Transient and stationary characteristics of a packet buffer modelled as an <i>MAP/SM/1/b</i> system

Rusek, Krzysztof; Janowski, Lucjan; Papir, Z.

doi:10.2478/amcs-2014-0033

Cited by 8 publications

(4 citation statements)

References 28 publications

(45 reference statements)

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“…In the course of experiments, a tcpdump probe was forced to filter streams of UDP packets. Examples of models based on queuing theory, including buffer control policies, can be found, for example, in [42,43]. Power consumption monitoring was supported by a standard one-phase electricity meter for active energy measurement equipped with RS485 interface.…”

Section: System Model Identificationmentioning

confidence: 99%

“…For an overview of approaches to server model identification in the context of control system design, see, for example, [38][39][40][41]. Examples of models based on queuing theory, including buffer control policies, can be found, for example, in [42,43]. Finally, let it be noticed that benchmarking capabilities are being constantly improved, especially in the Linux system environment, which sets up promising directions for further research.…”

Section: System Model Identificationmentioning

confidence: 99%

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Energy‐efficient CPU frequency control for the Linux system

Karpowicz

2015

Concurrency and Computation

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Efficiency of energy usage in computing systems improves, however, still not at the rate matching the climbing demand for computing capacity. To address this urging problem, computing elements of the latest generation, that is, CPUs/graphics processing units, memory units, and network interface cards, have been designed to operate in multiple modes with differentiated energy consumption levels. Mode switching and high-frequency performance monitoring functions have also been exposed by co-designed abstract programming interfaces. The challenge of energy-efficient computing is to develop hardware control mechanisms taking advantage of the new capabilities. This paper aims at giving an insight into the structure of optimal energy-aware CPU frequency scaling rules. It gives a characterization of solutions to the optimal control problem of energy-efficient real-time packet inspection performed by a Linux server. A class of CPU frequency switching rules, exploiting dynamic voltage and frequency scaling mechanisms, is constructed based on experimentally identified model of server operations. The control rules are demonstrated to outperform the default CPU frequency scaling governor for the Linux kernel, both in terms of achievable power savings and service quality. 421Agency's 50 GFLOPS/W goal ¶ by year 2020 [4,5]. Indeed, limiting power consumption in data centers has become an important engineering problem [6][7][8].Average CPU utilization of a server in data center rarely approaches 100%, most of the time oscillating somewhere between 10% and 50% [9]. Over-provisioning of computing capacity, strongly correlated with the running CPU frequency, has been applied as a sound strategy to eliminate potential breakdowns caused by sudden workload fluctuations or internal disruptions, for example, hardware or software faults. Unfortunately, although efficient from the point of view of service quality, the strategy may be a significant source of energy waste. The challenge of energy-efficient CPU frequency scaling, addressed in the present paper, is therefore to design control rules reducing power consumption subject to quality of service constraints in highly stochastic environment of a server operating system.Dynamic voltage and frequency scaling (DVFS) mechanisms have been commonly used to reduce power usage in routers and application servers. Translation of the commands responsible for the CPU frequency control in the Linux system has been provided by the cpufreq kernel module [10]. The module allows to adjust performance of CPU by activating a desired control policy implemented as a CPU governor. Typically, the calculated control inputs are mapped to admissible performance P-states, as described by the Advanced Configuration and Power Interface standard || , and passed to a processor driver (e.g., acpi_cpufreq). The sleeping state, or Advanced Configuration and Power Interface C-state, which CPU enters during idle periods, is independently and simultaneously determined by the cpuidle kernel module [11].Recent versions of ...

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Section: System Model Identificationmentioning

confidence: 99%

Section: System Model Identificationmentioning

confidence: 99%

Energy‐efficient CPU frequency control for the Linux system

Karpowicz

2015

Concurrency and Computation

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“…New results on transient and equilibrium stochastic characteristics of queueing models with finite waiting rooms can also be found, e.g., in the work of Rusek et al (2014), where the MAP-type arrival process is assumed, and in the paper by Woźniak et al (2014), where, additionally, a cost-optimization problem of the GI/M/1/N -type model with single server vacations was solved via an evolutionary approach. Kempa (2011) considered a Markovian model with a finite waiting room and a general-type dropping function, separately for the case of single and batch arrivals.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of an M/G/n-type queue with bounded capacity and packet dropping

Tikhonenko

Kempa

2016

International Journal of Applied Mathematics and Computer Science

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A queueing system of the M/G/n-type, n ≥ 1, with a bounded total volume is considered. It is assumed that the volumes of the arriving packets are generally distributed random variables. Moreover, the AQM-type mechanism is used to control the actual buffer state: each of the arriving packets is dropped with a probability depending on its volume and the occupied volume of the system at the pre-arrival epoch. The explicit formulae for the stationary queue-size distribution and the loss probability are found. Numerical examples illustrating theoretical formulae are given as well.

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“…Indeed, our model is closely related to partially observable semi-Markov decision processes (POSMDPs) (White, 1976;Mahadevan, 1998;Lim et al, 2011). A POSMDP is a semi-Markov model (e.g., Li et al, 2007;Oniszczuk, 2009;Rusek et al, 2014) equipped with actions and observations. The relationships between our model and POSMDPs are described in Section 3.3.3.…”

Section: Related Workmentioning

confidence: 99%

Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

Fukumoto

Wakuya

Furukawa

2015

International Journal of Applied Mathematics and Computer Science

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The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such as hidden Markov models and neural networks. However, little attention has been paid to bottom-up approaches for learning POMDP models. In this paper, we present a novel bottom-up learning algorithm for hierarchical POMDP models and prove that, by using this algorithm, a perfect model (i.e., a model that can perfectly predict future observations) can be learned at least in a class of deterministic POMDP environments.

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Transient and stationary characteristics of a packet buffer modelled as an MAP/SM/1/b system

Cited by 8 publications

References 28 publications

Energy‐efficient CPU frequency control for the Linux system

Energy‐efficient CPU frequency control for the Linux system

Performance evaluation of an M/G/n-type queue with bounded capacity and packet dropping

Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

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