An efficient host overload detection algorithm for cloud data center based on exponential weighted moving average

2021 ◽  
Vol 34 (4) ◽  
Author(s):  
Sudhanshu Kulshrestha ◽  
Sanjeev Patel
Keyword(s):  
Data Center ◽  
Moving Average ◽  
Detection Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Preference Based Resource Allocation In Cloud Data Center

2018 ◽  
Vol 6 (2) ◽  
pp. 287-292
Author(s):  
M.R. Dave ◽  
◽  
H.B. Patel ◽  
B. Shrimali ◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Data Center ◽  
Cloud Data Center ◽  
Cloud Data

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

2020 ◽  
pp. 1-21
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Moving Average ◽  
Integrated System ◽  
Measurement Noise ◽  
Noise Estimation ◽  
Doppler Velocity ◽  
Forgetting Factor ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

A Game Theoretic Approach to Estimate Fair Cost of VM Placement in Cloud Data Center

2018 ◽  
Vol 12 (4) ◽  
pp. 3509-3518
Author(s):  
Sourav Kanti Addya ◽  
Ashok Kumar Turuk ◽  
Bibhudatta Sahoo ◽  
Anurag Satpathy ◽  
Mahasweta Sarkar
Keyword(s):  
Data Center ◽  
Theoretic Approach ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Game Theoretic ◽  
Game Theoretic Approach
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