A Value‐of‐Information‐based management framework for fog services

Poltronieri, Filippo; Tortonesi, Mauro; Morelli, Alessandro; Stefanelli, Cesare; Suri, Niranjan

doi:10.1002/nem.2156

Cited by 5 publications

(6 citation statements)

References 46 publications

(82 reference statements)

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“…The need to optimize complex service fabrics in highly heterogeneous environments fostered the development of innovative concepts and tools, including Value-of-Information (VoI). Originally born from the seminal research by Howard in 1966 [23] as an extension to the Shannon's Information Theory for economics, and widely explored in decision sciences, the VoI concept has been recently investigated in different research areas related to the network and service management [9]. In [8] VoI was applied to optimize bandwidth consumption and information delivery in low-bandwidth and disrupted wireless networks.…”

Section: Related Workmentioning

confidence: 99%

“…In turn, ranking services and microservice components according to the total amount of VoI they provide to end users represents a natural and effective approach to realize self-adaptive services for Fog computing applications. To this end, interested readers can refer to the work in [9], in which the mathematical framework for VoI calculation is presented.…”

Section: The Case For Voi-based Approaches In the CCmentioning

confidence: 99%

“…By using the amount of VoI delivered to end users as a resource assignment criterion, a system will be able to naturally and seamlessly prioritize the assignment of resources to microservices that are providing the highest value to their end users -either because they are serving a considerable amount of users or because they are providing highly valuable information [9]. Factors such as the availability of resources or the changing demands of applications could be taken into account to estimate the relevance of requests and dynamically allocate resources in the CC environment.…”

Section: The Case For Voi-based Approaches In the CCmentioning

confidence: 99%

“…In this regards, Value of Information (VoI) represents a compelling approach to rank information and services according to the actual value they provide to the end users are particularly attractive [8], [9]. In fact, VoI-based approaches enable to maximize in a straightforward fashion resource utilization from the user perspective across the entire CC, also addressing the issue of resource sharing among competing services.…”

Section: Introductionmentioning

confidence: 99%

“…VOICE aims to push forward the state of the art for service elasticity management in the CC by explicitly involving application components in resource allocation expansion or contraction operations, to ensure the best QoS levels according to conditions and application runtime requirements. VOICE builds on top of previous research on adaptive VoI-based service models and runtimes [9], CIbased optimization solutions [12], and innovative Digital Twin approaches [13], [14] to provide a full fledged orchestration solution designed for the CC.…”

Section: Introductionmentioning

confidence: 99%

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VOICE: Value-of-Information for Compute Continuum Ecosystems

Zaccarini,

Cantelli,

Fazio

et al. 2024

2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)

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The "Compute Continuum" (CC), which encompasses and harmonizes the Edge, Fog, and Cloud computing paradigms, emerges as an opportunity for an efficient deployment and adoption of innovative services ranging from eHealth to virtual reality. However, the effective management of services all along the CC presents the challenge of service deployment in relatively resource scarce or poorly connected locations. We argue that CC environments call for novel resource management strategies with a holistic perspective of the whole computing ecosystem, that also consider the QoS reconfiguration and tuning of services in the resource reallocation process. Tackling these challenges, we introduce VOICE, a cutting-edge platform designed for service management in CC ecosystems. Thanks to a Value of Information (VoI)-based technique that emphasizes the significance of data filtering, VOICE supports dynamic service components allocation throughout the CC. To evaluate the choice of VoI as enablingtechnology for VOICE, we leverage computational intelligence (CI) approaches and compare it with more traditional strategies (e.g. maximization of satisfied requests or latency minimization). Final results show how VoI can be a valuable proposal to optimize the usage of resource scarcity that characterizes CC environments.

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Section: Related Workmentioning

confidence: 99%

Section: The Case For Voi-based Approaches In the CCmentioning

confidence: 99%

Section: The Case For Voi-based Approaches In the CCmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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VOICE: Value-of-Information for Compute Continuum Ecosystems

Zaccarini,

Cantelli,

Fazio

et al. 2024

2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)

Self Cite

View full text Add to dashboard Cite

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Value is King: The MECForge Deep Reinforcement Learning Solution for Resource Management in 5G and Beyond

et al. 2022

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Multi-access edge computing (MEC) is a key enabler to fulfill the promises of a new generation of immersive and low-latency services in 5G and Beyond networks. MEC represents a defining function of 5G, offering significant computational power at a reduced latency, allowing to augment the capabilities of user equipments while preserving their battery life. However, the demands generated by a plethora of innovative and concurrent IT services requiring high quality of service and quality of experience levels will likely overwhelm the—albeit considerable—resources available in 5G and Beyond scenarios. To take full advantage of its potential, MEC needs to be paired with innovative resource management solutions capable of effectively addressing the highly dynamic aspects of the scenario and of properly considering the heterogeneous and ever-changing nature of next generation IT services, prioritizing the assignment of resources in a highly dynamic and contextual fashion. This calls for the adoption of Artificial Intelligence based tools, implementing self-* approaches capable of learning the best resource management strategy to adapt to the ever changing conditions. In this paper, we present MECForge, a novel solution based on deep reinforcement learning that considers the maximization of total value-of-information delivered to end-user as a coherent and comprehensive resource management criterion. The experimental evaluation we conducted in a simulated but realistic environment shows how the Deep Q-Network based algorithm implemented by MECForge is capable of learning effective autonomous resource management policies that allocate service components to maximize the overall value delivered to the end-users.

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