Governing Passions

Greengrass, Mark

doi:10.1093/acprof:oso/9780199214907.001.0001

Cited by 47 publications

(18 citation statements)

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“…The greedy placement algorithm has a complexity of O(|Q| log(|Q|) + |H|). As seen before, the computational complexity of resource allocation algorithm is O((|Q| + 1) 3 Affinity-based scheme. In the affinity-based scheme, firstly the VNFs are placed on the hosts such that the VNFs having high transition probability between them are placed on the same host.…”

Section: Generalization: Examined Solutions and Running Timesmentioning

confidence: 91%

“…The response times for other queuing disciplines, including those accounting for priority levels and/or preemption, cannot be expressed in closed form. It is also worth stressing that present-day implementations of multi-access edge computing (MEC) [3] are based on FIFO discipline, and do not support preemption.…”

Section: Problem Formulation and Complexitymentioning

confidence: 99%

“…With regard to the objective function, (8) is now linear with respect to µ(q), while (5) is convex, even if it does not look so prima facie. Indeed, its second derivative is 3 , which is positive for any µ(q) > Λ(q). That condition is required for system stability; therefore, we can conclude that constraint (5) is convex over the all region of interest.…”

Section: Cpu Allocationmentioning

confidence: 99%

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VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

Agarwal

Malandrino

Chiasserini

et al. 2019

IEEE/ACM Trans. Networking

112

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One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, and efficiently. To this end, we formulate a queuingbased model and use it at the network orchestrator to optimally match the vertical's requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out accounting for multiple scenarios representative of real-world services, shows that MaxZ performs substantially better than state-ofthe-art alternatives and consistently close to the optimum. INTRODUCTION5G networks are envisioned to provide the computational, memory, and storage resources needed to run multiple third parties (referred to as vertical industries or verticals) with diverse communication and computation needs. Verticals provide network operators with the specification of the services they want to provide, e.g., the virtual network functions (VNFs) they want to use to process their data and the associated quality of service.Mobile network operators are in charge of mapping the requirements of the verticals into infrastructure management decisions. This task is part of the network orchestration, and includes making decisions concerning (i) the placement of the VNFs needed by the verticals across the infrastructure; (ii) the assignment of CPU, memory and storage resources to the VNFs; (iii) the routing of data across network nodes.These decisions interact with each other in ways that are complex and often counterintuitive. In this paper, we focus on the allocation of computational and network resources, and make such decisions jointly, accounting for (i) the requirements of each VNF and vertical; (ii) the capabilities of the network operator's infrastructure; (iii) the capacity • S. Agarwal is with IIT Guwahati, India. F. Malandrino and C.-F. Chiasserini are with Politecnico di Torino, Italy and CNR-IEIIT, Italy. S. De is with IIT Delhi, India. • A preliminary version [1] of this work was presented at the IEEE INFOCOM 2018 conference.

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Section: Generalization: Examined Solutions and Running Timesmentioning

confidence: 91%

Section: Problem Formulation and Complexitymentioning

confidence: 99%

Section: Cpu Allocationmentioning

confidence: 99%

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VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

Agarwal

Malandrino

Chiasserini

et al. 2019

IEEE/ACM Trans. Networking

112

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show abstract

“…For example, fog logic execution points may include local nodes and a wide variety of remote ones, as shown in Fig. 1 (i.e., both Amazon Web Service (AWS) Greengrass [4] and Microsoft Azure IoT Edge [5] allow executing functions both locally and remotely). In these settings, local devices provide low response latency but may not always be reliable.…”

Section: Iot Nodementioning

confidence: 99%

“…Peter Levine, a partner at an A-list venture capital firm Andreessen Horowitz, has recently called fog computing the next multi-billion dollar tech market [3]. The promise of fog computing for enabling the next generation of advances in IoT is underscored by the growing developments of fog computing architectures [4], [5] and ongoing industry-wide standardization efforts [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Virtualized Control Over Fog: Interplay Between Reliability and Latency

İnaltekin

Gorlatova

Chiang

2018

IEEE Internet Things J.

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This paper introduces an analytical framework to investigate optimal design choices for the placement of virtual controllers along the cloud-to-things continuum. The main application scenarios include low-latency cyber-physical systems in which real-time control actions are required in response to the changes in states of an IoT node. In such cases, deploying controller software on a cloud server is often not tolerable due to delay from the network edge to the cloud. Hence, it is desirable to trade reliability with latency by moving controller logic closer to the network edge. Modeling the IoT node as a dynamical system that evolves linearly in time with quadratic penalty for state deviations, recursive expressions for the optimum control policy and the resulting minimum cost value are obtained by taking virtual fog controller reliability and response time latency into account. Our results indicate that latency is more critical than reliability in provisoning virtualized control services over fog endpoints, as it determines the swiftness of the fog control system as well as the timeliness of state measurements. Based on a realistic drone trajectory tracking model, an extensive simulation study is also performed to illustrate the influence of reliability and latency on the control of autonomous vehicles over fog.

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Internet‐of‐Things‐Infrastructure‐as‐a‐Service: The democratization of access to public Internet‐of‐Things Infrastructure

Ilyas

Ahmad

Saleem

2020

Int J Communication

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We propose a new cloud service model called Internet-of-Things (IoT)-Infrastructure-as-a-Service (IoT-IaaS). Under an IoT-IaaS, an IaaS cloud service provider can go beyond offering IoT data streams and offer access to IoT infrastructure to users, the same way data center infrastructure is shared in the IaaS model. We developed a reference design for the proposed IoT-IaaS cloud service model, built from open source components. We demonstrate the proposed reference design in the form of a virtualized-IoT (vIoT) testbed. The vIoT testbed uses the OpenStack cloud management system and Raspberry Pi embedded computers (standing in for IoT devices) configured as Novacompute nodes and LXD containers. We also developed a simple mechanism for shared access to high data rate sensors (a camera) and low data rate sensors (temperature-humidity) by multiple tenants. We tested the performance of container launch times, memory access (with and without ZRAM optimization kernel module enabled), CPU, and file I/O for applications running inside Linux containers and compared it with the same performance when running on the Raspberry Pi's host OS. We conclude that there is no significant performance penalty for executing the edge component of an IoT application inside a Linux container, rather than directly on the device's host OS.

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Governing Passions

Cited by 47 publications

References 0 publications

VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

Virtualized Control Over Fog: Interplay Between Reliability and Latency

Internet‐of‐Things‐Infrastructure‐as‐a‐Service: The democratization of access to public Internet‐of‐Things Infrastructure

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