Dynamic Cloud Network Control Under Reconfiguration Delay and Cost

Wang, Chang‐Heng; Llorca, Jaime; Tulino, Antonia M.; Javidi, Tara

doi:10.1109/tnet.2019.2892148

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…However, the state on which the agents are trained contains only the current resource and system information, therefore the agent can hardly understand the long-term effect of its actions. Authors of [13]- [15] developed cost models and used them for cost-aware resource provisioning in edge cloud environment [13] and for optimizing network functions in a telco environment [14], [15]. The model in [13] also considers the load prediction for the next time interval, however the applicability of the models for long-term traffic or resource predictions are not discussed.…”

Section: Cost Optimization In Resource Scalingmentioning

confidence: 99%

LSSO: Long Short-term Scaling Optimizer

Fodor

Toka

Sonkoly

2022

2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)

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Demand forecast-based resource allocation is a key element of both application and network service management and resource cost optimization in cloud environments. A central mechanism called scaling or auto-scaling is responsible for adjusting the allocated resources dynamically to current or predicted demands. Most of today's solutions support short-term optimization, where the long-term effect of scaling actions is not considered, therefore the overall operational cost can be sub-optimal. In this paper, we address this issue and provide a long and short-term scaling optimization method called LSSO. Our contribution is threefold. First, we introduce the formal description of the LSSO scaling problem motivated by real cloud native applications. Second, we transform the problem into a tractable graph representation and show that the optimal solution of the original problem emerges as a shortest path problem in the graph. As a result, the transformation and the optimal solution can be computed in polynomial time. Third, we evaluate and validate the proposed algorithm by numerical analysis on multiple datasets using a cost model which considers the running and scaling costs. Results suggest that if the dynamics of the traffic is fairly predictable and the scaling cost is not negligible, LSSO is able to reduce the operation costs significantly. The exact cost gain depends on the ratio of the running and scaling costs, but in realistic operation regimes it can reach even 18% and the method can also tolerate some inaccuracies in the forecast.

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Section: Cost Optimization In Resource Scalingmentioning

confidence: 99%

LSSO: Long Short-term Scaling Optimizer

Fodor

Toka

Sonkoly

2022

2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)

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“…First, reconfiguration cost when new files are retrieved from the original file server to the SBSs can be incorporated into our framework. A few works on Lyapunov optimization addressed the reconfiguration issue [42]. The practical approach is that the decision maker updates the corresponding file when the expected benefit (say, the objective function for newly cached file minus reconfiguration cost, e.g., bandwidth cost) is greater than the objective function for the previously cached file [14].…”

Section: Concluding Remarkmentioning

confidence: 99%

Elastic FemtoCaching: Scale, Cache, and Route

Kwak

Paschos²,

Iosifidis

2021

IEEE Trans. Wireless Commun.

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The advent of elastic Content Delivery Networks (CDNs) enable Content Providers (CPs) to lease cache capacity on demand and at different cloud and edge locations in order to enhance the quality of their services. This paper addresses key challenges in this context, namely how to invest an available budget in cache space in order to match spatio-temporal fluctuations of demand, wireless environment and storage prices. Specifically, we jointly consider dynamic cache rental, content placement, and request-cache association in wireless scenarios in order to provide just-in-time CDN services. The goal is to maximize the an aggregate utility metric for the CP that captures both service benefits due to caching and fairness in servicing different end users. We leverage the Lyapunov driftminus-benefit technique and Jensen's inequality to transform our infinite horizon problem into hour-by-hour subproblems which can be solved without knowledge of future file popularity and transmission rates. For the case of non-overlapping small cells, we provide an optimal subproblem solution. However, in the general overlapping case, the subproblem becomes a mixed integer non-linear program (MINLP). In this case, we employ a randomized cache lease method to derive a scalable solution. We show that the proposed algorithm guarantees the theoretical performance bound by exploiting the submodularity property of the objective function and pick-and-compare property of the randomized cache lease method. Finally, via real dataset driven simulations, we find that the proposed algorithm achieves 154% utility compared to similar static cache storage-based algorithms in a representative urban topology.

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Minimizing Resource Allocation for Cloud-Native Microservices

Erdei

Toka

2023

J Netw Syst Manage

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With the continuous progress of cloud computing, many microservices and complex multi-component applications arise for which resource planning is a great challenge. For example, when it comes to data-intensive cloud-native applications, the tenant might be eager to provision cloud resources in an economical manner while ensuring that the application performance meets the requirements in terms of data throughput. However, due to the complexity of the interplay between the building blocks, adequately setting resource limits of the components separately for various data rates is nearly impossible. In this paper, we propose a comprehensive approach that consists of measuring the resource footprint and data throughput performance of such a microservices-based application, analyzing the measurement results by data mining techniques, and finally formulating an optimization problem that aims to minimize the allocated resources given the performance constraints. We illustrate the benefits of the proposed approach on Cortex, an extension to Prometheus for storing monitored metrics data. The data-intensive nature of this illustrative example stems from real-time monitoring of metrics exposed by a multitude of applications running in a data center and the continuous analysis performed on the collected data that can be fetched from Cortex. We present Cortex’s performance vs resource footprint trade-off, and then we build regression models to predict the microservices’ resource consumption and draw a mathematical programming formulation to optimize the most important configuration parameters. Our most important finding is the linear relationship between resource consumption and application performance, which allows for applying linear regression and linear programming models. After the optimization, we compare our results to Cortex’s recommendation, leading to a CPU reservation reduced by 50–80%.

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Dynamic Cloud Network Control Under Reconfiguration Delay and Cost

Cited by 10 publications

References 21 publications

LSSO: Long Short-term Scaling Optimizer

LSSO: Long Short-term Scaling Optimizer

Elastic FemtoCaching: Scale, Cache, and Route

Minimizing Resource Allocation for Cloud-Native Microservices

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