A conceptual framework of control, learn, and knowledge for computer power management

This conceptual paper observes the human inactivity in computer power management and discovers that; the efficiency of the computer power management (CPM)can be achieved by the eligibility of the human inactivity period. This period reduces the efficiency of CPM. This study examines the self-adaptation(SA) and the knowledge repository (KR)concepts, to model the framework of a new approach in computer power management. The essential elements and features from theseconceptswere adapted and applied as a techniqueto a new implementation of CLK-CPM. As a result, this study has proposed a modelof thetheoretical framework and demonstratesit through its conceptual framework for the technique

Adaptive approach in handling human inactivity in computer power management

Human inactivity is handled by adapting the behavioral changes of the users.Human inactivity refers to as unpredictable workload of a complex system that is caused by increments of amount in power consumption and it can be handled automatically without the need to set a fixed time for changing the computer state.This is happens due to lack of knowledge in a software system and the software self-adaptation is one approach in dealing with this source of uncertainty. This paper observes human inactivity and Power management policy through the application of reinforcement learning approach in the computer usage and finds that computer power usage can be reduced if the idle period can be intelligently sensed from the user activities. This study introduces Control, Learn and Knowledge model that adapts the Monitor, Analyze, Planning, Execute control loop integrates with Q Learning algorithm to learn human inactivity period to minimize the computer power consumption.An experiment to evaluate this model was conducted using three case studies with same activities. The result show that the proposed model obtained those 5 out of 12 activities shows the power decreasing compared to other

Adaptive Approach in Handling Human Inactivity in Computer Power Management

Human inactivity is handled by adapting the behavioral changes of the users. Human inactivity refers to as unpredictable workload of a complex system that is caused by increments of amount in power consumption and it can be handled automatically without the need to set a fixed time for changing the computer state. This is happens due to lack of knowledge in a software system and the software self-adaptation is one approach in dealing with this source of uncertainty. This paper observes human inactivity and Power management policy through the application of reinforcement learning approach in the computer usage and finds that computer power usage can be reduced if the idle period can be intelligently sensed from the user activities. This study introduces Control, Learn and Knowledge model that adapts the Monitor, Analyze, Planning, Execute control loop integrates with Q Learning algorithm to learn human inactivity period to minimize the computer power consumption. An experiment to evaluate this model was conducted using three case studies with same activities. The result show that the proposed model obtained those 5 out of 12 activities shows the power decreasing compared to other

Deriving a Generic Energy Consumption Model for Network Enabled Devices

Abstract-Energy saving has become a global issue when people use network enabled equipment in the office or at home. However few methods exist to measure and monitor energy use per user or per application, or to control equipment power states. We propose a generic energy consumption model that is based on the power state of network attached equipment, and that supports power management capabilities. This includes measures for each power state (on/off/sleep) and for per bit energy consumption, per interface, per application and at the network QoS (Quality of Services) level. Given the power state of a network device, a network manger could remotely inspect the energy consumption and make changes to the power management setting; for this to happen we introduce a new MIB (Management Information Base) schema to capture the attributes of relevance. Using an agent based modeling framework, we introduce the overall autonomic architecture that makes it possible to minimize energy consumption of network enabled equipment

ADAPTIVE POWER MANAGEMENT FOR COMPUTERS AND MOBILE DEVICES

Power consumption has become a major concern in the design of computing systems today. High power consumption increases cooling cost, degrades the system reliability and also reduces the battery life in portable devices. Modern computing/communication devices support multiple power modes which enable power and performance tradeoff. Dynamic power management (DPM), dynamic voltage and frequency scaling (DVFS), and dynamic task migration for workload consolidation are system level power reduction techniques widely used during runtime. In the first part of the dissertation, we concentrate on the dynamic power management of the personal computer and server platform where the DPM, DVFS and task migrations techniques are proved to be highly effective. A hierarchical energy management framework is assumed, where task migration is applied at the upper level to improve server utilization and energy efficiency, and DPM/DVFS is applied at the lower level to manage the power mode of individual processor. This work focuses on estimating the performance impact of workload consolidation and searching for optimal DPM/DVFS that adapts to the changing workload. Machine learning based modeling and reinforcement learning based policy optimization techniques are investigated. Mobile computing has been weaved into everyday lives to a great extend in recent years. Compared to traditional personal computer and server environment, the mobile computing environment is obviously more context-rich and the usage of mobile computing device is clearly imprinted with user\u27s personal signature. The ability to learn such signature enables immense potential in workload prediction and energy or battery life management. In the second part of the dissertation, we present two mobile device power management techniques which take advantage of the context-rich characteristics of mobile platform and make adaptive energy management decisions based on different user behavior. We firstly investigate the user battery usage behavior modeling and apply the model directly for battery energy management. The first technique aims at maximizing the quality of service (QoS) while keeping the risk of battery depletion below a given threshold. The second technique is an user-aware streaming strategies for energy efficient smartphone video playback applications (e.g. YouTube) that minimizes the sleep and wake penalty of cellular module and at the same time avoid the energy waste from excessive downloading. Runtime power and thermal management has attracted substantial interests in multi-core distributed embedded systems. Fast performance evaluation is an essential step in the research of distributed power and thermal management. In last part of the dissertation, we present an FPGA based emulator of multi-core distributed embedded system designed to support the research in runtime power/thermal management. Hardware and software supports are provided to carry out basic power/thermal management actions including inter-core or inter-FPGA communications, runtime temperature monitoring and dynamic frequency scaling