Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an Advance Algorithm

ISSN (e): 2250 – 3005 || Volume, 05 || Issue, 10 ||October – 2015 ||
International Journal of Computational Engineering Research (IJCER)
www.ijceronline.com Open Access Journal Page 7
Efficient Resource Allocation to Virtual Machine in Cloud
Computing Using an Advance Algorithm
Rajeev Kumar 1
, Aditya Sharma2
1
Deptt. of C.S.E.,Arni University, Kangra,India
2
Deptt. of C.S.E, Arni University, Kangra, India
I. INTRODUCTION
Cloud computing has attracted attention as an important platform for software deployment, with perceived
benefits such as elasticity to fluctuating load, and reduced operational costs compared to running in enterprise
data centers. While some software is written from scratch especially for the cloud, many organizations also wish
to migrate existing applications to a cloud platform. A cloud environment is one of the most shareable
environments where multiple clients are connected to the common environment to access the services and the
products. A cloud environment can be public or the private cloud. In such environment, all the resources are
available on an integrated environment where multiple users can perform the request at same time. In such case ,
some approach is required to perform the effective scheduling and the resource allocation.
II. RESOURCE ALLOCATION
There are different algorithm that defines the load balancing to provide resources on the criteria as following
2.1 Token Routing: The main objective of the algorithm is to minimize the system cost by moving the tokens
around the system. But in a scalable cloud system agents cannot have the enough information of distributing the
work load due to communication bottleneck. So the workload distribution among the agents is not fixed. The
drawback of the token routing algorithm can be removed with the help of heuristic approach of token based load
balancing. This algorithm provides the fast and efficient routing decision. In this algorithm agent does not need
to have an idea of the complete knowledge of their global state and neighbor’s working load. To make their
decision where to pass the token they actually build their own knowledge base. This knowledge base is actually
derived from the previously received tokens. So in this approach no communication overhead is generated.
2.2 Round Robin: In this algorithm, the processes are divided between all processors. Each process is assigned
to the processor in a round robin order. The process allocation order is maintained locally independent of the
allocations from remote processors. Though the work load distributions between processors are equal but the job
processing times for different processes are not same. So at any point of time some nodes may be heavily loaded
and others remain idle. This algorithm is mostly used in web servers where Http requests are of similar nature
and distributed equally.
2.3 Randomized: Randomized algorithm is of type static in nature. In this algorithm process can be handled by
a particular node n with a probability p. The process allocation order is maintained for each processor
independent of allocation from remote processor.
This algorithm works well in case of processes are of equal loaded. [10]. However, problem arises when loads
are of different computational complexities. Randomized algorithm does not maintain deterministic approach. It
works well when Round Robin algorithms generate solver head for process queue.
ABSTRACT:
The focus of the paper is to generate an advance algorithm of resource allocation and load
balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine.
In VM while processes are allocate they executes in queue , the first process get resources , other
remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the
algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR)
Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
KEYWORDS: VM(Virtual machine)

Efficient Resource Allocation to Virtual Machine…
2.4 Central queuing: This algorithm works on the principal of dynamic distribution. Each new activity arriving
at the queue manager is inserted into the queue. When request for an activity is received by the queue manager it
removes the first activity from the queue and sends it to the requester. If no ready activity is present in the queue
the request is buffered, until a new activity is available. But in case new activity comes to the queue while there
are unanswered requests in the queue the first such request is removed from the queue and new activity is
assigned to it. When a processor load falls under the threshold then the local load manager sends a request for
the new activity to the central load manager.
2.6 Connection mechanism: Load balancing algorithm can also be based on least connection mechanism which
is a part of dynamic scheduling algorithm. It needs to count the number of connections for each server
dynamically to estimate the load. The load balancer records the connection number of each server. The number
of connection increases when a new connection is dispatched to it, and decreases the number when connection
finishes or timeout happens.
III. ALGORITHM
The algorithm provide parallel processes to each virtual machine rather than serial processes one by one.
Figure 1. VM Function
*a. *Input the M number of Clouds with L1, L2, L3………….., Ln (n tends to no. of last virtual
machine))number of Virtual Machines associated with each cloud.
*b. *Define the available memory and load for each virtual machine.
*c. *Assign the priority to each cloud.
*d. *Input n number of user process request with some parameters specifications like arrival time, process time,
required memory etc.
*e. *Arrange the process requests in order of memory requirement
*f. *For i=1 to n
*g. *{
*h. *Identify the priority Cloud and Associated VM having Available
Memory(L1,L2,L3…………Ln)>Required Memory(i)
*i. *Perform the initial allocation of process to that particular VM and
the Cloud
*j. *}
*k. *For i=1 to n
*l. *{
*m. *Identify the Free Time slot on priority cloud to perform the allocation. As the free slot identify, record the
start time, process time, turnaround time and the deadline of the process.
*n. *}
*o. *fori=1 to n
*p. *(
*q.* start queue Q1.
*r. *{
*s. Process i1allocate to VM L1.
*t. *Print "Migration Done”
*u. Process i2, i3………in allocate to VM L2, L3………………,Ln respectively.
*w.* Q1, i1, p1 ends till i(n+1) allots to L1 again.
*X. * start new Queue Q2 [i (n+1)],
Q3 {I (2n+1)},……………. Respectively.
*y. *}
*z. *}

IV. EXPERIMENTAL REVIEW
Larger waiting time and Response time
In round robin architecture the time the process spends in the ready queue waiting for the processor to get
executed is known as waiting time and the time [13] the process completes its
Figure: Process scheduling in shortest round robin
Intelligent time slice generation
A new way of intelligent time slice calculation has been proposed which allocates the frame exclusively for each
task based on priority, shortest CPU burst time and context switch avoidance time.
Let the original time slice (OTS) is the time slice to be given to any process if it deserves no special
consideration
The intelligent time slice of process P1 is same as the original time slice of four milliseconds and time slice of
four milliseconds is assigned to process P1. After the execution of four milliseconds time slice the CPU is
allocated to process P2. Since the CPU burst of process P2 is lesser than the assumed CPU burst (ATS), one
milliseconds of SC has been included. The process P3 has the highest priority, so priority component is added
and the total of five milliseconds is allocated to process P3. The Balanced CPU burst for process P4 is leaser
than OTS, context switch component is added and a total of eight millisecond time slice is given to process P4.
Process P5 is given a total of five milliseconds with one millisecond of priority component is added to original
time slice. After executing a cycle the processor will again be allocated to process P1 for the next cycle and
continuously schedules in the same manner.
Steps for scheduling are as follows
Step 1:
Master system (VMM) receives information regarding virtual machine from slave (VM-1….n). If the master
node capability doesn’t catch the data, it will determine the virtual machine to be dead. This study proposed by
parameter W.
 If W=0 is set up, it will define the virtual machine to be working and still alive now.
 If W=1 then node is dead.
 If W=2 then node is in previous state.
Step 2: If Master node receives the data from slave, then it gets the information’s regarding data (memory used,
CPU time etc...)
Step 3: Then Master node builds the weighted table containing the details which is collected from step 2.

Step 4: Then the master node sorts (Round-robin method) all the virtual machines according to their
performance. Which is 1≦i≦N.Where N is the number of the virtual machines.
Step 5: The scheduling capability generates the weighted table.
Step 6: The virtual machine control capability receives the weighted table from the Step 5, and distributes the
task to the virtual machines according to the weighted value.
V. RESULT
The proposed algorithm and existing round robin algorithm implemented like graphical simulation. Java
language is used for implementing VM load balancing algorithm. Assuming the application is deployed in one
data centre having virtual machines (with 2048Mb of memory in each VM running on physical processors
capable of speeds of 100 MIPS) and Parameter Values are as under Table discuss the Parameter value’s which
are used for Experiment.
Parameter Value
Data Center OS Linux
VM Memory 2048mb
Data Center Architecture X86
Service Broker Policy Optimize Response
Time
VM Bandwidth 1000
Table 1. Parameter values used for Experiment
These experimental results shows that weighted round robin method improves the performance by consuming
less time for scheduling virtual machines.
Figure 2. The results based on Round robin algorithm
Figure 3. The Data Centre for Round robin algorithm

Figure 4. The results based on Weighted Round robin table
Figure 5. The Data Centre for Weighted Round robin table
Figure 6. Comparison of results between Round Robin and weighted round robin For Overall response
time
Figure 7. Comparison of results between Round Robin and Weighted Round Robin for Data Center
processing time.

VI. CONCLUSION
The above algorithm distributes the process allocation in such a way that process does not concede
each other and the waiting state time for process is very much less .as well as all recourses (VMs, memory) are
using efficiently. That means the dead lock accruing chances are very much lees. .if the processes may allocate
to virtual machine at time. Processes may execute fast and chances of deadlock accruing is less. So we need an
algorithm that can describe how process execution can be done on virtual machine fast. A comparative study of
round robin architecture shortest round robin and intelligent time slice for round robin architecture is made. It is
concluded that the proposed architectures are superior as it has less waiting, response times, usually less
preemption and context switching thereby reducing the overhead and saving of memory space. Future work can
be based on these architectures modified and implemented for hard real time system where hard deadline
systems require partial outputs to prevent catastrophic events.
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