EXTRACTION AND PREDICTION OF SYSTEM PROPERTIES USING VARIABLE-N-GRAM MODELING AND COMPRESSIVE HASHING

In modern computer systems, memory accesses and power management are the two major performance limiting factors. Accesses to main memory are very slow when compared to operations within a processor chip. Hardware write buffers, caches, out-of-order execution, and prefetch logic, are commonly used to reduce the time spent waiting for main memory accesses. Compiler loop interchange and data layout transformations also can help. Unfortunately, large data structures often have access patterns for which none of the standard approaches are useful. Using smaller data structures can significantly improve performance by allowing the data to reside in higher levels of the memory hierarchy. This dissertation proposes using lossy data compression technology called ’Compressive Hashing’ to create “surrogates”, that can augment original large data structures to yield faster typical data access. One way to optimize system performance for power consumption is to provide a predictive control of system-level energy use. This dissertation creates a novel instruction-level cost model called the variable-n-gram model, which is closely related to N-Gram analysis commonly used in computational linguistics. This model does not require direct knowledge of complex architectural details, and is capable of determining performance relationships between instructions from an execution trace. Experimental measurements are used to derive a context-sensitive model for performance of each type of instruction in the context of an N-instruction sequence. Dynamic runtime power prediction mechanisms often suffer from high overhead costs. To reduce the overhead, this dissertation encodes the static instruction-level predictions into a data structure and uses compressive hashing to provide on-demand runtime access to those predictions. Genetic programming is used to evolve compressive hash functions and performance analysis of applications shows that, runtime access overhead can be reduced by a factor of ~3x-9x

Improving Energy Efficiency and Security for Pervasive Computing Systems

Pervasive computing systems are comprised of various personal mobile devices connected by the wireless networks. Pervasive computing systems have gained soaring popularity because of the rapid proliferation of the personal mobile devices. The number of personal mobile devices increased steeply over years and will surpass world population by 2016.;However, the fast development of pervasive computing systems is facing two critical issues, energy efficiency and security assurance. Power consumption of personal mobile devices keeps increasing while the battery capacity has been hardly improved over years. at the same time, a lot of private information is stored on and transmitted from personal mobile devices, which are operating in very risky environment. as such, these devices became favorite targets of malicious attacks. Without proper solutions to address these two challenging problems, concerns will keep rising and slow down the advancement of pervasive computing systems.;We select smartphones as the representative devices in our energy study because they are popular in pervasive computing systems and their energy problem concerns users the most in comparison with other devices. We start with the analysis of the power usage pattern of internal system activities, and then identify energy bugs for improving energy efficiency. We also investigate into the external communication methods employed on smartphones, such as cellular networks and wireless LANs, to reduce energy overhead on transmissions.;As to security, we focus on implantable medical devices (IMDs) that are specialized for medical purposes. Malicious attacks on IMDs may lead to serious damages both in the cyber and physical worlds. Unlike smartphones, simply borrowing existing security solutions does not work on IMDs because of their limited resources and high requirement of accessibility. Thus, we introduce an external device to serve as the security proxy for IMDs and ensure that IMDs remain accessible to save patients\u27 lives in certain emergency situations when security credentials are not available

A run-time, feedback-based energy estimation model for embedded devices

We present an adaptive, feedback-based, energy estimation model for battery-powered embedded devices such as sensor network gateways and hand-held computers. Our technique maps hardware and software counters to energy consumption values using a set of first order, linear regression equations. Our system is novel in that it combines online and offline techniques to enable runtime power prediction. Our system employs an offline instantiated model that it continuously updates using feedback from a readily available battery monitor within the device. We empirically evaluate our model and detail its robustness, accuracy, and computational cost. We also analyze the stability of the model in the presence of feedback errors. We demonstrate that our approach can achieve an error rate of 1 % (extant techniques: 2.6 % to 4%) for computationally bound tasks and 6.6 % (extant techniques: 11%) for communication bound tasks

Energy Awareness for Multiple Network Interface-Activated Smartphone

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 최성현.최신 스마트폰은 LTE와 Wi-Fi와 같은 네트워크 인터페이스 여러 개를 동시에 사용하여 전송율을 증가시키거나 네트워크 접속성을 향상시킬 수 있다. 이런 경우에, 스마트폰의 에너지 소모는 LTE와 Wi-Fi를 동시에 사용함에 따라 증가할 수 있다. 특히, 제한된 용량을 가지는 배터리로 동작을 하는 스마트폰에서 에너지 소모는 중요한 이슈이기 때문에, 에너지 증가와 성능 향상 사이의 trade-off를 고려하는 것이 요구된다. 에너지 인지 기술과 함께 스마트폰의 LTE와 Wi-Fi 인터페이스를 전략적으로 잘 사용함으로써, 배터리 에너지 소모를 줄임과 동시에 스마트폰 어플리케이션의 성능을 최적화 하는 것이 가능해진다. 본 논문에서는 LTE와 Wi-Fi 링크를 동시에 활용할 수 있는 스마트폰에서 에너지 인지를 가능하게 하는 다음 세 가지 전략을 고려하였다. (i) LTE와 Wi-Fi를 동시에 사용하는 스마트폰의 전력 소모 모델링, (ii) 스마트폰의 배터리 소모율의 실시간 예측 기법, (iii) dynamic adaptive streaming over HTTP (DASH) 기반의 비디오 스트리밍의 성능 최적화. 먼저, 동시에 여러 네트워크를 활성화하여 사용하는 스마트폰의 정밀한 전력 소모 모델링을 제시한다. 패킷 처리에 의해 소모되는 전력과 네트워크 인터페이스에서 소모되는 전력을 분해하여, 다중 네트워크 인터페이스를 활성화시키는 경우의 정밀한 전력 소모 모델을 구성한다. 다양한 시나리오에서 측정된 전력과 모델을 통해 예측된 전력을 비교하며 제안하는 전력 소모 모델의 정확성을 평가하였다. 기존 전력 소모 모델에 비해 단일 네트워크 통신의 경우에도 7%-35% 만큼 추정 오차를 줄였으며, 다중 네트워크 통신의 경우 추정 오차를 25% 줄임을 보였다. 두 번째로, 스마트폰의 실시간 에너지 인지를 가능하게 하기 위해서, 스마트폰에서 사용하고 있는 Li-ion 배터리의 특성을 고려하여 배터리 소모율을 추정하는 기법을 고안한다. Li-ion 배터리는 온도와 노화 상태에 따라서 가용 용량과 내부 저항이 달라지기 때문에, 온도와 노화 상태에 따라서 배터리 소모율이 달라질 수 있다. 배터리 특성을 모델링하는 것은 어려운 일이기 때문에, effective resistance 개념을 도입하여 용량과 내부 저항을 모르고도 배터리 소모율을 예측할 수 있는 BattTracker를 고안한다. BattTracker는 실시간 배터리 소모율을 최대 0.5초 마다 추정할 수 있다. 실제 스마트폰으로 다양한 실험을 통하여 BattTracker가 배터리 소모 예측을 5% 오차율 이내로 예측함을 보였으며, 이를 활용하면 높은 시간 해상도로 스마트폰의 에너지 인지 동작이 가능해진다. 마지막으로, 애너지 인지 기법과 LTE와 Wi-Fi 링크를 동시에 활용하는 것을 dynamic adaptive streaming over HTTP (DASH) 기반 비디오 스트리밍 어플리케이션에 적용한다. 스마트폰에서 LTE와 Wi-Fi 링크를 동시에 활용하는 것은 다양한 측면에서 DASH 기반의 비디오 스트리밍의 성능을 향상시킬 수 있다. 그러나, 배터리 에너지와 LTE 데이터 사용량을 절약하면서 끊김없는 고화질의 비디오를 스트리밍하는 것은 도전적인 일이다. 따라서, DASH 비디오를 시청하는 사용자의 Quality of Experience (QoE)를 향상시키기 위하여 LTE와 Wi-Fi를 동시에 활용하는 DASH video chunk 요청 기법인 REQUEST를 제안한다. REQUEST는 주어진 배터리 에너지와 LTE 사용량 예산 내에서 최적에 가까운 품질의 비디오를 끊김없이 스트리밍해 주는 것을 가능하게 한다. 다양한 환경에서의 시뮬레이션과 실 환경에서의 측정을 통하여, REQUEST가 기존 비디오 스트리밍 기법에 비해 평균 비디오 품질, 재버퍼링, 자원 낭비량 관점에서 상당히 우수함을 보인다. 요약하자면, 우리는 LTE와 Wi-Fi의 동시 사용하는 스마트폰에서의 전력 모델링 방법론, 실시간 배터리 소모율 추정 기법, DASH 기반 비디오 스트리밍 성능 최적화를 제안한다. 이 연구를 통해서, 우리는 프로토타입 구현과 실험 장비들을 통한 실측 기반으로 LTE와 Wi-Fi를 동시에 활용하는 스마트폰을 위한 에너지 인지 기술을 제안한다. 제안하는 기법들의 성능은 상용 스마트폰에 구현하여 실 환경에서의 성능 평가를 통해 검증하였다.State-of-the-art smartphones can utilize multiple network interfaces simultaneously, e.g., LTE and Wi-Fi, to enhance throughput and network connectivity in various use cases. In this situation, energy consumption of smartphones can increase while using both LTE and Wi-Fi interfaces simultaneously. Since energy consumption is an important issue for smartphones powered by batteries with limited capacity, it is required to consider the trade-off between energy increase and performance enhancement. By judiciously utilizing both LTE and Wi-Fi interfaces of smartphones and energy awareness techniques, it is enabled to optimize the performance of smartphones applications while saving battery energy. In this dissertation, we consider the following three strategies to enable the energy awareness for smartphones which utilize both LTE and Wi-Fi links: (i) Power modeling for smartphone which utilizes both LTE and Wi-Fi links simultaneously, (ii) real-time battery drain rate estimation for smartphones, and (iii) optimizing the performance of dynamic adaptive streaming over HTTP (DASH)-based video streaming for smartphones. First, an accurate power modeling is presented for the smartphones, especially, those capable of activating/utilizing multiple networks simultaneously. By decomposing packet processing power and power consumed by network interfaces, we construct the accurate power model for multiple network interface-activated cases. The accuracy of our model is comparatively evaluated by comparing the estimated power with the measured power in various scenarios. We find that our model reduces estimation error by 7%–35% even for single network transmissions, and by 25% for multiple network transmissions compared with existing power models. Second, in order to enable real-time energy awareness for smartphones, we develop a battery drain rate monitoring technique by considering characteristics of Li-ion batteries which are used by smartphones. With Li-ion battery, battery drain rate varies with temperature and battery aging, since they affect battery characteristics such as capacity and internal resistance. Since it is difficult to model the battery characteristics, we develop BattTracker, an algorithm to estimate battery drain rate without knowing the exact capacity and internal resistance by incorporating the concept of effective resistance. BattTracker tracks the instantaneous battery drain rate with up to 0.5 second time granularity. Extensive evaluation with smartphones demonstrates that BattTracker accurately estimates the battery drain rate with less than 5% estimation error, thus enabling energy-aware operation of smartphones with fine-grained time granularity. Finally, we adapt an energy awareness and utilize both LTE and Wi-Fi links for a Dynamic Adaptive Streaming over HTTP (DASH)-based video streaming application. Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of DASH-based video streaming in various aspects. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. Thus, we propose REQUEST, a video chunk request policy for DASH in a smartphone, which can utilize both LTE andWi-Fi to enhance users Quality of Experience (QoE). REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste. In summary, we propose a power modeling methodology, a real-time battery drain rate estimation method, and performance optimization of DASH-based video streaming for a smartphone which utilizes both LTE and Wi-Fi simultaneously. Through this research, we propose several energy-aware techniques for the smartphone, which especially utilizes both LTE and Wi-Fi, based on prototype implementation and the real measurement with experimental equipment. The performance of the proposed methods are validated by implementation on off-the-shelf smartphones and evaluations in real environments.1 Introduction 1 1.1 Motivation 1 1.2 Overview of Existing Approaches 3 1.2.1 Power modeling of smartphone 3 1.2.2 Battery drain rate estimation 4 1.2.3 Optimizing the performance of video streaming 5 1.3 Main Contributions 6 1.3.1 PIMM: Power Modeling of Multiple Network Interface-Activated Smartphone 6 1.3.2 BattTracker: Real-Time Battery Drain Rate Estimation 7 1.3.3 REQUEST: Performance Optimization of DASH Video Streaming 8 1.4 Organization of the Dissertation 8 2 PIMM: Packet Interval-Based Power Modeling of Multiple Network Interface-Activated Smartphones 10 2.1 Introduction 10 2.2 Background 12 2.2.1 Power saving operations of Wi-Fi and LTE 12 2.2.2 Related work 13 2.3 Power Consumption Modeling 14 2.3.1 Modeling Methodology 15 2.3.2 Packet interval-based power modeling 21 2.4 Practical issues 30 2.4.1 Impact of packet length 30 2.4.2 Impact of channel quality 33 2.5 Performance Evaluation 34 2.5.1 On-line power estimation 35 2.5.2 Single network data connections 37 2.5.3 Multiple network data connections 42 2.5.4 Model generation complexity 45 2.6 Summary 46 3 BattTracker: Enabling Energy Awareness for Smartphone Using Li-ion Battery Characteristics 47 3.1 Introduction 47 3.2 Background 50 3.2.1 Smartphones battery interface 50 3.2.2 State of Charge (SoC) and Open Circuit Voltage (Voc) 50 3.2.3 Batterys internal resistance 53 3.2.4 Related Work 53 3.3 Li-ion Battery Characteristics 54 3.3.1 Impact of temperature and aging on Li-ion battery 54 3.3.2 Measuring battery characteristics 54 3.3.3 Battery characteristics models 57 3.4 Effective Internal Resistance 58 3.4.1 Effective internal resistance (re) 58 3.4.2 Battery drain rate and lifetime derived from re 60 3.5 BattTracker Design 61 3.5.1 Rd estimator 62 3.5.2 Voc estimator 62 3.5.3 re estimator 64 3.6 Performance Evaluation 65 3.6.1 Comparison schemes and performance metrics 66 3.6.2 Convergence time of re 67 3.6.3 Power consumption overhead of BattTracker 67 3.6.4 Comparison with measurement using equipment 68 3.6.5 BattTracker with aged batteries 70 3.6.6 BattTracker with various video applications 70 3.6.7 BattTracker with varying temperature 73 3.7 Summary 74 4 REQUEST: Seamless Dynamic Adaptive Streaming over HTTP for Multi-Homed Smartphone under Resource Constraints 75 4.1 Introduction 75 4.2 Background and Related Work 77 4.2.1 Background 77 4.2.2 Related Work 79 4.3 Motivation 80 4.3.1 Wi-Fi Throughput Fluctuation 80 4.3.2 Optimizing Resource Utilization 82 4.4 Proposed Chunk Request Policy 83 4.5 Problem Formulation 86 4.6 REQUEST Algorithm 90 4.6.1 Request Interval Adaptation 91 4.6.2 Chunk Request Adaptation 94 4.7 Performance Evaluation 96 4.7.1 Prototype-Based Evaluation 98 4.7.2 Trace-Driven Simulation 102 4.8 Summary 104 5 Concluding Remarks 106 5.1 Research Contributions 106 5.2 Future Work 107 Abstract (In Korean) 117Docto

Microarchitecture Choices and Tradeoffs for Maximizing Processing Efficiency.

This thesis is concerned with hardware approaches for maximizing the number of independent instructions in the execution core and thereby maximizing the processing efficiency for a given amount of compute bandwidth. Compute bandwidth is the number of parallel execution units multiplied by the pipelining of those units in the processor. Keeping those computing elements busy is key to maximize processing efficiency and therefore power efficiency. While some applications have many independent instructions that can be issued in parallel without inefficiencies due to branch behavior, cache behavior, or instruction dependencies, most applications have limited parallelism and plenty of stalling conditions. This thesis presents two approaches to this problem, which in combination greatly increases the efficiency of the processor utilization of resources. The first approach addresses the problem of small basic blocks that arise when code has frequent branches. We introduce algorithms and mechanisms to predict multiple branches simultaneously and to fetch multiple non-continuous basic blocks every cycle along a predicted branch path. This makes what was previously an inherently serial process into a parallelized instruction fetch approach. For integer applications, the result is an increase in useful instruction fetch capacity of 40% when two basic blocks are fetched per cycle and 63% for three blocks per cycle. For floating point benchmarks, the associated improvement is 27% and 59%. The second approach addresses increasing the number of independent instructions to the execution core through simultaneous multi-threading (SMT). We compare to another multithreading approach, Switch-on-Event multithreading, and show that SMT is far superior. Intel Pentium 4 SMT microarchitecture algorithms are analyzed, and we look at the impact of SMT on power efficiency of the Pentium 4 Processor. A new metric, the SMT Energy Benefit is defined. Not only do we show that the SMT Energy Benefit for a given workload with SMT can be quite significant, we also generalize the results and build a model for what other future processors’ SMT Energy Benefit would be. We conclude that while SMT will continue to be an energy-efficient feature, as processors get more energy-efficient in general the relative SMT Energy Benefit may be reduced.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61740/1/dtmarr_1.pd

ABSTRACT A Run-Time, Feedback-Based Energy Estimation Model For Embedded Devices

We present an adaptive, feedback-based, energy estimation model for battery-powered embedded devices such as sensor network gateways and hand-held computers. Our technique maps hardware and software counters to energy consumption values using a set of first order, linear regression equations. Our system is novel in that it combines online and offline techniques to enable runtime power prediction. Our system employs an offline instantiated model that it continuously updates using feedback from a readily available battery monitor within the device. We empirically evaluate our model and detail its robustness, accuracy, and computational cost. We also analyze the stability of the model in the presence of feedback errors. We demonstrate that our approach can achieve an error rate of 1 % (extant techniques: 2.6 % to 4%) for computationally bound tasks and 6.6 % (extant techniques: 11%) for communication bound tasks

Verfahren und Protokolle für energiebewusste, gemeinsame Ressourcenverwendung mit mobilen Geräten

Voraussetzung für die gemeinsame Nutzung von Betriebsmitteln mobiler Geräte sind Verfahren, die es erlauben, den Energiebedarf für die Bereitstellung von Ressourcen abzuschätzen, unter mehreren Ressourcenanbietern einen geeigneten auszuwählen und diesen für den erbrachten Energieaufwand zu entschädigen. Diese Arbeit stellt hierfür entwickelte Verfahren vor, die insbesondere das geringe Energiebudget mobiler Geräte berücksichtigen