Exact Analysis of Energy-Aware Multiserver Queueing Systems with Setup Times

Maccio, Vincent J.; Down, Douglas G.

doi:10.1109/mascots.2016.47

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…We denote the setup delay in small cell k by D k , which is assumed to obey an exponential distribution with E[ D k ]=1/ δ k . Exponentiality is assumed for the sake of tractability, similarly as in, eg, the previous studies . To keep track of the operational state of each small cell k , we denote by

B_{k}^{normals} false(t false) = false{0, 1 false}

, if the small cell k is nonbusy or busy, respectively.…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Delay‐ and energy‐aware load balancing in ultra‐dense heterogeneous 5G networks

Taboada

Aalto

Lassila

et al. 2017

Trans. Emerging Tel. Tech.

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In the context of ultra‐dense heterogeneous networks in 5G, we focus on the load balancing problem of elastic traffic in a single macrocell that is supported by a number of small cells. Each small cell is modeled by a single‐class processor sharing queue, whereas the macrocell is modelled by a multiclass processor sharing queue. We assume that the macrocell is always consuming energy, while a small cell can be switched off when it is idle with the setup delay penalty. As the main contribution, aimed at minimizing the weighted sum of the mean delay and the mean power consumption in the system, we develop static and dynamic load balancing policies that take into account the setup delay. All the obtained results are analytically founded. The performance of the static policy is evaluated using the achieved analytical expressions, which include the static optimal probabilities achieved before, whereas for the dynamic policy we need to perform simulations using previously obtained numerical results from the first policy iteration procedure. As concluded, numerical results indicate that the dynamic policy outperforms the static policy under realistic settings.

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B_{k}^{normals} false(t false) = false{0, 1 false}

, if the small cell k is nonbusy or busy, respectively.…”

Section: System Modelmentioning

confidence: 99%

“…Exponentiality is assumed for the sake of tractability, similarly as in, eg, the previous studies. [26][27][28] To keep track of the operational state of each small cell k, we denote by B s k (t) = {0, 1}, if the small cell k is nonbusy or busy,…”

Section: Elastic Traffic Modelmentioning

confidence: 99%

Delay‐ and energy‐aware load balancing in ultra‐dense heterogeneous 5G networks

Taboada

Aalto

Lassila

et al. 2017

Trans. Emerging Tel. Tech.

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show abstract

“…Another approach was used by Xu and Tian [13] with (e, d)control policies for M/M/c systems: when d servers are idle, e servers are switched off. Lastly, an extensive analysis for M/M/c queues in server farms and data-centers was performed by Maccio and Down [14], [15], [16], [17]. In [14] and [16] the authors extracted structural properties pertaining to the optimal policy to be applied for controlling M/M/c queues.…”

Section: A Related Literaturementioning

confidence: 99%

“…In [14] and [16] the authors extracted structural properties pertaining to the optimal policy to be applied for controlling M/M/c queues. Then, in [15] and [16], they analyzed system performances when applying two specific policies satisfying those structural properties. In their most recent work on this topic [17], policies for an M/M/c configuration are studied under the asymptotic regime where the number of servers approaches infinity while the load per server remains constant.…”

Section: A Related Literaturementioning

confidence: 99%

Energy-Efficient Control Policy for Parallel and Identical Machines With Availability Constraint

Loffredo

Frigerio

Lanzarone

et al. 2021

IEEE Robot. Autom. Lett.

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Energy efficiency is becoming a key subject in manufacturing, especially if related to the major environmental impact of machining activities. Nowadays, great research efforts are performed to find new methodologies to improve sustainability of manufacturing processes. Energy consumption can be lowered controlling machine state during idle periods. This can be achieved with Energy-Efficient Control (EEC) policies that switch off/on the machine. The same approach can be applied simultaneously to more identical devices in a parallel machines workstation. This paper presents a novel model to identify EEC policies for manufacturing systems composed of parallel and identical machines with finite buffer capacity. In this configuration, each machine can be switched on with a stochastic startup time and switched off instantaneously also considering an availability constraint. The proposed model reduces energy consumption while assuring a target availability level. The control is executed using buffer level information. Numerical results confirm model benefits when applied to a real industrial system from the automotive sector.

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“…In addition, queuing models can be applied to evaluate the impact of an energy-saving configuration on the overall energy consumption in data centers. In [57,58], a data center is represented as a queuing system with homogeneous servers and a central queue. The servers work according to the specific policy; they switch on or off depending on the number of jobs in the system to ensure the expected energy cost.…”

Section: Related Workmentioning

confidence: 99%

Fluid-Flow Approximation in the Analysis of Vast Energy-Aware Networks

Nycz

Czachórski

2021

Mathematics

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The paper addresses two issues: (i) modeling dynamic flows transmitted in vast TCP/IP networks and (ii) modeling the impact of energy-saving algorithms. The approach is based on the fluid-flow approximation, which applies first-order differential equations to analyze the evolution of queues and flows. We demonstrate that the effective implementation of this method overcomes the constraints of storing large data in numerical solutions of transient problems in vast network topologies. The model is implemented and executed directly in a database system. It can analyze transient states in topologies of more than 100,000 nodes, i.e., the size which was not considered until now. We use it to investigate the impact of an energy-saving algorithm on the performance of a vast network. We find that it reduces network congestion and save energy costs but significantly lower network throughput.

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Exact Analysis of Energy-Aware Multiserver Queueing Systems with Setup Times

Cited by 12 publications

References 22 publications

Delay‐ and energy‐aware load balancing in ultra‐dense heterogeneous 5G networks

Delay‐ and energy‐aware load balancing in ultra‐dense heterogeneous 5G networks

Energy-Efficient Control Policy for Parallel and Identical Machines With Availability Constraint

Fluid-Flow Approximation in the Analysis of Vast Energy-Aware Networks

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