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Recent studies demonstrate that handling a patient’s health-related data needs dynamic Quality of Service (QoS) to cope up with significant changes in the functioning of a Patient Monitoring Wireless Body Area Network (PA-WBAN). This kind of system demands a remarkable improvement in the QoS as it is dealing with the reliable transmission of a large volume of assorted data in a frequently changing environment. The QoS in a PA-WBAN is not only about how it senses or reads data from various body parts but is how it deciphers the sensed data. In the patient monitoring system, if the required data is not available in a readable format or not in a specific time, then this data becomes useless and is of no use. However, to harmonize these requirements, two approaches are proposed in this paper. The first protocol is developed for obtaining dynamic QoS for reliable packet transmission. It offers both node and packet-level dynamic-priority assignment policy, which further helps in fair and dynamic resource allocation, queuing, scheduling, retransmission, drop, and delay. It provides fair queuing and percentile scheduling policies, which estimates service rate for each priority queue and serves only highly significant packets with high waiting time during scheduling. Additionally, it offers application-specific reliability through a predictive retransmission and loss recovery policy. During retransmission and loss recovery, it calculates a retransmission rate for each sensor node and retransmits only that amount of packets from each sensor node. It also controls congestion with its dynamic priority-based rate adjustment and packet drop policies. It further provides a concept of time-bound based packet transmission policy that minimizes delay and jitter more appropriately. The second protocol is designed for attaining optimized QoS in the dynamic and assorted PA-WBAN. It applies a Lion Cooperative Hunt Optimization (LCHO) technique for the optimization of multi-objective QoS. Both theoretical and simulation results examine the usefulness of the proposed protocols and illustrate its advantage over the existing protocols.

Recent years have seen a slew of papers on datacenter congestion control mechanisms. In this editorial, we ask whether the bulk of this research is needed for the common case where congestion control involves hosts responding to simple congestion signals from the network and the performance goal is reducing some average measure of flow completion time. We raise this question because we find that, out of all the possible variations one could make in congestion control algorithms, the most essential feature is the switch scheduling algorithm. More specifically, we find that congestion control mechanisms that use Shortest-Remaining-Processing-Time (SRPT) achieve superior performance as long as the rate-setting algorithm at the host is reasonable. We further find that while SRPT's performance is quite robust to host behaviors, the performance of schemes that use scheduling algorithms like FIFO or Fair Queuing depend far more crucially on the rate-setting algorithm, and their performance is typically worse than what can be achieved with SRPT. Given these findings, we then ask whether it is practical to realize SRPT in switches without requiring custom hardware. We observe that approximate and deployable SRPT (ADS) designs exist, which leverage the small number of priority queues supported in almost all commodity switches, and require only software changes in the host and the switches. Our evaluations with one very simple ADS design shows that it can achieve performance close to true SRPT and is significantly better than FIFO. Thus, the answer to our basic question - whether the bulk of recent research on datacenter congestion control algorithms is needed for the common case - is no.

As implied by the word “packet scheduling”, the shared transmission resource should be intentionally assigned to some users at a given time. The process of assigning users’ packets to appropriate shared resource to achieve some performance guarantee is so-called packet scheduling. It is anticipated that packetized transmissions over links via proper packet scheduling algorithms will possibly make higher resource utilization through statistical multiplexing of packets compared to conventional circuit-based communications. A packet-switched and integrated service environment is therefore prevalent in most practical systems nowadays. However, it will possibly lead to crucial problems when multiple packets associated to different kinds of Quality of Service (QoS) (e.g. required throughput, tolerated delay, jitter, etc) or packet lengths competing for the finite common transmission resource. That is, when the traffic load is relatively heavy, the first-come-first-serve discipline may no longer be an efficient way to utilize the available transmission resource to satisfy the QoS requirements of each user. In such case, appropriate packet-level scheduling algorithms, which are designed to schedule the order of packet transmission under the consideration of different QoS requirements of individual users or other criteria, such as fairness, can alter the service performance and increase the system capacity . As a result, packet scheduling algorithms have been one of the most crucial functions in many practical wired and wireless communication network systems. In this chapter, we will focus on such topic direction for complete investigation. Till now, many packet scheduling algorithms for wired and wireless communication network systems have been successfully presented. Generally speaking, in the most parts of researches, the main goal of packet scheduling algorithms is to maximize the system capacity while satisfying the QoS of users and achieving certain level of fairness. To be more specific, most of packet scheduling algorithm proposed are intended to achieve the following desired properties: 1. Efficiency: The basic function of packet scheduling algorithms is scheduling the transmission order of packets queued in the system based on the available shared resource in a way that satisfies the set of QoS requirements of each user. A packet scheduling algorithm is generally said to

The increasing number of different, incompatible congestion control algorithms has led to an increased deployment of fair queuing. Fair queuing isolates each network flow and can thus guarantee fairness for each flow even if the flows' congestion controls are not inherently fair. So far, each queue in the fair queuing system either has a fixed, static maximum size or is managed by an Active Queue Management (AQM) algorithm like CoDel. In this paper we design an AQM mechanism (Learning Fair Qdisc (LFQ)) that dynamically learns the optimal buffer size for each flow according to a specified reward function online. We show that our Deep Learning based algorithm can dynamically assign the optimal queue size to each flow depending on its congestion control, delay and bandwidth. Comparing to competing fair AQM schedulers, it provides significantly smaller queues while achieving the same or higher throughput.

Modern data centers often host multiple applications with diverse network demands. To provide fair bandwidth allocation to several thousand traversing flows, Approximate Fair Queueing (AFQ) utilizes multiple priority queues in switch to approximate ideal fair queueing. However, due to limited number of queues in commodity switches, AFQ easily experiences high packet loss and low link utilization. In this paper, we propose Elastic Fair Queueing (EFQ), which leverages limited priority queues to flexibly achieve both high network utilization and fair bandwidth allocation. EFQ dynamically assigns the free buffer space in priority queues for each packet to obtain high utilization without sacrificing flow-level fairness. The results of simulation experiments and real implementations show that EFQ reduces the average flow completion time by up to 82% over the state-of-the-art fair bandwidth allocation mechanisms.

Traditional ad-hoc network packet scheduling schemes cannot fulfill the requirements of proximity-based ad-hoc social networks (ASNETs) and they do not behave properly in congested environments. To address this issue, we propose a user popularity-based packet scheduling scheme for congestion control in ASNETs called Pop-aware. The proposed algorithm exploits social popularity of sender nodes to prioritize all incoming flows. Pop-aware also provides fairness of service received by each flow. We evaluate the performance of Pop-aware through a series of simulations. In comparison with some existing scheduling algorithms, Pop-aware performs better in terms of control overhead, total overhead, average throughput, packet loss rate, packet delivery rate and average delay., Comment: 18 pages, 8 figures

Modern content delivery networks (CDNs) allow their customers (i.e., operators of web services) to customize the processing of requests by uploading and executing code at the edges of the CDN's network. To achieve scale, CDNs have forgone heavyweight virtualization techniques. Instead, all requests often execute within the same OS or even process. However, performance interference may arise when these requests have differing demands on multiple system resources. In this paper, we study the sources of performance interference based on workloads from real customers, identify the lack of multi-resource fairness as the culprit, and show that existing schedulers available in commodity OSs are insufficient to enforce fairness between customers. We then design Sandpaper, a new and practical multi-resource request scheduler for mitigating performance interference in CDN edge environments. Sandpaper enforces fairness despite constraints, such as sitting within the application runtime and running atop the OS's underlying resource schedulers. By leveraging key insights about the differences between theoretical system models and real systems, Sandpaper bridges the trade-off between resource utilization and multi-resource fairness that plagues existing schedulers. We implement Sandpaper atop Varnish, an open-source CDN edge proxy, and show that it mitigates performance interference while maintaining high resource utilization and with little performance overhead.

With the applications of Internet of Things (IoT) for smart cities, the real-time performance for a large number of network packets is facing serious challenge. Thus, how to improve the emergency response has become a critical issue. However, traditional packet scheduling algorithms cannot meet the requirements of the large-scale IoT system for smart cities. To address this shortcoming, this paper proposes EARS, an efficient data-emergency-aware packet scheduling scheme for smart cities. EARS describes the packet emergency information with the packet priority and deadline. Each source node informs the destination node of the packet emergency information before sending the packets. The destination node determines the packet scheduling sequence and processing sequence according to emergency information. Moreover, this paper compares EARS with a first-come, first-served, multilevel queue algorithm and a dynamic multilevel priority packet scheduling algorithm. Simulation results show that EARS outperforms these previous scheduling algorithms in terms of packet loss rate, average packet waiting time, and average packet end-to-end delay.

Keeping the delay experienced by packets while travelling from a source to a destination below certain thresholds is essential to successfully deliver a number of Internet services nowadays. Most of the packet delay can be usually ascribed to the time spent in the many queues encountered by the packet. In this context, the term bufferbloat has been recently coined to denote the uncontrolled growth of the queuing time due, among others, to the excessive size of the buffers and the attitude of TCP to increase the sending rate until a packet is dropped. In this paper, we focus on the queues employed by the traffic control infrastructure and by the network device drivers. Reducing the queuing time due to the former is the objective of a plethora of scheduling algorithms developed in the past years and referred to as Active Queue Management (AQM) algorithms. Conversely, the impact of the additional queuing in the buffer of the network device driver on performance and on the effectiveness of AQM algorithms has instead received much less attention. In this paper, we report the results of an experimental analysis we conducted to gain a better insight into the impact that network device buffers (and their size) have on performance. We also give an in-depth presentation of Dynamic Queue Limits (DQL), an algorithm recently introduced in the Linux kernel to dynamically adapt the size of the buffers held by network device drivers. The experiments we conducted show that DQL not only enables to reduce the queuing time in the network device buffers, which is essential to ensure the effectiveness of AQM algorithms, but also enables to keep latency stable, which is important to reduce the jitter. In order to faithfully reproduce through simulations the dynamics revealed by the experimental study we conducted, we implemented DQL for the popular ns-3 network simulator. In this paper, we describe the design of such implementation and report the results of a simulation study we conducted to show the ability of the simulator to accurately reproduce the experimental results.

The aim of this study was to simulate pedestrian movements in the queuing process in subway stations using microscopic simulation models. The queuing process was divided into two stages – walking and selecting a queue, and entering a queue and waiting in that queue. To represent the boundary of these two stages, a novel queuing line concept was developed. This queuing line dynamically changes with factors such as pedestrian preference, queue shape and queue length. Furthermore, two corresponding microscopic simulation models were developed to handle those two stages on the basis of the social force model and the queuing rule, respectively. Typically, the queuing rule is determined by the desired force, the impact of the nearest pedestrian ahead in the same direction and a self-stopping mechanism. Two examples – a ticket office and a security check in a subway station – were used to verify the validity of the proposed models.

Charging electric vehicles (EVs) at home is attractive to EV users. However, when the penetration level of EVs becomes high, a distribution grid suffers from problems such as under-voltage and transformer overloading. EV users also experience a fairness problem, i.e., the limited capacity is unfairly shared among EVs. To solve these problems, a physical fair-queueing framework is established for EV charging. In this framework, a distribution sub-grid is first mapped to a multi-server queueing system, and then a fluid-model based queueing scheme called physical multi-server generalized processor sharing (pMGPS) is designed. pMGPS ensures perfect fairness but cannot be used practically due to its nature of fluid model. To this end, a packetized scheme called physical start-time fair queueing (pSTFQ) is developed to schedule tasks of EV charging. The fairness performance of the pSTFQ scheduling scheme is characterized by the ratio of energy difference between pSTFQ and pMGPS. This critical performance metric is studied through theoretical analysis and is also evaluated via simulations. Performance results show that the pSTFQ scheduling scheme achieves an energy difference ratio of less than 4 percent in various scenarios without causing under-voltage and transformer overloading problems.

© 1967-2012 IEEE. Multicasting is emerging as an efficient method to deliver the same data to a group of users, thereby saving network resources. The fairness between different multicast groups is an important quality-of-service (QoS) indication, but it has not been given significant attention. In this paper, we propose a normalized signal-To-noise ratio (SNR)-based fair scheduling for multiple multicast groups in multicast systems. The system fairness and capacity are then analyzed and compared for both fair scheduling and greedy scheduling over independent but non-identically distributed (i.n.d.) fading channels. Closed-form expressions in terms of the system spectral efficiency, outage probability, system fairness, and average bit error rate (BER) are derived in an uncoded/coded M-Ary quadrature amplitude modulation based adaptive transmission multicast system over i.n.d. Rayleigh fading channels. Numerical results show that compared with greedy scheduling, fair scheduling achieves considerably high fairness at the cost of slight system capacity loss, regardless of the number of multicast groups. Our focus is on the physical layer without rate loss, but we also briefly discuss applications of the proposed scheduling in a cross-layer design subject to the loss rate QoS constraint.

In this paper, we study a multi-server queuing system wherein each user is constrained to get service only from a specified subset of servers. Fair packet scheduling in such a setting poses novel challenges that we address in this paper. Specifically, we observe that max-min fair allocation of the available resource over different servers (notably bandwidth) in the presence of placement constraints results in different levels of fair service-rates. To achieve the max-min fair service rates, we propose a novel packet scheduler which is inspired by the deficit-round robin (DRR) algorithm. The scheduler allocates tokens to flows in a round-by-round manner, where token allocation to flows at the beginning of each round is weighted max-min fair. So, we have called it multi-server max-min fair DRR (MSMF-DRR). The performance of the MSMF-DRR algorithm in terms of achieving fairness is shown through a worst-case performance analysis. In addition to analytical results, numerical experiments are also carried out to illustrate service isolation and the delay guarantee that are provided by the algorithm. Generally, a scheduler for such a constrained multi-server queuing system can be applicable in many modern data-networking applications, especially in cloud computing wherein virtual machines and/or processes vie for different IT resources distributed over heterogenous servers, while different processes may have preferences over servers owing to their quality-of-service requirements and the heterogeneity of servers.

Queuing network models have been extensively used for performance evaluation in many modern manufacturing and communication systems. The phenomenon of feedback reflects many practical situations, e.g. reworking in the production systems. However, existing research on open queuing network with feedback mainly concentrates on the models with infinite buffers or the models with finite buffers but exponentially distributed inter-arrival and service times. Research on open queuing networks with finite buffers, feedback and general inter-arrival and service times has not been reported. In this paper, a Rate Iterative Method embedded with the Generalised Expansion Method, is proposed for modelling this type of queuing network. System performance measures include the mean throughput, work-in-process and sojourn time all calculated by the proposed method. The accuracy and the efficiency of the proposed method are tested by comparing the results with other methods or simulation results from the experiments. Finally...

Ad hoc sensor network is a compilation of ad hoc nodes with sensing capability. Recently, the network structure changes rapidly in modern applications and imposes priority for heterogeneous flows which may change dynamically to fulfill the end-user requirement. In some scenarios, the priorities of the flows need to change in middle of the communication. However the major problem is that the packets of different applications are not served in the destination by their expected priority ratio even if the priority does not change. Considering the above problem, this paper presents a fair packet scheduling policy which collects information from each individual flow according to its priority and auto-updates the priorities of flows when required. The presented work focuses on the prioritized packet scheduling policy with the help of multiple queues, whose lengths are determined by priority of each application. To the best of our knowledge, this is the first research effort to consider a scenario where change in priority of flow occurs dynamically. The proposed protocol has been simulated using NS2 for two applications, where each application carries multiple flows. The simulation result shows the internal buffer queues are occupied and transmitted by their respective packets according to their priority ratio. Moreover, it reduces overall packet loss which leads to serve more packets at destination than the existing fairness protocols and the packet service rate reflects instantly soon after the priority changes.

In this paper, we consider a beyond-5G scenario, where two types of users, denoted as scheduled and uncoordinated nodes, coexist on the same set of radio resources for sending data to a base station. Scheduled nodes rely solely on a centralized scheduler within the base station for the assignment of resources, while uncoordinated nodes use an unslotted Carrier Sense Multiple Access (CSMA) protocol for channel access. We propose and evaluate through simulations: (a) a novel centralized resource scheduling algorithm, called Neighbors-Aware Proportional Fair (N-PF) and (b) a novel packet length adaptation algorithm, called Channel-Aware (CA) Packet Length Adaptation algorithm for the scheduled nodes. The N-PF algorithm considers the uplink channel state conditions and the number of uncoordinated nodes neighboring each scheduled node in the aggregate scheduling metric, in order to maximize packet transmission success probability. The CA algorithm provides an additional degree of freedom for improving the performance, thanks to the fact that scheduled nodes with lower number of hidden terminals, i.e., having higher packet capture probability, are assigned longer packet transmission opportunities. We consider two benchmark schemes: Proportional Fair (PF) algorithm, as a resource scheduling algorithm, and a discrete uniform distribution (DUD) scheme for packet lengths distribution. Simulation results show that the proposed schemes can result in significant gain in terms of network goodput, without compromising fairness, with respect to two benchmark solutions taken from the literature.

Fair bandwidth sharing is important for the Internet architecture to be more accommodative of the heterogeneity. The Internet relies primarily on the end-systems to cooperatively deploy congestion control mechanisms for achieving high network utilization and some degree of fairness among flows. However, the cooperative behavior may be abandoned by some end-systems that act selfishly to be more competitive through bandwidth abuse. The result can be severe unfairness and even congestion collapse. Fairness-driven active queue management, thus, becomes essential for allocating the shared bottleneck bandwidth fairly among competing flows. This paper proposes a novel stateless active queue management algorithm, termed CHOKeH, to enforce fairness in bottleneck routers. CHOKeH splits the queue into dynamic regions at each packet arrival and treats each region differently for performing matched-drops using a dynamically updated drawing factor, which is based on the level of queue occupancy and the buffer size. In this way, CHOKeH can effectively identify and restrict unfair flows from dominating the bandwidth by discarding more packets from these flows. The performance of CHOKeH is studied through extensive simulations. The results demonstrate that CHOKeH is well suited for fair bandwidth allocation even in the presence of multiple unresponsive flows and across a wider range of buffer sizes. The results also show the ability of CHOKeH to provide inter-protocol and intra-protocols fairness and protection for short-lived flows. With a low per-packet-processing complexity, CHOKeH is amenable to implementation in core routers to offer an effective incentive structure for end-systems to self-impose some form of congestion control.

Wireless Sensor Networks (WSNs) has become a popular research topic due to its resource constraints. Energy consumption and transmission delay is crucial requirement to be handled to enhance the popularity of WSNs. In order to overcome these issues, we have proposed an Efficient Packet Scheduling Technique for Data Merging in WSNs. Packet scheduling is done by using three levels of priority queue and to reduce the transmission delay. Real-time data packets are placed in high priority queue and Non real-time data packets based on local or remote data are placed on other queues. In this paper, we have used Time Division Multiple Access (TDMA) scheme to efficiently determine the priority of the packet at each level and transmit the data packets from lower level to higher level through intermediate nodes. To reduce the number of transmission, efficient data merge technique is used to merge the data packet in intermediate nodes which has same destination node. Data merge utilize the maximum packet size by appending the merged packets with received packets till the maximum packet size or maximum waiting time is reached. Real- time data packets are directly forwarded to the next node without applying data merge. The performance is evaluated under various metrics like packet delivery ratio, packet drop, energy consumption and delay based on changing the number of nodes and transmission rate. Our results show significant reduction in various performance metrics

We consider a model of an open exponential queuing network where each node comprises several multi-class M R / M /1 queues that share a common waiting space (a buffer) of limited capacity. A customer arriving to a node with fully occupied buffer is lost. An assumption is made that each class input traffic to a node, which is a superposition of the class external Poisson flow and the class flows coming from other nodes, is a Poisson process. Under this assumption a method of an approximate analysis is presented. It is based on solving iteratively a system of non-linear equations for the unknown nodal flow rates. It is shown that the gradient iterations solve the multi-class network equations. For the single-class model we use the direct substitution iterations. In the latter case existence and uniqueness of the solution, obtained by the iterative algorithm, is rigorously proven. It is demonstrated for a few network configurations that the network and node performance characteristics received by analytic approach are close to those obtained by simulation method. Our contribution is a performance evaluation methodology that could be usefully employed in queuing network design.

Often one would like to allocate shared resources in a fair way. A common and well-studied notion of fairness is Max-Min Fairness , where we first maximize the smallest allocation, and subject to that the second smallest, and so on. We consider a networking application where multiple commodities compete over the capacity of a network. In our setting, each commodity has multiple possible paths to route its demand (for example, a network using Multiprotocol Label Switching (MPLS) tunneling). In this setting, the only known way of finding a max-min fair allocation requires an iterative solution of multiple linear programs. Such an approach, although polynomial time, scales badly with the size of the network, the number of demands, and the number of paths, and is hard to implement in a distributed environment. More importantly, a network operator has limited control and understanding of the inner working of the algorithm. In this article we introduce Upward Max-Min Fairness, a novel relaxation of Max-Min Fairness, and present a family of simple dynamics that converge to it. These dynamics can be implemented in a distributed manner. Moreover, we present an efficient combinatorial algorithm for finding an upward max-min fair allocation. This algorithm is a natural extension of the well-known Water Filling Algorithm for a multiple path setting. We test the expected behavior of this new algorithm and show that on realistic networks upward max-min fair allocations are comparable to the max-min fair allocations both in fairness and in network utilization.

We focus on the performance analysis of the buffer-aided relaying system which allows data and energy packets to arrive independently and depart interactively. First, we profile the cooperative relaying system model as a data arrival and energy arrival coupling queuing model. Considering the influence of channel condition on the data departure rate, a new relay transmit protocol which permits exhausting more energy packet to send one data packet in the bad channel environment is proposed. Second, the joint data packet and energy packet handling problem is ascribed to a Coupled Processor Queuing Model which could achieve its steady state transition probability by Quasi-Birth and Death method. Third, the expressions of throughput, delay, and packet drop rate for both data queue and energy queue are also derived. Simulations are demonstrated to verify the analytical results under different data arrival rate, energy arrival rate, and relaying strategy.

In multihop wireless ad hoc networks, it is important to maintain the outcome fairness of throughput and to maximize the throughput. In this paper, we propose a novel opportunistic scheduling framework by considering outcome fairness and throughput simultaneously. Since the data rate fluctuates intensively due to channel errors, we first devise a data rate estimation method with an adaptive sliding window to accurately and adaptively estimate the data rate. Then, we present a framework together with two mechanisms. The first proposed mechanism is ROSA-WOM, with the weighted objective function method to configure the data rate. The other one is ROSA-MGCF, and it is a maximum total goodput method under a constrained fairness index. The proposed mechanisms are able to schedule the flows fairly even when the data rate of all flows is more than the channel capacity. We establish a testbed to evaluate these two mechanisms. The experiment results show that our proposed mechanisms can not only trade off with different fairness and throughput requirements, but also effectively provide robust service isolation, outcome fairness, and high throughput in the presence of channel errors.

The usage of various packet scheduling disciplines in computer networking devices as a mechanism to ensure the quality of service is described. Stages for selection of necessary parameters values of packet scheduling during VoIP-traffic transmission in computer networks are defined. VoIP-traffic was set as a research object because there are strict requirements of VoIP-applications to the network transmission parameters. With the aid of training and experimental simulation system the numerous experiments for parameters selection of the most common packet scheduling disciplines were carried out (FIFO, WFQ, non-preemptive priority queueing). The example that illustrates the ability to adjust the weighting coefficients of WFQ packet scheduling discipline is presented. Approximate analytical dependences are obtained and they will significantly reduce system administrators’ efforts to assess and modify the parameters of packet scheduling in network devices. A method of automating selection of the optimal packet scheduling discipline is formulated.

Packets that traverse more hops to the destination will get lower throughput and result in the inter-flow fairness issue.We propose a virtual queue management scheme that does not require the modification of any communication protocol.According to the number of flows, our scheme adjusts the parameters to achieve each flow's fair sharing of channel resource.Our proposed scheme can mitigate the inter-flow fairness problem and effectively improves the quality of video transmission. Due to intra-flow and inter-flow interference problems, the throughput performance decreases dramatically in a multi-hop wireless network. These two kinds of bandwidth unfair sharing problems could cause serious collisions and congestion, hence affecting the performance of multi-hop wireless networks. That is to say, data packets that need to traverse more hops to arrive at the destination will get lower throughput and result in the inter-flow fairness problem. Furthermore, the quality of video transmission is especially poor in traditional multi-hop wireless network environments. Therefore, in this paper, we propose a virtual queue management scheme that does not require the modification of any communication protocol. According to the number of flows, it adjusts the queue management scheme to achieve each flow's fair sharing of channel resource. It also improves the quality of video transmission. Through NS2 simulations, the results show that our proposed scheme can mitigate the inter-flow fairness problem and effectively improves the quality of video transmission. Display Omitted

Software defined networking greatly simplifies network management by decoupling control functions from the network data plane. However, such a decoupling also opens SDN to various denial of service attacks: an adversary can easily exhaust network resources by flooding short-lived spoofed flows. Toward this issue, we present a comprehensive study of DoS attacks in SDN, and propose multi-layer fair queueing (MLFQ), a simple but effective DoS mitigation method. MLFQ enforces fair sharing of an SDN controller’s resources with multiple layers of queues, which can dynamically expand and aggregate according to controller load. Both testbed-based and emulation-based experiments demonstrate the effectiveness of MLFQ in mitigating DoS attacks targeted at SDN controllers.

This article discusses my personal view of the events that led to the publication of the paper "Analysis and Simulation of a Fair Queueing Algorithm" that won a Test-of-Time Award in 2007.

Recent model-based congestion control algorithms such as BBR use repeated measurements at the endpoint to build a model of the network connection and use it to achieve optimal throughput with low queuing delay. Conversely, applying this model-based approach to Active Queue Management (AQM) has so far received less attention. We propose the new AQM scheduler cocoa based on fair queuing, which adapts the buffer size depending on the needs of each flow without requiring active participation from the endpoint. We implement this scheduler for the Linux kernel and show that it interacts well with the most common congestion control algorithms and can significantly increase throughput compared to fair CoDel while avoiding overbuffering.

Multi-tenant datacenters and cloud networks must provide both isolation and interactive service to tenant applications, many of which are sensitive to tail flow completion times. Network operators must also ensure high utilization of network capacity to reduce cost. Existing approaches that statically partition network capacity, in either time or space, provide good isolation but suffer from under-utilization. Existing schemes that dynamically allocate capacity to tenants incur either decreased fairness or high tail flow completion times. To overcome these limitations, we propose Ether. Ether is able to overcome these limitations because it can prioritize bursty flows during short congestion episodes while still ensuring fairness at long timescales. In this paper, we present a preliminary design of Ether and discuss its feasibility in today's programmable switches. Our evaluations show that, at high loads, Ether achieves 23% improvement in tail flow completion times (FCT) when compared with idealized fair queueing (FQ) while still providing similar fairness as FQ. In contrast, pFabric, which optimizes FCT, worsens fairness by a factor of 1.8 when compared with Ether.

Fair queuing (FQ) algorithms have been widely adopted in computer systems to share resources among multiple users. Modern operating systems and hypervisors use variants of FQ algorithms to implement the critical OS resource management -- the thread scheduler. While the existing FQ algorithms enforce fair CPU allocation on a per-core basis, there lacks an algorithm to fairly allocate CPU on multiple cores. This common deficiency in state-of-the-art multicore schedulers causes unfair CPU allocations to parallel programs using blocking synchronization, leading to severe performance degradation. Parallel threads that frequently block due to synchronization exhibit deceptive idleness and are penalized by the thread scheduler. To this end, we propose a preemptive multi-queue fair queuing (P-MQFQ) algorithm that uses a centralized queue to fairly dispatch threads from different programs based on their received CPU bandwidth from multiple cores. We demonstrate that P-MQFQ can be approximated by augmenting the existing load balancing in the OS without requiring to implement the centralized queue or undermining scalability. We implement P-MQFQ in Linux and Xen, respectively, and show significantly improved utilization and performance for parallel programs.

As the volume of traffic flows surges, providing Quality-of-Service (QoS) guarantees to flows by fair queueing has never been more challenging in Network Function Virtualization (NFV). There has been a recent effort in both industry and academia to develop fair queueing algorithms across multiple resources in NFV. However, all existing works fail to support hierarchical scheduling, a crucial feature that also provides QoS guarantees to grouped flows on tenant boundaries. In this paper, we present two new multi-resource fair queueing algorithms that support hierarchies, collapsed Hierarchical Dominant Resource Fair Queueing (collapsed H-DRFQ) and dove-tailing H-DRFQ, both of which provide hierarchical share guarantees. Through formal analysis, we find that the dove-tailing H-DRFQ outper-forms collapsed H-DRFQ by providing a smaller delay bound. However, according to the simulation results, both algorithms have their pros and cons. Dove-tailing H-DRFQ benefits to the flows with more complex hierarchies, while collapsed H-DRFQ is better for the flows with simpler attribution structures. Meanwhile, our simulation shows that both H-DRFQ algorithms can achieve near-perfect fairness.

The use of directional communications in Mobile Ad Hoc Networks (MANET) helps to overcome the most of undesired issues occurring at Medium Access Control (MAC) layer involved by omnidirectional antennas. Although, the employment of Directional Virtual Carrier Sensing (DVCS) techniques are not sufficient to limit decisively the deafness problem, the most recent approaches such as [1] exploiting Round-Robin principle help to enhance efficiency in reducing this issue. However, because these kinds of approaches employ queues for frame management, while considering to reduce the deafness through a Round-Robin scheduling approach, it is necessary also to address the most common issues implied by these strategies that are the waiting queue and the time slice problems. These problems, unfortunately, tend to considerably reduce benefits involved by a sectorized-queue based solution. In this paper a novel Self Clocked Fair Queueing MAC (SCFQ-MAC) approach is proposed with the goal to minimize the effects of the mentioned issue. By exploiting a SCFQ scheduling it is possible to reduce the average waiting time of frames in queues and consequently, to improve system performance especially in terms of fairness.

A unhackneyed multifunctional paradigm is cloud computing. Suppliers of cloud services i.e., with the assistance of these elastic dynamic features, CSPs can efficiently manage the input loads available in the cloud environment in accordance with the end users' dynamic requirements. The effective secure management of loads in the cloud environment gives a rise to introduce new managerial secure methods, which are responsible for security, distributing the loads among all the available VMs dynamically in cloud data centers within a given fixed period of time. To achieve these goals various methodologies are exists in market but most of them are unable to find the optimality as well as the security of resources means they are actually unable to fulfill the dynamic changeable demands of the end users. In this manuscript, we impose an effective as well as efficient load balancing strategy to address these hurdles effectively: a secure heuristic load balancing approach for cloud data centers based on RSA and SFQ. The effective balance of incoming loads in cloud data centers is our approach. With the help of Start Fair Queuing (SFQ) algorithm, we are able to the fair allocation of VMs in our proposed approach. Hence, it maximizes the resource utilization. With the help of RSA algorithm, we are able to take care of security concerns of our proposed approach.

Optical communication network plays an predominant role in the life of the user(s) system by providing high demand i.e. digital signal levels, fair queuing and channel reuse for high capacity links. In optical communications, Optical Component and Parameter (OCP) are becoming more accurate in order to provide the significance of the optical devices. Optical networks are enhanced in order to afford the optical technologies connectivities to provide high end communication in terms of sustainability and survivability. In survivability concept, two layers plays a significant/vital role between source to destination i.e. end to end connectivities is much reliable in terms of node connectivities, flow connectivities hence local to global fair queuing is achieved with the lower bound to upper bound and vice versa. Further the path propagation determines the range of path propagation determines the range of packets and its estimation is measured by using the concept of node mobility and scalability. It achieves the global range of optical packet transmission reception and design issues in network architecture analysis.

Cloud computing provides on demand scalable resources for large scale distributed computing tasks. Scheduling of the large scale user tasks plays a significant role in improving performance of the cloud services. This paper presents a performance analysis of STFQ (Start Time Fair Queuing), WRR (Weighted Round Robin)and BVT (Borrowed Virtual Time) task scheduling algorithms for the cloud environment. The simulation results using CloudSim are used for comparing the performance of these three algorithms.