Toward Energy‐Aware Scheduling Using Machine Learning

Berral, Josep Ll.; Goiri, Íñigo; Nou, Ramón; Julià, Ferran; Fitó, J. Oriol; Guitart, Jordi; Gavaldà, Ricard; Torres, Jordi

doi:10.1002/9781118342015.ch8

Cited by 9 publications

(3 citation statements)

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“…Issues related to power management are becoming increasingly relevant, inter alia in the context of modern distributed systems, including those using virtualization and cloud computing In [7], the authors present an analysis of power-saving techniques and examine the capabilities of machine learning in automatic power management systems. However, the article lacks a broader discussion of machine learning algorithms and aspects of possible adaptation.…”

Section: Related Workmentioning

confidence: 99%

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

Nawrocki

Śnieżyński

2020

Wireless Pers Commun

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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.

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Section: Related Workmentioning

confidence: 99%

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

Nawrocki

Śnieżyński

2020

Wireless Pers Commun

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“…Chapter 5 presents work on machine learning applied to predicting components and variables to drive decision managers concerning to autonomic computing issues like self-optimization for a computing machine/datacenter (Stage 2). This chapter is based on already published material (EEnergy10 [30], GreenBook12 [31]).…”

Section: Document Outlinementioning

confidence: 99%

“…The work presented in this chapter has been published in the "international ACM eEnergy 2010 Conference" [30] (2010) and as a chapter in the book "Energy-Efficient Distributed Computing Systems" [31] (2012).…”

Section: Conclusion For Energy-aware Schedulingmentioning

confidence: 99%

Improved self-management of datacenter systems applying machine learning

Berral¹

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Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the world and belong to different entities and authorities. Self-management lets these distributed computing systems improve their resource and energy management, a very important issue when resources have a cost, by obtaining, running or maintaining them. Here we propose to improve Autonomic Computing techniques for resource management by applying modeling and prediction methods from Machine Learning and Artificial Intelligence. Machine Learning methods can find accurate models from system behaviors and often intelligible explanations to them, also predict and infer system states and values. These models obtained from automatic learning have the advantage of being easily updated to workload or configuration changes by re-taking examples and re-training the predictors. So employing automatic modeling and predictive abilities, we can find new methods for making "intelligent" decisions and discovering new information and knowledge from systems. This thesis departs from the state of the art, where management is based on administrators expertise, well known data, ad-hoc studied algorithms and models, and elements to be studied from computing machine point of view; to a novel state of the art where management is driven by models learned from the same system, providing useful feedback, making up for incomplete, missing or uncertain data, from a global network of datacenters point of view. - First of all, we cover the scenario where the decision maker works knowing all pieces of information from the system: how much will each job consume, how is and will be the desired quality of service, what are the deadlines for the workload, etc. All of this focusing on each component and policy of each element involved in executing these jobs. -Then we focus on the scenario where instead of fixed oracles that provide us information from an expert formula or set of conditions, machine learning is used to create these oracles. Here we look at components and specific details while some part of the information is not known and must be learned and predicted. - We reduce the problem of optimizing resource allocations and requirements for virtualized web-services to a mathematical problem, indicating each factor, variable and element involved, also all the constraints the scheduling process must attend to. The scheduling problem can be modeled as a Mixed Integer Linear Program. Here we face an scenario of a full datacenter, further we introduce some information prediction. - We complement the model by expanding the predicted elements, studying the main resources (this is CPU, Memory and IO) that can suffer from noise, inaccuracy or unavailability. Once learning predictors for certain components let the decision making improve, the system can become more ¿expert-knowledge independent¿ and research can focus on an scenario where all the elements provide noisy, uncertainty or private information. Also we introduce to the management optimization new factors as for each datacenter context and costs may change, turning the model as "multi-datacenter" - Finally, we review of the cost of placing datacenters depending on green energy sources, and distribute the load according to green energy availability.

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