Edge to Cloud Continuum is a concept that integrates cloud computing and cellular networks that has been gaining popularity due to its potential to provide a seamless user experience and address the challenges of mana...
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ISBN:
(纸本)9798350399806
Edge to Cloud Continuum is a concept that integrates cloud computing and cellular networks that has been gaining popularity due to its potential to provide a seamless user experience and address the challenges of managing complex multi-domain networks involving massive IoT devices. Enabling intelligence in the Edge to Cloud Continuum can further enhance its capabilities, offering benefits such as reduced latency, improved scalability, enhanced resource utilization, and increased context awareness. this paper provides insights into the opportunities and challenges of enabling intelligence in Edge to Cloud Continuum, highlighting the potential of this technology. this study presents a comprehensive review of the existing literature on enabling intelligence in Edge to Cloud Continuum, to reach the research questions that will construct the PhD. Various tools and technologiesthat can be used to integrate intelligence into the Edge to Cloud Continuum system were explored and analyzed. In addition, this study provides a detailed work plan for the upcoming months of the project.
Serverless functions have emerged as a useful abstraction to manage the complexity of distributed and heterogeneous edge-cloud infrastructure. Current cloud-centric orchestration services and serverless platforms are ...
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ISBN:
(纸本)9781665460873
Serverless functions have emerged as a useful abstraction to manage the complexity of distributed and heterogeneous edge-cloud infrastructure. Current cloud-centric orchestration services and serverless platforms are not suitable for the edge-cloud continuum due to their mobility-unawareness. In this paper, we present a mobility-aware framework for edgecloud systems, where the operational mechanisms of placement, scaling, and routing of serverless functions work in tandem. To that end, we formulate the concept of pressure that captures complex system behavior in a single metric. the pressure-based framework handles geo-distributed workload and user mobility by reducing overall function latency and increasing data throughput while efficiently using edge resources. Our contributions include a novel framework revolving around pressure and a realworld proof of concept evaluation. Our approach combines the benefits of a centralized control-plane with a decentralized dataplane. the results show the efficacy of the platform to address operational goals and make effective and deterministic tradeoffs between system utilization and application performance. the novel concept of pressure shows great extensibility and builds the base for a plethora of future works.
this paper aims to contribute to the challenging issue of one microscopic simulation round for dynamic traffic assignment. It relays on the selfish behaviour of the vehicle agent that benefits from a more accurate est...
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Multi-access edge computing (MEC) technology is a potential solution to the conflict between resource-constrained unmanned aerial vehicles (UAVs) and various emerging computationally intensive, delay-sensitive applica...
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the growing popularity of data-intensive applications in cloud computing necessitates a cost-effective approach to harnessing distributed processing capabilities. However, the wide variety of instance types and config...
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distributed Constraint Optimization Problem (DCOP) is an important model for multi-agents, has been widely used in various fields. When a large scale of DCOP implement on the supercomputer, various parameters need to ...
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Withthe development of machine learning and big data technologies, distributed training has become an important way to improve computational efficiency. However, in the distributed training environment, the performan...
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Blockchain and distributed Ledger Technology (DLT) are becoming important driving forces for a new round of scientific and technological development. the construction of blockchain information infrastructure has risen...
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A complementary computational approach is proposed for the time-varying Takagi-Sugeno fuzzy model system (TVTSFMS). the proposed approach integrates orthogonal-functional approach (OFA), hybrid Taguchi genetic algorit...
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A complementary computational approach is proposed for the time-varying Takagi-Sugeno fuzzy model system (TVTSFMS). the proposed approach integrates orthogonal-functional approach (OFA), hybrid Taguchi genetic algorithm (HTGA), and a stabilizability condition (SC) for use in designing stable and quadratic-optimal parallel-distributed-compensation (SQOPDC) controllers for optimal control problems. First, the SC was set according to linear matrix inequalities (LMIs). Next, OFA was used to derive an algorithm that only required algebraic computation to solve the TVTSFMS. Finally, the HTGA could be used to search the SQOPDC controller for the TVTSFMS. the SQOPDC controller obtained by the proposed complementary computational approach was evaluated in a case study of a vibratory pendulum design;the successful design verified the usability of the proposed hybrid intelligent computing method.
the continuous expansion of Edge computing calls for efficient scheduling techniques for the employed microservices. However, typical container schedulers often fall short when used in an Edge cluster with heterogeneo...
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ISBN:
(纸本)9781665460873
the continuous expansion of Edge computing calls for efficient scheduling techniques for the employed microservices. However, typical container schedulers often fall short when used in an Edge cluster with heterogeneous devices and unstable network connections, because they do not account for any network Quality of Service (QoS) requirements. this makes it hard for applications to fulfill their Service Level Objectives (SLOs). In this paper we present Polaris Scheduler, an SLO-aware scheduler for the Edge that is developed as part of the Linux Foundation Centaurus project. Polaris Scheduler optimizes the placement of an application's microservices to improve the fulfillment of their SLOs. To this end, it supports modeling of the Edge topology as a Cluster Topology Graph to capture the network quality characteristics and allows users to specify the dependencies among their application's microservices, as well as the network QoS requirements (bandwidth, latency, jitter, and packet drop) for each of them in the form of a Service Graph. Polaris Scheduler relies on a plugin-based approach to allow support for multiple SLOs. We implement our scheduler with plugins for meeting network QoS SLOs as a Kubernetes scheduler and evaluate it using a realistic traffic analysis and hazard detection use case.
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