2018). An EC
environment exhibits a dynamic workload and often
has limited resources. In order to design such frame-
work, a deep understanding of EC nature (e.g. work-
load patterns and users’ behavior) and a thorough in-
vestigation of the workload prediction methods, their
characteristics and impact on their accuracy are re-
quired (Islam et al., 2012; Nikravesh et al., 2015b).
Aljulayfi, A. and Djemame, K.
A Machine Learning based Context-aware Prediction Framework for Edge Computing Environments.
DOI: 10.5220/0010379001430150
In Proceedings of the 11th International Conference on Cloud Computing and Services Science (CLOSER 2021), pages 143-150
ISBN: 978-989-758-510-4
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
The advantages of these activities are twofold: 1) to
support the IoT applications’ Quality of Service
(QoS) by avoiding under-provisioning (Ajila and
Bankole, 2013; Aldossary and Djemame, 2018;
Calheiros et al., 2015; Kumar and Singh, 2018;
Lorido-Botran et al., 2014; Moreno-vozmediano et
al., 2019), and 2) to efficiently utilize the EC re-
sources thus avoiding over-provisioning and improv-
ing the system’s scalability.
Altogether, a timely research challenge is the de-
sign of the CAPF for forecasting EC workload. This
paper extends our previous work on the SAS Archi-
tecture (Aljulayfi and Djemame, 2019) and focuses
on the proactive adaptation support. Further, it anal-
yses a real EC dataset from Shanghai Telecom
(Sguangwang.com, 2018) in order to identify work-
load patterns and propose the most suitable workload
prediction model. Moreover, it compares the accu-
racy of the most well-known ML algorithms: LR,
SVR, and NN which includes investigating the effect
of window size. Finally, the CAPF is designed in ac-
cordance to the investigations’ results. The main con-
tributions of this paper are summarized as follows:
(C1) An analysis of a real EC workload dataset is per-
formed in order to understand the EC workload
pattern and train the ML prediction models.
(C2) A comparison of the most well-known ML al-
gorithms’ accuracy considering the window size
effect is conducted aiming towards workload
prediction framework.
(C3) A design of CAPF through SAS to support auto-
scaling decision using the most accurate and
suitable ML prediction algorithms is presented.
The remainder of this paper is organized as fol-
lows: Section 2 discusses the related work. This is fol-
lowed by Section 3 which presents the SAS architec-
ture. Section 4 shows the research methodology. The
results and discussion are in Section 5. Section 6 il-
lustrates the CAPF. Finally, the paper’s conclusion
and future work in Section 7.
This section discusses the proposed proactive adapta-
tion models to support auto-scaling systems, which
can be classified into resource utilization- and work-
load-based. The resource utilization-based studies
predict resource utilization e.g. CPU utilization to
support the auto-scaling decision. A considerable
body of research adopts this method. For example, a
CPU-utilization prediction model using the Regres-
sion-Markov chain (RMC) method targeting the ap-
plications’ QoS is proposed in (Li et al., 2018). The
results show that the RMC provides better accuracy
as compared to LR due to large fluctuation and ran-
domness. Some other studies adopt ML methods.
Three prediction models for CPU-utilization,
throughput, and response time for Cloud Computing
(CC) are proposed by (Bankole and Ajila, 2013) using
a synthetic linear workload. Furthermore, LR, SVR,
and NN ML methods are used. The results show that
the SVR outperforms LR and NN in predicting both
CPU-utilization and throughput whereas the NN out-
performs other methods in predicting the response
time. This work is extended in (Ajila and Bankole,
2013) by considering the random workload pattern.
In (Islam et al., 2012) a synthetic linear workload
pattern is generated in order to develop prediction
models to support scaling decisions. Moreover, this
work compares the accuracy of LR and NN with and
without the sliding window consideration. It reports
that the sliding window has a positive impact on the
models’ accuracies. The effect of the NN on the auto-
scaling decision technique is also evaluated using a
threshold and compared with SVR (Nikravesh et al.,
2014). Additionally, an investigation is conducted to
select the best proportion of the dataset split consid-
ering e.g. 50%/50% for training/testing. This work is
extended in (Nikravesh et al., 2015a) and aims to in-
vestigate the effect of different workload patterns (i.e.
growing, periodic, and unpredicted). Besides the slid-
ing window technique is considered.
A workload prediction model using SVR and NN
for growing, periodic, and unpredicted workload pat-
terns is proposed in (Nikravesh et al., 2015b). More-
over, the influence of window size on the selected al-
gorithms is considered. The adopted hypothesis is
claiming that the prediction auto-scaling system ac-
curacy can be improved by selecting the best predic-
tion algorithms based on the workload pattern. The
research is extended in (Nikravesh et al., 2017) to in-
vestigate the risk minimization principle using the
same methods and workload patterns. In addition, the
SVR, NN Multi-layer Perceptron (MLP), and MLP
with Weight Decay (MLPWD) are considered. The
NN is also adopted in (Kumar and Singh, 2018) to
develop a workload prediction model that is able to
learn the best mutation strategy along with optimal
crossover rate. The model is evaluated using two real
datasets and compared with maximum, average, and
back propagation network methods.
In (Moreno-vozmediano et al., 2019), an auto-
scaling system using the SVR model is introduced.
Besides, a performance model based on queuing the-
ory is proposed to determine the number of resources
that must be provisioned. The SVR model is com-
pared with e.g. LR method. Further, several SVR con-
CLOSER 2021 - 11th International Conference on Cloud Computing and Services Science
figurations are investigated considering the kernel
type. The results reveal that the SVR using different
configurations outperform the other methods. In (B.
Liu et al., 2020) both Autoregressive Moving Aver-
age (ARMA) and Elma Neural Network (ENN) are
used where the ENN is responsible for correcting the
prediction error of ARMA and providing the final
prediction value.
Most of the presented studies focus on the CC en-
vironment and use a synthetic workload. To the best
of our knowledge, this paper is the first to propose a
CAPF for the EC environments based on real EC
workload with a support of a proactive SAS. This
framework is designed thanks to a thorough compar-
ison of the most effective ML algorithms used in the
literature with consideration of the window size effect
to improve prediction accuracy.
This section briefly illustrates our SAS architecture
by zooming in to show auto-scaling components only
due to the page limit. It is shown in Figure 1 where
the full version including the research roadmap can
be found in (Aljulayfi and Djemame, 2019). The SAS
uses the MAPE-based (Monitor, Analyse, Plan, Exe-
cute) loop with a focus on the Analyse activity as it is
the paper’s scope. The use of MAPE-based allows the
system to have a full and continuous management
over the operational environment thanks to MAPE
loop. Additionally, the design of the SAS architecture
aims to have a hybrid adaptation, but this paper only
focuses on the proactive side.
Figure 1: Self-adaptive system architecture.
The data analyser (i.e. analyse activity) is respon-
sible for analysing the monitoring data which is pro-
vided by Monitor activity. In order to support hybrid
adaptation, this activity is divided into two main com-
ponents as follows: 1) Context-aware Prediction
Framework (CAPF): is responsible for predicting the
number of tasks requests in the future by consuming
the historical workload that stored in Request Repos-
itory where these requests will be scheduled as con-
tainers. This component (highlighted in grey) sup-
ports C3. Further details about its internal compo-
nents is available in Section 6 as it is designed after
conducting the paper’s investigation. 2) Resource
Utilization Analyser: is responsible for reactive adap-
tation process which is used as a back-up for the
CAPF in case of events are not predicted.
This section presents the methods that are used to-
wards achieving the research objectives.
4.1 Dataset Analysis
The paper makes use of the Shanghai Telcom dataset
which simulates the EC workload (Sguangwang.com,
2018) and reported in (Guo et al., 2019; Wang, Guo,
et al., 2019; Wang, Zhao, Huang, et al., 2019; Wang,
Zhao, Xu, et al., 2019). It provides six months of mo-
bile phones records accessing the Internet via base
stations which are distributed over Shanghai city. The
dataset has 7 attributes: month, date, start time, end
time, latitude, longitude, and user ID. The analysis of
the full data set shows that it has 6,952,921 records,
9739 mobile devices, and 3042 base stations. Further,
a thorough analysis of the dataset is conducted in or-
der to understand the workload patterns and mobile
phone users’ behavior. However, this section only
presents part of the workload analysis that is related
to this paper.
A preliminary data analysis revealed the workload
of the first month (i.e. June) has the lowest percentage
of records with missing data e.g. base station location.
Figure 2: Workload pattern.
A Machine Learning based Context-aware Prediction Framework for Edge Computing Environments
the same month as it is representative for the rest days
in the same month in the sense that the overall work-
load pattern is periodic. The workload of this day is
shown in Figure 2 per minute after removing the ex-
treme outliers which hide the data pattern. From Fig-
ure 2, it can be seen clearly that the overall pattern of
the data is a fluctuation with decreasing, increasing,
and steady (fluctuating) behavior. Therefore, to pro-
pose a robust context-aware prediction model, the day
will be divided into three categories based on the
workload. These categories as shown in Figure 2 are
1) decreasing which includes late night and early
morning (red), 2) increasing which includes morning
(green), and 3) Fluctuating which includes afternoon
to evening (orange). Further, one hour from each cat-
egory is selected (i.e. 2nd, 12th, 14th hours) which
will be used in training and testing the prediction
models. The training and testing splitting percentage
will be discussed in Section 4.4.
4.2 Machine Learning Algorithms
Three of the most popular and widely used ML algo-
rithms, LR, SVR, and NN are considered. These al-
gorithms are able to predict the future workload effi-
ciently based on historical data (Baig et al., 2020;
Islam et al., 2012; C. Liu et al., 2017; Sapankevych
and Sankar, 2009).
The LR is the simplest and most widely used su-
pervised ML algorithm for prediction (Baig et al.,
2020; James et al., 2017). In this paper, the simplest
case of LR is used because we have only one input
variable. The SVR is an efficient learning method that
implements the Support Vector Machine principle but
produces continuous variable. The advantage of using
SVR is its ability to map the time-series to a higher
dimension using kernel function (Nikravesh et al.,
2017). The NN or Artificial Neural Network (ANN)
is a supervised learning algorithm that can be used for
both regression and classification problems
(Nikravesh et al., 2015b). A type of ANN is MLP
which is a feed-forward network that is used for a
range of problems including forecasting (Nikravesh
et al., 2017; Zhang et al., 1998). This network archi-
tecture is adopted in this paper because it is the most
popular and efficient network architecture that is used
for forecasting.
4.3 Sliding Window Technique
The sliding window technique uses the last 𝑛 samples
of the data feature in order to forecast the future value
(Nikravesh et al., 2017). The use of the sliding win-
dow technique is important to perform a supervised
ML when having only one feature in the dataset aim-
ing to train the prediction algorithm (Nikravesh et al.,
2015b). In this paper, the number of requests per time
unit feature is only available. Therefore, in order to
apply ML algorithms, the sliding window technique
is used. Indeed, the window size is an important factor
which has a significant influence on the ML predic-
tion accuracy. However, selecting the appropriate
window size is challenging because we have to find
the best window size that allows the model to capture
the data pattern and application behavior (Amiri and
Mohammad-khanli, 2017). This means a small win-
dow size might not be representative while a large
window size might cause overfitting (Nikravesh et al.,
2015b). Therefore, this paper aims to investigate the
effect of window size on the ML algorithms accuracy.
4.4 Experimental Design
This section presents the design of the experiments
and the overall approach. As mentioned, three predic-
tion models will be proposed, each model targets a
part of the day. In order to do so, firstly, we must find
the best splitting percentage that allows the ML algo-
rithms to capture the data pattern and relationship.
Further, this is done for each day part (i.e. decreasing,
increasing, and fluctuating). Then, based on the best
splitting percentage, the ML algorithms’ accuracy
will be evaluated using: Mean Absolute Error (MAE),
Root Mean Squared Error (RMSE), and Mean Abso-
lute Percentage Error (MAPE). This means for each
workload pattern, the prediction models are trained
and tested with consideration of the effect of the win-
dow size on the prediction accuracy. Finally, the
CAPF will be designed based on these investigations.
Table 1: SVR and NN configurations.
Kernel RBF
rate 0.38
No. of hidden la
ers 1
Number of hidden neurons 4
time 10000
As part of the experimental design, the implemen-
tation of ML algorithms and their configurations must
be considered. In this paper, all the selected algorithms
are implemented using the well-known ML tool
WEKA 3.8
. In terms of the configurations, one pre-
dictor is used in LR simplest case. For SVR and MLP
CLOSER 2021 - 11th International Conference on Cloud Computing and Services Science
(i.e. NN), we used the same configuration of
(Nikravesh et al., 2015a, 2015b) as shown Table 1 be-
cause we have same scenario and workload pattern.
This section presents and discusses the experimental
results. The discussion for workload will be separate
as each workload represents a different pattern.
5.1 Percentage Splitting
Before comparing the ML models', it is important to
specify the best training duration that allows the mod-
els to capture and learn the data pattern. This section
presents the results of the experiments considering the
proportion of the dataset to include in the train split:
80/20 (i.e. 80% training and 20% testing) and 70/30
(i.e. 70% training and 30% testing). This means each
workload (i.e. decreasing, increasing, and fluctuat-
ing) is split and evaluated using these percentages.
The overall results show that the 80/20 is the best
split percentage as it allows the selected algorithms to
capture the data pattern and provide the most accurate
results. Further, the 80/20 split outperforms the 70/30
overall evaluation metrics and different window
sizes. Therefore, the 80/20 splitting percentage results
will be considered in the following sections.
5.2 Ml Algorithms Comparison
This section compares the accuracy of the ML predic-
tion algorithms considering the testing results and ad-
dresses accordingly contribution (C2).
Data with the decreasing workload reveals that
SVR outperforms both LR and NN in overall predic-
tion accuracy metrics. This can be seen clearly from
Table 2 which shows the evaluation metrics for de-
creasing workload. Additionally, the best ML predic-
tion value is also provided by the SVR when the win-
dow size is 3 using MAE and RMSE. If LR is com-
pared with NN, the LR outperforms NN in most
For the increasing workload pattern, the predic-
tion results in Table 3 show that SVR outperforms
both LR and NN in most cases; this is similar to the
decreasing workload pattern. However, by looking
closely at the results, the best prediction values over
the evaluation metrics are provided by NN when the
window size equals 9. Although SVR has better ac-
curacy in most cases, the NN provides the best accu-
racy in the increasing data pattern, thus, NN will be
adopted for this data pattern.
Table 2: MAE, MAPE, and RMSE values (decreasing).
2 2.56 2.5 3.04 34.43 32.6 42.63 3.1 2.95 3.78
3 2.55 2.41 3.29 34.21 30.32 45.81 3.08 2.84 4.01
4 2.56 2.52 2.52 34.3 33.02 28.44 3.09 2.98 2.93
5 2.57 2.57 4.61 34.61 34.79 62.37 3.11 3.13 5.37
6 2.55 2.49 2.47 34.16 32.46 31.35 3.07 2.94 2.86
7 2.69 2.49 2.82 37.37 32.73 39.43 3.37 2.97 3.53
8 2.53 2.5 3.25 33.65 32.61 45.18 3.03 2.95 3.98
9 2.51 2.42 3.91 33.02 30.71 35.36 2.98 2.86 4.56
Table 3: MAE, MAPE, RMSE values (increasing).
2 4.25 4.24 5.33 16.67 16.83 22.54 4.76 4.84 6.78
3 4.25 4.23 4.53 16.75 16.84 16.35 4.8 4.84 5.21
4 4.37 4.25 5.45 17.54 16.9 22.95 5.2 4.87 6.87
5 4.25 4.25 4.28 16.91 16.92 15.99 4.87 4.87 4.7
6 4.82 4.25 5.72 18.99 16.92 24.08 5.44 4.87 7.13
7 4.25 4.24 5.81 17.02 16.89 24.49 4.93 4.86 7.28
8 4.25 4.25 6.21 16.92 16.91 25.93 4.88 4.86 7.54
9 4.25 4.24 3.87 16.87 16.9 14.87 4.85 4.87 4.57
The prediction results of the fluctuating workload
are shown in Table 4. Unlike the decreasing and in-
creasing patterns, the result reveals that NN has better
accuracy as compared to both LR and SVR in most
cases. Moreover, its accuracy is the best when the
window size is 9 using MAPE and RMSE. Therefore,
the NN with window size 9 will be adopted in the
Table 4: MAE, MAPE, RMSE values (fluctuating).
2 4.97 4.84 4.26 20.93 20.41 17.26 6.23 6.11 5.13
3 4.86 4.7 4.29 20.87 19.8 16.96 6.45 5.94 5.06
4 4.74 4.65 5.52 19.92 19.54 23.3 5.97 5.89 6.84
5 4.72 4.6 4.75 19.83 19.31 19.97 5.95 5.81 5.96
6 4.65 4.59 4.57 19.51 19.13 18.97 5.86 5.73 5.61
7 4.63 4.48 4.63 19.31 18.56 19.58 5.77 5.54 5.91
8 4.63 4.66 4.06 19.36 19.41 16.88 5.79 5.8 5.06
9 4.66 4.69 4.15 19.75 19.63 15.88 5.88 5.91 4.93
5.3 Sliding Window Effect
The increase in window size does not have a signifi-
cant impact on LR and SVR algorithms over all met-
rics in both decreasing and increasing data as shown
in Figures 3 and 4, respectively. To be more specific,
from Figure 3 and 4, the changes in the accuracy of
both LR and SVR are roughly steady when the win-
dow size increase. Although the SVR accuracy is al-
most steady, it provides the best accuracy when the
window size is 3 as compared to other ML algorithms
in decreasing data. Additionally, the difference be-
tween LR and SVR are neglected in the most cases of
different window size values. In terms of the increas-
ing data, the SVR accuracy seems to be steady over
A Machine Learning based Context-aware Prediction Framework for Edge Computing Environments
window size as shown in Figure 4. In contrast, the in-
crease of window size causes highly fluctuating NN
accuracies over MAE, MAPE, and RMSE in both de-
creasing and increasing data. Further, in the increas-
ing data, NN provides the best accuracy when the
window size equals to 9.
Figure 3: Window size effect (decreasing).
Figure 4: Window size effect (increasing).
Figure 5: Window size effect (fluctuating).
Unlike decreasing and increasing data, increasing the
window size has a positive impact on all ML algo-
rithms overall evaluation metrics in fluctuating data.
This effect can be seen clearly in Figure 5 which
shows a decreasing trend. Further, the sliding window
technique slightly improves the accuracy of both LR
and SVR. In the case of NN, it has a significant im-
pact on its accuracy over MAE, MAPE, and RMSE.
Additionally, the best accuracy is provided by NN
when the window size is 9 using MAPE and RMSE.
5.4 Results Summary
This section highlights the main findings of the ex-
periments and their position in the context of the re-
lated work. NN outperforms LR and SVR in both in-
creasing and fluctuating workloads whereas SVR out-
performs NN and LR in decreasing workload. The
reason NN exhibits the best accuracy is its ability to
capture all noise in the data whereas SVR tries to find
a smooth curve to cover them (Nikravesh et al.,
2017). Based on this logic, SVR should outperform
NN in increasing workload. However, the increasing
workload has some form of fluctuation which reduces
the SVR accuracy.
In terms of the sliding window, the results show
that for some workload patterns increasing the window
may have a significant impact on the prediction accu-
racy. For example, increasing the window size has a
positive impact on the ML algorithms in the fluctuating
workload because the large window size allows the
models to learn the relationships between features
(Islam et al., 2012; Nikravesh et al., 2015b). In con-
trast, the increase of window size does not have an im-
pact on some ML algorithms such as LR and SVR
which means that their accuracies are almost steady
over the window size values (Nikravesh et al., 2017).
This section proposes the CAPF fulfilling contribu-
tion (C3). It is designed according to the above exper-
iments that select the best ML prediction algorithms
with consideration of the window size. Further, it is
integrated with the SAS architecture in the CAPF
component that is shown in Figure 1 (i.e. highlighted
in grey). The framework aims to forecast the future
workload that will be submitted to the EC by the IoT
devices. This framework consists of two main com-
ponents as shown in Figure 6 (highlighted in grey)
which are: 1) Context analyser: identifies the context
CLOSER 2021 - 11th International Conference on Cloud Computing and Services Science
of the application, including time dependence, in or-
der to select the ML model suits its workload pattern
(i.e. decreasing, increasing, and fluctuating). 2) Algo-
rithm selector: selects the best ML algorithm based
on the workload pattern that is identified by the Con-
text Analyser. The selection of algorithms will be
based on the experiments that we performed to select
the best ML algorithm for each pattern. In other
words, it uses either SVR or NN for predicting the
future workload based on the day’s part.
Figure 6: Context-aware prediction framework.
This paper has presented a CAPF to support auto-
scaling decisions in EC environments. This frame-
work predicts the future EC workload using either
SVR or NN ML algorithms. These algorithms are
considered the best ML algorithms to be used for EC
workloads which is based on the comparison that has
been performed. Further, the comparison process in-
cludes a thorough investigation on the window size
effect. All these steps have done using the Shanghai
Telecom dataset which represents a real EC work-
load. The results reveal that window size has a signif-
icant impact on workload patterns and ML algorithms
as best size allows the ML algorithms to capture the
workload pattern and behavior.
The SAS architecture is currently under develop-
ment with the aim of supporting, e.g. elasticity, scala-
bility, and QoS. In term of elasticity support, the pre-
dicted workload will be implemented to evaluate the
effectiveness of the developed models in operational
environment under several applications’ scenarios.
Further, this involves evaluating the performance of
the proposed SAS in the EC with consideration to var-
ious adaptation approaches which are proactive, reac-
tive, and hybrid adaptation. The scalability support
must also be considered to efficiently utilize the EC re-
source and maximize the number of running applica-
tions in the EC environment in sense that the EC have
limited resources. It requires meeting the applications’
QoS of IoT devices which have very sensitive require-
ments such as low latency. The support of scalability
and QoS will also involve the consideration of their
trade-offs, which is key in service provision.
Ajila, S. A., and Bankole, A. A. (2013). Cloud client
prediction models using machine learning techniques.
International Computer Software and Applications
Conference, 134–142.
Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., and Merle, P.
(2018). Elasticity in Cloud Computing: State of the Art
and Research Challenges. IEEE Transactions on
Services Computing, 11(2), 430–447.
Aldossary, M., and Djemame, K. (2018). Performance and
energy-based cost prediction of virtual machines auto-
scaling in clouds. 44th Euromicro Conference on
Software Engineering and Advanced Applications,
Aljulayfi, A. F., and Djemame, K. (2019). A Novel QoS
and Energy-aware Self-adaptive System Architecture
for Efficient Resource Management in an Edge
Computing Environment. 35th Annual UK
Performance Engineering Workshop, 39–54.
Amiri, M., and Mohammad-khanli, L. (2017). Survey on
prediction models of applications for resources
provisioning in cloud. Journal of Network and
Computer Applications, 82, 93–113.
Arcaini, P., Riccobene, E., and Scandurra, P. (2015).
Modeling and Analyzing MAPE-K Feedback Loops for
Self-Adaptation. 10th International Symposium on
Software Engineering for Adaptive and Self-Managing
Systems, 13–23.
Baig, S., Iqbal, W., Lluis, J., and Carrera, D. (2020).
Adaptive sliding windows for improved estimation of
data center resource utilization. Future Generation
Computer Systems, 104, 212–224.
Bankole, A. A., and Ajila, S. A. (2013). Cloud client
prediction models for cloud resource provisioning in a
multitier web application environment. 2013 IEEE 7th
International Symposium on Service-Oriented System
Engineering, SOSE 2013, 156–161.
Calheiros, R. N., Masoumi, E., Ranjan, R., and Buyya, R.
(2015). Workload prediction using ARIMA model and
its impact on cloud applications’ QoS. IEEE
Transactions on Cloud Computing, 3(4), 449–458.
D’Angelo, M. (2018). Decentralized self-adaptive
computing at the edge. IEEE/ACM 13th International
Symposium on Software Engineering for Adaptive and
Self-Managing Systems, 144–148.
Delicato, F. C., Pires, P. F., and Batista, T. (2017). Resource
Management for Internet of Things. Springer.
Dolui, K., and Datta, S. K. (2017). Comparison of edge
computing implementations: Fog computing, cloudlet
and mobile edge computing. 2017 Global Internet of
Things Summit (GIoTS), 1–6.
Framingham, M. (2019). The Growth in Connected IoT
Devices. IDC Analyze the Future. https://www.idc.
Galante, G., and De Bona, L. C. E. (2012). A survey on
cloud computing elasticity. 2012 IEEE/ACM Fifth
International Conference on Utility and Cloud
Computing A
, 263–270.
A Machine Learning based Context-aware Prediction Framework for Edge Computing Environments
Guo, Y., Wang, S., Zhou, A., Xu, J., Yuan, J., and Hsu, C.
H. (2019). User allocation-aware edge cloud placement
in mobile edge computing. Software - Practice and
Experience, January 2019, 1–14.
Gupta, H., Dastjerdi, A. V., Ghosh, S. K., and Buyya, R.
(2017). iFogSim: A toolkit for modeling and simulation
of resource management techniques in the Internet of
Things, Edge and Fog computing environments.
Software - Practice and Experience, 47(9), 1275–1296.
Islam, S., Keung, J., Lee, K., and Liu, A. (2012). Empirical
prediction models for adaptive resource provisioning in
the cloud. Future Generation Computer Systems, 28(1),
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2017).
Introduction to Statistical Learning with Applications
in R. Springer.
Kaur, K., Dhand, T., Kumar, N., and Zeadally, S. (2017).
Container-as-a-Service at the Edge: Trade-off between
Energy Efficiency and Service Availability at Fog Nano
Data Centers. IEEE Wireless Communications, 24(3),
Kavanagh, R., Djemame, K., Ejarque, J., Badia, R. M., and
Garcia-perez, D. (2019). Energy-aware Self-Adaptation
for Application Execution on Heterogeneous Parallel
Architectures. IEEE Transactions on Sustainable
Computing, 1–15.
Kramer, J., and Magee, J. (2007). Self-Managed Systems:
an Architectural Challenge. 2007 Future of Software
Engineering, 259–268.
Krupitzer, C., Roth, F. M., VanSyckel, S., Schiele, G., and
Becker, C. (2015). A survey on engineering approaches
for self-adaptive systems. Pervasive and Mobile
Computing, 17, 184–206.
Kumar, J., and Singh, A. K. (2018). Workload prediction in
cloud using artificial neural network and adaptive
differential evolution. Future Generation Computer
Systems, 81, 41–52.
Li, G., Song, J., Wu, J., and Wang, J. (2018). Method of
Resource Estimation Based on QoS in Edge
Computing. Wireless Communications and Mobile
Computing, 2018.
Liu, B., Guo, J., Li, C., and Luo, Y. (2020). Workload
forecasting based elastic resource management in edge
cloud. Computers and Industrial Engineering,
139(0360–8352), 1–12.
Liu, C., Liu, C., Shang, Y., Chen, S., Cheng, B., and Chen,
J. (2017). An adaptive prediction approach based on
workload pattern discrimination in the cloud. Journal
of Network and Computer Applications, 80, 35–44.
Lorido-Botran, T., Miguel-Alonso, J., and Lozano, J. A.
(2014). A Review of Auto-scaling Techniques for
Elastic Applications in Cloud Environments. Journal of
Grid Computing, 12(4), 559–592.
Moreno-vozmediano, R., Montero, R. S., Huedo, E., and
Llorente, I. M. (2019). Efficient resource provisioning
for elastic Cloud services based on machine learning
techniques. Journal of Cloud ComputingAdvances,
Systems and Applications, 8(1).
Nikravesh, A. Y., Ajila, S. A., and Lung, C. H. (2017). An
autonomic prediction suite for cloud resource
provisioning. Journal of Cloud Computing, 6(1).
Nikravesh, A. Y., Ajila, S. A., and Lung, C. H. (2015a).
Evaluating Sensitivity of Auto-scaling Decisions in an
Environment with Different Workload Patterns. IEEE
39th Annual International Computers, Software &
Applications Conference, 415–420.
Nikravesh, A. Y., Ajila, S. A., and Lung, C. H. (2014).
Measuring prediction sensitivity of a cloud auto-scaling
system. Proceedings - IEEE 38th Annual International
Computers, Software and Applications Conference
Workshops, COMPSACW 2014, 690–695.
Nikravesh, A. Y., Ajila, S. A., and Lung, C. H. (2015b).
Towards an Autonomic Auto-scaling Prediction
System for Cloud Resource Provisioning. Proceedings
- 10th International Symposium on Software
Engineering for Adaptive and Self-Managing Systems,
SEAMS 2015, 35–45.
Sapankevych, N. I., and Sankar, R. (2009). Using Support
Vector Machines: A Survey. IEEE Computational
Intelligence Magazine, 2, 24–38.
Sguangwang.com. (2018). The Telecom Dataset (Shanghai
Telecom). http://sguangwang.com/TelecomDataset.html
Shi, W., and Dustdar, S. (2016). The Promise of Edge
Computing. Computer, 49(5), 78–81.
Singh, S., and Chana, I. (2015). QoS-Aware Autonomic
Resource Management in Cloud Computing: A
Systematic Review. ACM Computing Surveys, 48(3),
Toczé, K., and Nadjm-Tehrani, S. (2018). A Taxonomy for
Management and Optimization of Multiple Resources
in Edge Computing. Wireless Commu. and Mobile
Computing, 2018, 1–20.
Wang, S., Guo, Y., Zhang, N., Yang, P., Zhou, A., and
Shen, X. S. (2019). Delay-aware Microservice
Coordination in Mobile Edge Computing: A
Reinforcement Learning Approach. IEEE Transactions
on Mobile Computing, 1–1.
Wang, S., Zhao, Y., Huang, L., Xu, J., and Hsu, C. H.
(2019). QoS prediction for service recommendations in
mobile edge computing. Journal of Parallel and
Distributed Computing, 127, 134–144.
Wang, S., Zhao, Y., Xu, J., Yuan, J., and Hsu, C. H. (2019).
Edge server placement in mobile edge computing.
Journal of Parallel and Distributed Computing, 127,
Xu, M., and Buyya, R. (2019). Brownout Approach for
Adaptive Management of Resources and Applications
in Cloud Computing Systems. ACM Computing
Surveys, 52(1), 1–27.
Zhang, G., Eddy Patuwo, B., and Y. Hu, M. (1998).
Forecasting with artificial neural networks: The state of
the art. International Journal of Forecasting, 14(1), 35–
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