Delay Tradeoff Research Articles

Optimal Throughput–Delay Tradeoff in MANETs With Supportive Infrastructure Using Random Linear Coding

The performance of throughput, delay, and their tradeoff in mobile ad hoc networks (MANETs) has been investigated on different assumptions. Nevertheless, few papers consider the supportive infrastructure in MANETs. With the help of supportive infrastructure, i.e., cellular networks, the performance of throughput and delay in MANETs can be improved. The impact of supportive infrastructure on the throughput–delay tradeoff in MANETs is an open research point. In this paper, we are interested in the optimal throughput–delay tradeoff in MANETs with supportive infrastructure. We investigate the supportive infrastructure that only provides transmission pipes between distant nodes. We study the optimal throughput–delay tradeoff in MANETs on different assumptions such as different densities of nodes and base stations (BSs), discrete/continuous mobility models with various velocities, and the fast/slow mobility assumption. We obtain the asymptotically optimal throughput–delay tradeoff and propose transmission policies based on random linear coding (RLC) to achieve the optimal throughput–delay tradeoff asymptotically. From the obtained optimal throughput–delay tradeoff, we find that supportive infrastructure reduces the delay bound from velocity, which is a lower bound of delay caused by nodes' velocity. In particular, if supportive infrastructure can cover the entire network, then the delay bound from velocity vanishes when per-node throughput does not exceed a threshold. Moreover, we find that there also exists a delay bound from the transmission range of the instant transmission. In addition, we observe that, for the optimal throughput–delay tradeoff in the large throughput region, the case in the continuous mobility model outperforms the case in the discrete mobility model, and the case on the slow mobility assumption outperforms the case on the fast mobility assumption.

Processor-Network Speed Scaling for Energy–Delay Tradeoff in Smartphone Applications

Many smartphone applications, e.g., file backup, are intrinsically delay-tolerant so that data processing and transfer can be delayed to reduce smartphone battery usage. In the literature, these energy--delay tradeoff issues have been addressed independently in the forms of Dynamic Voltage and Frequency Scaling (DVFS) problems and network selection problems when smartphones have multiple wireless interfaces. In this paper, we jointly optimize the CPU speed and network speed to determine how much more energy can be saved through the joint optimization when applications can tolerate delays. We propose a dynamic speed scaling scheme called SpeedControl that jointly adjusts the processing and networking speeds using four controls: application scheduling, CPU speed control, wireless interface selection, and transmit power control. Through invoking the Lyapunov drift-plus-penalty technique, the scheme is demonstrated to be near optimal because it substantially reduces energy consumption for a given delay constraint. This paper is the first to reveal the energy--delay tradeoff relationship from a holistic perspective for smartphones with multiple wireless interfaces, DVFS, and multitasking capabilities. The trace-driven simulations based on real measurements of CPU power, network power, WiFi/3G throughput, and CPU workload demonstrate that SpeedControl can reduce battery usage by more than 42% through trading a 10 minutes delay when compared with the same delay in existing schemes; moreover, this energy conservation level increases as the WiFi coverage extends.

On the Throughput-Delay Tradeoff in Georouting Networks

We study the scaling properties of a georouting scheme in a wireless multi-hop network of $n$ mobile nodes. Our aim is to increase the network capacity quasi-linearly with $n$ , while keeping the average delay bounded. In our model, we consider mobile nodes moving according to an independent identically distributed random walk with velocity $v$ and transmitting packets to randomly chosen fixed and known destinations. The average packet delivery delay of our scheme is of order $1/v$ , and it achieves network capacity of order $({n}/{\log n\log \log n})$ . This shows a practical throughput-delay tradeoff, in particular when compared with the seminal result of Gupta and Kumar, which shows network capacity of order ${(n/\log n)}^{1/2}$ and negligible delay and the groundbreaking result of Grossglauser and Tse, which achieves network capacity of order $n$ but with an average delay of order $\sqrt {n}/v$ . The foundation of our improved capacity and delay tradeoff relies on the fact that we use a mobility model that contains straight-line segments, a model that we consider more realistic than classic Brownian motions. We confirm the generality of our analytical results using simulations under various interference models.

Rate, Energy, and Delay Tradeoffs in Wireless Multicast: Network Coding versus Routing

We build on the framework of joint scheduling and network coding optimization. The formulation is extended to include rate, energy, and delay in network coding and routing paradigms. We then study energy-rate and delay-rate relationships to see how minimum energy and delay change as functions of multicast rate demand. The main observation is that as the rate demand approaches maximum achievable rate, the solution tends to increasingly use more diverse, longer paths. This translates into non-linearly higher energy and delay for higher input rates. In the case of energy, we are also able to show that network coding provides more benefits (when compared to routing) at higher rates. Another observation is related to the scheduling over maximal independent sets (MISs). We present results on comparing the performance of scheduling over all, exponentially growing MISs and small randomly selected subsets of MISs. Our results point to the effectiveness of the latter in achieving near-optimal rate and energy while reducing the complexity of the problem.

Energy Study for 28 nm Fully Depleted Silicon-On-Insulator Devices

In this paper, we propose to study the impact of 28nm FDSOI technology on energy study. In fact, due to the effects of the Moore’s law, the process variations in current technologies are increasing and have a major impact on power and performance which results in parametric yield loss. Due to this, process variability and the difficulty of modeling accurately transistor behavior impede the dimensions scaling benefits. The Fully Depleted Silicon-On-Insulator (FDSOI) technology is one of the main contenders for deep submicron devices as they can operate at low voltage with superior energy efficiency compared with bulk CMOS. In this paper, we study the static energy on 28nm FDSOI devices to implement sub-threshold circuits. Study of delay vs. static power trade-off reveals the FDSOI robustness with respect to process variations.

Manycast Overlay in Split-Incapable Networks for Supporting Bandwidth-Intensive Applications

Recent trends in science applications call for long-range and large-scale collaboration among laboratories and super-computing sites. Long gone are the days of entering data manually into a spreadsheet on a local workstation. The world's most powerful and ground-breaking experiments generate exabytes of information, which must be distributed to multiple labs for analysis and interpretation. Such trends reveal the unwavering importance of new communication paradigms, like multicasting and manycasting, which provide point-to-multipoint data transfers. Typically, these all-important mechanisms are provided at the optical layer, where split-capable cross-connects split input signals into multiple output signals all-optically. Unfortunately, some of the world's largest and most powerful networks do not have the hardware infrastructure to support such functionality, but allow for point-to-point communication exclusively. In such split-incapable (SI) networks, multicast and manycast must be provided as a logical overlay to the pre-existing and limited unicast infrastructure. In this paper, we present two overlay models for providing manycast support in SI networks: Manycasting with Drop at Member Node (MA-DMN) and Manycasting with Drop at Any Node (MA-DAN). Through the development of integer linear programs (ILPs) and heuristics, we evaluate these models in terms of both optimal solutions and efficient approximations for both small-scale and large-scale networks and consider both static and dynamic traffic scenarios. Our results demonstrate that despite a small tradeoff in additional complexity and delay from signal conversion to the optical domain, our models provide efficient utilization of network resources and greatly surpass the standard naive approach of establishing paths to every destination.

Energy Efficiency and Delay Tradeoff in Device-to-Device Communications Underlaying Cellular Networks

This paper investigates the problem of revealing the tradeoff between energy efficiency (EE) and delay in device-to-device (D2D) communications underlaying cellular networks. Considering both stochastic traffic arrivals and time-varying channel conditions, we formulate it as a stochastic optimization problem, which optimizes EE subject to the average power, interference-control, and network stability constraints. With the help of fractional programming and the Lyapunov optimization technique, we develop an algorithm, referred to as the TRADEOFF, to solve the problem. To deal with the nonconvex and NP-hard power allocation subproblem in the TRADEOFF, we adopt the prismatic branch and bound algorithm to find its globally optimal solution, where only a linear programming needs to be solved in each iteration. Thus, the TRADEOFF serves as an important benchmark to evaluate performance of other heuristic algorithms and is usually cost-efficient. The theoretical analysis and simulation results show that the TRADEOFF achieves an EE-delay tradeoff of $[O(1/V),O(V)]$ with $V$ being a control parameter and can strike a flexible balance between them by simply tuning $V$ .

Base-Station Sleeping Control and Power Matching for Energy–Delay Tradeoffs With Bursty Traffic

In this paper, we study sleeping control (SC) and power matching (PM) for a single cell in cellular networks with bursty traffic. The base station (BS) sleeps whenever the system is empty and wakes up when N users are assembled during the sleep period. The service capacity of the BS in the active mode is controlled by adjusting its transmit power. The total power consumption and average delay are analyzed, and based on this, the impact of parameter N and transmit power on the energy-delay tradeoff is studied. It is shown that, given the average traffic load, the more bursty the traffic is, the less total power consumed, although the delay performance of more bursty traffic is better only under certain circumstances. The optimal energy-delay tradeoff is then obtained through joint SC and PM optimization. The relationship between the optimal control parameters and the asymptotic performance are also provided. Moreover, the influence of the traffic autocorrelation is explored, which shows less impact on the system performance compared with that of the burstiness. Numerical results show the energy saving gain of the joint SC and PM scheme, as well as the impact of burstiness on the optimal energy-delay tradeoff.

Optimal Power Allocation With Delay Constraint for Signal Transmission From a Moving Train to Base Stations in High-Speed Railway Scenarios

Widespread deployment of high-speed railways in recent years has created huge demand for high-mobility broadband wireless communications. To provide broadband wireless access for passengers on the train, a well-acknowledged approach is to apply a two-hop architecture, under which passengers communicate with base stations (BSs) via an access point (AP) installed in the train. We consider the uplink transmission from the AP to the BSs on the ground along the railway. The key problem we discuss is how to match user-data arrival process and time-varying channel service process with a delay constraint at the AP. We first assume the constant-rate data arrival and the wireless channel affected only by large-scale fading in high-speed railway scenarios. We present the optimal power-allocation policy given the delay constraint. Based on the policy, we show that there exists a power–delay tradeoff in our system model, with the delay within a limited range. Besides, we find that the average transmit power and the train velocity have a tradeoff, which is similar to the power–delay tradeoff. Besides, given average transmit power constraint, the delay constraint is inversely proportional to the train velocity. Finally, when small-scale fading is considered, we modify the optimal power-allocation policy and provide a convenient approach to counteract Nakagami- $m$ fading in high signal-to-noise ratio (SNR) regimes. The modified power-allocation policy can be well adapted to different fading statistics in different terrestrial environments experienced by the high-speed train.

Cognitive Radio Networks With Probabilistic Relaying: Stable Throughput and Delay Tradeoffs

This paper studies fundamental throughput and delay tradeoffs in cognitive radio systems with cooperative secondary users. We focus on randomized cooperative policies, whereby the secondary user (SU) serves either its own queue or the primary users (PU) relayed packets queue with certain service probability. The proposed policy opens room for trading the PU delay for enhanced SU delay, and vice versa, depending on the application QoS requirements. Towards this objective, the system's stable throughput region is characterized. Furthermore, the moment generating function approach is employed and generalized for our system to derive closed-form expressions for the average packet delay for both users. The accuracy of these expressions is validated through simulations. Analytical and simulation results reveal that the service probability can steer the system into prioritizing PU's traffic at the expense of SU's QoS, or vice versa, independently from the admission probability. Alternatively, the ability of the admission probability to control the throughput and delay at the PU or the SU depends on the selected value for the service probability as well as the channel conditions. Finally, it is shown how the service and admission probabilities could be used to achieve the desired QoS level to both PU and SU.

On Multicast Capacity and Delay in Cognitive Radio Mobile Ad Hoc Networks

In this paper, we focus on the capacity and delay tradeoff for multicast traffic pattern in cognitive radio mobile ad hoc networks (MANETs). In our system model, the primary network consisting of n primary nodes overlaps with the secondary network consisting of m secondary nodes in a unit square. Assume that all nodes move according to an independent and identically distributed mobility model, and each primary node serves as a source that multicasts its packets to kp primary destination nodes, whereas each secondary source node multicasts its packets to ks secondary destination nodes. Under the cell partitioned network model, we study the capacity and delay for the primary networks under two communication schemes, i.e., noncooperative scheme and cooperative scheme. The communication pattern considered for the secondary network is cooperative scheme. Given that m = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</sup> (β > 1), we show that per-node capacities O(1/k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ) and O(1/k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ) are achievable for the primary network and the secondary network, with average delays Θ(n log k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ) and Θ(m log k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ), respectively. Moreover, to reduce the average delay in the secondary network, we employ a redundancy scheme and prove that a per-node capacity O(1/k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> √m log k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ) is achievable with average delay Θ(√m log k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ). We find that the fundamental delay-capacity tradeoff in the secondary network is delay/capacity ≥ O(mk <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> log k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ) under both cooperative and redundancy schemes.

A Benchmarking Algorithm to Determine Minimum Aggregation Delay for Data Gathering Trees and an Analysis of the Diameter-Aggregation Delay Tradeoff

Aggregation delay is the minimum number of time slots required to aggregate data along the edges of a data gathering tree (DG tree) spanning all the nodes in a wireless sensor network (WSN). We propose a benchmarking algorithm to determine the minimum possible aggregation delay for DG trees in a WSN. We assume the availability of a sufficient number of unique CDMA (Code Division Multiple Access) codes for the intermediate nodes to simultaneously aggregate data from their child nodes if the latter are ready with the data. An intermediate node has to still schedule non-overlapping time slots to sequentially aggregate data from its own child nodes (one time slot per child node). We show that the minimum aggregation delay for a DG tree depends on the underlying design choices (bottleneck node-weight based or bottleneck link-weight based) behind its construction. We observe the bottleneck node-weight based DG trees incur a smaller diameter and a larger number of child nodes per intermediate node; whereas, the bottleneck link-weight based DG trees incur a larger diameter and a much lower number of child nodes per intermediate node. As a result, we observe a complex diameter-aggregation delay tradeoff for data gathering trees in WSNs.

Effective Capacity Maximization With Statistical Delay and Effective Energy Efficiency Requirements

This paper presents the three-fold energy, rate and delay tradeoff in mobile multimedia fading channels. In particular, we propose a rate-efficient power allocation strategy for delay-outage limited applications with constraints on energy-per-bit consumption of the system. For this purpose, at a target delay-outage probability, the link-layer energy efficiency, referred to as effective-EE, is measured by the ratio of effective capacity (EC) and the total expenditure power, including the transmission power and the circuit power. At first, the maximum effective-EE of the channel at a target delay-outage probability is found. Then, the optimal power allocation strategy is obtained to maximize EC subject to an effective-EE constraint with the limit set at a certain ratio of the maximum achievable effective-EE of the channel. We then investigate the effect of the circuit power level on the maximum EC. Further, to set a guideline on how to choose the effective-EE limit, we obtain the transmit power level at which the rate of increasing EC (as a function of transmit power) matches a scaled rate of losing effective-EE. Analytical results show that a considerable EC-gain can be achieved with a small sacrifice in effective-EE from its maximum value. This gain increases considerably as the delay constraint becomes tight.

A Benchmarking Algorithm for Maximum Bottleneck Node Trust Score-based Data Gathering Trees in Wireless Sensor Networks

The author proposes a benchmarking algorithm to determine maximum bottleneck node trust score-based data gathering trees (MaxBNT-DG trees) for wireless sensor networks (WSNs) wherein the bottleneck node trust score of a path (minimum trust score for any node on the path, including those of the end nodes) from any node to the root node of the DG tree is the maximum. He compares the performance of the MaxBNT-DG trees with that of the maximum bottleneck link weight-based data gathering trees (MaxBLT-DG trees) for which the bottleneck link trust score (minimum trust score for constituent links) of a path from any node to the root node is the maximum. The author observes the MaxBNT-DG trees to incur a smaller tree diameter, a larger percentage of nodes as leaf nodes and a larger trust score per intermediate node; whereas, the MaxBLT-DG trees incur a lower aggregation delay, indicating a trust-aggregation delay tradeoff in WSNs. The MaxBNT-DG algorithm is also generic and can be extended to any other node criterion like residual energy, wake-up frequency, etc

Simultaneous exploration of optimal datapath and loop based high level transformation during area-delay tradeoff in architectural synthesis using swarm intelligence

This paper presents the first transformation methodology of a swarm intelligence algorithm into design space exploration (DSE) of optimal datapath and unrolling factor during area-performance tradeoff in high level synthesis (HLS). To the best of the authors’ belief, in the literature, the above transformation has not been performed so far for developing a fully automated integrated exploration methodology of optimal datapath and unrolling factor. DSE problem being intractable and complex in the presence of auxiliary variable such as loop unrolling factor requires administration of intelligent decision making strategies at multiple stages to yield optimal results. The major sub-contributions of this proposed algorithm includes: a) Deriving a model for execution delay computation of a loop unrolled control data flow graph (CDFG) based on available resources, b) handling area-execution delay tradeoff during our integrated DSE process; c) Sensitivity analysis of particle swarm optimization (PSO) parameters used in assisting the designer in pre-tuning the baseline parameters during final area-performance tradeoff. Results of the proposed approach indicated an average improvement in Quality of Results (QoR) of > 23% and reduction in runtime of > 92% compared to recent approaches.

Switch cost and packet delay tradeoff in data center networks with switch reconfiguration overhead

Cost minimization is a major concern in data center networks (DCNs). Existing DCNs generally adopt Clos network with crossbar middle switches to achieve non-blocking data switching among the servers, and the number of middle switches is proportional to the number of ports of the aggregation switches in a fixed manner. Besides, reconfiguration overhead of the switches is generally ignored, which may contradict the engineering practice. In this paper, we consider batch scheduling based packet switching in DCNs with reconfiguration overhead at each middle switch, which inevitably leads to packet delay. With existing state-of-the-art traffic matrix decomposition algorithms, we can generate a set of permutations, each of which stands for the configuration of a middle switch. By reconfiguring each middle switch to fulfill multiple configurations in parallel with others, we reveal that a tradeoff exists between packet delay and switch cost (denoted by the number of middle switches), while performance guaranteed switching with bounded packet delay can be achieved without any packet loss. Based on the tradeoff, we can minimize the number of middle switches (under a given packet delay bound) and an overall cost metric (by translating delay into a comparable cost factor), as well as formulating criteria for choosing a proper matrix decomposition algorithm. This provides a flexible way to reduce the number of middle switches by slightly enlarging the packet delay bound.

Characterizing Energy–Delay Tradeoff in Hyper-Cellular Networks With Base Station Sleeping Control

Base station (BS) sleeping operation is one of the effective ways to save energy consumption of cellular networks, but it may lead to longer delay to the customers. The fundamental question then arises: How much energy can be traded off by a tolerable delay? In this paper, we characterize the fundamental tradeoffs between total energy consumption and overall delay in a BS with sleep mode operations by queueing models. Here, the BS total energy consumption includes not only the transmitting power but also basic power (for baseband processing, power amplifier, etc.) and switch-over power of the BS working mode, and the overall delay includes not only transmission delay but also queueing delay. Specifically, the BS is modeled as an M/G/1 vacation queue with setup and close-down times, where the BS enters sleep mode if no customers arrive during the close-down (hysteretic) time after the queue becomes empty. When asleep, the BS stays in sleep mode until the queue builds up to N customers during the sleep period ( N-Policy) . Several closed-form formulas are derived to demonstrate the tradeoffs between the energy consumption and the mean delay for different wake-up policies by changing the close-down time, setup time, and the parameter N. It is shown that the relationship between the energy consumption and the mean delay is linear in terms of mean close-down time, but non-linear in terms of N. The explicit relationship between total power consumption and average delay with varying service rate is also analyzed theoretically, indicating that sacrificing delay cannot always be traded off for energy saving. In other words, larger N may lead to lower energy consumption, but there exists an optimal N* that minimizes the mean delay and energy consumption at the same time. We also investigate the maximum delay (delay bound) for certain percentage of service and find that the delay bound is nearly linear in mean delay in the cases tested. Therefore, similar tradeoffs exist between energy consumption and the delay bound. In summary, the closed-form energy-delay tradeoffs cast light on designing BS sleeping and wake-up control policies that aim to save energy while maintaining acceptable quality of service.

Throughput–Delay Tradeoff in Interference-Free Wireless Networks With Guaranteed Energy Efficiency

Existing works have addressed the tradeoffs between any two of the three performance metrics: throughput, energy efficiency (EE), and delay. In this paper, we unveil the intertwined relations among these three metrics under a unifying framework and particularly investigate the problem of EE-guaranteed throughput–delay tradeoff in interference-free wireless networks. We first propose two admission control schemes, referred to as the first-out and first-in schemes. We then formulate it as two stochastic optimization problems, aiming at throughput maximization (in the first-out scheme) or dropping rate minimization (in the first-in scheme) subject to requirement of EE (RoE), stability, admission control, and transmit power. To solve the problems, the EE-Guaranteed algorithm for throUghput-delAy tRaDeoff (eGuard), respectively called eGuard-I and eGuard-II in the first-out and first-in schemes, is devised. Moreover, with guaranteed RoE, we theoretically show that the eGuard (I and II) can not only push the throughput arbitrarily close to the optimal with tradeoffs in delay but also quantitatively control the throughput–delay performance on demand. Simulation results consolidate the theoretical analysis and particularly show the pros and cons of the two schemes.

Throughput and Delay Tradeoffs for Mobile Ad Hoc Networks With Reference Point Group Mobility

In this paper, we explore the throughput–delay tradeoff in a mobile ad hoc network (MANET) operating under the practical reference point group mobility model and also a general setting of node moving speed. In particular, we consider a MANET with unit area and $n$ nodes being divided evenly into $\Theta(n^{\alpha})$ groups, $\alpha\in[0,1] $ , where the center of each group moves according to a random direction model with speed of no more than $\upsilon\in[0,1] $ . We determine the regions of per-node throughput and average delay and their tradeoffs that can be achieved (in order sense) in such a network. For the regime of $\upsilon=0$ , we first prove that the per-node throughput capacity is $\Theta(n^{-\alpha/2} ) $ and then develop a routing scheme to achieve this capacity, resulting in an average delay of $\Theta(\max\{n^{1/2},n^{1-\alpha}\})$ for any $\alpha\in[0,1]$ . Regarding the regime of $\upsilon>0$ , we prove that the per-node throughput capacity can be improved to $\Theta(1) $ , which is achievable by adopting a new routing scheme with an average delay of $\Theta(\max\{n^{1-\alpha},n^{\alpha/2}/\upsilon\})$ for $\upsilon=o(1)$ and $\Theta(n)$ for $\upsilon=\Theta(1) $ . The results in this paper help us to have a deep understanding on the fundamental performance scaling laws and also enable an efficient throughput–delay tradeoff to be achieved in MANETs with correlated mobility.

Throughput–Delay Tradeoff for Wireless Multichannel Multi-Interface Random Networks

Capturing throughput-delay tradeoff in wireless networks has drawn considerable attention, as it could bring better usage experience by considering different requirements of throughput/delay demands. Traditional works consider only typical single-channel single-interface networks, whereas multichannel multi-interface (MCMI) networks will become mainstream since they provide concurrent transmissions in different channels, which in turn helps each node to obtain better performance. Unlike previous works, this paper investigates the throughput-delay tradeoff for MCMI random networks. Two queuing systems, i.e., the M/M/m queuing system and the m M/M/1 queuing system, are established for MCMI nodes, and a parameter in routing implementation named routing deviation is also considered in the analytical model. This paper studies concurrent transmission capacity (CTC) using the physical interference model and also explores the impact on CTC of different physical parameters. Moreover, the relations between throughput and delay are achieved using two queuing systems in MCMI random networks respectively. The deterministic results obtained with a group of real network configuration parameters demonstrate that the proposed tradeoff model could be applied to the real network scenarios.