Cost-Efficient Server Configuration and Placement for Mobile Edge Computing

He, Zhenli; Li, Kenli; Li, Keqin

doi:10.1109/tpds.2021.3135955

Cited by 27 publications

(8 citation statements)

References 31 publications

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“…To facilitate the evaluation, we introduce three comparison algorithms for performance comparison: Traditional Differential Evolution (DE) [ 35 ]: as the discrete problem solved in this paper cannot be directly solved using DE, we convert it into a continuous problem by employing a remainder-based transformation and an adaptive parameter tuning strategy. Genetic Algorithm (GA) [ 36 ]: this algorithm adopts a binary problem encoding approach, with the number of dimensions corresponding to the available configuration types. Particle Swarm Optimization (PSO) [ 37 ]: Similar to DE, PSO faces challenges in solving discrete problems; to address this, we apply PSO to the problem by taking the residuals.…”

Section: Experimenal Evaluationmentioning

confidence: 99%

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FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

Li,

Zhang,

Lin

et al. 2023

Sensors

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The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers’ concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments.

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Section: Experimenal Evaluationmentioning

confidence: 99%

“…Genetic Algorithm (GA) [ 36 ]: this algorithm adopts a binary problem encoding approach, with the number of dimensions corresponding to the available configuration types.…”

Section: Experimenal Evaluationmentioning

confidence: 99%

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

Li,

Zhang,

Lin

et al. 2023

Sensors

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“…MEC deployment revolves around looking for the best possible locations where to place edge servers to satisfy the QoS/low latency requirements of end users. Most of the works in the literature consider service QoS, load balance, response delay [68], cost efficiency [21], installation cost [4], and maximization of overall profit [70]. From the literature reviewed, we learned that the capacities of MEC servers should match the workload demand in order to maximize the overall energy efficiency of the network by reducing the time and energy spent by servers in an idle state.…”

Section: Remarks On Mec Deploymentmentioning

confidence: 99%

Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges

Shalavi¹,

Perin²,

Zanella³

et al. 2022

Preprint

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Mobile networks are becoming energy hungry, and this trend is expected to continue due to a surge in communication and computation demand. Multi-access Edge Computing (MEC), a key component of 5𝐺 and 6𝐺 networks, will entail energy-consuming services and applications, with non-negligible impact in terms of ecological sustainability. In this paper, we provide a comprehensive review of existing approaches to make edge computing networks greener, including but not limited to the exploitation of renewable energy resources, and context-awareness (e.g., user mobility, or dynamics in the traffic), analyzing their pros and cons. We hence provide an updated account of recent developments on MEC from an energetic sustainability perspective, addressing the initial deployment of computing resources, their dynamic (re)allocation (resource scheduling), as well as distributed and federated learning designs. In doing so, we highlight the energy aspects of these algorithms, advocating the need for energy-sustainable edge computing systems that are aligned with Sustainable Development Goals (SDGs) and the Paris agreement. To the best of our knowledge, this is the first work providing a systematic literature review on the efficient deployment and management of energy harvesting MEC, with special focus on the deployment, provisioning, and scheduling of computing tasks, including federated learning for distributed edge intelligence, toward making edge networks greener and more sustainable. At the end of the paper, open research avenues and challenges are identified for all the surveyed topics. CCS Concepts: • Networks → Network performance evaluation; Wireless access points, base stations and infrastructure; • Computer systems organization → Embedded systems; • Computing methodologies → Planning and scheduling.

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“…He et al minimized operational expenditures while maintaining system performance at a predetermined level by optimal server configuration and suboptimal server placement in mobile edge computing, where edge servers were treated as M/G/m queueing systems. 18 Huang et al solved the problem of optimal distribution of general tasks among heterogeneous servers and optimal speed setting for the servers (treated as M/G/1 queueing systems), where each server has its own preloaded dedicated tasks and the servers have different queueing disciplines in scheduling dedicated tasks and general tasks, such that the average power consumption is minimized and that the average response time of general tasks does not exceed a given bound (i.e., performance constrained power minimization). 19 Huang et al also minimized the average response time of generic tasks on heterogeneous embedded processors with dedicated tasks by optimal power allocation and load balancing, where the M/M/1 queueing model with prioritization and preemption was employed.…”

Section: Multiple Multiserver Systemsmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Workload management and server speed setting for cost‐performance ratio optimization

2022

Softw Pract Exp

Self Cite

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The cost‐performance tradeoff is a fundamental issue in a data center for cloud computing, which is closely related to two key metrics that both cloud consumers and service providers care the most, that is, quality of service and cost of service. While there are different definitions of quality of service, the average response time is a common choice of performance metric. While there are various considerations in cost of service, the average power consumption is a common choice of cost metric. Hence, the cost‐performance tradeoff becomes the power‐performance tradeoff. In this article, we deal with the power‐performance tradeoff at the data center level. We study cost‐performance ratio optimization by using the techniques of workload management and server speed setting. In particular, we make the following tangible contributions. We solve three optimization problems, that is, (1) the workload management problem—to find a workload distribution, such that the cost‐performance ratio is minimized; (2) the server speed setting problem—to find a server speed setting, such that the cost‐performance ratio is minimized; (3) the workload management and server speed setting problem—to find a workload distribution and a server speed setting, such that the cost‐performance ratio is minimized. All the three optimization problems are analytically defined as multivariable optimization problems based on M/M/m queueing systems for multiple heterogeneous multiserver systems, together with two power consumption models, that is, the idle‐speed model and the constant‐speed model. Our approach makes it possible to quantitatively evaluate and optimize the cost‐performance ratio of a data center within a rigorously developed framework. Each multivariable optimization problem is transformed to a nonlinear system of equations. Due to the sophistication of these equations, they are solved algorithmically by a numerical procedure. Furthermore, we provide approximate, accurate, and analytical solutions to the first two problems. Performance data are demonstrated for each problem, and the accuracy of our approximate solutions are also discussed. To the best of the author's knowledge, this is the first paper which analytically and algorithmically minimizes the cost‐performance ratio of a data center with multiple heterogeneous multiserver systems using the techniques of workload management and server speed setting.

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Cost-Efficient Server Configuration and Placement for Mobile Edge Computing

Cited by 27 publications

References 31 publications

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges

Workload management and server speed setting for cost‐performance ratio optimization

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