Energy efficient resource management in cloud datacenters

Abstract

We propose an energy efficient resource allocation approach that integrates Holt Winters forecasting model for optimizing energy consumption while considering performance in a cloud computing environment. The approach is based on adaptive decision mechanism for turning on/off machines and detection of over utilization. By this way, it avoids performance degradation and improves energy efficiency. The proposed model consists of three functional modules, a forecasting module, a workload placement module along with physical and virtual machines, and a monitoring module. The forecasting module determines the required number of processing unit (Nr) according to the user demand. It evaluates the number of submitted workloads (NoSW), mean execution time of submitted workloads in interval and mean CPU requirements of them to calculate approximately total processing requirement (APRtotal). These three values are forecasted separately via forecasting methodologies namely Holt Winters (HW) and Auto Regressive Integrated Moving Average (ARIMA). The Holt Winters gives significantly better result in term of Mean Absolute Percentage Error (MAPE), since the time series include seasonality and trend. In addition, the interval is short and the long period to be forecasted, the ARIMA is not the right choice. The future demand of processing unit is calculated using these data. Therefore, the forecasting module is based on Holt Winters forecasting methodology with 8.85 error rate. Therefore, the forecasting module is based on the Holt Winters. Workload placement module is responsible for allocation of workloads to suitable VMs and allocation of these VMs to suitable servers. According to the information received from forecasting module, decision about turning a server on and off and placement for incoming workload is making in this module. The monitoring module is responsible for observing system status for 5 min. The consolidation algorithm is based on single threshold whether to decide that the server is over utilized. In other words, if the utilization ratio of CPU exceeds the predefined threshold, it means that the server is over utilized otherwise, the server is under load. If the utilization of server equals the threshold, the server is running at optimal utilization rate. Unlike other studies, overloading detection does not trigger VM migration. Overloading is undesirable since it causes performance degradation but, it can be acceptable under some conditions. To decide allocation of incoming workloads, this threshold is not only and enough parameter. Beside the threshold, the future demands are also considered as important as systems current state. The proposed algorithm also uses different parameters as remaining execution time of a workload, active number of servers (Na), required number of servers (Nr) besides efficient utilization threshold. The system can be instable with two cases; (1) Na is greater than Nr that means there are underutilized servers and it causes energy inefficiency (2) Nr is greater than Na, if new servers are not switched on, it causes over utilized servers and performance degradation. The algorithm is implemented and evaluated in CloudSim which is commonly preferred in the literature since, it provides a fair comparison between the proposed algorithm with previous approaches and it is easy to adapt and implementation. However, workloads come to the system in a static manner and the usage rates of the works vary depending on time. Our algorithms provide dynamically submission. Therefore, to make fair comparison, the benchmark code is modified to meet dynamic requirement by working Google Cluster Data via MongoDB integration. The forecasting module is based on Holt Winters as described before. Therefore, the approach is named Look-ahead Energy Efficient Allocation – Holt Winters (LAA-HW). If we knew the actual values instead of forecasted values, the system would give the result as Look-ahead Energy Efficient Allocation –Optimal (LAA-O). The proposed model uses Na and Nr parameters to decide the system's trend whether the system has active servers than required. If Na is greater than the Nr, incoming workloads are allocated on already active servers. It causes bottleneck for workloads with short execution time and less CPU requirement as the Google Tracelog workloads. The mean cpu requirement of a day and the mean execution time of a day are 3% and 1,13 min 32 respectively. It gives the small Nr value and it causes less number of received workload than Local Regression-Minimum Migration Time (LRMMT). The number of migration is zero in our approach. The energy consumption for switching on and off in our model is less in comparison with the migration model.