Armen Dzhagaryan

Performance and Energy Consumption of Lossless Compression/Decompression Utilities on Mobile Computing Platforms

Data compression and decompression utilities can be critical in increasing communication throughput, reducing communication latencies, achieving energy-efficient communication, and making effective use of available storage. This paper experimentally evaluates several such utilities for multiple compression levels on systems that represent current mobile platforms. We characterize each utility in terms of its compression ratio, compression and decompression through-put, and energy efficiency. We consider different use cases that are typical for modern mobile environments. We find a wide variety of energy costs associated with data compression and decompression and provide practical guidelines for selecting the most energy efficient configurations for each use case. The best performing configurations provide 6-fold and 4-fold improvements in energy efficiency for compressed uploads and downloads over WLAN, respectively, when compared to uncompressed data transfers.

An mHealth Tool Suite for Mobility Assessment

The assessment of mobility and functional impairments in the elderly is important for early detection and prevention of fall conditions. Falls create serious threats to health by causing disabling fractures that reduce independence in the elderly. Moreover, they exert heavy economic burdens on society due to high treatment costs. Modern smartphones enable the development of innovative mobile health (mHealth) applications by integrating a growing number of inertial and environmental sensors along with the ever-increasing data processing and communication capabilities. Mobility assessment is one of the promising mHealth application domains. In this paper, we introduce a suite of smartphone applications for assessing mobility in the elderly population. The suite currently includes smartphone applications that automate and quantify the following standardized medical tests for assessing mobility: Timed Up and Go (TUG), 30-Second Chair Stand Test (30SCS), and 4-Stage Balance Test (4SBT). For each application, we describe its functionality and a list of parameters extracted by processing signals from smartphone’s inertial sensors. The paper shows the results from studies conducted on geriatric patients for TUG tests and from experiments conducted in the laboratory on healthy subjects for 30SCS and 4SBT tests.

Energy efficiency of lossless data compression on a mobile device: An experimental evaluation

Lossless compression and decompression are routinely used in mobile computing devices to reduce the costs of communicating and storing data. This paper presents the results of an experimental evaluation of common compression utilities on Pandaboard, a development platform similar to current commercial mobile devices. We study the compression ratio, compression and decompression throughput, and energy efficiency of different usage scenarios typical for mobile computing. We observe a wide variety of energy costs associated with data compression and provide practical guidelines for selecting the most energy-efficient configurations.

A Smartphone Application Suite for Assessing Mobility

Modern smartphones integrate a growing number of inertial and environmental sensors that can enable the development of new mobile health applications. In this paper we introduce a suite of smartphone applications for assessing mobility in elderly population. The suite currently includes applications that automate and quantify the following standardized medical tests for assessing mobility: Timed-Up-and-Go (TUG), 30 Seconds Chair Stand Test (30SCS), and a 4-stage Balance Test (4SBT). For each smartphone application we describe its functionality and a list of parameters extracted by processing signals from smartphones inertial sensors. The paper shows the results from studies conducted on geriatric patients for TUG tests and from studies conducted in the laboratory on healthy subjects for 30SCS and 4SBT tests.

On effectiveness of lossless compression in transferring mHealth data files

The health and fitness data traffic originating on mobile devices has been continually increasing, with an exponential increase in the number of personal wearable devices and mobile health monitoring applications. Lossless data compression can increase throughput, reduce latency, and achieve energy-efficient communication between personal devices and the cloud. This paper experimentally explores the effectiveness of common compression utilities on mobile devices when uploading and downloading a representative mHealth data set. Based on the results of our study, we develop recommendations for effective data transfers that can assist mHealth application developers.

An environment for automated power measurements on mobile computing platforms

Mobile computing devices such as smartphones, tablet computers, and e-readers have become the dominant personal computing platforms. Energy efficiency is a prime design requirement for mobile device manufacturers and smart application developers alike. Runtime power measurements on mobile platforms provide insights that can eventually lead to more energy-efficient operation. In this paper we describe mPowerProfile - an environment for automated power measurements of programs running on a mobile development platform. We discuss mPowerProfiles main functions and its utilization in several example studies based on the Pandaboard and Raspberry Pi platforms.

Quantifying Benefits of Lossless Compression Utilities on Modern Smartphones

The data traffic originating on mobile computing devices has been growing exponentially over the last several years. Lossless data compression and decompression can be essential in increasing communication throughput, reducing communication latency, achieving energy-efficient communication, and making effective use of available storage. This paper experimentally evaluates several compression utilities and configurations on a modern smartphone. We characterize each utility in terms of its compression ratio, compression and decompression throughput, and energy efficiency for representative use cases. We find a wide variety of energy costs associated with data compression and decompression and provide practical guidelines for selecting the most energy efficient configurations for each use case. For data transfers over WLAN, the best configurations provide a 2.1-fold and 2.7-fold improvement in energy efficiency for compressed uploads and downloads, respectively, when compared to uncompressed data transfers. For data transfers over a mobile broadband network, the best configurations provide a 2.7-fold and 3-fold improvement in energy efficiency for compressed uploads and downloads, respectively.

Analytical Models for Evaluating Effectiveness of Compressed File Transfers in Mobile Computing

The importance of optimizing data transfers between mobile computing devices and the cloud is increasing with an exponential growth of mobile data traffic. Lossless data compression can be essential in increasing communication throughput, reducing communication latency, achieving energy-efficient communication, and making effective use of available storage. In this paper we introduce analytical models for estimating effective throughput and energy efficiency of uncompressed data transfers and compressed data transfers that utilize common compression utilities. The proposed analytical models are experimentally verified using state-of-the-art mobile devices. These models are instrumental in developing a framework for seamless optimization of data file transfers.

Models for Evaluating Effective Throughputs for File Transfers in Mobile Computing

The importance of optimizing data transfers between mobile computing devices and the cloud is increasing with an exponential growth of mobile data traffic. Lossless data compression can be essential in increasing communication throughput, reducing communication latency, achieving energy-efficient communication, and making effective use of available storage. In this paper we introduce analytical models for estimating effective through-put of uncompressed data transfers and compressed data transfers that utilize common compression utilities. The proposed analytical models are experimentally verified using state-of-the-art smartphones as mobile devices. These models are instrumental in developing a framework for seamless optimization of file transfers in mobile computing.

Impact of thread and frequency scaling on performance and energy in modern multicores: a measurement-based study

Modern microprocessors integrate a growing number of components on a single chip, such as processor cores, graphics processors, on-chip interconnects, shared caches, memory controllers, and I/O interfaces. An ever-increasing complexity and the number of components present new challenges to software developers interested in finding operating points that strike an optimal balance between performance and energy consumed. In this paper we analyze the impact of thread scaling and frequency scaling on performance and energy in modern multicores. By exploiting recent additions to microprocessors that support energy estimation and power management, we measure execution times and energy consumed on an Intel Xeon 1240 v2 microprocessor when running the PARSEC benchmark suite. We conduct a number of experiments by varying the number of threads, 1 ≤ N ≤ 16, and processor clock frequency, 1.6 ≤ F ≤ 3.4 GHz. We find that the maximum performance is achieved when the number of threads matches or slightly exceeds the number of logical processors (8 ≤ N ≤ 12) and the clock frequency is at maximum (F = 3.4 GHz). The minimum energy is consumed when the processor clock frequency is in range 2.0 ≤ F ≤ 2.4 GHz. Finally, we find that the best performance at minimal energy is achieved when 8 ≤ N ≤ 12 and 2.8 ≤ F ≤ 3.1 GHz.