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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 154038, 11 pages
Asymptotic Identity in Min-Plus Algebra: A Report on CPNS
1School of Information Science & Technology, East China Normal University, No. 500 Dong-Chuan Road, Shanghai 200241, China
2Department of Computer and Information Science, University of Macau, Avenue Padre Tomas Pereira, Taipa, Macau
Received 23 March 2011; Accepted 20 April 2011
Academic Editor: Carlo Cattani
Copyright © 2012 Ming Li and Wei Zhao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Network calculus is a theory initiated primarily in computer communication networks, especially in the aspect of real-time communications, where min-plus algebra plays a role. Cyber-physical networking systems (CPNSs) are recently developing fast and models in data flows as well as systems in CPNS are, accordingly, greatly desired. Though min-plus algebra may be a promising tool to linearize any node in CPNS as can be seen from its applications to the Internet computing, there are tough problems remaining unsolved in this regard. The identity in min-plus algebra is one problem we shall address. We shall point out the confusions about the conventional identity in the min-plus algebra and present an analytical expression of the asymptotic identity that may not cause confusions.
We use the term cyber-physical networking systems (CPNS) instead of cyber-physical systems (CPS) as that in Song et al.  for the meaning of Internet of Things (IoT) that was stated by Commission of the European Communities  or Networks of Things (NoT) as discussed by Ferscha et al. , intending to emphasize the point that we are interested in the networking theory in CPS. Communication networks in CPNS include, but are never limited to, the Internet. Physical systems considered in CPNS are heterogeneous, ranging from telemedicine systems to geophysical ones, see, for example, Clifton et al. , Traynor , Chang . Obviously, data in various physical systems are heterogeneous, see, for example, Chang , Goodchild , Lai and Xing , Mandelbrot [9–11], Hainaut and Devolder , Cattani [13–17], Chen et al. [18–22], Mikhael and Yang , Bakhoum and Toma [24–26], Li [27–32], Li et al. [33–39], Messina et al. , Humi , Dong , Liu , Toma , Abuzeid et al. , [46–49], Werner , and West , just naming a few.
There are two challenge issues in CPNS. On the one hand, data models that are irrelevant of statistics of a random function are greatly desired. On the other hand, theory that may be used to linearize nonlinear data transmission systems but irrelevant of their nonlinearity is particularly expected, because communication systems, including the Internet, are, in nature, nonlinear due to queuing, see, for example, Akimaru and Kawashima , Yue et al. , Gibson , Cooper , Pitts and Schormans , McDysan , and Stalling . In short, we are interested in data models that are irrelevant of their statistics and system theory that is irrelevant of the nonlinearity of systems.
The early work regarding the above in italic may refer to Cruz [59–61], Zhao and Ramamritham , Raha et al. , Chang [64, 65], Boudec , Boudec and Patrick , Firoiu et al. , and Agrawal et al. . Following Cruz [59, 60], the theory for the above in italic is called network calculus, see, for example, [66, 67], Jiang and Liu . Chang  uses the term calculus, which is taken as the synonym of network calculus of Cruz in this paper.
The main application area of network calculus is conventionally to computer science, the Internet in particular, see, for example, Wang et al. [72, 73], Li and Zhao [74, 75], Fidler , Jiang , Jiang et al. , Liu et al. , Li et al. , Li and Kinghtly , Burchard et al. , Ng et al. , Raha et al. [84, 85], Starobinski and Sidi , Fukś et al. , Jia et al. , Golestani , and Lenzini et al. . However, we have to emphasize the point that its applications are never limited to computer science. Rather, it is a theory to model data irrelevant of their statistics and to deal with data transmission without the necessity in principle to consider the nonlinearity of transmission systems, as we shall explain in the next section. Therefore, it may be a promising tool to deal with data and systems in CPNS.
Basically, the fundamental theory of network calculus consists of three parts as described below.(i) model of arrival data ,(ii)relationship between , single system (or node or server) that is usually called service curve, and departure data ,(iii)departure data of a series of systems (nodes or servers) , driven by arrival data ,
The contributions of this paper are in the following three aspects: (i)the problem statement,(ii)the proof of the existence of the identity in the min-plus algebra in the domain of generalized functions,(iii)the asymptotic expression of the identity.
The rest of paper is organized as follows. Research background is discussed in Section 2. In Section 3, we will brief the min-plus algebra and state the problem regarding the identity in this algebra system. In Section 4, we shall address the existence of the identity in the min-plus algebra. The asymptotic expression of the identity is presented in Section 5. Discussions are given in Section 6, which is followed by conclusions.
2. Research Background
Data in CPNS are heterogeneous. They may be from sensors like radio-frequency identification (RFID), see, for example, , Ilie-Zudor et al. , Ahuja and Potti , data traffic in the Internet , transportation traffic (see [94–98]), ocean waves (see ), sea level (see [36, 99]), medical signals (see ), hydrological data (see ), financial data (see ), and so on. They may be Gaussian (see [29, 31]) or non-Gaussian (see [12, 102]). They may be in fractional order or integer order. In the case of fractional order, they may be unifractal or multifractal. The sample size of data of interest may be long enough for statistical analysis or very short, for example, a short conversation in mobile phone networks. On the other side, systems are also heterogeneous. Therefore, CPNS challenges us two tough issues. One is in data modeling and the other system modeling. We shall exhibit that the min-plus algebra in network calculus may yet serve as a tool in this regard.
2.1. Network Model
We first explain a single node in CPNS. Then, a model of tandem network is mentioned.
2.1.1. Nonlinearity of Node in CPNS
Without confusions, we use to represent the operator of node such that Recall that queuing is a phenomenon often occurring in CPNS. For instance, cars in highways are often queued. Clients in a library for borrowing or returning books need queuing. Suppose client suffers from delay . Then, Note that is a random variable in two senses. One is The other is Therefore, we have the following remark.
Remark 1 (nonlinearity). A node in CPNS is usually nonlinear. That is,
2.1.2. Number of Arrivals is Random
The number of arrivals, denoted by in Figure 1, is random.
Note 1. We need theory to deal with a nonlinear node with arrival clients, where is a random variable.
2.1.3. Tandem Network Model
A single node previously described is not enough in CPNS since a client may be served by a series of nodes, which we call tandem network, see Figure 2.
According to Remark 1, each node in Figure 2 is nonlinear. In addition, considering Note 1, we see that the number of arrival clients at the input of each node is random. Some clients may go through from to while others may not. For instance, client leaves the tandem network when it passes through . Further more, some clients, for example, , arrive at this tandem network at the input of . In general, how many clients leave the tandem network at the output of a specific node and how many clients arrive at the input of another specific node are uncertain.
Note 2. We need theory to handle a nonlinear system that is a tandem network as that in Figure 2 to assure the quality of service (QoS) of a specific client or of a specific class of clients within a given period of time.
2.2. Data Modeling
We consider two classes of data flow. One is arrival data in the aggregated case, or aggregated clients, and the other arrival data of a specific client. In terms of network communications, the former is usually called aggregated arrival traffic while later arrival traffic at connection level. Without confusions, we use the term traffic rather than client.
One of radical properties of arrival traffic (traffic for short) is remarked below.
Remark 2 (positive). Traffic is positive. That is, where is the set of real numbers.
Another radical property of traffic is that the maximum of is finite. More precisely, the value of may never be infinite. Thus, we have the following remark.
Remark 3 (finite range). The maximum of is finite. That is,
Remark 4 (randomness). The function is usually random. This implies that
2.2.1. Traffic at Connection Level
At connection level, for instance, for the connection, traffic is . One particularity of is that for usually lasts within a finite time interval, say, . The width of the interval may be short, such as a short conversation like a word “hello” or long, such as a long speech over a network. In any case, it is finite. Modeling with short interval is particularly desired and challenging.
Note 3. In the discrete case, the length of may be too short to the proper statistical analysis of in practice.
Note 4. Without confusions, we use to represent the interval in both the continuous case and the discrete one. In the continuous case, . In the discrete case, , where is the set of integer numbers, implying . We use to represent an interval the starting point of which is nonzero.
2.2.2. Aggregated Traffic
We adopt Figure 1 to discuss aggregated traffic. At time , aggregated traffic denoted by at a node is expressed by In contrary to , the particularity of is that for usually lasts within an interval longer than that of . As a matter of fact, if passes through a node, another arrival flow may arrive at the node. Consequently, in general, we should consider for .
2.3. Accumulated Traffic
Traffic, either or , discussed previously is instantaneous one. Data modeling of instantaneous traffic is essential, as we need understanding what its behaviors are at instantaneous time at the input of a node. However, from the point of view of the service of a node, we also need data modeling of accumulated traffic within a time interval, say, , without loss of generality, because it is desired for us to understand what the service performance of the node is for the purpose of proper design of a buffer size as well as scheduling policy of the node.
2.3.1. Accumulated Traffic at Connection Level
In the continuous case, the accumulated traffic of within the interval is denoted by . It is given by In the discrete case,
2.3.2. Accumulated Traffic in the Aggregated Case
Denote by the accumulated traffic in the aggregated case within the interval . Then, in the continuous case, we have In the discrete case,
The mathematical expressions of and appear similar except the subscript . However, differs from substantially in analysis in methodology. On the one hand, for should be assumed to be short such that conventional methods in statistics fail to its statistical analysis. On the other hand, for may be large enough such that it may be sectioned for the statistical analysis, see, for example, Li et al. .
2.3.3. A Basic Property of Accumulated Traffic
One property of accumulated traffic, either or , is the wide sense increasing. By wide sense increasing, we mean that or
Therefore, the data functions or series we face with are increasing ones in the wide sense.
2.3.4. Model of Data
For and , the following is called the model of data ,
Note 5. The model expressed by (16) is irrelevant of any information of statistics of . The advantage of this model is at the cost of using inequality instead of equality.
Note 6. The model of (16) is simple in computation. Thus, it may be effective in practice, particularly in environments of CPNS, where simple computations are always expected.
For accumulated traffic , we have Due to sufficiently large , we may set the starting time by . In this case, we have Moreover, we are allowed to section the above integral such that Without loss of generality, we use (17) to explain and .
Remark 5. The parameter represents the bound of the burstness or local irregularity of , because
Note that the above integral does not make sense if for the continuous even in the field of the Lebesgue’s integrals, see Dudley , Bartle and Sherbert , and Trench  for the contents of the Lebesgue’s integrals. However, it makes sense when it is considered in the domain of generalized functions, which we shall brief in the following section. A simple way to explain (20) is where and is the Dirac- function.
Remark 6. The parameter represents the bound of the average rate of , because
Remark 7. The parameter measures the local property of while is a measure of global property of .
3. Min-Plus Algebra and Problem Statement
Min-plus convolution is essential in the min-plus algebra. In this section, we first briefly review the conventional convolution in linear systems. Then, we shall visit min-plus convolution. Finally, we shall state the problem in the aspect of identity in the min-plus algebra.
3.1. Conventional Convolution
Denote by a real number that satisfies . If a function defined on , where is allowed to be and is allowed to be , is measurable and we say that .
Suppose that two functions . Then, one says that convolutes if where is the symbol implying the operation of convolution. We call it conventional convolution so as to distinguish it from the min-plus convolution we are discussing in this paper.
The conventional convolution is crucial for linear systems, see, for example, Gibson , Box et al. , Mitra and Kaiser , Papoulis , Harris , Mikusinski , Fuller , and Bendat and Piersol , just naming a few. It has the properties described by the following lemmas.
Lemma 1. In the algebra system , the conventional convolution is commutative.
Lemma 2 (closure of *). If , then .
Lemma 3. In the algebra system , where + implies the ordinary addition, * with respect to + is distributive.
Lemma 4. For , .
Lemma 5. The identity in is the Dirac- function that is defined by where is continuous at .
In fact, in the domain of generalized functions, we have Thus, in the sense of generalize functions. Consequently, is taken as the asymptotic identity in in the domain of generalized functions. Accordingly, the inverse of the conventional convolution discussed by, for instance, Mikusinski , Bracewell , Huang and Qiu , Abutaleb et al. , Rhoads and Ekstrom , Todoeschuck and Jensen , and Moreau et al. , exists because the necessary and sufficient condition that the inverse of an operation exists is that there exists the identity in that system, see, for example, Korn and Korn , Zhang , Riley et al. , Bronshtein et al. , and Stillwell , but it should be in the sense of generalized functions. As a matter of fact, the conventional convolution itself is in that sense, see, for example, Smith .
Theorem 1. The algebra system is a group.
Proof. First, the operation * is closed in . Second, * is commutative because, for any ,
Finally, there exists the left identity denoted by and the right one again denoted by in such that
Thus, is a group.
3.2. Min-Plus Convolution
Considering the property of wide sense increasing of accumulated traffic mentioned in Section 2.3, we denote by the set that contains all functions that are greater than or equal to zero and that are wide sense increasing.
Definition 1. Let . Then, the following operation is called min-plus convolution: where represents the operation of the min-plus convolution.
Example 1. Let for and 0 elsewhere. Then, .
Lemma 6 . (closure of ). Let . Then, .
Lemma 7. The operation is commutative. That is,
Define another operation that is denoted by such that
Then, we have an algebra system denoted by that follows the distributive law.
Lemma 8. The operation with respect to is distributive. That is, for , one has
The following rule useful in this research is stated as follows.
Lemma 9. Suppose . Then, for , one has where + is the ordinary addition.
It seems quite obvious when one takes as the identity in the min-plus algebra since However, we shall soon point the contradictions of below.
3.3. Problem Statement
In addition to the above contradiction, we now state another problem regarding (34). As a matter of fact, if we let and in Lemma 9, then, on the left side of (33) in Lemma 9, we have On the other side, on the right side of (33) in Lemma 9, we have Comparing the right sides of (39) with that of (40) yields another contradiction expressed by
The above discussions imply that the definition of the identity of (34) in the min-plus algebra, which is commonly used in literature, see, for example, [66–70], may not be rigorous at least. Therefore, the conventional representation of the identity, that is, (34), may be inappropriate since it may mislead computation results like those in (39) and (40). Consequently, rigorous definition of the identity needs studying.
4. Existence of Identity in Min-Plus Algebra
The problems regarding the definition of the conventional identity, which we stated in Section 3.3, give rise to a question whether or not the identity in the min-plus algebra exists. The answer to this question is rarely seen, to the best of our knowledge. Another question resulted from Section 3.3 is what the rigorous representation of the identity is. We shall provide the answer to the first question in this section. The answer to the second will be explained in the next section.
Definition 2. Let be the support of a function . It implies . The function is said to have a bounded support if there exist such that .
Definition 3. A function is said to have time continuous derivatives if its first derivatives exist and are continuous. If its derivatives of all orders exist and are continuous, is said to be infinitely differentiable. In this case, is said to be smooth.
Definition 4. A test function is a smooth with . The set of all test functions is denoted by .
Definition 5. A linear functional on is a map such that, for and , .
Definition 6. Denote by a sequence of test functions and another test function. We say that if the following holds:(1)there is an interval that contains and for all ,(2) uniformly for .
Definition 7. A functional on is continuous if it maps every convergent sequence in into a convergent sequence in . A continuous linear functional on is termed a generalized function. It is often called a distribution in the sense of Schwartz.
Definition 8. A function is locally integrable if for all .
Lemma 10. Any continuous, including piecewise continuous, function is locally integrable.
Lemma 11 (regular). Any locally integrable function is a generalized function defined by In this case, is called regular.
Lemma 12. Any generalized function has derivatives of all orders.
Lemma 13. There exists the Fourier transform of any generalized function.
Definition 9 (rapid function). A function of rapid decay is a smooth function such that as for all , where is the space of complex numbers. The set of all functions of rapid decay is denoted by .
Lemma 14. Every function belonging to is absolutely integrable.
4.2. Proof of Existence
Define the norm and inner product of by where . Combining any with its limit yields a Hilbert space that we denote again by without confusions.
Let be a system function such that it transforms its input to the output by
Denote the system by the operator . Then, we purposely force the functionality of such that it maps an element to another element . Note that is a linear operator. In fact, according to Lemma 8, we have In addition, from Lemma 9, we have Therefore, is a linear mapping from to .
Lemma 15 (archimedes criterion). For any positive real numbers and , there exists positive integer such that (see ).
Lemma 16 (archimedes). If , there exists such that (see ).
Lemma 17. An operator is invertible if and only if there exists constant such that for all , , where and are linear normed spaces (see ).
From the above discussions, we obtain the following theorem.
Theorem 2 (existence). For and and , if or , then both and are invertible. Consequently, the identity in the min-plus algebra exists.
Proof. Consider Since and , we have According to Lemmas 15 and 16, there exists such that Therefore, Similarly, if is such that , we have since , where is a constant. Thus, according to Lemma 17, Theorem 2 holds.
Note 7. In Theorem 2, we need the conditions of and . Since and are wide sense increasing, we need in fact and .
5. Representation of Identity in Min-Plus Algebra
Express the Dirac- function by
For the purpose of distinguishing the identity we present from the conventional one, we denote as the identity in what follows instead of as used in Section 3.
Theorem 3 (representation). The identity in the min-plus algebra is expressed by
Proof. Take the following into account Then, the identity in the discrete case is given by The identity in the continuous case is taken as the limit expressed by Considering the Poisson’s summation formula, we have In the limit case, This completes the proof.
Note 8. The identity expressed by (59) is an asymptotic one.
We mention an application of min-plus algebra to CPNS. Denote by the accumulated function characterizing the output of the node (Figure 3). Then, the min-plus convolution can be used to establish the relationship between , , and by
Suppose a traffic function passes through tandem nodes from the first node with the service curve to the th node with the service curve to reach the destination as indicated in Figure 4. Denote the departure traffic of the th node by . Then, where (see )
Note 9. Min-plus algebra can be used to linearize a nonlinear system as can be seen from (62). Thus, it may yet be used as a theory in the aspect of data transmission systems in CPNS.
We have proposed the problem regarding the conventional identity in the min-plus algebra. In addition, we have presented the proof that the identity in the min-plus algebra exists in the domain of generalized function. Moreover, we have given the asymptotic expression of the identity in the system of min-plus algebra.
This work was partly supported by the National Natural Science Foundation of China (NSFC) under the project Grant nos. 60873264, 61070214, and the 973 plan under the Project no. 2011CB302801/2011CB302802.
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