A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-Off Debates

Abstract

Given the complexity and heterogeneity in Cloud computing scenarios, the modeling approach has widely been employed to investigate and analyze the energy consumption of Cloud applications, by abstracting real-world objects and processes that are difficult to observe or understand directly. It is clear that the abstraction sacrifices, and usually does not need, the complete reflection of the reality to be modeled. Consequently, current energy consumption models vary in terms of purposes, assumptions, application characteristics and environmental conditions, with possible overlaps between different research works. There- fore, it would be necessary and valuable to reveal the state-of-the-art of the existing modeling efforts, so as to weave different models together to facilitate comprehending and further investigating application energy consumption in the Cloud domain. By systematically selecting, assessing and synthesizing 76 relevant studies, we rationalized and organized over 30 energy consumption models with unified notations. To help investigate the existing models and facilitate future modeling work, we deconstructed the runtime execution and deployment environment of Cloud applications, and identified 18 environmental factors and 12 workload factors that would be influential on the energy consumption. In particular, there are complicated trade-offs and even debates when dealing with the combinational impacts of multiple factors.

DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

“…They have reduced the energy cost by assigning the suitable amount of resources to the VMs. The authors in [20] survey the previous works on energy consumption of datacenters. They have divided the research areas to some parts and discussed them.…”

DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

“…They have not considered the data variety in their study. We have also considered data variety and reduced the processing resources such as energy or cost [21], [22] and [23].…”

Power Profiling of Context Aware Systems: A Contemporary Analysis and Framework for Power Conservation

“…Ravindarnath [9] and Arun [10] have independently provided similar surveys for mobile and cloud computing based devices. Li et al [11] has provided a survey of cloud power optimization based on the tasks given to a cloud system. Mittal [12] has surveyed various power conservation techniques for embedded systems.…”

“…Focused Techniques [8] Device power optimization Handheld mobile devices Software based energy efficiency [9] Enhancing energy efficient Mobile and cloud computing based devices Energy aware offloading techniques [10] Minimizing energy consumption Mobile cloud computing Effective task scheduling method [11] Cloud power optimization Cloud applications Energy consumption models [12] Power conservation Embedded systems Power management techniques [13] Energy efficiency Mobile devices Energy-aware profilers [14] Power consumption Context aware applications Energy profiler for sensor configuration [15] Energy efficiency Wearable sensors/healthcare application Approaches for context aware activity recognition [16] Power consumption Multimedia Content adaptation techniques [17] Energy profiling Software and hardware level DBMS [18] Power consumption and optimization P2P/Network communication File distribution and content streaming [19] Power efficiency Networks/mobile devices Power consumption in network communication [20] Energy XML. This history information can be purged to reduce the size as well as to identify the user preferences [35].…”