Abhinav Pathak

Fine-grained power modeling for smartphones using system call tracing

Accurate, fine-grained online energy estimation and accounting of mobile devices such as smartphones is of critical importance to understanding and debugging the energy consumption of mobile applications. We observe that state-of-the-art, utilization-based power modeling correlates the (actual) utilization of a hardware component with its power state, and hence is insufficient in capturing several power behavior not directly related to the component utilization in modern smartphones. Such behavior arise due to various low level power optimizations programmed in the device drivers. We propose a new, system-call-based power modeling approach which gracefully encompasses both utilization-based and non-utilization-based power behavior. We present the detailed design of such a power modeling scheme and its implementation on Android and Windows Mobile. Our experimental results using a diverse set of applications confirm that the new model significantly improves the fine-grained as well as whole-application energy consumption accuracy. We further demonstrate fine-grained energy accounting enabled by such a fined-grained power model, via amanually implemented eprof, the energy counterpart of the classic gprof tool, for profiling application energy drain.

What is keeping my phone awake?: characterizing and detecting no-sleep energy bugs in smartphone apps

Despite their immense popularity in recent years, smartphones are and will remain severely limited by their battery life. Preserving this critical resource has driven smartphone OSes to undergo a paradigm shift in power management: by default every component, including the CPU, stays off or in an idle state, unless the app explicitly instructs the OS to keep it on! Such a policy encumbers app developers to explicitly juggle power control APIs exported by the OS to keep the components on, during their active use by the app and off otherwise. The resulting power-encumbered programming unavoidably gives rise to a new class of software energy bugs on smartphones called no-sleep bugs, which arise from mis-handling power control APIs by apps or the framework and result in significant and unexpected battery drainage. This paper makes the first advances towards understanding and automatically detecting software energy bugs on smartphones. It makes the following three contributions: (1) we present the first comprehensive study of real world no-sleep energy bug characteristics; (2) we propose the first automatic solution to detect these bugs based on the classic reaching definitions dataflow analysis algorithm; (3) we provide experimental data showing that our tool accurately detected all 17 known instances of no-sleep bugs and found 34 new bugs in the 73 apps examined.

Bootstrapping energy debugging on smartphones: a first look at energy bugs in mobile devices

This paper argues that a new class of bugs faced by millions of smartphones, energy bugs or ebugs, have become increasingly prominent that already they have led to significant user frustrations. We take a first look at this emerging important technical challenge faced by the smartphones, ebugs, broadly defined as an error in the system (application, OS, hardware, firmware, external conditions or combination) that causes an unexpected amount of high energy consumption by the system as a whole. We first present a taxonomy of the kinds of ebugs based on mining over 39K posts (1.2M before filtering) from 4 online mobile user forum and mobile OS bug repositories. The taxonomy shows the highly diverse nature of smartphone ebugs. We then propose a roadmap towards developing a systematic diagnosing framework for debugging ebugs on smartphones.

Characterizing and modeling the impact of wireless signal strength on smartphone battery drain

Despite the tremendous market penetration of smartphones, their utility has been and will remain severely limited by their battery life. A major source of smartphone battery drain is accessing the Internet over cellular or WiFi connection when running various apps and services. Despite much anecdotal evidence of smartphone users experiencing quicker battery drain in poor signal strength, there has been limited understanding of how often smartphone users experience poor signal strength and the quantitative impact of poor signal strength on the phone battery drain. The answers to such questions are essential for diagnosing and improving cellular network services and smartphone battery life and help to build more accurate online power models for smartphones, which are building blocks for energy profiling and optimization of smartphone apps. In this paper, we conduct the first measurement and modeling study of the impact of wireless signal strength on smartphone energy consumption. Our study makes four contributions. First, through analyzing traces collected on 3785 smartphones for at least one month, we show that poor signal strength of both 3G and WiFi is routinely experienced by smartphone users, both spatially and temporally. Second, we quantify the extra energy consumption on data transfer induced by poor wireless signal strength. Third, we develop a new power model for WiFi and 3G that incorporates the signal strength factor and significantly improves the modeling accuracy over the previous state of the art. Finally, we perform what-if analysis to quantify the potential energy savings from opportunistically delaying network traffic by exploring the dynamics of signal strength experienced by users.

A measurement study of internet delay asymmetry

RTT has been widely used as a metric for peer/server selection. However, many applications involving closest peer/server selection such as streaming, tree-based multicast services and other UDP and TCP based services would benefit more from knowing one-way delay (OWD) rather than RTT. In fact, RTT is frequently used as as an approximate solution to infer forward and reverse delays by many protocols and applications which assume forward and reverse delay to be equal to half of RTT. In this paper, we compare and contrast one-way delays and corresponding RTTs using a wide selection of routes in the Internet. We first measure the extent and severeness of asymmetry in forward and reverse OWD in the Internet. We then attempt to isolate the causes of OWD asymmetry by correlating OWD asymmetry with the route asymmetry. Finally, we investigate the dynamics of delay asymmetry. We find there exists a weak correlation between the fluctuation of RTT and OWD but a strong correlation between OWD change and the corresponding route change.

Botnet spam campaigns can be long lasting: evidence, implications, and analysis

Accurately identifying spam campaigns launched by a large number of bots in a botnet allows for accurate spam campaign signature generation and hence is critical to defeating spamming botnets. The straight-forward approach of clustering all spam containing the same label such as an URL into a campaign can be easily defeated by techniques such as simple obfuscations of URLs. In this paper, we perform a comprehensive study of content-agnostic characteristics of spam campaigns, e.g. duration and source-network distribution of spammers, in order to ascertain whether and how they can assist the simple label-based clustering methods in identifying campaigns and generating campaign signatures. In particular, from a five-month trace collected by a relay sinkhole, we manually identified and then analyzed seven URL-based botnet spam campaigns consisting of 52 million spam messages sent over 2.09 million SMTP connections originated from over 150,000 non-proxy spamming hosts and destined to about 200,000 end domains. Our analysis shows that the spam campaigns, when observed from large destination domains, exhibit durations far longer than the five-day period as reported in a recent study. We analyze the implications of this finding on spam campaign signature generation. We further study other characteristics of these long-lasting campaigns. Our analysis reveals several new findings regarding workload distribution, sending patterns, and coordination among the spamming machines.

Hypnos: understanding and treating sleep conflicts in smartphones

To maximally conserve the critical resource of battery energy, smartphone OSes implement an aggressive system suspend policy that suspends the whole system after a brief period of user inactivity. This burdens developers with the responsibility of keeping the system on, or waking it up, to execute time-sensitive code. Developer mistakes in using the explicit power management unavoidably give rise to energy bugs, which cause significant, unexpected battery drain. In this paper, we study a new class of energy bugs, called sleep conflicts, which can happen in smartphone device drivers. Sleep conflict happens when a component in a high power state is unable to transition back to the base power state because the system is suspended when the device driver code responsible for driving the transition is supposed to execute. We illustrate the root cause of sleep conflicts, develop a classification of the four types of sleep conflicts, and finally present a runtime system that performs sleep conflict avoidance, along with a simple yet effective pre-deployment testing scheme. We have implemented and evaluated our system on two Android smartphones. Our testing scheme detects several sleep conflicts in WiFi and vibrator drivers, and our runtime avoidance scheme effectively prevents sleep conflicts from draining the battery.

Measuring and evaluating TCP splitting for cloud services

In this paper, we examine the benefits of split-TCP proxies, deployed in an operational world-wide network, for accelerating cloud services. We consider a fraction of a network consisting of a large number of satellite datacenters, which host split-TCP proxies, and a smaller number of mega datacenters, which ultimately perform computation or provide storage. Using web search as an exemplary case study, our detailed measurements reveal that a vanilla TCP splitting solution deployed at the satellite DCs reduces the 95th percentile of latency by as much as 43% when compared to serving queries directly from the mega DCs. Through careful dissection of the measurement results, we characterize how individual components, including proxy stacks, network protocols, packet losses and network load, can impact the latency. Finally, we shed light on further optimizations that can fully realize the potential of the TCP splitting solution.

Realizing the full potential of PSM using proxying

The WiFi radio in smartphones consumes a significant portion of energy when active. To reduce the energy consumption, the Power Saving Mode was standardized in IEEE 802.11 and two major implementations, Static PSM and Dynamic PSM, have been widely used in mobile devices. Unfortunately, both PSMs have inherent drawbacks: Static PSM is energy efficient but imposes considerable extra delays on data transfers; Dynamic PSM incurs little extra delay but misses energy saving opportunities. In this paper, we first analyze a one-week trace from 10 users and show that more than 80% of all traffic are Web 2.0 flows, which are of very small sizes and short durations. Targeting these short but dominant flows, we propose a system called Percy, to achieve the best of both worlds (Static and Dynamic PSM), i.e., to maximize the energy saving while minimizing the delay of flow completion time. Percy works by deploying a web proxy at the AP and suitably configuring the PSM parameters, and is designed to work with unchanged clients running Dynamic PSM, and unchanged APs and Internet servers. We evaluate our system via trace-driven testbed experiments. Our results show that Percy saves 40-70% energy compared to Dynamic PSM configurations of Nokia, iPhone and Android, while imposing low extra delay that can hardly be perceived by users.

Latency inflation with MPLS-based traffic engineering

While MPLS has been extensively deployed in recent years, little is known about its behavior in practice. We examine the performance of MPLS in Microsofts online service network (MSN), a well-provisioned multi-continent production network connecting tens of data centers. Using detailed traces collected over a 2-month period, we find that many paths experience significantly inflated latencies. We correlate occurrences of latency inflation with routers, links, and DC-pairs. This analysis sheds light on the causes of latency inflation and suggests several avenues for alleviating the problem.