The performance of cluster computing depends on how concurrent jobs share multiple data center resource types like CPU, RAM and disk storage. Recent research has discussed efficiency and fairness requirements and identified a number of desirable scheduling objectives including so-called dominant resource fairness (DRF). We argue here that proportional fairness (PF), long recognized as a desirable objective in sharing network bandwidth between ongoing flows, is preferable to DRF. The superiority of PF is manifest under the realistic modelling assumption that the population of jobs in progress is a stochastic process. In random traffic the strategy-proof property of DRF proves unimportant while PF is shown by analysis and simulation to offer a significantly better efficiency-fairness tradeoff.
The study of bandwidth sharing under a dynamic model of traffic also began at the end of the 90s @cite_21 . The most significant results derived over an ensuing period of several years are summarized in the paper by Bonald al @cite_10 . It turns out that PF brings significantly better flow completion time performance than max-min fairness for a network with a mix of wired and wireless links (e.g., see Fig. 17.14 in @cite_12 ). Flows in such networks require unequal resource shares, like jobs in the compute cluster, so that these bandwidth sharing results anticipate the excellent performance of PF demonstrated here.
@cite_21 @cite_10 @cite_12
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We discuss the relevance of fairness as a design objective for congestion control mechanisms in the Internet. Specifically, we consider a backbone network shared by a dynamic number of short-lived flows, and study the impact of bandwidth sharing on network performance. In particular, we prove that for a broad class of fair bandwidth allocations, the total number of flows in progress remains finite if the load of every link is less than one. We also show that provided the bandwidth allocation is "sufficiently" fair, performance is optimal in the sense that the throughput of the flows is mainly determined by their access rate. Neither property is guaranteed with unfair bandwidth allocations, when priority is given to one class of flow with respect to another. This suggests current proposals for a differentiated services Internet may lead to suboptimal utilization of network resources. We consider the performance of a network like the Internet handling so?called elastic traffic where the rate of flows adjusts to fill available bandwidth. Realized throughput depends both on the way bandwidth is shared and on the random nature of traffic. We assume traffic consists of point to point transfers of individual documents of finite size arriving according to a Poisson process. Notable results are that weighted sharing has limited impact on perceived quality of service and that discrimination in favour of short documents leads to considerably better performance than fair sharing. In a linear network, max---min fairness is preferable to proportional fairness under random traffic while the converse is true under the assumption of a static configuration of persistent flows. Admission control is advocated as a necessary means to maintain goodput in case of traffic overload. This paper addresses the problem of fairness in wireless ad-hoc networks. Usually, the problem of fairness in wireless ad-hoc networks is addressed in a classic approach inherited from wired networks. The common assumption is that nodes flows have pre-assigned fair shares. The task becomes then to modify wired networks' fair queueing algorithms to address wireless network nature. Wired networks have efficient means of allocating fiar shares through admission control and additionally, the fair shares remain constant throughout the session duration due to the static nature of the nodes. In ad-hoc networks, it is meaningless to assume statically pre-assigned fair shares, since on one hand, not only the nodes move, but also the routers are mobile, and on the other hand, contention is location dependent, such that in terms of absolute guarantees, fairness would at most mean "avoiding starvation": thus applying rate proportional fiar queueing algorithms is beyond the original goal of such algorithm, viz. flow isolation protection and bandwidth guarantee. In this work we argue in favor of multi-level scheduling for wireless ad-hoc networks with max-min fair allocation of the fair shares at the lower-most layer (MAC layer). This paper, mainly lays down the theoretical framework by which one can calculate the fair shares that would achieve max-min fairness in an ad-hoc network. We design distributed algorithms that allow each node to determine its max-min per-link fair share in a global ad-hoc network without knowledge of the global topology of the network. The results are then used in conjunction with a novel practical scheduling algorithm for IEEE 802.11 to show how fairness is achieved Abstract The performance of cluster computing depends on how concurrent jobs share multiple data center resource types such as CPU, RAM and disk storage. Recent research has discussed efficiency and fairness requirements and identified a number of desirable scheduling objectives including so-called dominant resource fairness (DRF). We argue here that proportional fairness (PF), long recognized as a desirable objective in sharing network bandwidth between ongoing data transfers, is preferable to DRF. The superiority of PF is manifest under the realistic modeling assumption that the population of jobs in progress is a stochastic process. In random traffic the strategy-proof property of DRF proves unimportant while PF is shown by analysis and simulation to offer a significantly better efficiency–fairness tradeoff.