The International Journal of Computer Science and Information Security is a monthly periodical on research articles in general computer science and information security which provides a distinctive technical perspective on novel technical research work, whether theoretical, applicable, or related to implementation. Target Audience: IT academics, university IT faculties; and business people concerned with computer science and security; industry IT departments; government departments; the financial industry; the mobile industry and the computing industry. Coverage includes: security infrastructures, network security: Internet security, content protection, cryptography, steganography and formal methods in information security; multimedia systems, software, information systems, intelligent systems, web services, data mining, wireless communication, networking and technologies, innovation technology and management. Thanks for your contributions in July 2010 issue and we are grateful to the reviewers for providing valuable comments. IJCSIS July 2010 Issue (Vol. 8, No. 4) has an acceptance rate of 36 %.
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 4, July 2010 Analytical Comparison of Fairness Principles for Resource Sharing in Packet-Based Communication Networks Yaser Miaji and Suhaidi Hassan InterNetWorks Research Group, UUM College of Arts and Sciences Universiti Utara Malaysia, 06010 UUM Sintok, MALAYSIA firstname.lastname@example.org email@example.com ABSTRACT potential responsibility in structuring the fairness principle. Some applications require more sensitive pamper and care Current Internet users are enormously increased and such as voice and interactive application such as video application that they are using is magnificently bandwidth conversation and so forth. The sensitivity of these devoured. With this manner, Internet is no longer a fair and applications significantly involved in fairness principle. protective environment. The diversity in the Internet applications required a reconsideration of the mechanisms Moreover, providing Quality of Service (QoS) is one used to deliver each packet pass through a router in order to provide better fairness and more protective place. big dimension which should be achieved if not fully at Furthermore, the observer of the Internet packet could easily least to the large extent. QoS requirements rhyme heavily identify the purpose of the delay which is indeed caused by with user and application requirements. Even though, the queuing in the output buffer of the router. Service Providers (SP) is one potential dimension which tighten fairness principle, their requirements is highly Therefore, to reduce such delay for those sensitive depend on financial matters. applications such as real-time applications, scholars develop many fairness principle which by turn could improve the Fairness principle is indeed, applied in routers or to QoS and hence the fairness and the protection aspect. This be more specific in the process of scheduling the study highlight most famous fairness principles used in the transmission of the packets over a shared link. Fairness literature and some other novel ideas in the concept of fairness. The analytical comparison of these principles shows principle should provide three primary function selection, the weakness and the strength of each principle. promptness, and QoS consideration. Selection is the Furthermore, it illuminates which fairness principle is more basically which packet deserves to be transmitted. appropriate in which environment. Promptness means when the selected packet will be transmitted. QoS requires considering the delay, loss and Keywords-components; Fairness, max-min, proportional error of overall network performance. fairness, balanced, max-min charge Scholars, since the discovery of the sensitive and 1. INTRODUCTION bandwidth hanger applications, dedicate their research in providing superior fairness and larger protection for these Internet utilization in public and private sector is applications over others less sensitive. This paper magnificently growing with extraordinary manner. The demonstrates most available and used fairness principles in occupation of the World Wide Web is unpredictable over scheduling packets depending on application sensitivity time frame. Daily usage of the Internet resources with and user usage. The rest of the paper is organized as current scrambles in network access is hard to be estimated following. Next section gives the state diagram of the and hence the distribution of these resources is dynamic. literature and brief information about the evolution of the This dynamic behavior leads to vagueness in constructing fairness principle. This is followed by thorough conceptual the essential principle of fairness for resource utilization. and analytical illustration of five fairness definition namely; max-min fairness, proportional fairness, utility fairness, balanced fairness, and max-min charge fairness. Furthermore, not only the dynamic attitude of the Section four compares and contrasts all six principles and resource utilization is an issue, the behavior and the finally the conclusion and future works are drawn. characteristics of the application itself also, play a 149 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 4, July 2010 2. MIND-MAP OF FAIRNESS LITERATURE vision of fairness by providing the principle of charge allocation rather than bandwidth allocation and it named as In this section, related works to the fairness is presented in max-min charge. Max-min charge is a new fairness state diagram or min-map diagram to correlate and track principle based on charge allocation instead of the evolution of fairness principle. Exhibit 2.1 shows the conventional bandwidth allocation. Next section mind-map diagram which explains the evolution of presents all the above mentioned five fairness principles fairness principle. In 1967, Kleinrock  published his conceptually and analytically. article in sharing one common resource. Although the article is primarily designed for addressing this specific issue from processor sharing prospective, it opened sites in discussing fairness in networks since process sharing environment shares some similarity with resource sharing in the Internet or networks. Kleinrock then wrote his book which consists of two volume in queuing systems [2, 3]. In this book the essential ideas and explanation of max-min fairness principle is been demonstrated with the aid of mathematic. Jaffe  incorporates the max-min fairness principle explicitly in network resource sharing. This concept is been presented in data networks book written by Bertsekas et al. . Nevertheless, the concept and regulations which rule max-min fairness and lead to its result are not convenience and does not provide the efficient fairness from Kelly point of view [6, 7]. Consequently, he proposed an alternative fairness principle named as proportional fairness. This concepts is further developed by Massoulie and Roberts . Bothe principles; max-min and Exhibit 2.1: Mind-map literature of fairness proportional are further compared and thoroughly principles analyzed by Denda et al. . However, the advocates of proportional fairness has comprehensively illustrate the principle in . 3. PRINCIPLES OF FAIRNESS Despite the success of the most famous principles; Approaching an optimum fairness in shared elastic max-min and proportional, they have some weaknesses environment such as the Internet is complicated and which are discovered by Bonald and Proutiere . frustrated. As a consequence, different proposals have Balanced fairness is their proposal which is inspired by been drawn to accomplish the mission in several Erlang  ideas, has different approaches. All three prospective. This section provides rigorous knowledge in principles; max-min, proportional and balanced fairness the most five adopted fairness notions. This are presented in Bonland et al. paper . Bonland has comprehensive illustration will reach the conceptual and provided some comparison using analytical demonstration. analytical approach of each o these five notions. Next Another fairness view is called utility fairness introduced section compares and contrasts these five principles. by Cao and Zegura . Utility fairness has adopted the concept of utility proposed in . All the above Before the explanation of the five notions mentioned mentioned fairness definition have been presented in  earlier, a scenario of shared resource is been assumed. So, by Hosaagrahara. let consider the following scenario. Consider a contended user n with demands varies However, these four principles; max-min, from one user to another. Those users are sharing the one proportional, balanced and utility fairness are in principle resource R. Additionally, each user is allocated a specific correlated and based on bandwidth allocation with portion of the different approaches in determining the proper algorithm resource R according to a policy P. There are two main to chose the next packet in line. The entire principle of stipulations for such allocation; bandwidth allocation has been criticized in Briscoe article . Therefore, Miaji and Hassan in  proposed a new a.The resource which is allocated is finite and limited. 150 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 4, July 2010 b.There is no resource feedback from users’ side. user 4 and 5 will take equal resource allocation no matter what they demand for. Consequently, any policy abides by these two conditions is said to be active and defined as follows Additionally, any user attempts to increase its ]61[: allocation will result in decrease in the resource allocated to another. Furthermore, it could be obviously seen that the Definition 3.1: The policy P is said to be active if, for attempts to increase the demand will not influence the all possible demands D, it results in an allocation A such decision of allocation . that: Exhibit 3.1 provides us with much information which 1. has not been illustrated yet. The essential inspiration of 2. max-min fairness is the Pareto superiority as well as Pareto efficiency which were suggested by Pareto [19, 20]. In fact, Pareto proposed his notion in political economic and it has Now, let establish the investigation in the five fairness two main concept; superiority and efficiency for two principles. active allocation. Firstly, if we have to allocate to two different resources , is considered 3.1 Max-min Fairness as Pareto superior with respect to if expands the allocation of at least one entity while not reducing the Let first simplify the principle of ma-min fairness be the allocation of any other entity; for instance, at least one user following example. Let assume that there are buckets prefers over . In the case of exhibit 3.1, user 4 which are corresponding to the demand of the users. prefers to obtain 40 units over 50 units and no other user Moreover, let assume that all buckets share the same tab request it. This preference will affect other users . which corresponds to the resource R. Therefore, since the resource is limited and the buckets cannot, indeed, provide Secondly, an allocation is considered as Pareto any resource enhancement which there is no other resource optimal if it is active and Pareto superior to all other active except the one which is shared as seen in exhibit 3.1. allocations. Indeed, Max-Min fairness shows its Pareto optimality and hence it is unique since it is the only notion which meets the conditions of the Pareto optimality . Now, let take the analytical vision of the notion of Max-Min fairness. So, let presume that is the allocation dedicated for with demand in flow and is the allocation specified for with demand . If we assume the then the following theorem could be deduced; Theorem 3.1: The Max-Min fairness is unique. Proof: Let and two users with demands and respectively and the resource allocated for them is Exhibit 3.1: Users Share the same resource respectively as well. So, if then the allocation results could be; According to max-min principle no user will obtain more than its demand and also, all not fully served users will be equally allocated in term of the resource. Only first one is possible since the remaining two are Therefore, user 1, 2, and 3 will take exactly what they not Max-Min fair. demand since their demands is the lowest. In comparison, 151 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 4, July 2010 Moreover, consider is the service received by , less throughput . then if that means this allocation is not Max-Min fair because in Max-Min fair the following should be true: . Hence the following definition is true for Max-Min fair: Definition 3.1: A policy is considered Max-Min fair if and only if satisfies the following conditions [22, 23]: 1- A is active; 2- Any attempts to increase and allocation for specific user result in a decrease in another user with equal or less value. Therefore, Max-Min policy should have the following Exhibit 3.2 a: Max-Min fairness properties; Property 3.1: No user gets resource allocation than what it have been requested. Property 3.2: users with same demands will be allocated similar resource. Property 3.3: Any increase in the demand will not affect the allocation procedure. 3.2 PROPORTIONAL FAIRNESS The idea of the proportional fairness is, indeed, proposed after the discovery of some gap in the fairness of Exhibit 3.2 b: Proportional fairness2 Max-Min. We will simplify this concept by illustrating a wireless node example . Nevertheless, this maximization will not be fair since those nodes with bad radio channel will suffer from It well known that the fairness goal is not to starvation. In Max-Min concept as shown in exhibit 3.2a, maximize the overall throughout or the bit rate or increase those nodes with bad radio channel will be allocated more the efficiency, it rather to be fair in allocating the bandwidth since the main aim of such principle is to bandwidth in accordance to the current network status. maximize the minimum. However, from proportional From this sense, consider a constant 1 wireless network fairness point of view, this solution is not the optimum. where there are two status of a node either good or bad. Therefore, in order to achieve high throughput and hence Therefore, there is a trade of between efficiency and to maximize the bit rate or increase the efficiency, it is fairness. Proportional fairness is trying to solve and hence better to allocate more bandwidth, transmission power and minimize this trade of by proposing the concept of so on to those good nodes since the bad one will allocating bandwidth in proportion to charge [6, 7]. experience more loss and required more bandwidth with 1 This situation is likely to be impossible 2The width of the wireless communication especially in the case of mobile wireless link corresponds to the bandwidth allocated to this environment. specific user. 152 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 4, July 2010 Logarithmic approach has been dedicated to such approach. So, proportional fairness concept proposed the notion of price per unit used or shared (see exhibit 3.2b). If we assume that user is charge of an amount of for unit shared. Therefore, in proportion to this user will be allocated . As a consequence the problem of maximization could be formed as following; Maximize (a) (b) So, the allocation for each user is depend on the amount it is charged. This gives some restriction in the Exhibit 3.3: example of utility fairness utilization of such concept which will be discussed later in the analysis and comparison section. Cao  in his article proposed and proof the following theorem; 3.3 Utility Fairness The concept of utility fairness is easily to be inferred from If is the allocated bandwidth for session, is its name. This notion is based on the utility or the the link capacity, the real utility function for session is application. It basically, derives the bandwidth allocation , is the error in the advertised bandwidth and is in accordance to the characteristic of the application to be the difference between the utility achieved by session transmitted through the link. Therefore, packets which has and the allocation deserved by the same session then; elastic or more tolerance in term of delay or loss or any other specified criterion, are allocated bandwidth depending on its specifications, behavior, and characteristics . A quantitative measure of the error in utility allocation is given by such theorem which resulted from Therefore, in the case of the identical utility or packet the inaccurate information. Moreover, it reveals that there specification or in other words applications, packets will is a strong relationship between the error of utility be treated as in Max-Min fairness. On the other hand, as allocated to an individual source and the accuracy of the application or packets diverse in its characteristics or advertised utility functions; nevertheless, it is not affected manners, the allocation scheme will also, changed and is by the number of sources sharing the same bottleneck link highly depends on the utility. and hence no harms from any exponential increase in the users side. To simplify the idea of utility life example is been provided. Now, consider an apple which needs to be 3.4 Balanced fairness divided among three people fairly as in exhibit 3.3. The simple and basic way is to allocate one third of this apple The proper definition of balanced fairness is the unique to each person equally as shown in exhibit 3.3a. However, balanced allocation such that belongs to the this sort of division is considered unfair if the boundary f the capacity set in any state If Φ circumstances of the people are not equal. corresponds to the balance function, the following equation is true in any state So, now consider the first person is a child how will any way, cannot eat more than a quarter of the apple. The (3.1) second person is in diet and he also, cannot eat more than a quarter of the apple and the third is very hungry energetic youth. Consequently, according to the utility as one half is Therefore, is recursively defined as the allocated to the youth, quarter for the child and the last minimum positive constant β such that the vector quarter portion is allocated for the person in diet (see belongs to . exhibit 3.3b). Balanced fairness is a new notion of bandwidth allocation with the very gratifying property that flow level performance metrics are insensitive to detailed traffic 153 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 4, July 2010 characteristics . This is particularly important for data misbehaved user. Some charges will be applied to such network engineering since performance can be predicted users and hence minimize the allocation. from an estimate of overall traffic volume alone and is independent of changes in the mix of user applications 4. COMPARISON OF FAIRNESS PRINCIPLES . The current status of the Internet provides only best-effort 3.5 Max-Min charge service. Consequently, providing enhancement for traffic flows for bandwidth reservation purposes is almost absent, Max-Min charge has taken new different vision of fairness or to be more precise bounding delay and jitter is not up to in packet switching networks. The authors claim that to the expectation level or even not met. Moreover, any provide better fairness and proper protection to any user in further modification in the protocols to be able to adopt the a common shared resource, some aggressive penalty concept of reservation high efficiency of Quality of should applied for those who are maliciously use the Service (QoS) required a crucial modification in the core sharing procedure . of the Internet which is unachievable. These boundaries rigorously reduce the ability of flows to demand Let take the analogy of multiple buckets sharing one guarantees from the Internet, and the capability of the fountain or resource as in exhibit 3.4. So, let consider Internet to put forward and accomplish such guarantees. that greedily attempts to gain more bandwidth by initiating several session with multiple connation and If these constraints taken in account, the most hence reserves more bandwidth than the others. Such appropriate notion to be considered is max-min fairness. manner could breaches both the protection of other users The principle of proportional fairness necessitates flows to who indeed fairly be using the resource and the fairness transmit information about their bandwidth requirements by making get double service than the others. and reservations to each router along their rout. The principle of utility fairness is unclear in term of the specification of the utility function and it rather demands flows to convey their utility. Nevertheless, minimum information about the flows among all notions is required by the principle of max-min fairness; a flow has a demand of unity if it has a packet enqueued and has a demand of zero otherwise. This information is, indeed, promptly available to each router and therefore the max-min principle of fairness is the most amenable to implementation. Likewise, max-min fairness is presently the most accepted principle of fairness in the research community. 5. CONCLUSION The nature of the Internet traffics is random and dynamic; Exhibit 3.4: analogy of max-min charge therefore, such behaviors should be taken in account once the issue of resource allocation is investigated. It has Nevertheless, Max-Min fairness has nothing to do proven that max-min fairness, proportional fairness and regarding such issue since it concern about fairness among balanced fairness provide stability to the network flows and not users. However, Max-Min charge assigns a particularly when the vector of traffic intensities depends specific values and parameters to each user. The on the interior of the capacity set. It is also, proven that following equation has been deducted to improve the balance property have not been met by max-min fairness protection level: notion with the exception of the trivial case where the network condenses to a set of independent links. This (3.2) justifies the limitation of the analytical results for this allocation and strengthens the assumption that such results should not be excluded. Proportional fairness is not By this notion, the only user who will suffer from any balanced either except in some specific cases. increase in the demand or in queue number is the 154 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 4, July 2010 Balanced fairness, conversely, is balanced by "Rate control for communication networks: construction and consequently leads to a well-mannered shadow prices, proportional fairness and queueing network. Nevertheless, it is lack of practical stability," Journal of the Operational implementation; nonetheless, balanced fairness could to a Research society, vol. 49, pp. 237-252, 1998. certain extent be contemplated as a mathematical tool practical for the performance evaluation of more practical  L. Massoulie and J. Roberts, "Bandwidth allocations like max-min fairness and proportional sharing: objectives and algorithms," 1999. fairness.  R. Denda, A. 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Khanna, "On allocating goods to maximize fairness," 2009, pp. Yaser Miaji received the B.E. 107-116. form Riyadh College of Technology, Saudi Arabia and M.E.  A. Sridharan and B. Krishnamachari, "Maximizing degrees, from University of New South Wales, Australia. in 1997 network utilization with max–min fairness in and 2007, respectively. He is currently a doctoral researcher in Computer Science in the University Utara Malaysia. wireless sensor networks," Wireless Networks, vol. Previously, he works as a lecturer in the College of 15, pp. 585-600, 2009. Telecommunication and Electronic in Jeddah from 1998-2206.  T. Bonald, A. Proutiere, J. Roberts, and J. Virtamo, His research interest includes digital electronics, computer "Computational aspects of balanced fairness," 2003, network, distributed system and genetic algorithm. He is a member of InternetWorks research group, IEEE, ACM ISOC and pp. 801–810. STMPE. Suhaidi Hassan PhD SMIEEE is an associate professor in computer systems and communication networks and the Assistant Vice Chancellor of the Universiti Utara Malaysia’s College of Arts and Sciences. He received his PhD in Computing from University of Leeds in United Kingdom, MS in Information Science from University of Pittsburgh, PA and BS in Computer Science from Binghamton University, NY. He currently heads the InterNetWorks Research Group at the Universiti Utara Malaysia and chairs SIG InterNetWorks of the Internet Society Malaysia Chapter. 156 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
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