Selçuk-Teknik Dergisi BIG, MEDIUM AND LITTLE (BML) SCHEDULING IN FOG ENVIRONMENT

Abstract

Fog computing has got great attention due to its importance especially in Internet of Things (IoT) environment where computation at the edge of the network is most desired. Due to the geographical proximity of resources, Fog computing exhibits lower latency compared to cloud; however, inefficient resource allocation in Fog environment can result in higher delays and degraded performance. Hence, efficient resource scheduling in Fog computing is crucial to get true benefits of the cloud like services at the proximity of data generation sources. In this paper, a Big-Medium-Little (BML) scheduling technique is proposed to efficiently allocate Fog and Cloud resources to the incoming IoT jobs. Moreover, cooperative and non-cooperative Fog computing environments are also explored. Additionally, a thorough comparative study of existing scheduling techniques in Fog-cloud environment is also presented. The technique is rigorously evaluated and s h o w s promising results in terms of makespan, energy consumption, latecny and throughput.

Full text

Selçuk-Teknik Dergisi
ISSN 1302-6178
Journal of Selcuk-Technic
Özel Sayı 2020 (ICAT’20)
Special Issue 2020 (ICAT’20)
243
BIG,
MEDIUM
AND
LITTLE (BML)
SCHEDULING IN FOG ENVIRONMENT
Bashir Yusuf BICHI1,+, Saif UI ISLAM1, Anas Muazu KADEMI3
1COMSATS University, Department of Computer Science, Park Road, Islamabad,
Pakistan
2Yasar University, Department of Computer Engineering, No 37-39 Bornova, Izmir,
Turkey
bbichi2009@kustwudil@.edu.ng, saiflu2004@gmail.com, anas.kademi@yasar.edu.tr
Abstract
Fog
computing
has
got
great
attntion
due
to
its
importance
especially
in
Internet
of
Things
(IoT)
environment
where
computation
at
the
edge
of
the
network
is
most
desired.
Due
to
the
geographical
proximity
of
resources,
Fog
computing
exhibits
lower
latency
compared
to
cloud;
however,
inefficient
resource
allocation
in
Fog
environment
can
result
in
higher
delays
and
degraded
performance.
Hence,
efficient
resource
scheduling
in
Fog
computing
is
crucial
to
get
true
benefits
of
the
cloud
like
services
at
the
proximity
of
data
generation
sources.
In
this
paper,
a
Big-Medium-Little
(BML)
scheduling
technique
is
proposed
to
efficiently
allocate
Fog
and
Cloud
resources
to
the
incoming
IoT
jobs.
Moreover,
cooperative
and
non-cooperative
Fog
computing
environments
are
also
explored.
Additionally,
a
thorough
comparative
study
of
existing
scheduling
techniques
in
Fog-cloud
environment
is
also
presented.
The
technique
is rigorously
evaluated
and
shows
promising
results
in
terms
of
makespan,
energy
consumption,
latecny
and
throughput.
Keywords: Cloud
node,
Fog
node,
Max-Min,
Min-Min,
Big,
Medium,
Little,
Task,
Resource,
Cooperative
and
Non-Cooperative
Systems.
 This paper has been presented at the ICAT'20 (9th International Conference on Advanced Technologies)
held in Istanbul (Turkey), August 10-12, 2020.

Selçuk-Teknik Dergisi
ISSN 1302-6178
Journal of Selcuk-Technic
Özel Sayı 2020 (ICAT’20)
Special Issue 2020 (ICAT’20)
244
1. Introduction
In
todays
world,
distributed
computing
is
continuously
drawing attention
with
the
sole
purpose
of
bringing
computing
resources
closer
to
clients.
The
advancement
of
this
idea
leads to
the
introduction
of
grid
computing
which
later
advances
to
cloud
computing,
and
now
even
closer,
by
what
is called
fog
or
edge
computing.
Cloud
and
Fog
Computing
are
two
terms
that
are
almost
the
same,
the
main
difference
in
between
the
two
is
that
fog
computing
was
introduces
to
bring
computing
resources
closer
to
needing
computing
environment
(i.e.
Clients
and
IoT
devices)
for
task
processing.
Bringing
the
two
ecosystems
(i.e.
Cloud
and
Fog)
together
adds
efficiency
and
reduces
delay
in
processing
task
as compared
to
sending
task
to
far
distant
environment
(Cloud).
Every
day
billions
of
tasks
are
generated,
and
each
task
needs
to
be
processed
in
the
shortest
possible
time.
Therefore,
the need
for
Fog
has
become
a
necessity
to
provide
clients
with efficient
and
timely
task
processing
capabilities.
According
to [16]
Fog
computing
has
some
outstanding
advantages
which
include
real-time
processing
of
task,
it
also
bridges
(IoT)
with the
internet
computing
infrastructure.
Another
major
advantage
is
that
it
reduces
the
latency
and
improves
quality
of
service
(QoS)
of
a
server
by
bringing
the
computation
services,
storage
and
networking
services
closer
to
the
edge
[20].
These
advantages
give
clients
an
environment
to
get
access
to
various
resources
at
earliest
possible
time
and
highly
efficient
manner. For
Fog
to
attain
efficiency
it
needs
to
collaborate
with
the cloud
environment
or
other
fog
environments
which
is
required
to
introduce
the
concept
of
load
sharing
between
fog
nodes
or
fog
and
cloud
environment.
Fog
computing
plays
a
vital
role.
Toward
reducing
delay
in
providing
service
to
the
IoT
[1]
or clients
in
need
of
computing
resources,
for
example
mobile
devices
(such
as
those
along
the
highways)
demand
high quality
streaming
via
proxies
and
access
points
position
along the
highway
and
other
places
from
a
nearby
Fog
node
[3], therefore
Fog
nodes
must
be
equipped
with
such
high-quality
streaming
capabilities
to
meet
the
client
demand.
Bringing
the
edge
closer
to
the
IoT
nodes
is
an
important
milestone
towards
minimizing
delay
in
processing
a
given
request,
as stated
in
[3].
Fog
is
the
best
solution
because
it
is
considered
more
efficient
and
easy
to
access.
Though
we
have
seen
the advantages,
the
edge
needs
to
balance
the
set
of
requests
and

Selçuk-Teknik Dergisi
ISSN 1302-6178
Journal of Selcuk-Technic
Özel Sayı 2020 (ICAT’20)
Special Issue 2020 (ICAT’20)
245
tasks
along
its
resources
for
effective
use
of
such
resources.
According
to
[5][9],
load
balancing
is
necessary
to
enhance
the overall
performance
of
the
distributed
environment.
Multiple
load
balancing
techniques
were
introduced
with
the
objective
of
giving
each
node
a
fair
computing
time.
There
are
different
load
balancing
algorithms
proposed
in
[17]
and
[19],
in
the second
algorithm
the
technique
allows
for
requested
data
to move
from
one
tier
to
another
i.e.
the
IoE
(Internet
of
Every
thing)
tier
to
Fog
tier
and
to
Cloud
tier
until
the
request
finds
a node
that
will
process
the
data,
while
the
former
balances
load using
a
technique
known
as
graph
re-
partitioning.
Having
seen the
importance
of
Fog
towards
providing
efficient
services,
these
data
needs
to
be
secured
and
free
from
intrusion,
[18] [21]
discusses
some
challenges
in
the
link
between
the
Fog and
IoT,
these
challenges
include
authentication/access
control
where
there
are
issues
regarding
the
security
of
data
along
the Fog
node,
other
issues
include
man-in-the
middle
attack.
In the
authentication
issue
the
adversary
which
is
often
called
malicious
user
may
change
their
smart
meter
or
spoof
an
IP address,
while
the
man-in-the-middle attack
may
temper
with
the
gateway
services
of
the
Fog
environment.
All
these
threats
are
not
predominant,
but
are
considered
as
a
potential
issue
that
may
either
disrupt
services
to
the
IoT or
may
increase
the
latency
level
in
which
the
user
may
spend trying
to
get
services.
1.1. Motivation
Our
approach
was
to
build
a
scheme
based
on
Cloud level
max-min,
min-
min
scheduling
scheme.
The
approach
goes
beyond
the
concept
of
Cloud
by
lowering
d o w n
to
the
Fog
level
by
extending
the
mentioned
schemes
into
what
we
call
Big,
Medium,
and
Little
(BML)
Scheduling
in Fog
Environment”.
Load
balancing
is
very
important
when handling
a
heterogeneous
environment
that
processes
a
request
or
task
sent
from
a
remote
IoT
to
a
given
server,
it
also
helps
to
enhance
the
performance
of
a
system.
The
’loadh balancer’
receives
request
from
IoT
or
any
other
device
that send
a
request
and
tries
to
balance
these
series
of
request across different
resources
(VM)
that
are
within
the
system
[24].
Many
task
scheduling
algorithms
were
introduced
in the
cloud
environment
with
the
aim
of
balancing
the
load
across
different
resources
of
the
server
such
as
in
[20]
with
the
aim
of
obtaining
better
resource
utilization,
among
these

Selçuk-Teknik Dergisi
ISSN 1302-6178
Journal of Selcuk-Technic
Özel Sayı 2020 (ICAT’20)
Special Issue 2020 (ICAT’20)
246
algorithms
are
the
max-min
and
min-min
algorithm.
This
paper
proposes
a
scheduling
algorithm
that
was
based
on
the
idea
of the
two
mentioned
algorithms,
and
applies
these
algorithms
to
the
fog
environment
for
efficient
task
processing.
These
algorithms
will
then
be
compared
in
terms
of
their
make-span
and
utilization.
As
observed
in
the
algorithms
proposed
in
[2]
[5]
[6]
[15] [12]
[20]
[25]
in
the
cloud
environment
which
proved
very
effective
in
improving
the
environment,
but
the
algorithms
were
not
tested
in
the
Fog
environment
to
ascertain
its
behaviors.
Therefore,
we
rigorously
evaluated
the
algorithms
to
investigate
their
behavior
in
fog
environment,
at
the
same
time
we
compared
these
algorithms
with
our proposed
scheme
with
the
aim
of
observing
how
the
algorithm
works
in
term
of
its
make-
span
and
utilization
rate.
One
of the
main
reason
of
fog
environment
is
to
bring
computing
resources
closer
to
the
needy
environment
so
that
those
tasks
in
need
of
processing
time
will
be
processed
quickly,
and not
to
be
deprived
of
a
fair
processing
time.
the
scheme
we
proposed
helps
by
providing
a
task
with
a
moderate
processing
time
as
well
as
timely
completion
of
the
task.
The
proposed
algorithms
contribute
immensely
in
the
fog
environment
in
the
following:
1. Reducing make-span: The make-span determines the maximum time at which all
the resources will complete executing a given task, therefore reducing the
maximum make-span implies reducing the time a task will wait seeking computing
time.
2. Higher Utilization rate: Making the system as busy as possible is another important
issue, as cost of processing can yield to more profit to the providers.
3. Fair Processing Time: The algorithm also give task a fair processing time, i.e. a
task is not deprived of and not given too much processing time as well, this can
translate into user and providers concession.
4. Cooperation between Cloud to Fog and Fog to Fog environment for computing
resources.
The rest of this paper is organized as follows; Task Scheduling algorithms was
discussed in section 3, section 4 focuses on transmission and propagation delay in an
environment that consist of cooperative and non-cooperative system of task processing
between fog-to-fog and/or fog-to-cloud node directly. Section 5 discussed on the
mathematical models that were used to derive the relationship between the edge not i.e.

Selçuk-Teknik Dergisi
ISSN 1302-6178
Journal of Selcuk-Technic
Özel Sayı 2020 (ICAT’20)
Special Issue 2020 (ICAT’20)
247
fog and the upper node i.e. cloud. Lastly section six discussed on the simulated result,
discussions, conclusion and remarks.
2. Related
Works
Since
the
advancement
of
grid
computing
which
yields
to cloud,
researchers
have
been
putting
efforts
toward
finding an
efficient
strategy
in
handling
the
ever-
growing
demand
of resources,
there
are
number
of
things
being
done.
The research
work is
grouped
into
two
context:
scheduling
scheme
context
and environmental
context.
In
the
scheduling
context,
our
work
was build
based
on
max-min
and
min-
min
scheduling
algorithms
in
cloud
environment
which
aimed
at
providing
an
appropriate
scheduling
scheme
for
a
set
of
task
for
the cloud
resources.
[2][5][6][12][15][20][25][26]
work
on
the basic
idea
of
max-min
or
min-min
algorithm
or
both
but
in the
context
of
cloud
environment
with
the
aim
of
providing
appropriate
scheduling
scheme
for
the
set
of
tasks
in
the
cloud environment.
[10][23]
this
leads
to
the
algorithms
that
were used
as
motivation
to
the
fog
environment.
The
table
(table
1)
examines
the
different
scheduling
algorithms
that
were
in one
way
or
another
related
to
our
work
on
scheduling
scheme
for
fog
environment.
[10]
proposes
a
scheme
that
work
by predicting
the
completion
time
of
a
task
on
dynamic
and
static mode
of
allocation
to
the
available
resources
while
[23]
proposed
another
scheme
known
as
multi-tenant
Load
Distributed
Algorithm
where
task
are
allocated
based
on
priorities.
All
the related
works
are
examine
and
described
in
Table
1.
Table 1. Related work
Related
work
Problem/Environment
Technique used
Advantage
Limitation
Future work
Tool
[2]
Comparison of
different scheduling
algorithm in Cloud
Environment
Min-Min, Max-
Min MCT, MET
Based on the
compared criteria,
Min-Min
perfumes
in terms of
makespan, degree
of in-balance and
throughput.
Max-Min and
Min-Min algorithm
are suitable for
small scale task
scheduling
Min-min also gives
higher priority to
smaller task while
max-min give
higher priority to
Improve min-min
algorithm by
optimizing cost
for
task scheduling
CloudSim

Selçuk-Teknik Dergisi
ISSN 1302-6178
Journal of Selcuk-Technic
Özel Sayı 2020 (ICAT’20)
Special Issue 2020 (ICAT’20)
248
larger tasks
[5]
Resource
management for
Cloud Environment
Max-Min,
Proposed
Max-Min,
Produce lower
makespan
Small scale task
processing
MATLAB
[6]
Min-Max algorithm
in Cloud
Environment
Min-Min, Min-
Max
Max-Min
Improved the
resource
utilization
Despite pair tasks
are processed at a
time larger task
may still be paired
together
Price of resources
Energy
consumption
Java
Programming
[10]
Priced Time petri
net in Fog
environment
Predict time cost
and prices cost of a
task
Improved
efficiency of
resource
utilization
Prediction may not
give accurate
result, mean small
task can have
higher cost while
larger one may
have lower cost.
Extend resource
allocation
strategy,
average
completion
time, fairness.
Dawn parallel
machine and
Linux
cluster
Extend
[12]
Cluster based
Max-Min for
Cloud Environment
Cluster based
Max-Min
algorithm, Improve
Max-Min, Enhance
Max-Min
Produce lower
makespan than
Improved Max-
Min
and Enhance
Max-Min
Larger task gets to
be processed first
than the smaller
once
K-means
clustering, Fuzzy
C-mean
clustering
CloudSim
[15]
Improve Max-Min
algorithm for
Cloud Environment
Min-Min Max-Min
Improved
Max-Min
Improve
utilization
and performance
of
the system, larger
tasks are handled
by slower resource
while smaller once
are handled by
larger tasks are
assigned to slower
resource which all
adds up to
improving
scalability,
availability and
stability of
Not mentioned
CloudSim
[20]
Modified Max-Min
for Cloud
Environment
Max-Min Modified
Max-Min
algorithm
Improve
utilization
and performance
of
resource
As in [15] larger
tasks are assigned
to slower resources
improve
utilization,
performance
CloudSim
[23]
Load Distribution
in Fog
Environment
mtLDAF
improved
efficiency
of resources. Task
are sent to
resource
improving load
balance across
Fog-Cloud layers
Not mentioned
Java

Selçuk-Teknik Dergisi
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Özel Sayı 2020 (ICAT’20)
Special Issue 2020 (ICAT’20)
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3.
Proposed
Big,
Medium
and
Little
Scheduling
Presently there aree over 20 billion networks devices across the [4], and
these
devices
will
work
effectively
when
connected
via
efficient
and
reliable
host
like
Fog
and
Cloud
ecosystem.
Therefore,
for
efficient and
better
services
a
proper
scheduling
technique
will
be
needed
to
assign
different
task
to
a
given
resource.
Task schedulings
are
categorized
into
two
[22]–
static
and
dynamic.
In static
the
task
information
is
known
prior
to
the
scheduling,
while
in
dynamic
task
are
assigned
to
a
given
resource
as
they
arrive
i.e.
without
any
prior
knowledge
of
the
arriving
task.
Scheduling
algorithm
for
Fog
environment
was
proposed
in [10]
where
the
user
has
the
upper
hand
in
choosing
a
resource
from
the
group
of
pre-
allocated
resources
autonomously,:
this
algorithm
is
called
Price
Time
Petri
Nets
(PTPN).
Scheduling
algorithm
is
aimed
at
minimizing
the
completion
time
of a
given
task
[10].
Another
algorithm
proposed
for
the
Fog environment
is
the
multi-tenant
load
distribution
algorithm
[23],
the
proposed
load
balancing
algorithm
considers
two
key parameters (delay
and
priority)
when
a
task
is
sent
to
the
fog
environment,.
The
algorithm is supposed to minimizes delay
and
at
the
same
time
increases
the
utilization
of
resources.
In
this
paper
we
proposed
a
load
balancing
based on priority
[25]
User-Priority
Guided Min-Min
Scheduling
Algorithm in Cloud
Environment
LBIMM and
PA-LBIMM
Reduce makespan
and produce better
performance
priority customers
enjoys better
services when
compared to the
general
users. Deadline of a
task, high
heterogeneity of
interconnected
network,
geographical
location of task to
improve
PA-LBIMM
Not mentioned
MATLAB
[26]
Enhancing Load
balance in Cloud
Environment
LBMM and
ELBMM
Better makespan
and resource
utilization
Larger task may
occupy a resource
for a long time.
CloudSim

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Özel Sayı 2020 (ICAT’20)
Special Issue 2020 (ICAT’20)
250
algorithm
for
the
Fog
environment
based
on
some
existing
schemes
in
the
cloud
computing
environment.
The
idea
of
scheduling
task
was
to
make
appropriate
assignment
of
task
to
a
resource
(which
could
be
virtual
resource)
in
case
of
fog
or
cloud
environment.
Balancing
the
load
on
these
nodes
can
help
in
minimizing
delay
and
as
well
allow
for
higher
utilization
of
such
resource.
The
main
purpose
of
Fog
is
to
bring
a processing
server
close
to
the
user
(i.e.
IoT
or
client)
this
allows
for
a
greater
efficiency
as
stated
in
[3],
therefore
it will
be
even
more
efficient
when
these
nodes
cooperate
with
each
other.
Different
scheduling
algorithms
were
discussed
in
[2]
each
with
the
purpose
of
minimizing
the
makespan
of a
resource.
Makespan
is
a
measure
of
throughput
among
set of
computing
resources.
The
makespan
is
considered
as
a queue
which
hold
request
or
task
that
need
to
be
processed
[15],
therefore
reducing
the
makespan
time
will
be
necessary
to
minimize
the
delay
in
which
such
task
will
wait
until
the time
it
will
be
executed.
[2]
[12]
discussed
a
set
of
task scheduling
algorithms
in
the
cloud
environment
and
these
algorithms
are
the
bases
for
our
proposed
scheme
for
Fog environmentThe
algorithms
are;
a. Max-min:
this
scheduling
scheme
always
assign
task with
maximum
expected
completion
time
to
a
resource
that
give
minimum
completion
time
of
that
given.
There
are
other
schemes
that
are
based
on
max-min
algorithm
which
aimed
at
reducing
the
makespan,
for instance
improved
max-min
algorithm
as
implemented
in
[14] gives
lower
makespan
when
compared
to
the
max-min
algorithm.
The
Max-min
Algorithm
as
given
in
[22] select
the
task
that
requires
long
processing
time
and assign
it
to
a
resource
while
the
smaller
task
wait
till
all the
larger
task
are
completed.
b. Min-Min
Scheduling:
in
this
scheme
task
with
minimum completion
time
is
always
assigned
to
its
corresponding
resource.
The
scheme
starts
with
a
set
of
unscheduled
tasks
then
it
determines
the
minimum
completion
time
for
each
task
on
all
resources,
the
task
is
then
assigned
to
the
resources
or
machine
that
give
the
list
completion
time
[8]
[25].
There
are
many
other
task
scheduling
algorithms
that
were
proposed,
however
we
restricted
ourself
to
the
algorithms
mentioned
above
since our
proposed
schemes
only
covers
some
unique
characteristics
of
the
Cloud
schemes.
All
these

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algorithms
are
employed
in
the
cloud
environment
and
our
focus
is
on
fog
node
in
relation
to
the
cloud
node,
so
we
take
the
idea
of the
algorithms
and
use
in
fog
environment.
The
algorithms
reduce
the
maximum
makespan
of
a
given
resources
that
is
processing
a
set
of
tasks.
The
makespan
is nothing
but
the
measure
of
throughput
of
a
resource.
The
pseudo
code
below
in
algorithm
1,
algorithm
2,
and,
algorithm
3
depict
the
nature
of
our
proposed
Big,
Medium
and
Little
Scheduling
scheme
in
Fog
environment.
For
all
tasks
submitted
to
the
meta-task Ti
For
all
resource
R
j
c
(ij)
=
E
(ij)
+
r
j
while
meta-task
is
not
empty
do
Find
n=number
of
all
minimum
values
less
than
Tm
Find
set
of
all
minimum
values
less
than
Tm
Take
Tm
mod
n=
Tn
Assign
task
Tn
to
resource
R
n,
n
<>
m
Remove
task
Tm
form
the
meta-task
Update
r
j
for
selected
R
j
Update
C
(ij)
for
all
task
end
Algorithm
1:
Medium
Scheduling
Algorithm
For
all
submitted
tasks
in
meta-task
Ti
For
all
resources
R
j
C
(ij)
=
E
(ij)
+
r
j
while
meta-task
is
not
empty
do
Find
task
Tk
consumes
maximum
completion
time.
Assign
Tk
to
the
resource
R
j
which
gives
minimum
execution
time
Remove
Tk
from
meta-tasks
set update
r
j
for
selected
R
j
update
C
(ij)
for
all
task
end
Algorithm
2:
Big
Scheduling
Algorithm
For
all
submitted
tasks
in
meta-task
Ti For
all
resource
Rj
C
(ij)
=
E
(ij)
+
r
j
while
meta-task
is
not
empty
do
Find
the
task
Tk
consumes
maximum
completion
time.
Assign
task
Tk
to
the
resource
R
j
with
minimum
execution
time.

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Remove
the
task
Tk
from
meta-tasks
set
Update
r
j
for
selected
R
j
Update
C
(ij)
for
all
task
end
Algorithm
3:
Small
Scheduling
Algorithm
The algorithm
allows
for
the
resultant
n
modulo
value
to assign
the
task
to
the
resources
that
give
minimum
completion
time
when
compared
to
the
maximum
completion
time.
The
advantage
is
to
give
the
task
a
moderate
time
it
may
require
in
executing
the
given
task
at
hand. In
this
paper
we
will
investigate
the
makespan,
the
utilization
rate
of a
resource
and
energy
consumption
of
a
given
resource,
the
energy
consumption
is
discussed
in
[7]
and
shown
in
equation
(4),
makespan
and
average
resource
utilization are
two
very
important
metrics
when
handling
scheduling
algorithms,
as
pointed
out
in
[12]
[13]
[25]
the
makespan
and average
resource
utilization
are
define
using
the
following
mathematical
relations;
makespan
= (1)
Resourceusage(RU)
= 

(2)
Averageutilization
=





(3)
 (4)
Were
P
max
is
the
peak
power
consumption
and
P
min
is
the minimum
power
consumption,
U
is
the
utilization
of
a
given resource.
The
graphs
below
describe
how
the
three
(Big, Medium,
and
Little)
schemes
works
in
the
Fog
environment.
We
consider
a
set
of
five
randomly
selected
tasks
and
resource
(T1=200,
T2=250,
T3=150,
T4=300
and
T5=100)
in
MI
and three
resources
(R1=50,
R2=100,
and
R3=40)
in
MIPS
then the
meta-task
table
is
as
follows;
The
Big
scheme
works
by
taking
the
maximum
among
the set
of
tasks
e.g.
the
maximum
value
in
task
4
(T4)
is
7.5. Therefore,
we
take
7mod2=1,
2
is
the
number
of
all
the
task
on task
4
that
are
less
than
7.5
i.e.
6,3,
now
if
from
the
resulting
value
we
take
6,
but
since
our
resulting
value
is
1
we
take 2.
We
then
go
back
to
our
initial
meta-task
and
find
in
what resource
2
falls.
finally
that
resource
will
be
picked. In the
example
given,
the
resource
R2
will
be
assigned
the
task. The
graph
below
shows
how
each
task
is
assign
to
a
given resource
based
on
the
meta-
task
in
Table II.

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Fig.
1. Gantt chart of Little Scheme
Fig.
2. Gantt
chart
of
Big
Scheme
Fig.
3.
Gantt
chart
of
Medium
Scheme
Based
on
the
above
Gantt
chart
in
the
figure
(fig.1),
(fig.2), and
(fig.3),
the
little
scheme
produced
a
makespan
of
7.5;
Big scheme
produced
a
makespan
of
10
seconds
and
our
medium
scheme
produced
a
makespan
of
7
second.
Hence, from
the
results
we
conclude
that
the
medium
scheme
gives
less
makespan
and at
the
same
time
gives
each
task
a
fair
amount
of
time
that
the task
may
need
to
fully
utilize
the
resource.

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Table 2. Meta-Task
R1
R2
R3
T1
4
2
5
T2
5
2.5
6.25
T3
3
1.5
3.75
T4
6
3
7.5
T5
2
1
2.5
Fig.
4. -A1-A3-A4-A5:
Cooperative
Systems.
A1-A2:
Non-cooperative.
4.
Cooperative
and
Non-Cooperative
Systems
The
main
purpose
of
Fog
is
to
bring
computing
resources
closer
to
the
IoT,
or
the
client,
in
this
section
we
presented
what
is
called
cooperative
and
Non-
Cooperative
system mode
of
operation,
this
scenario
is
module
as
a
set
 for
the
Cooperative
Systems and
 for
the
Non-
Cooperative
Systems.
In Cooperative
system
the
Fog
environment
get
to
collaborate
with
its
neighboring
Fog
node
for
computing
resources.
The conceived
idea
is
presented
in
the
Figure
(fig.4).
with
the
purpose
of
allowing
the
fog
to
contact
its
neighboring
fog
whenever
it
needs
external
computing
resources.
For
a
set
of
n
tasks equation
(5)
and
(6)
show
how
these
set
of
tasks
will
be
processed
at
each
layer
level.
In
Cooperative
system
the
Fog
environment
get
to
collaborate
with
its
neighboring
Fog
node
for
computing
resources,
for instance
if
we
have
n
number
of
task
to
be
process
in
h
Fog environment
and
potentially
the
cloud
environment,
these tasks
will
be
sent
from
the
pool
of
resources
as
shown
in
the figure
above

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(fig.4).
tThe
system
model
below
shows
how
all
the tasks
will
be
process
across
the
cooperative
fog
environment.


  
 (5)
The
conceived
idea
presented
in
the
figure
above
(fig.4-A1- A3-A4-A5)
allows
the
edge
to
send
task
to
its
neighboring
fog
whenever
it
needs
external
computing
resources.
The
flow chart
below
explains
further
how
the
task
will
be
handle.
If
a
node
has
C
capacity,
that
node
will
select
task
to
its
maximum
capacity
and
sent
the
rest
to
its
neighboring
Fog
node
until the
task
if
there
is
any
reached
the
Cloud
environment
as describe
in
the
flow
chart
in
the
figure
(fig.5).
Fig. 5. Cooperative Systems
The
Non-Cooperative
Systems
is
the
second
approach
where
fog
node
processes
the
requested
task
and
possibly
sends
some
to
the
cloud
environment
as
shown
in
equation
(6).
The idea
of
fog
collaboration
with
its
neighboring
fog
node
for
computing
resources
was
discussed
in
[1]
but
with
a
slightly different
approach.
 
(6)
The figure (fig.2-A1-A2) below depicts the non-cooperative systems, fog
environment will select tasks according to its capacity and send the rest are send to the
cloud environment as indicated in the task flow in the figure (fig6).

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Fig. 6. Non- Cooperative Systems
5.
Discussion
and
Mathematical
Formulation
For
the
environment
showed
in
figure
(Fig.4),
some
metF
rics
are
considered
which
include
the
propagation
delay
and transmission
delay
between
each
node.
As
earlier
mentioned,
the
simulation
environment
is
assumed
to
be
cooperative
and
non-cooperative.
The
link
from
the
task
pool
to
the
nearest
Fog node
is
L
distance
and
another
L
distance
to
the
next
nearest
Fog,
the
Last
nearest
node
is
also
a
distance
apart
from
the Cloud
node
by
another
L
distances.
Therefore,
the
transmission
and
propagation
delay
of
each link needs to be computed. , and  :
are the Transmission and Propagation delay between Fog node i and  respectively.
 and : is the average latency at Fog and Cloud node respectively as shown in equation
(7) and (8) respectively. AvrgTh: is the average throughput of the link as shown in
equation (9), : Average processing delay by a resource at Fog node i as shown in
equation (10), : Average processing delay as shown in equation (11) by a resource at
Cloud node. : Average processing time at Fog or Cloud Node.
 

   

 (7)
 (8)

 (9)
 (10)

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 (11)
3. Simulation and Results
We
evaluate
our
proposed
scheme
by
randomly
sending
set
of
tasks
to
the
Fog
environment
in
a uniform
distribution
pattern
from
the
task
pool.
wWe
assumed
the
pool
to
send
500,
1000,
1500,
2000,
2500,
and
3000
set
of tasks.
The
set
of
tasks
size
ranges
from
50KB
to
2000KB,
The
Fog
nodes
were
assumed
to
be
IEEE
802.11a/g
which
have
link
rate
of
100
Mbps
each
and
that
of
the
Cloud
environment
is
assumed
to
be
1Gbps.
And
at
each
simulation
we
assumed
the
nodes
to
have
the
following
processing
capacity
100,
200,
300, 400,
500,
and
600
tasks
at
a
time.
we
assumed
five
resources
each
with
computing
power
as
follows;
R1=200,
R2=150,
R3=100,
R4=245,
and
R5=270,
the
simulation
environment
is
MATLAB
R2014b.
The
results
of
our
simulations
are
given as
follows;
 Makespan:
As
explain
earlier
makespan
is
a
measure
of
throughput
of
all
the
resources
of
a
given
node.
The graph
in
figure
(fig.7)
shows
the
makespan
at
each
level of
execution
for
the
set
of
tasks
that
were
sent
from
the task
pool
to
the
fog
environment
for
the
three
algorithms
i.e.
the
Big
algorithm,
the
Medium
algorithm
and
the Little
algorithm
for
the
fog
environment.
Based
on
the graph
in
figure
(fig.7),
Medium
algorithm
obtained
less makespan
when
executing
a
given
task
as
compare
to the
other
algorithms.
Fig. 7. Graph of Makespan for the three algorithms

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 Utilization:
The
Utilization
rate
from
equation
(3)
shows the
level
at
which
computing
resources
are
utilized
throughout
the
period
at
which
tasks
are
processed,
the
figure
(fig.8)
shows
the
graph
of
utilization
of
the three
algorithms.
From
the
result
obtained
in
fig.8
the utilization
rate
of
medium
algorithm
is
greater
than
the other two. Based on the figure (fig 8) can conclude
that max-min-mod algorithm make used of almost all the available resources
within, the system which make the environment fully utilized unlike in other
algorithm as stated in [6] that big algorithm has a very low utilization rate.
Fig. 8. Utilization
rate
of
the
three
Algorithm
 Energy Consumption: In the simulation and based on equation (4) we used some
predefined values that shows Load consumption and power values (in watt) that the
resources (Virtual) will contribute in the overall power consumption of the system,
the server predefined values are from Hp ProLiant G4 86 server. The figure (fig.9)
below shows that energy consumption contributed by the resources for our three
algorithms.

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Fig.
9.
Energy
Consumption
of
the
three
Algorithm
The graph of energy consumption in the Fig. 9 indicates that medium scheme
algorithm contributes more to the energy consumption of entire system. This is because
the medium algorithm has the tendency of using more resources in some case as compared
to the big and little algorithms.
A. COOPERATIVE
SYSTEMS
AND
NON-
COOPERATIVE
SYSTEMS
As
stated
that
the
simulation
is
based
on
two
systems i.e.
cooperative,
where
a
given
fog
node
collaborate
with
its neighboring
node
for
computing
resources
and
non-cooperative
were
fog
node
collaborate
with
the
cloud
directly.
The
scenario
is
that
if
a
fog
node
received
set
of
task
from
the
pool,
it
picks
tasks
to
its
capacity
and
offloads
the
remaining
to
the
nearby fog
nod
or
cloud.
we
investigate
the
relationship
between
the
fog
nodes
and
cloud
node
in
terms
of
the
latency
of
the
link
which
involves
the
transmission
and
propagation
delay
of
each
link.
We
also
consider
the
links
directly
and ignore
other
routing
devices
that
may
be
found
between
the links,
as
we
are
interested
in
node
to
node
communication
only.
The
results
below
are
based
on
the
medium
algorithm
for
task
scheduling,
the
link
rate
of
each
fog
nod
is
considered
to
be
100
Mbps
and
1
Gpbs
for
the
Cloud
environment.
wWe also
assumed
a
distance
between
the
pool
of
task
to
the
first fog
not
to
be
10Km,
and
other
fog
environment
are
separated
by
30KM
from
each
other,
and
the
Cloud
environment
is
at 500Km
from
the
last
fog
environment.
1. Average
Processing
Delay
Figure
fig.10
shows
the
average
processing
time
of
set
of tasks
by
a
given

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260
resources
in
all
the
Fog
environments
and that
of
the
cloud
environment.
The
nodes
process
these
set of
tasks
almost
concurrently,
this
collaboration
between
the
nodes
give
all
the
task
a
fair
time
in
which
it
will
be
processed
completely,
and
another
issue
is
that
the
task
independently
processes
by
each
Fog
node.
Fig.
10.
Average
Processing
Delay
In
the
Non-cooperative
systems,
the
Fog
node
sends
task directly
to
the
Cloud
environment
for
processing,in
the
figure (Fg.11)
the
Fog
sends
remaining
task
directly
to
the cloud
environment,
and
from
our
results
the
time
that
the
tasks
are
processed
in
somewhat
less
as
when
compared
to the
time
the
task
will
wait
at
the
Fog
environments
seeking
for
processing.
Therefore,
we
can
directly
assume
that
in this
case
Non-Cooperative
System
works
more
efficiently and
with
less
time
consumption
than
the
Cooperative
Systems.
2. Latency and Throughput: Cooperative and Non-Cooperative
As
stated
the
latency
of
the
link
is
the
time
a
task
takes before
it
gets
to
the
processing
stage,
while
the
utilization
of
the
link
is
the
rate
at
which
the
link
to
a
given
node
is been
utilized.
Based
on
the
result
of
our
simulation
in
the figure
(Fig.12),
the
latency
at
Cooperative
system
is
higher
when
compared
with
that
of
the
Non-Cooperative
Systems.
However,
the
graph
also
indicated
the
difference
between
the two
environment
is
negligible
but
for
a
large
stream
of
tasks the
Cooperative System can be considered more suitable than the the Non-Cooperative
Systems.

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261
Fig. 11. Average Processing Delay: Non-Cooperative System
Fig. 12. Latency: Cooperative and Non-Cooperative Environment
The throughput of the link is given below in figure (Fig.13) and based on the results
we find that the level at which the Cooperative System is utilized is far higher than that
of the Non-Cooperative Systems. Maximum Utilization of a System is the interest of
service providers, therefore the Cooperative Systems in this regard is far better than the
Non-Cooperative Systems.

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Special Issue 2020 (ICAT’20)
262
Fig. 13. Throughput: Cooperative and Non-Cooperative Environment
6. Conclusion
Efficiency in executing a task is the state of the art for cloud service providers and
due to the ever-growing pool of tasks that is always getting larger and larger by the day,
it becomes necessary for the service providers to look for efficient techniques to handle
these tasks. Because of the
geographical
proximity
of
resources,
Fog
computing
exhibits
lower
latency
compared
to
cloud computing and
inefficient
resource
allocation
in
Fog
environment
can
result
in
higher
delays
and
degraded
performance.
Efficient
resource
scheduling
in
Fog
computing
is
crucial
to
get
true
benefits
of
the
cloud
like
services
at
the
proximity
of
data
generation
sources.
The
Big-Medium-Little
(BML)
scheduling
technique
efficiently
allocate
Fog
and
Cloud
resources
to
the
incoming
IoT
jobs. Our proposed scheme together with the idea of
Cooperative and Non-Cooperative Systems timed the ever-growing pool of tasks by
collaboration between the Fog and Cloud environments. The
technique
as
evaluated,
shows
an improved
result
in
terms
of
makespan,
energy
consumption,
latency
and
throughput: an improved
max-min
and
min-min
scheduling
algorithms
in
cloud
environment
which
provide
an
appropriate
scheduling
scheme
for
a
set
of
tasks
for
the cloud
resources. In essence, the efficiency can be achieved by putting a very
efficient scheme that requires less time to complete a given task, and helps in providing
fast and real-time task executing within a limited amount of time.

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