Virtual machine migration in cloud infrastructures: Problem formalization and policies proposal

Papadopoulos, Alessandro Vittorio; Maggio, Martina

doi:10.1109/cdc.2015.7403274

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…Their utilization is quantized to certain values depending on predefined service plans (Mills et al 2011). This allows to partition VMs in a number S of classes that differ in the amount of resources available to users (Sugerman et al 2001;Papadopoulos and Maggio 2015;Gaggero and Caviglione 2016). According to the IaaS paradigm, users request a desired number of VMs with a given lifetime.…”

Section: Definition Of the Vm Placement Problemmentioning

confidence: 99%

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

et al. 2020

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

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Section: Definition Of the Vm Placement Problemmentioning

confidence: 99%

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

et al. 2020

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“…Whereas, the hardware requirement-based migration is a method used to perform virtual machine migration based on hardware requirements [12][13][14][15][16][17]. The destination host machine is determined according to the policy definition conditions of the hardware element.…”

Section: Migration Of Virtual Machinementioning

confidence: 99%

Experiments and Evaluation of a Container Migration Data-Auditing System on Edge Computing Environment

et al. 2023

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With the proliferation of IoT sensors and devices, storing collected data in the cloud has become common. A wide variety of data with different purposes and forms are not directly stored in the cloud but are sent to the cloud via edge servers. At the edge server, applications are running in containers and virtual machines to collect data. However, the current deployment and movement mechanisms for containers and virtual machines do not consider any conventions or regulations for the applications and the data it contains. Therefore, it is easy to deploy and migrate containers and virtual machines. However, the problem arises when it is deployed or migrated, which may violate the licensing terms of the contained applications, the rules of the organization, or the laws and regulations of the concerned country. We have already proposed a data-audit control mechanism for the migration of virtual machines. The proposed mechanism successfully controls the unintentional and malicious migration of virtual machines. We expect similar problems with containers to occur as the number of edge servers increases. Therefore, we propose a policy-based data-audit control system for container migration. The proposed system was verified in the implemented edge computing environment and the results showed that adding the proposed data-audit control mechanism had a minimal impact on migration time and that the system was practical enough. In the future, we intend to conduct verification not in a very compact and short-range environment such as this one but on an existing wide-area network.

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“…The broad challenges of Virtual Machine (VM) placement across DCs are taxamonised in [20] and formalised in [25]. The literature contains work on the placement of applications and their constituent VMs in DCs, to minimise cost [5], energy consumption [4], data-locality [24], network usage [6].…”

Section: Introductionmentioning

confidence: 99%

Distributed Approach to the Holistic Resource Management of a Mobile Cloud Network

Tärneberg

Papadopoulos

Mehta

et al. 2017

2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC)

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The Mobile Cloud Network is an emerging cost and capacity heterogeneous distributed cloud topological paradigm that aims to remedy the application performance constraints imposed by centralised cloud infrastructures. A centralised cloud infrastructure and the adjoining Telecom network will struggle to accommodate the exploding amount of traffic generated by forthcoming highly interactive applications. Cost effectively managing a Mobile Cloud Network computing infrastructure while meeting individual application's performance goals is nontrivial and is at the core of our contribution. Due to the scale of a Mobile Cloud Network, a centralised approach is infeasible. Therefore, in this paper a distributed algorithm that addresses these challenges is presented. The presented approach works towards meeting individual application's performance objectives, constricting system-wide operational cost, and mitigating resource usage skewness. The presented distributed algorithm does so by iteratively and independently acting on the objectives of each component with a common heuristic objective function. Systematic evaluations reveal that the presented algorithm quickly converges and performs near optimal in terms of system-wide operational cost and application performance, and significantly outperforms similar naïve and random methods.

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Virtual machine migration in cloud infrastructures: Problem formalization and policies proposal

Cited by 11 publications

References 24 publications

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

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

Experiments and Evaluation of a Container Migration Data-Auditing System on Edge Computing Environment

Distributed Approach to the Holistic Resource Management of a Mobile Cloud Network

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