Analysis of Multi Level Feedback Queue Scheduling Using Markov Chain Model with Data Model Approach

Int. J. Advanced Networking and Applications
Volume: 07 Issue: 06 Pages: 2915-2924 (2016) ISSN: 0975-0290
2915
Analysis of Multi Level Feedback Queue
Scheduling Using Markov Chain Model with Data
Model Approach
Shweta Jain
Department of Computer Applications, Shri R.G.P. Gujarati Professional Institute, Indore-10
Email: shwjain@yahoo.com
Dr. Saurabh Jain
Institute of Computer Applications, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore
Email: iamsaurabh_4@yahoo.co.in
----------------------------------------------------------------------ABSTRACT-----------------------------------------------------------
When a process gets the CPU, the scheduler has no idea of the precise amount of CPU time the process will need.
Process scheduling algorithms are used for better utilization of CPU. The number of processes arriving to the
CPU at a time comes in mass volume which causes a long waiting queue. In Multilevel feedback queue scheduling,
the scheduler moves from one queue to another in order to perform the processing follow the transition
mechanism. This paper analysed a general transition scenario for the functioning of CPU scheduler in multilevel
queue with feedback mechanism. We proposed a Markov chain model to analyze this transition phenomenon
with a general class of scheduling scheme. Simulation study is performed to evaluate the comparative study with
the help of varying values of α and d in a mathematical model.
Keywords - Markov chain model, Multi-level feedback queue scheduling, Process queue, Transition probability
matrix, Wait State.
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Date of Submission: May 31, 2016 Date of Acceptance: Jun 25, 2016
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I. INTRODUCTION
Multilevel feedback queue (MLFQ) is most suitable and
ideal scheduling algorithm for separating processes into
categories based on their need for the CPU. It allows a
process to move between queues. If we have not given the
relative length of various processes then none of these
scheduling: Shortest Process Next, Shortest Remaining
Time and Highest Response Ratio Next can be used.
Another way of establishing preferences for shorter jobs is
to penalize jobs that have been running longer. In other
words, if we cannot focus on the time remaining to
execute, so we can focus on the time spent in execution so
far. MLFQ scheduling is used on preemptive basis with
dynamic priority mechanism. When a process first enters a
system, it is placed in first queue. When it returns to the
ready state after its first execution, it is placed in second
queue. After each subsequent execution, it goes to the next
lower-priority queue. A shorter process will complete
quickly without migrating very far down the hierarchy of
ready queues. A longer process can be gradually drifted
downward. Thus some processes in queues are used
simple FCFS mechanism but few queues are treated as
round robin (RR) fashion. One common variation of the
MLFQ mechanism is to have a process circulate RR
several times through each queue before it moves to the
next lower queue. Usually the number of cycles through
each queue is increased as the process moves to the next
lower queue.
RELATED WORK
MLFQ may be one of most potential scheduling
technique for CPU. It is the extension of multi-level queue
scheduling algorithm where it is the results of combination
of basic scheduling algorithms such as FCFS and RR
scheduling algorithm. In MLFQ, where the processes can
move from one queue to another queue but in MLQ
scheduling processes are assigned to a fixed queue. There
are various type of approaches proposed in these papers by
different authors to increase the overall performance of the
MLFQ and other scheduling algorithms. Jain et al. [1]
presented a Linear Data Model based study of Improved
Round Robin CPU Scheduling algorithm with features of
Shortest Job First scheduling with varying time quantum
whereas Chavan and Tikekar [2] derived an Optimum
Multilevel Dynamic Round Robin scheduling algorithm,
which calculates intelligent time slice and changes after
every round of execution.
Suranauwarat [3] used simulator to learn
scheduling algorithms in an easier and a more effective
way. Sindhu et al. [4] proposed an algorithm which can
handle all types of process with optimum scheduling
criteria. Li et al. [5] analyzed existing fair scheduling
algorithms are either inaccurate or inefficient and non-
scalable for multiprocessors to produce larger scale multi-
core processors that solves this problem by a new
scheduling algorithm called Distributed Weighted Round-
Robin (DWRR). Hieh and Lam [6] discussed smart
schedulers for multimedia users. Saleem and Javed [7]
developed a comprehensive tool which runs a simulation
in real time, and generates data to be used for evaluation.
Raheja et al. [8] proposed a new scheduling algorithm

2916
called Vague Oriented Highest Response Ratio Next
(VHRRN) scheduling algorithm which computes the
dynamic priority using vague logic in Fuzzy Systems
(FUZZ) and [9] also proposed a 2-layered architecture of
multilevel queue scheduler based on vague set theory
(VMLQ) that scheduler handles the impreciseness of data
as well as improving the starvation problem of lower
priority tasks which optimizes the performance metrics
and improves the response time of system through
simulation.
Shukla and Jain [10] have discussed the use of
Markov chain model for multilevel queue scheduler and
also proposed a data model based Markov chain model to
study the transition phenomenon, designed a scheduling
scheme and compared through deadlock-waiting index
measure with simulation study[11]. Shukla et al. [12]
analyzed round robin scheme using Markov chain model.
Helmy and Dekdouk [13] introduced Burst Round Robin,
a proportional-share scheduling algorithm as an attempt to
combine the low scheduling overhead of round robin
algorithms and favor shortest jobs. Maste et al. [14]
proposed a new variant of MLFQ algorithm, in which time
slice is assigned to each queue such that it changes with
each round of execution dynamically i.e used dynamic
time quantum and used neural network to adjust this time
slice to optimize turnaround time with static time slice for
each queue.
Yadav and Upadhayay [15] suggested a novel
approach which will improve the performance of MLFQ.
Chahar and Raheja [16] analysed basic multilevel queue
and multilevel feedback queue scheduling techniques and
thereafter discussed a review of techniques proposed by
different authors. Rao and Shet [17] articulated the task
states of New Multi Level Feedback Queue [NMLFQ]
Scheduler and depicted the contingent of task transitions
with triggers which leads to a change of state with time
instants and intervals are elucidated and [18] also analysed
distinguishing problems with existing MLFQ scheduling
algorithm to develop a New Multi Level Feedback Queue
(NMLFQ) illustrated along with synchronization through
semaphore with the relationships of job sets, record of
tasks and queues in scheduler involving respective steps
describing object oriented code to justify the algorithm.
Jain and Jain [19] discussed the various approaches of
scheduling algorithm and probability-based Markov chain
analysis to determine the performance of these algorithms.
This paper referred different CPU scheduling and
their various aspects by Silberschatz and Galvin [20],
Stalling [21], Tanenbaum and Woodhull [22], Dhamdhere
[23] and Deitel [24] whereas stochastic processes and
Markov chain model by Medhi [25]. Motivation from this
analysis, this paper proposed a class of scheduling scheme
under the assumption of Markov chain model and using a
data model approach.
II. GENERAL CLASS OF MULTI-LEVEL
FEEDBACK QUEUE SCHEDULING
Multilevel Feedback Queue algorithm have
number of queues and the processes move between these
queues, a scheduling algorithm for each queue, a method
used to determine when to upgrade a process, a method
used to determine when to demote a process and a method
used to determine on which queue a process begins to
have each time it returns to the ready state. It is based on
the model of Multi-Channel Single server queue.
Fundamental advantages possessed by the multilevel
feedback queue are having processes of moving from one
queue to another queue, for instance with lower priority or
higher priority. In this scheme, if the priority of one
process is more than another process, then first one is
executed and if the priority of one process is same to
another process then its running on FSFC (First Come,
First Serve) manner. If a job is entered into the system,
then the work comes into the category highest priority in
the topmost queue. If the job requires extra time when
running, its priority is lowest or displacement of the lower
queue. The lowermost queue works in FCFS manner. If
the processing is completed before time, permanent jobs at
the same level.
ASSUMPTION OF THE MODEL
Suppose a multi-level feedback queue scheduling
with five active queues Q1, Q2, Q3, Q4, Q5 which are treated
here as states each having large number of processes Pj,
Pj', Pj", Pj'", Pj"" (j=1, 2, 3, 4, 5….) respectively for
processing and one waiting states Q6. These queues are
characterized and organized on the basis of some features
like priority, size, or weight. The new arrival of processes
are allocated in random manner in these queues. Define Qi
(i=1, 2, 3, 4, 5), the states of scheduling system and a
specific state Q6 which is a waiting state. First five states
are for arrival and inputting of processes while the last one
associate with waiting of the scheduler. A quantum is
considered as a small pre-defined slot of time given for
processing in different queues to the processes. Symbol n
denotes the nth
quantum allotted by the scheduler to a
process for execution (n = 1, 2, 3, 4, 5 …). So some
assumption steps for the model are:
1. All the first five queues Q1, Q2, Q3, Q4 and Q5 are
allowed to accept a new process with initial
probabilities pb1, pb2, pb3, pb4, and pb5 with the
probability condition
2. Q6=W is used as waiting state in the transition
system. Any special conditions over waiting or
restricting transition can be considered within this
scheduling scheme.
3. The Scheduler has a random movement over all
states Qi (i=1, 2, 3, 4, 5) on quantum variation.
4. A new arrival process is picked by the scheduler with
a predefined priority of any queue Qi and allots a
quantum of time for processing.
5. While the process remains with the CPU if the
allotted quantum is not over. If a process completes
within this quantum, then it leaves the queue Qi and
if a process remains incomplete within quantum,
scheduler assigns next quantum to the next process
of the same queue. The previous incomplete process
moves to next queue Qi+1 where (i+1) ≤ 5 and waits

2917
there for next quantum to be allotted for its
processing.
6. The scheduler can jump from one state to other state
at the end of a quantum. The quantum allotment
procedure continues by scheduler within Qi until all
Qi are empty. In that case the scheduler moves
towards Q6.The scheduler can also move to Q6 after
the completion of any quantum although all Qi ( i = 1,
2, 3, 4, 5) are full with processes.
Figure 2.1: The General Multilevel Feedback Queue System
III. MARKOV CHAIN MODEL FOR THE
PROPOSED SYSTEM
Define Q1 as state 1, Q2 as state 2, Q3 as state 3,
Q4 as state 4, Q5 as state 5 and Q6 as waiting state W. The
symbol n denotes the nth
quantum of time assigned by
scheduler for executing a process (n=1, 2, 3, 4…..).
Assume the movement of scheduler system is random over
the different processing states and waiting state.
Figure 3.1: Unrestricted Transition Diagram
Figure 3.1 depicts the transition diagram which
shows the transition from one state to another state.
Let{x(n)
, n≥1} be a Markov chain where x(n)
denotes the state of the scheduler at the quantum of time.
The state space for the random variable x(n)
is{ Q1, Q2, Q3,
Q4, Q5, Q6} where Q6=W is waiting state and scheduler X
moves stochastically over different processing states and
waiting states within different quantum of time.
Predefined selections for initial probabilities of states are:
Let Sij (i, j=1,2,3,4,5,6) be the unit step
trans18ition probabilities of scheduler over six proposed
states then transition probability matrix for :
Figure 3.2: Transition Matrix
Unit-step Transition Probabilities for the wait state W are
as follows:
If Sij (i, j=1,2,3,4,5) be the unit-step transition
probabilities of scheduler over six proposed states then
transition probability matrix for X(n)
will be
After first quantum, the state probabilities can be
determined by the following expressions:

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Similarly, after second quantum, the state probabilities can
be determined by the following expressions
In a similar way, the expression for the nth
quantum:
IV. SIMULATION STUDY WITH GRAPHICAL
ANALYSIS ON MATHEMATICAL DATA MODEL
The basic and scientific approach for graphical
data analysis related to state transition probabilities is
managed by a mathematical data model with two
parameters α and d where value of i stands number of
queues.
Figure 4.1: Data Model Matrix
CASE 1: When α=0.1 and
1. d = 0.002
Figure 4.1.1: α=0.1 and d = 0.002
2. d = 0.004
Figure 4.1.2: α=0.1 and d = 0.004
3. d = 0.006

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Figure 4.1.3: α=0.1 and d = 0.006
4. d = 0.008
Figure 4.1.4: α=0.1 and d = 0.008
5. d = 0.010
Figure 4.1.5: α=0.1 and d = 0.010
1. d = 0.002
Figure 4.2.1: α=0.12 and d = 0.002
2. d = 0.004
Figure 4.2.2: α=0.12 and d = 0.004
3. d = 0.006
Figure 4.2.3: α=0.12 and d = 0.006
4. d = 0.008

2920
Figure 4.2.4: α=0.12 and d = 0.008
5. d = 0.010
Figure 4.2.5: α=0.12 and d = 0.010
1. d = 0.002
Figure 4.3.1: α=0.14 and d = 0.002
2. d = 0.004
Figure 4.3.2: α=0.14 and d = 0.004
3. d = 0.006
Figure 4.3.3: α=0.14 and d = 0.006
4. d = 0.008
Figure 4.3.4: α=0.14 and d = 0.008
5. d = 0.010

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Figure 4.3.5: α=0.14 and d = 0.010
1. d = 0.002
Figure 4.4.1: α=0.16 and d = 0.002
2. d = 0.004
Figure 4.4.2: α=0.16 and d = 0.004
3. d = 0.006
Figure 4.4.3: α=0.16 and d = 0.006
4. d = 0.008
Figure 4.4.4: α=0.16 and d = 0.008
5. d = 0.010
Figure 4.4.5: α=0.16 and d = 0.010
1. d = 0.002

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Figure 4.5.1: α=0.18 and d = 0.002
2. d = 0.004
Figure 4.5.2: α=0.18 and d = 0.004
3. d = 0.006
Figure 4.5.3: α=0.18 and d = 0.006
4. d = 0.008
Figure 4.5.4: α=0.18 and d = 0.008
5. d = 0.010
Figure 4.5.5: α=0.18 and d = 0.010
V. DISCUSSION ON GRAPHS
Case 1: When α=0.1 and increasing value of d from 0.002
to 0.010, we observed pattern of the transition
probabilities of Q1 to Q5 are similar in varying quantum,
But few are very close (fig.4.1.1 and fig.4.1.5) and few are
comparatively far (fig.4.1.2, fig.4.1.3 and fig.4.1.4) to each
other. Now we find the waiting state Q6 is moving high,
going to down up to second quantum after that nearly
straight according to quantum increases. As d increases
this probability being reduces that can be seen in fig.4.1.3
and fig.4.1.4.
Case 2: At α=0.12, d=0.002 (fig.4.2.1) waiting state
w=Q6 the system is more likely to shift over to high. This
is not a good sign for scheduling procedure. With
increasing values of d, the scheduler of processing all
queues from Q1 to Q5 get consolidated in fig 4.2.1 and
4.2.2 but in fig.4.2.3-- fig 4.2.5 queues get fared to each
other. Along with this, a remarkable drop in the wait state
could also be observed near to zero from fig.4.2.1 to
fig.4.2.5
Case 3: With α=0.14 and varying values of d, we find that
almost all the pattern in fig.4.3.1- fig.4.3.5 queues from Q1
to Q5 remains constant over little high as increasing of d.
But waiting state Q6 is going to drop continuously to zero
especially in fig. 4.3.2- fig.4.3.5.
Case 4: At α=0.16 and d from 0.002 to 0.010, we analyzed
system transition probabilities of Q1 to Q5 has moved

2923
higher chance of receiving the scheduler. With increment
of d values these going to lower in fig.4.4.1 and nullifies
to zero in fig 4.4.2- fig. 4.4.5.
Case 5: At α=0.18 and for d=0.004-0.010, we find equal
chances of receiving the scheduler for all the queues Q1 to
Q5 moving higher except d=0.002 (comparatively closer).
With the increment of d, we find that the chance of
scheduler going on to Q6 (the waiting state) nullifies to its
minimum in fig.4.5.1-fig.4.5.5.
VI. CONCLUSION
This paper proposed Markov chain analysis for
multi-level feedback queue scheduling scheme finding the
effects of wait state with the overall throughput and
performance of the system. With the varying values of α
and d, we observe that the system determines some of the
interesting combination of it which provides some clue
regarding better choice of queues over others for high
priority processes. This analysis also highlights a path for
designing of system with chances of graceful degradation
with wait state constantly lower over the increasing
quantum. In each and every graph, with successive value
of d in the different specified conditions, an interesting
trend of waiting probability can be determined. Therefore
this paper also suggests this fact that the initial
combinations of α and d are the better choice as they are
showing less chance of system going on waiting state then
their higher counterparts.
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Author’s Biography
Mrs. Shweta Jain received her
M.C.A. degree from Barakatullah
University, Bhopal, in 1999. She
worked as Software Engineer since
1999 to 2004 in various
organizations. She served as
Associate Professor in Computer
Science and Application Department
in Shri R.G.P. Gujarati Professional
Institute, Indore, for 10 years, since 2006. Now she is
pursuing her Ph.D. in Computer Science. Her areas of
interest include Operating systems and Artificial
Intelligence. She has published research papers in
International and National Conferences and Journals.
Dr Saurabh Jain has completed
M.C.A. degree in 2005 and Ph.D.
(CS) in 2009 from Dr. H.S. Gour
Central University, Sagar. He
worked as Lecturer in the
department of Comp. Science &
Applications in the same
University since 2007 to 2010.
Currently, He is working as an
Associate Professor and Coordinator in institute of
Computer Applications in Shri Vaishnav Vidyapeeth
Vishwavidyalaya, Indore since 2010. He did his research
in the field of Operating system. In this field, he authored
and co-authored 25 research papers in
National/International Journals and Conference
Proceedings. His current research interest is to analyze the
scheduler’s performance under various algorithms.

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