Probabilistic analysis of context caching in Internet of Things applications

Khargharia, Himadri Sikhar; Jayaraman, Prem Prakash; Banerjee, Abhik; Zaslavsky, Arkady; Hassani, Alireza; Abken, Amin; Kumar, Ashwani

doi:10.1109/scc55611.2022.00025

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…The most frequently investigated reason is invalidation (also referred to as 'expiration') [11,12]. It is the main criticism we find for probabilistically caching popular context entities in [27] where our opinion is supported by Wu et al [21]. Scaling vertically (e.g., adding more CPUs to process context) or horizontally (e.g., indefinitely adding more cache memory) [28] is an attractive solution for maximizing performance efficiency but is a massively expensive option.…”

Section: Related Workmentioning

confidence: 93%

“…semantically similar context queries can request similar context information which, through monitoring, only previously observed context information discreetly using identifiers (e.g., as in [ 27 ]) will be incorrectly missed in the cache memory.…”

Section: Related Workmentioning

confidence: 99%

“…Then, [ 21 , 38 ] define events that result in a sudden “spike” in popularity during a very short period of time (considering an accident as an example). As AT correlates with the hit rate, it is also a potent indicator for such a scenario [ 27 ]. Finally, the reliability to derive the context information in time ( Reli) is subject to the time and the context itself.…”

Section: Reactive Context Cache Selectionmentioning

confidence: 99%

“…Therefore, the distribution of applicable consumer SLA parameters can differ significantly. CoQC is distinctive from the clustering approach in [ 27 ] which only identifies context entities by their types (e.g., cars and car parks). The major drawback of this approach to clustering in [ 27 ] is the inability to predict the reusability of context information for previously unseen context queries, as this approach ignores the semantic similarities of using that which the lack of prior information for selective caching decision-making can be overcome.…”

Section: Reactive Context Cache Selectionmentioning

confidence: 99%

“…As we indicated in Section 3 , context-aware caching in the literature has primarily dealt with maximizing the HR of a system [ 3 , 4 , 20 ]. Khargaria et al [ 27 ] also implemented a context-caching algorithm based on the popularity of context entities. Therefore, all authors attempted to cache the most frequently accessed data in the cache.…”

Section: Evaluationsmentioning

confidence: 99%

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Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach

Weerasinghe

Zaslavsky

Loke

et al. 2023

Sensors

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Making internet-of-things (IoT)-based applications context-aware demands large amounts of raw data to be collected, interpreted, stored, and reused or repurposed if needed from many domains and applications. Context is transient but interpreted data can be distinguished from IoT data in many aspects. Managing context in cache is a novel area of research that has been given very little attention. Performance metric-driven adaptive context caching (ACOCA) can have a profound impact on the performance and cost efficiency of context-management platforms (CMPs) when responding to context queries in realtime. Our paper proposes an ACOCA mechanism to maximize both the cost and performance efficiency of a CMP in near realtime. Our novel mechanism encompasses the entire context-management life cycle. This, in turn, distinctively addresses the problems of efficiently selecting context for caching and managing the additional costs of context management in the cache. We demonstrate that our mechanism results in long-term efficiencies for the CMP that have not been observed in any previous study. The mechanism employs a novel, scalable, and selective context-caching agent implemented using the twin delayed deep deterministic policy gradient method. It further incorporates an adaptive context-refresh switching policy, a time-aware eviction policy, and a latent caching decision management policy. We point out in our findings that the additional complexity of adaptation introduced to the CMP through ACOCA is significantly justified, considering the cost and performance gains achieved. Our algorithm is evaluated using a real-world inspired heterogeneous context-query load and a data set based on parking-related traffic in Melbourne, Australia. This paper presents and benchmarks the proposed scheme against traditional and context-aware caching policies. We demonstrate that ACOCA outperforms the benchmarks in both cost and performance efficiency, i.e., up to 68.6%, 84.7%, and 67% more cost efficient compared to traditional data caching policies to cache context, redirector mode, and context-aware adaptive data caching under real-world-like circumstances.

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