From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys Journal at this time of publishing in arxiv.or

On improving the performance of optimistic distributed simulations

This report investigates means of improving the performance of optimistic distributed simulations without affecting the simulation accuracy. We argue that existing clustering algorithms are not adequate for application in distributed simulations, and outline some characteristics of an ideal algorithm that could be applied in this field. This report is structured as follows. We start by introducing the area of distributed simulation. Following a comparison of the dominant protocols used in distributed simulation, we elaborate on the current approaches of improving the simulation performance, using computation efficient techniques, exploiting the hardware configuration of processors, optimizations that can be derived from the simulation scenario, etc. We introduce the core characteristics of clustering approaches and argue that these cannot be applied in real-life distributed simulation problems. We present a typical distributed simulation setting and elaborate on the reasons that existing clustering approaches are not expected to improve the performance of a distributed simulation. We introduce a prototype distributed simulation platform that has been developed in the scope of this research, focusing on the area of emergency response and specifically building evacuation. We continue by outlining our current work on this issue, and finally, we end this report by outlining next actions which could be made in this field

Towards Adaptive Caching for Parallel and Discrete Event Simulation

We investigate factors that impact the effectiveness of caching to speed up discrete event simulation. Walsh and Sirer have shown that a variant of function caching (staged simulation) can improve the performance of simulation in a networking application (Walsh and Sirer 2003). Consider, however, that the effectiveness of a caching scheme depends significantly on cache size, the cost of consulting the cache, the cache hit rate, and the cost of completing the computation in the case of a cache miss. We hypothesize that adaptive techniques can be used to optimize caching parameters (e.g. cache size), and demonstrate an adaptive scheme that decides whether to utilize caching at an LP depending on observed cache performance and event processing times. This paper focuses on a quantitative evaluation of these relationships using our own caching implementation with the P-Hold synthetic workload application (Fujimoto 1990) running on the GTW simulation kernel (Das et al. 1994). Experiments show that as the cache size is increased, performance improves to a point, then degrades, and also that the adaptive technique can substantially improve speedup.