Energy Efficient Scheduling of Application Components via Brownout and Approximate Markov Decision Process

Abstract

Unexpected loads in Cloud data centers may trigger overloaded situation and performance degradation. To guarantee system performance, cloud computing environment is required to have the ability to handle overloads. The existing approaches, like Dynamic Voltage Frequency Scaling and VM consolidation, are effective in handling partial overloads, however, they cannot function when the whole data center is overloaded. Brownout has been proved to be a promising approach to relieve the overloads through deactivating application non-mandatory components or microservices temporarily. Moreover, brownout has been applied to reduce data center energy consumption. It shows that there are trade-offs between energy saving and discount offered to users (revenue loss) when one or more services are not provided temporarily. In this paper, we propose a brownout-based approximate Markov Decision Process approach to improve the aforementioned trade-offs. The results based on real trace demonstrate that our approach saves 20% energy consumption than VM consolidation approach. Compared with existing energy-efficient brownout approach, our approach reduces the discount amount given to users while saving similar energy consumption.

Dynamic performance–Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds

“…Xu and Buyya [ 24 ] increase the functionality of the brownout to reduce data center energy consumption. They proposed a brownout-based approximate Markov Decision Process approach to improve tradeoffs between energy-saving and user discounts.…”

“…Xu et al [23] discuss the brownout model to reduce data center energy consumption. This approach can reduce energy consumption by selectively and dynamically deactivating optional Xu and Buyya [24] increase the functionality of the brownout to reduce data center energy consumption. They proposed a brownout-based approximate Markov Decision Process approach to improve tradeoffs between energy-saving and user discounts.…”

IoTPulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing

“…Moreover, the employee health data, real-time data collection and processing has not been fully addressed since it is challenging to continuously monitor an employee's health [64] [70]. Motivated by the successful applications of IoT [66] [68] 4 [69] in other fields of technological development integrated with the fortes of ML, an IoT based EHIS called IoTPulse has been proposed to predict the alcohol addiction using ML model with high accuracy. An alcohol consumer can be monitored using IoT enabled health monitoring devices.…”

BrownoutCon: A software system based on brownout and containers for energy-efficient cloud computing

“…In [13], we presented the brownout enabled system model and proposed several heuristic policies to find the microservices or application components that should be deactivated for energy saving purpose. The results showed that a trade-off existed between energy consumption and discount, and in [14], we adopted approximate Markov Decision Process to improve the trade-off.…”

“…The BrownoutCon is designed and implemented by following the brownout enabled system model in our previous works [13] [14]. Mandatory containers and optional containers are introduced in the system model, which are identified according to whether the containers can be temporarily deactivated or not.…”