Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning

Abstract

In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that as a result of the experience gathered and the learning process performed, the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

“…The mobile device's behavior is predicted and sufficiently detailed to analyze the context. The authors analyze the proposed technique using four machine learning algorithms, i.e., Linear Logistic Regression, Linear Discernment Analysis, Support Vector Machine, and K-Nearest [27]. The authors consider this in the mobile cloud computing framework.…”

Improving prediction of computational job execution times with machine learning

“…Using machine learning methods to predict run‐time features of computational workloads is a vast area of research, covering such diverse topics as Docker container anomaly detection, 9 power management on mobile devices, 5 or prediction of the execution time of Spark jobs 10 . Here, we confine ourselves to reviewing works related to scientific computing on distributed computing infrastructures.…”

“…Predicting resource consumption and execution time of computational tasks is crucial for such diverse applications as job management in Big Data analytics 1 or in HPC systems, 2 scientific workflow management, 3 optimization of resource allocation for virtual machines in infrastructure as a service clouds, 4 optimization of energy consumption on mobile devices, 5 or cost-effective workload scheduling. 6 In this paper, we present an experimental evaluation of various machine learning methods for predicting the execution time of computational jobs in the context of two motivating scenarios: scientific workflow management and data processing in the ALICE experiment in CERN.…”

“…Predicting resource consumption and execution time of computational tasks is crucial for such diverse applications as job management in Big Data analytics 1 or in HPC systems, 2 scientific workflow management, 3 optimization of resource allocation for virtual machines in infrastructure as a service clouds, 4 optimization of energy consumption on mobile devices, 5 or cost‐effective workload scheduling 6 …”

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“…Though, it captured raw data packet is not ideally stored (and developed) in DataNet form. For example, this research's packet data is in PCAP/PCAPNG format [21]. A basic kit includes details which do not include Classification, ex.…”