Decentralized Control of Distributed Cloud Networks with Generalized Network Flows

Cai, Yong; Llorca, Jaime; Tulino, Antonia M.; Molisch, Andreas F.

doi:10.48550/arxiv.2204.09030

Cited by 4 publications

(10 citation statements)

References 17 publications

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“…We employ the simple approach (see Section VI-D2) as the baseline for comparison, i.e., treating data-streams for different destination nodes as separated unicast flows. A more comprehensive comparison of the proposed approach with existing multicast techniques will be reported in [2].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Suppose a packet of duplication status q is selected for operation (processing or transmission) on a certain interface, we need to decide whether it will be duplicated or not. 2 If a packet is duplicated, only one copy is operated on the interface, while the other copy is reloaded to the queueing system at the end of the time slot (i.e., it is not involved in any other decisions in the current time slot).…”

Section: Queueing Systemmentioning

confidence: 99%

“…Proof: The result is derived by applying the fact to the queueing system in Section IV-A [18]. Details can be found in [2].…”

Section: Capacity Regionmentioning

confidence: 99%

“…However, the algorithm also becomes more computationally demanding, making it not suitable to apply to large scale networks. Developing an efficient, approximate algorithm is the topic of our ongoing research work, and a polynomial-time heuristic algorithm will be reported in [2]. 3 This is determined by the combinatorial nature of the multicast problem.…”

Section: Complexity Issuementioning

confidence: 99%

“…In recent years, multicast data-streams (contents with multiple destinations) have become an increasingly dominant component of network traffic, 1 especially in the coming Internet of things (IoT) era. For example, multi-user conferencing An extended version of this paper is submitted to the IEEE Transactions on Communications [2]. 1 To clarify, the term multicast in this paper refers to content delivery to multiple destinations, not related to the wireless communication technique of transmitting data to multiple nodes simultaneously, as considered in [7].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Multicast Service Chain Control: Packet Processing, Routing, and Duplication

Cai

Llorca

Tulino

et al. 2021

ICC 2021 - IEEE International Conference on Communications

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Distributed computing (cloud) networks, e.g., mobile edge computing (MEC), are playing an increasingly important role in the efficient hosting, running, and delivery of real-time stream-processing applications such as industrial automation, immersive video, and augmented reality. While such applications require timely processing of real-time streams that are simultaneously useful for multiple users/devices, existing technologies lack efficient mechanisms to handle their increasingly multicast nature, leading to unnecessary traffic redundancy and associated network congestion. In this paper, we address the design of distributed packet processing, routing, and duplication policies for optimal control of multicast stream-processing services. We present a characterization of the enlarged capacity region that results from efficient packet duplication, and design the first fully distributed multicast traffic management policy that stabilizes any input rate in the interior of the capacity region while minimizing overall operational cost. Numerical results demonstrate the effectiveness of the proposed policy to achieve throughput-and costoptimal delivery of stream-processing services over distributed computing networks.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Queueing Systemmentioning

confidence: 99%

“…Proof: The result is derived by applying the fact to the queueing system in Section IV-A [18]. Details can be found in [2].…”

Section: Capacity Regionmentioning

confidence: 99%

Section: Complexity Issuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Multicast Service Chain Control: Packet Processing, Routing, and Duplication

Cai

Llorca

Tulino

et al. 2021

ICC 2021 - IEEE International Conference on Communications

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Joint Compute-Caching-Communication Control for Online Data-Intensive Service Delivery

Cai¹,

Llorca²,

Tulino³

et al. 2022

Preprint

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Emerging Metaverse applications, designed to deliver highly interactive and immersive experiences that seamlessly blend physical reality and digital virtuality, are accelerating the need for distributed compute platforms with unprecedented storage, computation, and communication requirements. To this end, the integrated evolution of next-generation networks (e.g., 5G and beyond) and distributed cloud technologies (e.g., fog and mobile edge computing), have emerged as a promising paradigm to address the interaction-and resource-intensive nature of Metaverse applications. In this paper, we focus on the design of control policies for the joint orchestration of compute, caching, and communication (3C) resources in next-generation distributed cloud networks for the efficient delivery of Metaverse applications that require the real-time aggregation, processing, and distribution of multiple live media streams and pre-stored digital assets. We describe Metaverse applications via directed acyclic graphs able to model the combination of real-time stream-processing and content distribution pipelines. We design the first throughput-optimal control policy that coordinates joint decisions around (i) routing paths and processing locations for live data streams, together with (ii) cache selection and distribution paths for associated data objects. We then extend the proposed solution to include a max-throughput database placement policy and two efficient replacement policies. In addition, we characterize the network stability regions for all studied scenarios. Numerical results demonstrate the superior performance obtained via the novel multi-pipeline flow control and 3C resource orchestration mechanisms of the proposed policy, compared with state-of-the-art algorithms that lack full 3C integrated control.

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Ultra-Reliable Distributed Cloud Network Control with End-to-End Latency Constraints

Cai¹,

Llorca²,

Tulino³

et al. 2022

Preprint

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We are entering a rapidly unfolding future driven by the delivery of real-time computation services, such as industrial automation and augmented reality, collectively referred to as augmented information (AgI) services, over highly distributed cloud/edge computing networks. The interaction intensive nature of AgI services is accelerating the need for networking solutions that provide strict latency guarantees. In contrast to most existing studies that can only characterize average delay performance, we focus on the critical goal of delivering AgI services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network operational cost. To this end, we design a novel queuing system able to track data packets' lifetime and formalize the delay-constrained least-cost dynamic network control problem. To address this challenging problem, we first study the setting with average capacity (or resource budget) constraints, for which we characterize the delay-constrained stability region and design a near-optimal control policy leveraging Lyapunov optimization theory on an equivalent virtual network. Guided by the same principle, we tackle the peak capacity constrained scenario by developing the reliable cloud network control (RCNC) algorithm, which employs a two-way optimization method to make actual and virtual network flow solutions converge in an iterative manner. Extensive numerical results show the superior performance of the proposed control policy compared with the state-of-the-art cloud network control algorithm, and the value of guaranteeing strict end-to-end deadlines for the delivery of next-generation AgI services.

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Decentralized Control of Distributed Cloud Networks with Generalized Network Flows

Cited by 4 publications

References 17 publications

Optimal Multicast Service Chain Control: Packet Processing, Routing, and Duplication

Optimal Multicast Service Chain Control: Packet Processing, Routing, and Duplication

Joint Compute-Caching-Communication Control for Online Data-Intensive Service Delivery

Ultra-Reliable Distributed Cloud Network Control with End-to-End Latency Constraints

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