Mobile Computing – Optimization For Multimedia WorkloadEssay Preview: Mobile Computing – Optimization For Multimedia WorkloadReport this essayADAPTIVE ALGORITHMIC POWEROPTIMIZATION FOR MULTIMEDIA WORKLOAD IN MOBILE ENVIRONMENTSPragnesh GoyaniSoftware EngineerMotorola Inc., Plantation, FloridaHitesh JoshiSoftware EngineereFunds Inc., Sunrise, FloridaABSTRACTThis paper addresses the problem of power consumption in mobile devices with multimedia and presents algorithmic optimization techniques to achieve reduction of power usage. We also present researched approaches for adaptive minimization of the total energy consumption in multimedia wireless communications subject to achieving a given quality of service. We discuss the energy optimization techniques, and collaborative and non-collaborative techniques used for power management, after which we discuss in detail, the adaptive encoding and decoding algorithms as well as RTOS based and Server assisted techniques.
(2a). The goal of this paper is to provide a general approach to power consumption, for the general purpose of managing power consumption globally. The aim is to introduce a unified way, but in this way specific techniques to manage power consumption by optimizing and optimized their energy consumption. A basic problem is to measure power consumption, while for mobile operating system we can measure power consumption by means of various tools including the mobile networking (MAC), the power efficient power saving platform, the energy saving system, the power efficient storage platform, a power efficiency sensor, energy management application, and the energy saving application. We can also define efficient energy management as the act of managing the energy requirements of the system, such as for energy consumption, use of energy, storage, power consumption, etc. This type of approach can be used as a way of managing the power consumption of the mobile operating system and other mobile equipment. It would be the same general approach as in earlier work in this study, and is based on the concept of “efficiency control.” It is interesting to note that, while this work has not done any work exploring efficiency, we have discussed both the approach and its implications for other work in this area as well. It is a good idea to focus now on a specific example of power utilization, to get an understanding about why this need exists in this area. We will further discuss how to optimize it, how to optimize the efficiency, and how to use it as part of the optimization process for mobile mobile operating system, server, and computing. We take the idea and methodology of energy management for mobile operating system and apply them separately and for different scenarios. More on the efficiency control in mobile operating system. This section will not deal with some of the most important problems for mobile computing. To give you a background, first, it is important to analyze the energy distribution of an operating system, so to perform any kind of computation on it, it is necessary for an optimal use of the system. It is very important to distinguish the functions of the various functions of all these systems. In mobile operating system there are some basic functions such as power sharing, power saving, power consumption, and energy consumption. The same concept applies to these functions as well, as the same need for computation is also present when we have a power management software on different operating systems – for example, for smartphones and tablets, it is very important to analyze this use of the power generation functions and their operations in general. In this paper we will use the concept of efficiency in order to define the actual energy management processes employed in mobile operating system. Therefore, it would be also good to address this problem of power consumption, energy consumption, and power management in other contexts. First, we have to investigate the energy usage, the power consumption per second. Energy consumption according to a number of factors is used to measure energy consumption, based on its cost. Our energy consumption measurements in this study were based on the data set published in the paper by Weisdorf et al (2008b). One such data set was published in Physical Review A: Physical Sciences, 2012, pp. 713-736. We have measured energy consumption by a number of parameters which in general we cannot understand for free any specific amount of time. So we developed several methodologies which can easily be understood for free to calculate the amount of energy consumption. We also found that we can use it to estimate energy consumption via a number of energy saving methods. This is a new data set published in the Nature Physics Journal; it was collected from the same data set published by Weisdorf et al (2008b). This set is available on the open SITE for all papers and articles on the same topic as this paper. To give you a background on some previous work, let me explain first the importance of the concept of “efficiency control”, and the importance of what is being said.
(2a). The goal of this paper is to provide a general approach to power consumption, for the general purpose of managing power consumption globally. The aim is to introduce a unified way, but in this way specific techniques to manage power consumption by optimizing and optimized their energy consumption. A basic problem is to measure power consumption, while for mobile operating system we can measure power consumption by means of various tools including the mobile networking (MAC), the power efficient power saving platform, the energy saving system, the power efficient storage platform, a power efficiency sensor, energy management application, and the energy saving application. We can also define efficient energy management as the act of managing the energy requirements of the system, such as for energy consumption, use of energy, storage, power consumption, etc. This type of approach can be used as a way of managing the power consumption of the mobile operating system and other mobile equipment. It would be the same general approach as in earlier work in this study, and is based on the concept of “efficiency control.” It is interesting to note that, while this work has not done any work exploring efficiency, we have discussed both the approach and its implications for other work in this area as well. It is a good idea to focus now on a specific example of power utilization, to get an understanding about why this need exists in this area. We will further discuss how to optimize it, how to optimize the efficiency, and how to use it as part of the optimization process for mobile mobile operating system, server, and computing. We take the idea and methodology of energy management for mobile operating system and apply them separately and for different scenarios. More on the efficiency control in mobile operating system. This section will not deal with some of the most important problems for mobile computing. To give you a background, first, it is important to analyze the energy distribution of an operating system, so to perform any kind of computation on it, it is necessary for an optimal use of the system. It is very important to distinguish the functions of the various functions of all these systems. In mobile operating system there are some basic functions such as power sharing, power saving, power consumption, and energy consumption. The same concept applies to these functions as well, as the same need for computation is also present when we have a power management software on different operating systems – for example, for smartphones and tablets, it is very important to analyze this use of the power generation functions and their operations in general. In this paper we will use the concept of efficiency in order to define the actual energy management processes employed in mobile operating system. Therefore, it would be also good to address this problem of power consumption, energy consumption, and power management in other contexts. First, we have to investigate the energy usage, the power consumption per second. Energy consumption according to a number of factors is used to measure energy consumption, based on its cost. Our energy consumption measurements in this study were based on the data set published in the paper by Weisdorf et al (2008b). One such data set was published in Physical Review A: Physical Sciences, 2012, pp. 713-736. We have measured energy consumption by a number of parameters which in general we cannot understand for free any specific amount of time. So we developed several methodologies which can easily be understood for free to calculate the amount of energy consumption. We also found that we can use it to estimate energy consumption via a number of energy saving methods. This is a new data set published in the Nature Physics Journal; it was collected from the same data set published by Weisdorf et al (2008b). This set is available on the open SITE for all papers and articles on the same topic as this paper. To give you a background on some previous work, let me explain first the importance of the concept of “efficiency control”, and the importance of what is being said.
This paper discusses the following five algorithms and their overheads:Algorithm #1:Window Follower Algorithm for Encoding (Motion Estimation)Algorithm #2:Transmission Adaptation Encoding AlgorithmAlgorithm #3:MPEG4 Adaptive Parameters Encoding AlgorithmAlgorithm #4:Dynamic Voltage Scaling — Adaptive Clock/Voltage SettingAlgorithm #5:Buffer Insertion Decoding AlgorithmAlgorithm #6:Adaptive Multimedia Streaming AlgorithmThis paper also presents several simulation results achieved by other authors, which show the effectiveness of the discussed algorithms in reduction of energy or power consumption.
KEYWORDSLow-power, multimedia streaming, adaptive encoding, adaptive decoding, voltage scaling, frequency scalingMOTIVATIONUnlike other enabling technologies for mobile information systems, the specific energy of commercially available rechargeable batteries has improved at only about 2 percent per year over the past half century [9]. Considering this track record of available solutions on energy optimization, wireless multimedia systems has to be optimized for low energy consumption subject to a desired quality of service. These solutions have to be adaptive in nature, considering the complexity of the multimedia workload and modest resources the mobile systems posses.
INTRODUCTIONA key challenge for mobile devices today is the management of power considering the variable nature of workload, the heterogeneity of multimedia content and the inconsistent wireless network quality. Energy consumption of the mobile devices is not only dominated by wireless communication, but data processing or computation takes heavy toll on power as well. Since the conception of multimedia in mobile phones, high computational power as well as communicational power has become an important requirement. Videos (MPEG), Audio (MP3) and Photos (JPEG) on cell phones are becoming more common now, and new technologies like MPEG4/H263 for multimedia are being used for multimedia processing. Current high-end mobile devices integrate wireless wideband data modems, video cameras, net browsers, and phones into small packages. Being of modest sizes and weights, these devices have inadequate resources – lower processing power and limited battery life. Multimedia applications used on these devices have distinctive Quality of Service (QoS) and processing requirements which make them very resource hungry [5].
Energy usage of a 3G phone in Video Streaming mode is provided in Table 1 below.System ComponentEnergy consumption (mW)RF Receiver and Cellular modem1200 mWApplication Processor and Memories600 mWMemories200 mWUser Interface (audio, display, keyboard, with backlights)1000 mWTotal3000 mWTable 1: Energy consumption for different system componentsThis usage can only increase with the increasing demand of higher resolution videos. Among its limitations, although battery power has significant importance, it is probably the least researched and has experienced the least improvement.
Research has been done in both hardware and software areas where power consumption can be reduced. As far as the physical parameters are concerned, there are many limitations for mobile wireless communications, including high and variable error rate, available bandwidth variation and limitation and limited battery power [1]. [1] proposes a scheduling algorithm that decides which flow should be dealt with at what time, enabling the wireless network interface to sleep for longer periods, hence saving battery power. Lan et al [9] investigates low power video transmission techniques based on H263 video coding standards, which are majorly based on software knobs.
Several interesting solutions have been proposed at various computational levels — system cache and external memory access optimization, dynamic voltage scaling (DVS) [10, 11], dynamic power management of disks and network interfaces, efficient compilers and application or middleware based adaptations [12, 13]. Adaptive algorithmic power optimization is another solution that can maintain the QoS in spite of reducing power usage.
Encoding algorithms perform data compression before transmission and reduce information’s bandwidth requirement by reducing the bit rate. On the other hand, decoding algorithms help to playback multimedia information (saved on the mobile unit or streaming from server) in a compressed format. Power efficient encoding/decoding algorithms can help overcome the battery power limitations of the mobile unit, and when these algorithms are made adaptive to workload and physical parameters, better battery performance is achieved.
NON-COLLABORATIVE