Msrtia: A proposal to reduce the response time for load balancing on cloud computing

E RESPONSE TIME FOR LOAD BALANCING ON CLOUD COMPUTING 
Figure 8. MSRTIA schema 
Description of the MSRTIA algorithm: 
The idea is to calculate the Cloudlet aggregation value of 
requests, chooses the request with largest value, then pairs 
with the fastest executing VM following the formula: 
public double getAverageSize(Cloudlet cloudlet) { 
double resAverage; 
resAverage = cloudlet.getCloudletLength() * 0.5 + 
cloudlet.getCloudletOutputSize() * 0.2 + 
cloudlet.getCloudletFileSize() * 0.3; 
return resAverage; } 
The MSRTIA algorithm will repeat until the Makespan 
tables are empty. At that time the requests will be 
processed faster, shorten the finishing time, increased 
load balancing capability for cloud computing. 
The response time is explained in details as below. 
Basis of assessment 
The efficiency of load balancing can be based on many 
factors, but the most important factors are loading and 
performance. Loading is the CPU queue index and 
utilization [15]. Performance is the average response time 
to user requests. Load balancing algorithm is based on 
input parameters such as: configuring virtual machines, 
configuring Cloudlet tasks, arrival time, and time to 
complete tasks, then estimating the expected completion 
time of each task. Response time is the processing time 
plus the cost of transmitting request, queuing through the 
network nodes. Expected response time is calculated 
according to the following formula [16]: 
Expected Response Time = F – A + Tdelay 
F: time to complete the task, A: arrival time of the task, 
Tdelay: transfer time of the task 
Because the algorithm that performs load balancing is that 
of DatacenterBroker, the level of the algorithm only 
affects the processing time in a local environment of a 
data centre. Hence the communication delay parameter 
can be omitted, so Tdelay = 0. 
Determining the expected time to complete task [16]: 
If the scheduling policy is Space shared – Space shared or 
Time shared - Space shared, it is determined by the 
following formula: 
eft(p) = est(p) + (3.1) 
capacity = (3.2) 
If the scheduling policy is Space shared – Time shared or 
Timeshared-Timeshared, it is determined by the formula 
eft(p) = ct + (3.3) 
capacity = (3.4) 
 eft(p) is the expected completion time of 
Cloudlet p; 
 est is the arrival time of Cloudlet p; 
 rl is the total number of instructions that 
Cloudlet p needs to execute on a processor; 
 capacity is the average processing power (in 
MIPS) of a core for Cloudlet p; 
 ct is the current simulation time; 
 cores (p) is the number of cores required by 
Cloudlet; 
 np is the number of actual cores that the host is 
considering; 
 cap is the processing power of the core. 
The capacity parameter specifies the actual capacity for 
task processing on each VM. Apparently capacity 
depends on the scheduling of computing resources on the 
virtualized system. The total processing power on a 
physical host is unchanged and depends on the number of 
physical cores and processing power of each core. 
However, when this processing resource is shared for 
multiple tasks simultaneously, each task requires a certain 
number of cores and if the total number of cores is greater 
than the number of physical cores, the concept of virtual 
core appears, each virtual core will have lower processing 
power than the physical core. In other words, the capacity 
of a virtual core for a task can only be equal to or smaller 
than the physical core and how much depends on the 
resource sharing policy. Capacity is the processing power 
of a virtual core [15] [16]. 
Tran Cong Hung, Nguyen Ngoc Thang, Kieu Trong Duc 
From this analysis and based on the resource sharing 
policy to develop formulas for capacity. Resource sharing 
policy is specified through scheduling mechanism in 
cloud computing. We have two levels of scheduling: 
scheduling virtual machines to share physical host 
machine resources and scheduling tasks to share virtual 
machine resources. There are two scheduling 
mechanisms: Time shared and Space shared. Within the 
scope of this paper, we will perform algorithms and 
simulations based on the Time shared – Time shared 
policy, respectively, to virtual machines and tasks. 
Therefore, the calculation base for the proposed algorithm 
will be based on the formulas (3.3) and (3.4). 
IV. SIMULATION & EVALUATION 
Cloud environment emulator uses CloudSim 3.0 library 
and programming in JAVA language, includes 1 to 4 
VMs. It will create a random request environment for 
services on the cloud containing virtual cloud services, 
CloudSim provisioning and user provisioning services for 
testing [17]. 
Table 1. Data center configuration parameters 
Table 2. VM configurations parameters when initialized 
The requests (WebRequest) are represented by Cloudlet 
in CloudSim and the size of Cloudlets is randomly 
generated using the JAVA random function. 
Table 3. Requests configuration parameters 
The function to create randomly 1000 requests in Table 2: 
public DataInput() { 
this.v_length = 
ThreadLocalRandom.current().nextInt(1700, 3000); 
this.v_fileSize = 
ThreadLocalRandom.current().nextInt(5000,45000); 
this.v_outputSize= 
ThreadLocalRandom.current().nextInt(450,750); 
} 
Result and Evaluation 
Experiments apply Timeshared – Timeshared scheduling 
policy for VM – task and calculate response time 
according to formula (3.3) and (3.4) as described in Base 
of assessment. 
The simulation will make out 1000 requests with 4 times, 
each will have 4 VMs and the number of requests is 100, 
200, 500 and 1000 respectively. 
From the Figure 9-13, we can see that the response time 
of MSRTIA is less than Max-Min and Round-Robin for 
all scenarios with the number of requests from 10 to 1000. 
The more requests are tested, the better response time of 
MSRTIA demonstrates in comparison with Max-Min and 
Round-Robin. In other words, MSRTIA is effective 
especially for large amount of requests. 
Through 4 experiments, it shows that MSRTIA has the 
response time for VMs better and load balancing more 
efficiently than Max-Min and Round-Robin scheduling 
techniques. Specifically, the response time of MSRTIA is 
faster 9.63% than Max-Min and 15.32% than Round-
Robin algorithms. 
Compared to the previous methods, the proposed 
algorithm doesn’t need to perform the calculation for 
completion time of requests again. From which, MSRTIA 
will reduce unnecessary processing time and costs as well 
as minimize load unbalancing in the cloud system. 
Figure 9. Experimental result on 4 virtual machines with 
100 requests 
MSRTIA: A PROPOSAL TO REDUCE THE RESPONSE TIME FOR LOAD BALANCING ON CLOUD COMPUTING 
Figure 10. Experimental result on 4 virtual machines with 
200 requests 
Figure 11. Experimental result on 4 virtual machines with 
500 requests 
Figure 12. Experimental result on 4 virtual machines with 
1000 requests 
Figure 13. Experimental result after 4 times counted on 
average 
V. CONCLUSION 
MSRTIA calculates the Cloudlet aggregation value of 
requests and searches for the request owning the 
maximum value then assign to VM having the minimum 
completion time. It is very clear to see the results that 
MSRTIA has ameliorated the response time for load 
balancing, optimized the performance compared to Max-
Min and Round-Robin algorithms with the better rate 
9.63% and 15.32% respectively. 
For future research, the improvements may include the 
following: simulate the algorithm with more 
configuration cases in terms of data centres, VMs, 
different scheduling policies (Time shared – Space shared 
or vice versa), combined with other machine learning 
methods. 
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Tran Cong Hung, Nguyen Ngoc Thang, Kieu Trong Duc 
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MSRTIA: MỘT ĐỀ XUẤT ĐỂ GIẢM THỜI GIAN 
ĐÁP ỨNG CHO CÂN BẰNG TẢI TRÊN ĐIỆN 
TOÁN ĐÁM MÂY 
Tóm tắt: Điện toán đám mây là mô hình cung cấp mọi 
thứ liên quan đến công nghệ thông tin dưới dạng dịch vụ 
thông qua Internet. Lợi ích chính của nó là tiết kiệm chi 
phí đầu tư hệ thống ban đầu, tối ưu hóa việc xử lý dữ liệu, 
tính toán và lưu trữ dữ liệu. Ngày nay, điện toán đám mây 
phải đối mặt với nhiều thách thức trong việc đảm bảo chất 
lượng dịch vụ xuyên suốt. Trong đó vấn đề quá tải máy 
chủ vật lý hoặc máy chủ ảo của các trung tâm dữ liệu 
được đặc biệt quan tâm. Vì vậy, để đủ điều kiện cho các 
yêu cầu trên, thiết lập một phương pháp cân bằng tải hiệu 
quả và sử dụng các tài nguyên tối ưu hóa nhất là mục tiêu 
mà điện toán đám mây muốn đạt được. Trong bài báo này, 
chúng tôi đề xuất thuật toán MSRTIA dựa trên thuật toán 
lập lịch Max Min. Thuật toán của chúng tôi tính toán giá 
trị tổng hợp Cloudlet của các yêu cầu sau đó ghép yêu cầu 
với giá trị lớn nhất được tìm thấy với máy ảo thực thi 
nhanh nhất. Trong đó, giá trị tổng hợp của Cloudlet là sự 
kết hợp của các tham số độ dài, kích thước đầu ra và kích 
thước tệp. Kết quả mô phỏng chứng minh rằng MSRTIA 
có thời gian phản hồi nhanh hơn so với các thuật toán 
Max Min và Round-Robin. 
Từ khóa: Giá trị tổng hợp Cloudlet, Max-Min, Round-
Robin 
AUTHORS 
Tran Cong Hung was born in 
Vietnam in 1961. He received the 
B.E in electronic and 
Telecommunication engineering 
with first class honors from 
HOCHIMINH University of 
technology in Vietnam, 1987. He 
received the B.E in informatics 
and computer engineering from 
HOCHIMINH University of 
technology in Vietnam, 1995. He received the Master of 
Engineering degree in telecommunications engineering 
course from postgraduate department Hanoi University of 
technology in Vietnam, 1998. He received PhD. at Hanoi 
University of technology in Vietnam, 2004. His main 
research areas are B – ISDN performance parameters and 
measuring methods, QoS in high speed networks, MPLS. 
He is, currently, Associate Professor PhD. of Faculty of 
Information Technology II, Posts and Telecoms Institute 
of Technology in HOCHIMINH, Vietnam. 
Nguyen Ngoc Thang was born in 
Vietnam in 1993. He received B.E 
in Computer Science with first 
class honors from Industrial 
University of Ho Chi Minh City, 
MSRTIA: A PROPOSAL TO REDUCE THE RESPONSE TIME FOR LOAD BALANCING ON CLOUD COMPUTING 
Vietnam, 2015. He received the Master of Computer 
Science degree with first class honors from Saigon 
University, Vietnam in 2018. 
Kieu Trong Duc was born in 
Vietnam in 1989. He received his 
undergraduate degree in 2011, major 
in Information Technology from 
Saigon University, Viet Nam. 
Currently, he is a Master candidate 
in Computer Science of Saigon 
University, Vietnam. He is working 
for the Vietnam Mobile Telecom 
Services One Member Limited Liability Company.