This paper presents Swing, a framework that aggregates a swarm of mobile devices to perform collaborative computation on sensed data streams. It endows performance and efficiency to the new generation of mobile sensing applications, in which the computation is overly intensive for a single device. After studying the source of performance slowdown of the sensing applications on a single device, we design and implement Swing to manage (i) parallelism in stream processing, (ii) dynamism from mobile users, and (iii) heterogeneity from the swarm devices. We build an Android-based prototype and deploy sensing apps - face recognition and language translation - on a wireless testbed. Our evaluations show that with proper management policies, such a distributed processing framework can achieve up to 2.7x improvement in throughput and 6.7x reduction in latency, allowing intensive sensing apps to reach real-time performance goals under different device usages, network conditions and user mobility.

Ensuring fairness in a system with scarce, preferred resources requires time sharing. We consider a heterogeneous system with a few “big” and many “small” processors. We allocate heterogeneous processors using a novel token mechanism, which frames the allocation problem as a repeated game. At each round, users request big processors and spend a token if their request is granted. We analyze the game and optimize users’ strategies to produce an equilibrium. In equilibrium, allocations balance performance and fairness. Our mechanism outperforms classical, fair mechanisms by 1.7×, on average, in performance gains, and is competitive with a performance maximizing mechanism.

Task colocation improves datacenter utilization but introduces resource contention for shared hardware. In this setting, a particular challenge is balancing performance and fairness. We present Cooper, a game-theoretic framework for task colocation that provides fairness while preserving performance. Cooper predicts users' colocation preferences and finds stable matches between them. Its colocations satisfy preferences and encourage strategic users to participate inshared systems. Given Cooper's colocations, users' performance penalties are strongly correlated to their contributions to contention, which is fair according to cooperative game theory. Moreover, its colocations perform within 5% of prior heuristics.

Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe a sprinting architecture in which many, independent chip multiprocessors share a power supply and sprints are constrained by the chips’ thermal limits and the rack’s power limits. Moreover, we present the computational sprinting game, a multi-agent perspective on managing sprints. Strategic agents decide whether to sprint based on application phases and system conditions. The game produces an equilibrium that improves task throughput for data analytics workloads by 4--6× over prior greedy heuristics and performs within 90% of an upper bound on throughput from a globally optimized policy.

Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe a sprinting architecture in which many, independent chip multiprocessors share a power supply and sprints are constrained by the chips' thermal limits and the rack's power limits. Moreover, we present the computational sprinting game, a multi-agent perspective on managing sprints. Strategic agents decide whether to sprint based on application phases and system conditions. The game produces an equilibrium that improves task throughput for data analytics workloads by 4-6× over prior greedy heuristics and performs within 90% of an upper bound on throughput from a globally optimized policy.

Mobile workloads have diverse performance requirements. Some involve frequent user inputs and are computationally intensive while others are background services that minimally uses system resources. To exploit this diversity, architects propose asymmetric multiprocessing (AMP), which achieves both performance and energy efficiency by dynamically switching between high-performance and low-power cores. However, the benefits of existing AMPs that share main memory are limited by the high costs of switching between execution modes. Existing AMP architectures can exploit inter-application diversity but fail to exploit intra-application diversity. Exploiting the latter requires emerging AMP architectures that share the cache hierarchy and reduce switch latency by orders of magnitude. To explore the AMP design space, we propose a set of realistic mobile benchmarks on Android that consider user actions and inputs. We simulate the benchmarks and show that mobile apps benefit substantially from responsive AMPs that switch quickly to exploit fine-grained microphases.

Emerging mobile applications involve continuous sensing and complex computations on sensed data streams. Examples include cognitive apps (e.g., speech recognition, natural language translation, as well as face, object, or gesture detection and recognition) and anticipatory apps that proactively track and provide services when needed. Unfortunately, today's mobile devices cannot keep pace with such apps, despite advances in hardware capability. Traditional approaches address this problem by computation offloading. One approach offloads by sending sensed streams to remote cloud servers via cellular networks or to cloudlets via Wi-Fi, where a clone of the app runs [2, 3, 4]. However, cloudlets may not be widely deployed and access to cloud infrastructure may yield high network delays and can be intermittent due to mobility. Morever, users might hesitate to upload private sensing data to the cloud or cloudlet. A second approach offloads to accelerators by rewriting code to use DSP or GPU within mobile devices. However, using accelerators requires substantial programming effort and produces varied benefits for diverse codes on heterogeneous devices.

This paper describes a system for automatically rating content - mainly movies and videos - at multiple granularities. Our key observation is that the rich set of sensors available on today's smartphones and tablets could be used to capture a wide spectrum of user reactions while users are watching movies on these devices. Examples range from acoustic signatures of laughter to detect which scenes were funny, to the stillness of the tablet indicating intense drama. Moreover, unlike in most conventional systems, these ratings need not result in just one numeric score, but could be expanded to capture the user's experience. We combine these ideas into an Android based prototype called Pulse, and test it with 11 users each of whom watched 4 to 6 movies on Samsung tablets. Encouraging results show consistent correlation between the user's actual ratings and those generated by the system. With more rigorous testing and optimization, Pulse could be a candidate for real-world adoption.

The Wi-Fi interface is one of the major energy consuming components in smart phones. Wi-Fi scanning process contributes significantly to this. We present a solution based on partial scan, which produces full scan results, by performing only incomplete scan process. Using a decision tree algorithm, we propose such a prediction scheme, with the help of cached scan results.