The T-Policy for the M/G/1 Queue

Heyman, Daniel P.

doi:10.1287/mnsc.23.7.775

Cited by 160 publications

(71 citation statements)

References 6 publications

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“…We will consider the three following classical policies for switching on the server: (a) N -policy [26]: the server is turned on when the queue length reaches the number N. (b) T -policy [14,17]: the server checks the queue after T time units. If the server finds a non empty queue then it automatically initiates service; otherwise, the server takes a second vacation period of length T .…”

Section: Model Description Of the M/g/1 Vacation Models Under Studymentioning

confidence: 99%

“…Firstly, we will assume that the available information consists in the first and second moments of L N . For simplicity, we assume that such moments are given by the "classical" expressions (13) and (14). Alternatively, the use of empirical data to construct constraints based on sample moments provides another choice consistent with the philosophical principles of the maximum entropy approach.…”

Section: Model Description Of the M/g/1 Vacation Models Under Studymentioning

confidence: 99%

“…The T -policy [14,17] consists of turning the server on after T time units have elapsed from the epoch of server removal, if there is at least one customer present Figure 2. M/H 2 /1 queue with N = 10, ρ = 0.25: "classical" and ME densities.…”

Section: The M/g/1 Queue Operating Under the T-policymentioning

confidence: 99%

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Entropy maximization and the busy period of some single-server vacation models

Artalejo

Lopez-Herrero

2004

RAIRO-Oper. Res.

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Abstract. In this paper, information theoretic methodology for system modeling is applied to investigate the probability density function of the busy period in M/G/1 vacation models operating under the N -, T -and D-policies. The information about the density function is limited to a few mean value constraints (usually the first moments). By using the maximum entropy methodology one obtains the least biased probability density function satisfying the system's constraints. The analysis of the three controllable M/G/1 queueing models provides a parallel numerical study of the solution obtained via the maximum entropy approach versus "classical" solutions. The maximum entropy analysis of a continuous system descriptor (like the busy period) enriches the current body of literature which, in most cases, reduces to discrete queueing measures (such as the number of customers in the system).

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Section: Model Description Of the M/g/1 Vacation Models Under Studymentioning

confidence: 99%

Section: Model Description Of the M/g/1 Vacation Models Under Studymentioning

confidence: 99%

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Entropy maximization and the busy period of some single-server vacation models

Artalejo

Lopez-Herrero

2004

RAIRO-Oper. Res.

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“…Over the past few decades, a large class of policies for control of vacation queues has been discussed in the literature, viz., single and multiple vacation policies (Doshi 1986), N-policy (Yadin and Naor 1963), T-policy (Heyman 1977), NT-policy (Doganata 1990), D-policy (Balachandran 1973) and F-policy (Gupta 1995). The discrete-time queue with NT-policy was first studied by Alfa and Frigui (1996).…”

Section: Introductionmentioning

confidence: 99%

Some analysis results associated with the optimization problem for a discrete-time finite-buffer NT-policy queue

Alfa

2015

Oper Res Int J

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The prime objective of this paperis to give some analysis results concerning the discrete-time finite-buffer NT-policy queue, which can be utilized to determine the optimal threshold values. By recording the waiting time of the leading customer in server's vacation period, the model is successfully described as a vectorvalued Markov chain. Meanwhile, depending on the special block structure of the one-step transition probability matrix, the equilibrium queue length distri-bution is calculated through a more effective UL-type RG-factorization. Due to the number of customers served in the busy period does not have the structure of a Galton-Watson branching process, analysis of the regeneration cycle is regarded as a difficult problem in establishing the cost structure of the queueing system. However, employing the concept of i-busy period and some difference equation solving skills, the explicit expression for the expected length of the regeneration cycle is easily derived, and the stochastic decomposition structure of the busy period is also demonstrated. Finally, numerical results are offered to illustrate how the direct search method can be implemented to obtain the optimal management policy.

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“…Therefore to evaluate the optimal parameters of these policies, we modified M/G/1 with T [13], K [14] and D [15] policies for TOP, COP and ROP respectively.…”

Section: Introductionmentioning

confidence: 99%

Aggregation policies over RSVP tunnels

Malik

Kanhere

Hassan

VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005.

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Abstract-IETF's integrated service with resource aggregation techniques provide a mechanism to reduce the RSVP states kept at router. Therefore, it reduces the number of RSVP refresh messages exchanged across routers. But the high frequency of setup messages exchanged between the edge routers can hinders the performance of aggregation. In this paper, we look at the management of the aggregation of resources over IP-in-IP tunnel with a view of reducing the setup messages. We develop and compare the cost functions of three aggregation control policies: the Temporal Operating Policy, the Cardinal Operating Policy, and the Resource Threshold Operating Policy. We also figure out the conditions under which the operating costs of these policies are minimum. Furthermore, we compare the performance of these policies under different conditions and shown that the Temporal Operating Policy will be more cost effective than the other two policies.

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The T-Policy for the M/G/1 Queue

Cited by 160 publications

References 6 publications

Entropy maximization and the busy period of some single-server vacation models

Entropy maximization and the busy period of some single-server vacation models

Some analysis results associated with the optimization problem for a discrete-time finite-buffer NT-policy queue

Aggregation policies over RSVP tunnels

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