A Survey on the Placement of Virtual Resources and Virtual Network Functions

IEEE Communications Surveys & Tutorials, Journal Year: 2018, Volume and Issue: 21(2), P. 1409 - 1434

Published: Dec. 3, 2018

Cloud computing and network slicing are essential concepts of forthcoming 5G mobile systems. Network slices essentially chunks virtual connectivity resources, configured provisioned for particular services according to their characteristics requirements. The success cloud hinges on the efficient allocation resources (e.g., VCPU VMDISK) optimal placement virtualized functions (VNFs) composing slices. In this context, paper elaborates issues that may disrupt VNFs machines (VMs). This classifies existing solutions VM based nature, whether is dynamic or static, objectives, metrics. then proposes a classification VNF approaches, first, regarding general management VNFs, second, target type.

Language: Английский

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A Survey on Controller Placement in SDN

IEEE Communications Surveys & Tutorials, Journal Year: 2019, Volume and Issue: 22(1), P. 472 - 503

Published: Aug. 15, 2019

In recent years, Software Defined Networking (SDN) has emerged as a pivotal element not only in data-centers and wide-area networks, but also next generation networking architectures such Vehicular ad hoc network 5G. SDN is characterized by decoupled data control planes, logically centralized plane. The plane offers several opportunities well challenges. A key design choice of the placement controller(s), which impacts wide range issues ranging from latency to resiliency, energy efficiency load balancing, so on. this paper, we present comprehensive survey on controller problem (CPP) SDN. We introduce CPP highlight its significance. classical formulation along with supporting system model. discuss modeling choices associated metrics. classify literature based objectives methodologies. Apart primary use-cases data-center networks area examine application new domains mobile/cellular 5G, named wireless mesh VANETs. conclude our discussion open future scope topic.

Language: Английский

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Machine learning (ML)-centric resource management in cloud computing: A review and future directions

Journal of Network and Computer Applications, Journal Year: 2022, Volume and Issue: 204, P. 103405 - 103405

Published: May 6, 2022

Language: Английский

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Approaches for optimizing virtual machine placement and migration in cloud environments: A survey

Journal of Parallel and Distributed Computing, Journal Year: 2017, Volume and Issue: 111, P. 222 - 250

Published: Sept. 20, 2017

Language: Английский

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A placement architecture for a container as a service (CaaS) in a cloud environment

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2019, Volume and Issue: 8(1)

Published: May 29, 2019

Unlike a traditional virtual machine (VM), container is an emerging lightweight virtualization technology that operates at the operating system level to encapsulate task and its library dependencies for execution. The Container as Service (CaaS) strategy gaining in popularity likely become prominent type of cloud service model. Placing instances on classical scheduling problem. Previous research has focused separately either placement physical machines (PMs) or container, only tasks without containerization, machines. However, this approach leads underutilized overutilized PMs well VMs. Thus, there growing interest developing algorithm considers utilization both instantiated VMs used simultaneously. goal study improve resource utilization, terms number CPU cores memory size PMs, minimize active environment. proposed architecture employs heuristics, namely, Best Fit (BF) Max (MF), based fitness function simultaneously evaluates remaining waste In addition, another meta-heuristic uses Ant Colony Optimization (ACO-BF) with function. Experimental results show ACO-BF outperforms BF MF heuristics maintains significant improvement PMs.

Language: Английский

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VNF and CNF Placement in 5G: Recent Advances and Future Trends

IEEE Transactions on Network and Service Management, Journal Year: 2023, Volume and Issue: 20(4), P. 4698 - 4733

Published: March 31, 2023

With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler most of operators. NFV decouples functions from hardware devices. This decoupling allows services, called Virtualized Network Functions (VNFs), to be hosted on commodity which simplifies enhances service deployment management providers, improves flexibility, leads efficient scalable resource usage, lower costs. The proper placement VNFs in hosting infrastructures is one main technical challenges. significantly influences network's performance, reliability, operating VNF NP-Hard. Therefore, there need methods that can cope with complexity problem find appropriate solutions reasonable duration. primary purpose this study provide taxonomy optimization techniques used tackle problems. We classify studied papers based performance metrics, methods, algorithms, environment. Virtualization not limited simply replacing physical machines virtual or VNFs, but may also include micro-services, containers, cloud-native systems. In context, second part our article focuses Containers (CNFs) edge/fog computing. Many issues have been considered traffic congestion, utilization, energy consumption, degradation, etc. For each matter, various are proposed through different surveys research addresses specific manner by suggesting single objective multi-objective types algorithms such heuristic, meta-heuristic, machine learning algorithms.

Language: Английский

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Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

International Journal of Communication Systems, Journal Year: 2019, Volume and Issue: 32(14)

Published: July 1, 2019

Summary Cloud computing introduced a new paradigm in IT industry by providing on‐demand, elastic, ubiquitous resources for users. In virtualized cloud data center, there are large number of physical machines (PMs) hosting different types virtual (VMs). Unfortunately, the centers do not fully utilize their and cause considerable amount energy waste that has great operational cost dramatic impact on environment. Server consolidation is one techniques provide efficient use reducing active servers. Since VM placement plays an important role server consolidation, main challenges mapping VMs to PMs. Multiobjective generating interest among researchers academia. This paper aims represent detailed review recent state‐of‐the‐art multiobjective mechanisms using nature‐inspired metaheuristic algorithms environments. Also, it gives special attention parameters approaches used placing into end, we will discuss explore further works can be done this area research.

Language: Английский

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Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters

Soft Computing, Journal Year: 2020, Volume and Issue: 25(19), P. 12569 - 12588

Published: Dec. 12, 2020

Abstract The ubiquitous diffusion of cloud computing requires suitable management policies to face the workload while guaranteeing quality constraints and mitigating costs. typical trade-off is between used power adherence a service-level metric subscribed by customers. To this aim, possible idea use an optimization-based placement mechanism select servers where deploy virtual machines. Unfortunately, high packing factors could lead performance security issues, e.g., machines can compete for hardware resources or collude leak data. Therefore, we introduce multi-objective approach compute optimal strategies considering different goals, such as impact outages, required datacenter, perceived users. Placement are found using deep reinforcement learning framework best heuristic each machine composing workload. Results indicate that our method outperforms bin heuristics widely in literature when either synthetic real workloads.

Language: Английский

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A survey on the placement of virtual network functions

Journal of Network and Computer Applications, Journal Year: 2022, Volume and Issue: 202, P. 103361 - 103361

Published: March 19, 2022

Language: Английский

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A decentralized adaptation of model-free Q-learning for thermal-aware energy-efficient virtual machine placement in cloud data centers

Computer Networks, Journal Year: 2023, Volume and Issue: 224, P. 109624 - 109624

Published: Feb. 15, 2023

Language: Английский

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