Quality and Cost Aware Service Selection in IoT-Context Management Platforms

Jagarlamudi, Kanaka Sai; Zaslavsky, Arkady; Loke, Seng W.; Hassani, Alireza; Medvedev, Alexey

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics53846.2021.00028

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…We refer to this gap of time as the invalid gap for context provider (CP), where it cannot be used for refreshing. CPs are listed in order [ 6 ] since CoaaS need to be cost and quality-aware when selecting CPs for context retrieval. We use this to retrieve the context from the next best CP, the context from which could also be in the cache.…”

Section: Adaptive Context Refreshing With Policy Shiftingmentioning

confidence: 99%

“…As much as the data retrieval process for context derivation can benefit from optimizing techniques at the producer when the context providers are essentially not IoT devices (e.g., the calendar in the above example), deriving context would still yield an overhead that minimizes this benefit such that delivered context may no longer be relevant to the entity. Note that the raw data from which the context is derived from for each context entity, originate from “relevant” context providers [ 6 ] selected from billions of possible providers for billions of context consumers [ 1 ]. Therefore, streamlining the process of managing context retrieval or inventing an innovative method to reuse and repurpose retrieved and derived context using a caching technique is a compelling research problem.…”

Section: Introductionmentioning

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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Section: Adaptive Context Refreshing With Policy Shiftingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Weerasinghe

Zaslavsky

Loke

et al. 2023

Sensors

Self Cite

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“…The freshness of context (which inversely correlates with the age of context), its accuracy, completeness, and the latency of access to context are several parameters of Quality of Service (QoS). On the other hand, the price that the middleware incurs to access context information is referred to as the Cost of Context (CoC) [5]. Therefore, there is an urgent need to optimize data caching strategies to balance operational costs with the QoS provided.…”

Section: Motivation For Refresh Rate-based Caching and Prefetchingmentioning

confidence: 99%

“…However, we cannot ignore the fact that the provider set freshness is most reliable until the provider is adequately dependablebased on the consumer's context requirements (e.g., sensors can be unreliable due to wear, or dust/residue accumulation on receptors). Hence, quality-based selection of providers at retrieval time [5] and techniques such as alternate context provider retrievals [15] can be required to maintain the cost and quality of context. The role of context providers in determining the Quality of Context is out of the scope of this paper because, (i) CMPs such as CoaaS rely on cost and quality-based context provider selection mechanisms prior to retrieving IoT data [5], and (ii) assuming the providers and state-of-the-art protocols such as CoAP and AMQP are fast and reliable.…”

Section: Transient Nature Of Iot Datamentioning

confidence: 99%

Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware

Medvedev,

Hassani,

Belov

et al. 2023

Sensors

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One of the research directions in Internet of Things (IoT) is the field of Context Management Platforms (CMPs) which is a specific type of IoT middleware. CMPs provide horizontal connectivity between vertically oriented IoT silos resulting in a noticeable difference in how IoT data streams are processed. As these context data exchanges can be monetised, there is a need to model and predict the context metrics and operational costs of this exchange to provide relevant and timely context in a large-scale IoT ecosystem. In this paper, we argue that caching all transient context information to satisfy this necessity requires large amounts of computational and network resources, resulting in tremendous operational costs. Using Service Level Agreements (SLAs) between the context providers, CMP, and context consumers, where the level of service imperfection is quantified and linked to the associated costs, we show that it is possible to find efficient caching and prefetching strategies to minimize the context management cost. So, this paper proposes a novel method to find the optimal rate of IoT data prefetching and caching. We show the main context caching strategies and the proposed mathematical models, then discuss how a correctly chosen proactive caching strategy and configurations can help to maximise the profit of CMP operation when multiple SLAs are defined. Our model is accurate up to 0.0016 in Root Mean Square Percentage Error against our simulation results when estimating the profits to the system. We also show our model is valid using the t-test value tending to 0 for all the experimental scenarios.

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“…QoC-based filtering and aggregation are seen as important features of LAURA. Jagarlamudi et al (2021) proposed a Service Level Agreement (SLA) template integrating a QoC-aware mechanism, called the Relative Reputation (RR), to select context providers with high RR values. The QoC evaluator generates the RR unit representing the match between QoC outcomes and QoC requirements.…”

Section: Qoc Managementmentioning

confidence: 99%

Middleware Supporting PIS: Requirements, Solutions, and Challenges

Taconet

Batista

Borges

et al. 2022

The Evolution of Pervasive Information Systems

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Quality and Cost Aware Service Selection in IoT-Context Management Platforms

Cited by 8 publications

References 11 publications

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

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

Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware

Middleware Supporting PIS: Requirements, Solutions, and Challenges

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