NACER: A Network-Aware Cost-Efficient Resource Allocation Method for Processing-Intensive Tasks in Distributed Clouds

Ahvar, Ehsan; Ahvar, Shohreh; Crespi, Noël; García-Alfaro, Joaquín; Mann, Zoltán Ádám

doi:10.1109/nca.2015.37

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…The constraint is formulated in such a way that for x j,v = 1, Λ j,v = r j (Λ j,v ), whereas for x j,v = 0 (in which case also Λ j,v = 0 because of Constraint (3)), also Λ j,v = 0 so that there is no contradiction with Constraint (4). Constraint (8) computes the data rate on the inputs of a component instance as the sum of the data rates on the links ending in that input. Similarly, Constraint (9) ensures that the data rate on the outputs of a component instance is distributed on the links starting in that output.…”

Section: A Constraintsmentioning

confidence: 99%

“…Next, the mapping of the sources and source components is checked and updated if necessary (lines [6][7][8][9][10][11]: if a new source emerged, an instance of the corresponding source component is created; if the data rate of a source changed, then the output data rate of the corresponding source component instance is updated; if a source disappeared, then the corresponding source component instance is removed.…”

Section: Heuristic Approachmentioning

confidence: 99%

“…However, for a bigger substrate network with 20 nodes and 44 arcs, the solver reaches the time limit for much smaller source data rate and also the optimality gap is much bigger. For even bigger substrate networks, the performance 8 In Fig. 8, in the high-load area between event 20 and 50, some problem instances are too complex to be solved within the 60 seconds time limit we have set for the optimizer.…”

Section: Scalabilitymentioning

confidence: 99%

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JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services

Dräxler

Karl

Mann

2018

IEEE Trans. Netw. Serv. Manage.

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To adapt to continuously changing workloads in networks, components of the running network services may need to be replicated (scaling the network service) and allocated to physical resources (placement) dynamically, also necessitating dynamic re-routing of flows between service components. In this paper, we propose JASPER, a fully automated approach to jointly optimizing scaling, placement, and routing for complex network services, consisting of multiple (virtualized) components. JASPER handles multiple network services that share the same substrate network; services can be dynamically added or removed and dynamic workload changes are handled. Our approach lets service designers specify their services on a high level of abstraction using service templates. From the service templates and a description of the substrate network, JASPER automatically makes scaling, placement and routing decisions, enabling quick reaction to changes. We formalize the problem, analyze its complexity, and develop two algorithms to solve it. Extensive empirical results show the applicability and effectiveness of the proposed approach. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.(iii) the network flows between the service components can be re-routed through different, more suitable paths.Given this large number of degrees of freedom for finding the best adaptation, deciding scaling, placement, and routing independently can result in sub-optimal decisions for the network and the running services. Consider a service platform provider hosting a dynamically changing set of network services, where each network service serves dynamically changing user groups that produce dynamically changing data rates. Trade-offs among the conflicting goals of network services and platform operators can be highly non-trivial, for example:• Placing a compute-intensive service component on a node with limited resources near the source of requests (e.g., the location of users, content servers, etc.) minimizes latency but placing it on a more powerful node further away in the network minimizes processing time. • Letting a single instance of a data-processing component serve multiple sources minimizes compute resource consumption but using dedicated instances near the sources minimizes network load. • Changing the current configuration to a better one will hopefully pay off in the long run but keeping the current configuration avoids reconfiguration costs. • Fulfilling the resource requirements of one service versus the requirements of another service. To deal with these challenges, we propose JASPER, a comprehensive approach for the Joint optimizAtion of Scaling, PlacEment, and Routing of virtual network services. In JASPER, each network service is described by a service template, containing information about the components of the network service, the interconnections between the components, and the resource requirements of the components. Both...

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Section: A Constraintsmentioning

confidence: 99%

Section: Heuristic Approachmentioning

confidence: 99%

Section: Scalabilitymentioning

confidence: 99%

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JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services

Dräxler

Karl

Mann

2018

IEEE Trans. Netw. Serv. Manage.

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“…Chen et al (Chen et al, 2013) modeled the VM placement method in terms of electricity cost and WAN communication cost incurred between the communicated VMs. Ahvar et al (Ahvar et al, 2015) addressed the problem of DCs selection for inter-communicated VMs to minimize the inter-DCs communication cost. Malekimajd et al (Malekimajd et al, 2015) proposed an algorithm to minimize the communication latency in geodistributed clouds.…”

Section: Related Workmentioning

confidence: 99%

Power and Cost-aware Virtual Machine Placement in Geo-distributed Data Centers

Rawas

Zekri

El-Zaart

2018

Proceedings of the 8th International Conference on Cloud Computing and Services Science

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The proliferation of cloud computing due to its attracting on-demand services leads to the establishment of geo-distributed data centers (DCs) with thousands of computing and storage nodes. Consequently, many challenges exist for cloud providers to run such an environment. One important challenge is to minimize cloud users' network latency while accessing services from the DCs. The other is to decrease the DCs' energy consumption that contributes to high operational cost rates, low profits for cloud providers, and high carbon non-environment friendly emissions. In this paper, we studied the problem of virtual machine placement that results in less energy consumption, less CO2 emission, and less access latency towards largescale cloud providers operational cost minimization. The problem was formulated as multi-objective function and an intelligent machine-learning model constructed to improve the performance of the proposed model. To evaluate the proposed model, extensive simulation is conducted using the CloudSim simulator. The simulation results reveal the effectiveness of PCVM model compared to other competing virtual machine placement methods in terms of network latency, energy consumption, CO2 emission and operational cost minimization.

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“…In [131], we addressed the problem of selecting data centers for a large-scale task, requiring thousands of virtual machines, in a distributed cloud consisting of dozens of DCs, with the objective of minimizing inter-DC communication. We proved that the problem is strongly NP-hard and devised a heuristic for it based on the A* algorithm.…”

Section: Algorithmic Approachesmentioning

confidence: 99%

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

Vajk¹,

Harsányi

Poppe

et al. 2016

Period. Polytech. Elec. Eng. Comp. Sci.

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PrefaceThroughout László Vajta, Dean of BME VIK János Levendovszky, Deputy Dean of BME VIK Department of Automation and Applied InformaticsDepartment of Automation and Applied Informatics (AUT) is one of the largest and dynamically developing departments at BME with diverse scope competences such as control theory, embedded systems, software modelling, applied software development, Internet of Things, power electronics, mechatronics, and many others. The department's main activities are education, research and development. Training versatile electrical and software engineers with solid practical knowledge is our top priority. Our profile also includes developing high quality software and hardware solutions for industry partners. All of our activities are backed by strong research background.In 2015, the department has organized the 21 st European Wireless (EW) Conference. The EW 2015 conference hosted more than 140 participants from 30 different countries. The 2015 edition of EW was aimed at addressing a key theme on "5G and beyond."The next sections summarize the key research results of the department in year 2015. IoT and Smart CityThe Internet of Things (IoT) is transforming the surrounding physical objects into an ecosystem of information that enriches our everyday life. The IoT represents the convergence of advances in miniaturization, wireless connectivity, and increased data storage, and is driven by various sensors. Application and service development methods and frameworks are required to support the realization of solutions covering data collection, transmission, data processing, analysis, reporting, and advanced querying. Our SensorHUB framework URBMOBI is a mobile environmental sensor that (i) provides temporally and spatially distributed environmental data, (ii) fulfills the need for monitoring at various places without the costs for a large number of fixed measurement stations, (iii) integrates small and precise sensors in a system that can be operated on buses, trams, or other vehicles, (iv) concentrates on urban heat and thermal comfort, and (v) aims at providing climate services and integration with real-time climate models.utilizes the state-of-the-art open source technologies and provides a unified tool chain for IoT related application and ser-URBMOBI project has been worked out between 2013 and 2015 by the following consortium: RWTH Aachen University (Germany), Netherlands Organization for Applied Scientific Research TNO (Netherlands), ARIA Technologies (France), Budapest University of Technology and Economics (Hungary), and Meteorological and Environmental Earth Observation (Italy).The SOLSUN (Sustainable Outdoor Lighting & Sensory Urban Networks) project is about to demonstrate how intelligent city infrastructure can be created in a cost-effective and sustainable way by reusing existing street lighting as the communications backbone. We apply different technologies and methods to reduce energy consumption at the same time as turning streetlights into nodes on a scalable network that is al...

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NACER: A Network-Aware Cost-Efficient Resource Allocation Method for Processing-Intensive Tasks in Distributed Clouds

Cited by 13 publications

References 13 publications

JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services

JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services

Power and Cost-aware Virtual Machine Placement in Geo-distributed Data Centers

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

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