Yongbo Li

Accurately determining the network energy consumption of each software principal when multiple ones are active is the key to mobile energy optimization. Tail energy accounting, which attributes tail energy to individual software principals, remains an open problem. Besides, tail energy has also become a major energy drain, especially in mobile ad modules that generate frequent, intermittent network traffics by on-demand ad downloading. In this paper, we propose a systematic framework for mobile… Show more

Accurately determining the network energy consumption of each software principal when multiple ones are active is the key to mobile energy optimization. Tail energy accounting, which attributes tail energy to individual software principals, remains an open problem. Besides, tail energy has also become a major energy drain, especially in mobile ad modules that generate frequent, intermittent network traffics by on-demand ad downloading. In this paper, we propose a systematic framework for mobile ad prefetching and energy optimization, based on a novel tail energy accounting policy using cooperative game theory. In particular, we maximize the sum of deadline- and energy-aware ad utility, by jointly determining apps' aggressiveness in ad prefetching. The proposed tail energy accounting not only characterizes the energy profile of each app's ad module, a crucial input in energy optimization, but also enables an efficient solution by decoupling decision making of individual apps. The proposed framework is implemented on Android with negligible performance/network overhead. Using real-world apps and usage traces, we demonstrate a significant reduction in mobile network energy consumption by up to 45% compared with existing approaches. To the best of our knowledge, it is the first fully implemented ad management system transparent to apps and ad ecosystem. Show less

Mobile Edge Computing has fastly become a promising diagram to meet the ever-increasing demands imposed by emerging domains of applications and reduce the reliance on remote data centers in traditional cloud computing. In this paper, we address two problems unique in heterogeneous edge computing: the cost accounting and task assignment. To determine the cost of each unit of task being concurrently processed on an edge device, we propose to model the problem as a multichoice game, and use… Show more

Mobile Edge Computing has fastly become a promising diagram to meet the ever-increasing demands imposed by emerging domains of applications and reduce the reliance on remote data centers in traditional cloud computing. In this paper, we address two problems unique in heterogeneous edge computing: the cost accounting and task assignment. To determine the cost of each unit of task being concurrently processed on an edge device, we propose to model the problem as a multichoice game, and use Shapley Value for cost accounting. With the total cost decoupled, we are able to leverage the distributive Hungarian algorithm to solve the task assignment problem efficiently. We adopt a hybrid manner for evaluations: we profile the costs (including energy and data transmission costs) using a fully implemented workload offloading framework in an edge environment, then the cost profiles are used to drive the simulations. Results show that our policy of task assignment guided by multichoice Shapley value is able to consistently outperform the two other baselines: Random policy and the policy of Hungarian assignment algorithm based on Even energy accounting. The advantage of our policy is further enlarged when the heterogeneity level of the network or computing resource in edge environments is increased. We also show interesting patterns of the joint effects of different resources’ heterogeneity levels and the weighting factor between them, which provide useful inputs for edge resource optimization and management. Show less

PhD dissertation

This paper presents a semantics-aware rule recommendation and enforcement (SARRE) system for taming information leakage on Android. SARRE leverages statistical analysis and a novel application of minimum path cover algorithm to identify system event paths from dynamic runtime monitoring. Then, an online recommendation system is developed to automatically assign a fine-grained security rule to each event path, capitalizing on both known security rules and application semantic information. The… Show more

This paper presents a semantics-aware rule recommendation and enforcement (SARRE) system for taming information leakage on Android. SARRE leverages statistical analysis and a novel application of minimum path cover algorithm to identify system event paths from dynamic runtime monitoring. Then, an online recommendation system is developed to automatically assign a fine-grained security rule to each event path, capitalizing on both known security rules and application semantic information. The proposed SARRE system is prototyped on Android devices and evaluated using real-world malware samples and popular apps from Google Play spanning multiple categories. Our results show that SARRE achieves 93.8% precision and 96.4% recall in identifying the event paths, compared with tainting technique. Also, the average difference between rule recommendation and manual configuration is less than 5%, validating the effectiveness of the automatic rule recommendation. It is also demonstrated that by enforcing the recommended security rules through a camouflage engine, SARRE can effectively prevent information leakage and enable fine-grained protection over private data with a very small performance overhead. Show less