Cross-Layer Adaptation with Safety-Assured Proactive Task Job Skipping

During the operation of many real-time safety-critical systems, there are often strong needs for adapting to a dynamic environment or evolving mission objectives, e.g., increasing sampling and control frequencies of some functions to improve their performance under certain situations. However, a system's ability to adapt is often limited by tight resource constraints and rigid periodic execution requirements. In this work, we present a cross-layer approach to improve system adaptability by allowing proactive skipping of task executions, so that the resources can be either saved directly or re-allocated to other tasks for their performance improvement. Our approach includes three novel elements: (1) formal methods for deriving the feasible skipping choices of control tasks with safety guarantees at the functional layer, (2) a schedulability analysis method for assessing system feasibility at the architectural layer under allowed task job skippings, and (3) a runtime adaptation algorithm that efficiently explores job skipping choices and task priorities for meeting system adaptation requirements while ensuring system safety and timing correctness. Experiments demonstrate the effectiveness of our approach in meeting system adaptation needs.

Elastic AI: system support for adaptive machine learning in pervasive computing systems

Artificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.

The Correctness of Service in Runtime Adaptation for Context-Aware Mobile Cloud Learning

Service-Based Applications (SBAs) have become increasingly pervasive. These applications rely on the third-parties services available on the cloud, and services must be aware of and adapt to their changing contexts in highly dynamic environments. SBAs with context-aware capabilities have provided the users with personalized services based on their user's (intrinsic) and device's (extrinsic) contextual information, as well as the Quality of Services (QoS). The correctness of service substitution in runtime adaptation is substantial for the continuity of user activity on the system. In Mobile Cloud Learning (MCL) environment most works only focus on intrinsic context factors such as learner's profile, learner's location, etc. We then introduce a comprehensive Dynamic Service Adaptation of Context-Aware Mobile Cloud Learning (DACAMoL), which is designed to reason for bothcontextual factors and QoS inservice discovery, ranking, and selection. The framework represents the contextual information, service descriptions, and QoS using a semantic-based approach to improve the correctness of service substitution. In this paper, wepresent a quasi-experiment study to demonstrate the DACAMoL framework with a mobile app called Mudahnya BM. Mudahnya BM is a learning app to learn basic knowledge of Malay language that build using RESTful backend services. The study involved 30 participants and 33 randomized scenarios tested using One-Sample Wilcoxon Signed Rank test. The results show significantly better service substitutions with 32 out of 33educational servicesare correctly adapted (i.e. 95% of the population).

Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System

FPGAs are promising target architectures for hardware acceleration of database query processing, as they combine the performance of hardware with the programmability of software. In particular, they are partially reconfigurable at runtime, which allows for the runtime adaptation to a variety of queries. However, reconfiguration costs some time, and a region of the FPGA is not available for computations during its reconfiguration. Techniques to avoid or at least hide the reconfiguration latencies can improve the overall performance. This paper presents optimizations based on query look-ahead, which follows the idea of exploiting knowledge about subsequent queries for scheduling the reconfigurations such that their overhead is minimized. We evaluate our optimizations with a calibrated model for various parameter settings. Improvements in execution time can be “calculated” even if only being able to look one query ahead.