scholarly journals Model for Evaluating the Effectiveness of the Cloud Center with a High Degree of Virtualization in Terms of Group Requests

2020 ◽  
pp. 82-98
Author(s):  
Nazar Peleh ◽  
Olha Shpur ◽  
Mykhailo Klymash
Keyword(s):  
Markov Process ◽  
Response Time ◽  
Service Time ◽  
Coefficient Of Variation ◽  
Approximation Technique ◽  
Use Of Resources ◽  
Semi Markov Process ◽  
Important Challenge ◽  
Group Flow ◽  
High Degree

The current state of information technology is to develop and implement new approaches to the computational process. Evaluating the effectiveness of cloud centers is an important challenge for research, but it is complicated by the dynamic of cloud environments and a variety of user requests. This evaluation is vital in cases where virtualization is used to provide well-defined computing resources for users. The proposed model for evaluating the effectiveness of cloud centers in a high degree of virtualization to solve this problem has been proposed. Compared to existing, it considers the ability to service requests for group requests and the distributed time of service requests. The model is based on a two-stage approximation technique. The main non-Markov process is first modeled as an embedded semi-Markov process, then modeled as an approximated Markov process but only when receiving group request flows. The technique of constructing Markov links to build the model has been used. This model provides a full probability distribution of request waiting time, response time to execute requests, and the number of requests in the system. The results show that the performance of cloud centers is highly dependent on the coefficient of variation (CoV), request service time, and the size of the group flow (i.e., the number of requests in the group flow of requests). The larger the flow rate and/or the value of the coefficient of variation of the service time of requests, the longer the response time. But this helps reduce the use of resources by cloud providers. As a result, the work shows that in the conditions of large group flow of requests and/or large value of CoV, it is possible to increase the efficiency of cloud centers by grouping requests using the criterion of homogeneity.

A SEMI MARKOV PROCESS APPROACH IN EVALUATION OF SERVICE TIME OF BEACON MESSAGE TRANSMISSION IN THE DSRC MEDIUM ON VEHICULAR ENVIRONMENT

2021 ◽  
Vol 10 (5) ◽  
pp. 2571-2579
Author(s):  
C. Bazil Wilfred ◽  
M. Selvarathi ◽  
P. Anantha Christu Raj
Keyword(s):  
Markov Process ◽  
Markov Model ◽  
Service Time ◽  
High Speed ◽  
Process Approach ◽  
The Other ◽  
Message Transmission ◽  
Semi Markov Process

On a highway each vehicle periodically tries to communicate with the RSU by transmitting beacon messages and other general messages like position, speed, destination etc. Beacon messages need to be given high priority and high-speed service since they are important for the broadcasting vehicle and also for the other vehicles within the stipulated radius. Instead of employing a Markov model, we employ a Semi Markov process and evaluate the service time transmission of the tagged state with a particular emphasis on the QOS of the beacon messages

Partially observable semi-Markov reward processes

1993 ◽  
Vol 30 (3) ◽  
pp. 548-560 ◽  
Author(s):  
Yasushi Masuda
Keyword(s):  
Markov Process ◽  
Counting Process ◽  
Probabilistic Interpretation ◽  
Real Domain ◽  
Semi Markov Process ◽  
Reward Process ◽  
Markov Reward ◽  
Partially Observable ◽  
Joint Behavior

The main objective of this paper is to investigate the conditional behavior of the multivariate reward process given the number of certain signals where the underlying system is described by a semi-Markov process and the signal is defined by a counting process. To this end, we study the joint behavior of the multivariate reward process and the multivariate counting process in detail. We derive transform results as well as the corresponding real domain expressions, thus providing clear probabilistic interpretation.

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

2021 ◽  
pp. 036119812110302
Author(s):  
Chao Wang ◽  
Weijie Chen ◽  
Yueru Xu ◽  
Zhirui Ye
Keyword(s):  
Approximation Method ◽  
Service Time ◽  
Diffusion Approximation ◽  
Coefficient Of Variation ◽  
Queuing Theory ◽  
Queuing Model ◽  
Bus Stop ◽  
Proposed Model ◽  
Bus Stops ◽  
Bus Service

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

Continuous Semi-Markov Process by HARLAMOV, B.

Biometrics ◽  
2008 ◽  
Vol 64 (4) ◽  
pp. 1301-1301
Author(s):  
Mei-Jie Zhang
Keyword(s):  
Markov Process ◽  
Semi Markov Process

scholarly journals Maximal success durations for a semi-Markov process

1987 ◽  
Vol 24 (2) ◽  
pp. 203-224 ◽  
Author(s):  
David E. Fousler ◽  
Samuel Karlin
Keyword(s):  
Markov Process ◽  
Semi Markov Process
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