Migration Plan With Minimum Overall Migration Time or Cost

Jaumard, Brigitte; Pouya, Hamed

doi:10.1364/jocn.10.000001

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…Decision Value Algorithm [22] Response time, QoS SP GA [23] Resource wastage SP GA [24] Cost Load dispatching Greedy, GA [25] Cost Task distribution Mixed ILP [26] Network latency SP ILP [27] Power consumption SP Weight [28] Delay SP Markov [29] Resource usage Resource allocation Consensus [30] Energy SP First-fit [31] Delay SP Own algorithms Proposed scheme…”

Section: Metricmentioning

confidence: 99%

“…The following metrics were also considered for different service types. The operation cost of using the VMs when services are executed in CDC Minimize VC [12,25,34] PM cost (PC) The cost of using the PM in the specific period Minimize PC [36][37][38] Distance between VMs and service users (DS)…”

Section: Costmentioning

confidence: 99%

“…We wanted to test the generality and adaptability of new host CPU data to realize a multi-objective SP. In this work, we used real-world workload data that is provided from, PlanetLab [25]. These data were collected from the VMs of different servers.…”

Section: Evaluation Environmentmentioning

confidence: 99%

“…At this step, metric values are mapped to the appropriate linguistic values by the corresponding universal set. For example, if X is the universal set, then its elements are denoted by x, such that the fuzzy set F in X is a set where µF(x) is an MF of x in F. This step maps each element of X onto a value between 0 and 1 [25]. In this paper, we used MF to represent the fuzzy number.…”

mentioning

confidence: 99%

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Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

Son

Huh

2019

Applied Sciences

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With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

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Section: Metricmentioning

confidence: 99%

Section: Costmentioning

confidence: 99%

Section: Evaluation Environmentmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

Son

Huh

2019

Applied Sciences

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“…Moreover, a short description of different migration scenarios toward next generation networks (NGNs) was given in [9]. In [10], an optimization framework is presented, which builds a migration plan with minimum overall migration time or cost. To illustrate, the number of maintenance windows and technicians required to perform the network migration is estimated.…”

Section: Introductionmentioning

confidence: 99%

Efficient Migration to the Next Generation of Networks Based on Digital Annealing

Javad-Kalbasi

Valaee

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Networks are frequently changing due to new technologies. The growing demand for bandwidth is forcing many carriers to migrate their existing network to a network with a new technology in order to increase network performance. Telecommunication companies are looking for optimization algorithms to efficiently manage their network migration. In this paper, the network migration problem is considered as a set of circuit migration problems in which two technicians simultaneously migrate the two ends of a circuit in order to minimize the total accumulated sites in-service and total technician travels. While total accumulated sites in-service indicates how fast the sites can be upgraded during the migration process, total technician travels estimates the required cost. We first formulate our target problem as a constrained binary quadratic program which is NP-hard in general. Our approach for solving the derived optimization problem is based on converting it to a quadratic unconstrained binary optimization problem (QUBO) using the penalty method. Subsequently, we exploit Digital Annealer which is a massively parallel hardware architecture to minimize the derived QUBO. To evaluate our proposed method, we study extensive network migration instances on the 75-node CONUS network topology.

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