Abstract Middleboxes have become a vital part of modern networks by providing services such as load balancing, optimization of network traffic, and content filtering. A sequence of middleboxes comprising a logical service is called a Service Function Chain (SFC) . In this context, the main issues are to maintain an acceptable level of network path survivability and a fair allocation of the resource between different demands in the event of faults or failures. In this paper, we focus on the problems of traffic engineering, failure recovery, fault prevention, and SFC with reliability and energy consumption constraints in Software Defined Networks (SDN). These types of deployments use Fog computing as an emerging paradigm to manage the distributed small-size traffic flows passing through the SDN-enabled switches (possibly Fog Nodes). The main aim of this integration is to support service delivery in real-time, failure recovery, and fault-awareness in an SFC context. Firstly, we present an architecture for Failure Recovery and Fault Prevention called FRFP; this is a multi-tier structure in which the real-time traffic flows pass through SDN-enabled switches to jointly decrease the network side-effects of flow rerouting and energy consumption of the Fog Nodes. We then mathematically formulate an optimization problem called the Optimal Fog-Supported Energy-Aware SFC rerouting algorithm (OFES) and propose a near-optimal heuristic called Heuristic OFES (HFES) to solve the corresponding problem in polynomial time. In this way, the energy consumption and the reliability of the selected paths are optimized, while the Quality of Service (QoS) constraints are met and the network congestion is minimized. In a reliability context, the focus of this work is on fault prevention; however, since we use a reallocation technique, the proposed scheme can be used as a failure recovery scheme. We compare the performance of HFES and OFES in terms of energy consumption, average path length, fault probability, network side-effects, link utilization, and Fog Node utilization. Additionally, we analyze the computational complexity of HFES. We use a real-world network topology to evaluate our algorithm. The simulation results show that the heuristic algorithm is applicable to large-scale networks.
Recently, the authors in @cite_9 presented a scheduling and routing solution in SDN NFV time-triggered flows. In detail, they approximate the optimal solution over a corresponding static scheduling problem and solve it using ILP. As in our approach, hard constraints on the overall execution times are considered by @cite_9 . However, we point out that, unlike our approach: (i) the focus of @cite_9 is on the traffic routing and scheduling between SDN-enabled switches per time-flow, so that the resulting flow scheduler does not support, by design, failure and fault tolerance per link and switch of data time-flow; (ii) the joint flow and computing rate mapping afforded in @cite_9 is, by design, static; (iii) the scheduler in @cite_9 does not perform real-time reconfiguration rerouting, real-time traffic hosted by the serving controller; (iv) the work in @cite_9 does not consider SFC and rerouting; and (v) the scheduler in @cite_9 does not enforce per-flow QoS guarantees on the limited time minimum energy and or the minimum side-effect. Although the aforementioned solutions are interesting, however, none of them considers the problem of service chaining with respect to the energy consumption of the VMs.
@cite_9
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Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14 optimality-gap) while their execution time makes them usable for real-life networks. Many existing data center network (DCN) flow scheduling schemes minimize flow completion times (FCT) based on prior knowledge of flows and custom switch designs, making them hard to use in practice. This paper introduces, Pias, a practical flow scheduling approach that minimizes FCT with no prior knowledge using commodity switches. At its heart, Pias leverages multiple priority queues available in commodity switches to implement a Multiple Level Feedback Queue (MLFQ), in which a PIAS flow gradually demotes from higher-priority queues to lower-priority queues based on the bytes it has sent. In this way, short flows are prioritized over long flows, which enables Pias to emulate Shortest Job First (SJF) scheduling without knowing the flow sizes beforehand. Our preliminary evaluation shows that Pias significantly outperforms all existing information-agnostic solutions. It improves average FCT for short flows by up to 50 and 40 over DCTCP [3] and L2DCT [16]. Compared to an ideal information-aware DCN transport, p-Fabric [5], it only shows 4.9 performance degradation for short flows in a production datacenter workload. Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces. In this paper, we discuss a real-time on-chip communication service with a priority-based wormhole switching policy. A novel off-line schedulability analysis approach is presented. By evaluating diverse inter-relationships among the traffic-flows, this approach can predict the packet network latency based on two quantifiable different delays: direct interference from higher priority traffic-flows and indirect interference from other higher priority traffic-flows. Due to the inevitable existence of parallel interference, we prove that the general problem of determining the exact schedulability of real-time traffic-flow over the on- chip network is NP-hard. However the results presented do form an upper bound. In addition, an error in a previous published scheduling approach is illustrated and remedied. Utilizing this analysis scheme, we can flexibly evaluate at design time the schedulability of a set of traffic-flows with different QoS requirements on a real-time SoC NoC communication platform. We consider offline scheduling algorithms that incorporate speed scaling to address the bicriteria problem of minimizing energy consumption and a scheduling metric. For makespan, we give a linear-time algorithm to compute all non-dominated solutions for the general uniprocessor problem and a fast arbitrarily-good approximation for multiprocessor problems when every job requires the same amount of work. We also show that the multiprocessor problem becomes NP-hard when jobs can require different amounts of work. For total flow, we show that the optimal flow corresponding to a particular energy budget cannot be exactly computed on a machine supporting exact real arithmetic, including the extraction of roots. This hardness result holds even when scheduling equal-work jobs on a uniprocessor. We do, however, extend previous work by to give an arbitrarily-good approximation for scheduling equal-work jobs on a multiprocessor. Several networking architectures have been developed atop IEEE 802.3 networks to provide real-time communication guarantees for time-sensitive applications in industrial automation systems. The basic principle underlying these technologies is the precise transmission scheduling of time-triggered traffic through the network for providing deterministic and bounded latency and jitter. These transmission schedules are typically synthesized offline (computational time in the order of hours) and remain fixed thereafter, making it difficult to dynamically add or remove network applications. This paper presents algorithms for incrementally adding time-triggered flows in a time-sensitive software-defined network (TSSDN). The TSSDN is a network architecture based on software-defined networking, which provides real-time guarantees for time-triggered flows by scheduling their transmissions on the hosts (network edge) only. These algorithms exploit the global view of the control plane on the data plane to schedule and route time-triggered flows needed for the dynamic applications in the Industrial Internet of Things (Industry 4.0). The evaluations show that these algorithms can compute incremental schedules for time-triggered flows in subseconds with an average relative optimality of 68 . Data centers are a cost-effective infrastructure for hosting Cloud and Grid applications, but they do incur tremendous energy cost and CO2 emissions. Today's data center network architectures such as Fat-tree and BCube are over-provisioned to guarantee large network capacity and meet peak performance requirement. Networks suffer from inefficient power usage when data center traffic is not high. A solution to this problem is the adoption of network management platform such as OpenNaaS, which can be augmented with energy-aware capabilities. We developed a component for energy monitoring and routing in OpenNaaS. Energy-aware OpenNaaS can support different types of OpenFlow controller; it inherits and enhances network management capabilities, e.g. dynamically obtaining power and topology.In this paper we also discuss the evaluation and selection of energy-aware routing strategies based on an initial prototype of energy-aware OpenNaaS. The target strategies are fine-grained as they combine flow routing algorithms that make routing decisions for the flows and flow scheduling algorithms that schedule the flows on the same link. Differently from previous routing work which focuses on power-minimization problem in data center networks, we aim to optimize energy consumption. Our simulation shows that the combination of priority-based shortest routing and exclusive flow scheduling achieves about 5 -35 higher energy efficiency without performance degradation. This paper studies the problem of congestion control and scheduling in ad hoc wireless networks that have to support a mixture of best-effort and real-time traffic. Optimization and stochastic network theory have been successful in designing architectures for fair resource allocation to meet long-term throughput demands. However, to the best of our knowledge, strict packet delay deadlines were not considered in this framework previously. In this paper, we propose a model for incorporating the quality-of-service (QoS) requirements of packets with deadlines in the optimization framework. The solution to the problem results in a joint congestion control and scheduling algorithm that fairly allocates resources to meet the fairness objectives of both elastic and inelastic flows and per-packet delay requirements of inelastic flows. Long Term Evolution (LTE) is a cellular technology developed to support diversity of data traffic at potentially high rates. 3GPP's LTE is defined by the standardization body's Release 8 and 9. A key mechanism in the LTE traffic handling is the packet scheduler, which is in charge of allocating resources to active flows in both the frequency and time dimension. The scheduling scheme used largely impacts the throughput of individual users as well as throughput of the cell. It is worthwhile to evaluate the throughput and fairness conditions for different scheduling schemes before the actual deployment of LTE scheduler. Our main contribution in this study is to evaluate and compare the performance of six scheduling schemes designed for LTE network in terms of user's throughput and fairness. The findings from our performance evaluation presented to draw conclusions on the performance of the six schedulers, and point out the strengths and weakness that are common to schedulers under study. This would help design the scheme of the scheduler at the eNodeB appropriately. This paper studies the problem of scheduling in single-hop wireless networks with real-time traffic, where every packet arrival has an associated deadline and a minimum fraction of packets must be transmitted before the end of the deadline. Using optimization and stochastic network theory we study the problem of scheduling to meet quality of service (QoS) requirements under heterogeneous delay constraints and time-varying channel conditions. Our analysis results in an optimal scheduling algorithm which fairly allocates data rates to all flows while meeting long-term delay demands. We also prove that under a simplified scenario our solution translates into a greedy strategy that makes optimal decisions with low complexity. A range of new datacenter switch designs combine wireless or optical circuit technologies with electrical packet switching to deliver higher performance at lower cost than traditional packet-switched networks. These "hybrid" networks schedule large traffic demands via a high-rate circuits and remaining traffic with a lower-rate, traditional packet-switches. Achieving high utilization requires an efficient scheduling algorithm that can compute proper circuit configurations and balance traffic across the switches. Recent proposals, however, provide no such algorithm and rely on an omniscient oracle to compute optimal switch configurations. Finding the right balance of circuit and packet switch use is difficult: circuits must be reconfigured to serve different demands, incurring non-trivial switching delay, while the packet switch is bandwidth constrained. Adapting existing crossbar scheduling algorithms proves challenging with these constraints. In this paper, we formalize the hybrid switching problem, explore the design space of scheduling algorithms, and provide insight on using such algorithms in practice. We propose a heuristic-based algorithm, Solstice that provides a 2.9× increase in circuit utilization over traditional scheduling algorithms, while being within 14 of optimal, at scale. Scheduling a set of jobs over a collection of machines is a fundamental problem that needs to be solved millions of times a day in various computing platforms: in operating systems, in large data clusters, and in data centers. Along with makespan, flow-time, which measures the length of time a job spends in a system before it completes, is arguably the most important metric to measure the performance of a scheduling algorithm. In recent years, there has been a remarkable progress in understanding flow-time based objective functions in diverse settings such as unrelated machines scheduling, broadcast scheduling, multi-dimensional scheduling, to name a few. Yet, our understanding of the flow-time objective is limited mostly to the scenarios where jobs have simple structures; in particular, each job is a single self contained entity. On the other hand, in almost all real world applications, think of MapReduce settings for example, jobs have more complex structures. In this paper, we consider two classical scheduling models that capture complex job structures: 1) concurrent open-shop scheduling and 2) precedence constrained scheduling. Our main motivation to study these problems specifically comes from their relevance to two scheduling problems that have gained importance in the context of data centers: co-flow scheduling and DAG scheduling. We design almost optimal approximation algorithms for open-shop scheduling and precedence constrained scheduling, and show hardness results. This paper considers the problem of determining the achievable rates in multi-hop wireless networks. We consider the problem of jointly routing the flows and scheduling transmissions to achieve a given rate vector. We develop tight necessary and sufficient conditions for the achievability of the rate vector. We develop efficient and easy to implement Fully Polynomial Time Approximation Schemes for solving the routing problem. The scheduling problem is a solved as a graph edge-coloring problem. We show that this approach guarantees that the solution obtained is within 67 of the optimal solution in the worst case and, in practice, is typically within about 80 of the optimal solution. The approach that we use is quite flexible and is a promising method to handle more sophisticated interference conditions, multiple channels, multiple antennas, and routing with diversity requirements. Distributed controller architectures have been proposed for Software-Defined Networking (SDN) to ensure scalability and reliability. One major drawback of the existing architectures is the uneven load distribution among controllers stemming from the static binding between controllers and switches. To address this issue, several existing studies introduce dynamic binding by adopting some switch migration mechanisms that re-associate switches from overloaded controllers to underutilized controllers. However, the migration process adds a considerable amount of complexity to the system and may incur significant network latency. In this paper, we propose BLAC, a novel BindingLess Architecture for distributed Controllers (BLAC), in which load balance is achieved with the help of the proposed scheduling layer, which intercepts flow requests from switches and dispatches them to different controllers as determined by selected scheduling algorithms. The process is proceeded transparently with no extra modification required for off-the-shelf SDN switches. Besides, the scheduling layer can flexibly support various scheduling algorithms and causes neither disruption of service nor significant network delay. We build a prototype that can work with various distributed controller systems and conduct experiments to demonstrate its efficacy. The results show that our design outperforms the static-binding controller system in terms of both system throughput and response time without the complexity of the dynamic-binding controller system. Abstract Current OpenFlow specification is unable to set the service rate of the queues inside OpenFlow devices. This lack does not allow to apply most algorithms for the satisfaction of Quality of Service requirements to new and established flows. In this paper we propose an alternative solution implemented through some modifications of Beacon, one popular SDN controller. It acts as follows: using ‘almost’-real-time statistics from OpenFlow devices, Beacon will re-route flows on different queues to guarantee the observance of deadline requirements (e.g. the flow is still useful if, and only if, is completely received by a given time) and or an efficient queue balancing in an OpenFlow SDN switch. Differently from the literature, we do not propose any new primitive or modification of the OpenFlow standard: our mechanism, implemented in the controller, works with regular OpenFlow devices. Our changes in the SDN controller will be the base for the design of a class of new re-routing algorithms able to guarantee deadline constraints and queue balancing without any modification of the OpenFlow specification, as well as, of OpenFlow devices. Built upon a shared wireless medium, wireless network is particularly vulnerable to jamming attacks. The ability to recover from attacks and maintain an acceptable level of service degradation is a crucial aspect in the design of a wireless network. To address this issue, this paper investigates the network restoration solutions via the joint design of traffic rerouting, channel re-assignment, and scheduling. Efficient routing and channel assignment schemes can relieve the interference caused by both the normal network nodes and the jamming nodes. Therefore, based on the necessary conditions of schedulability, we first formulate the optimal network restoration problem as linear programming problem, which gives an upper bound on the achievable network throughput. After we solve the LP problems, we have a set of flows assigned to edges that have been assigned to different channels. And based on the LP solutions, we provide a greedy scheduling algorithm using dynamic channel assignment, which schedules both the network traffic and the jamming traffic. In particular, we consider two strategies, namely global restoration and local restoration, which can support a range of tradeoffs between the restoration latency and network throughput after restoration. To quantitatively evaluate the impact of jamming attacks during and after restoration, we define two performance degradation indices, transient disruption index (TDI) and throughput degradation index (THI). Finally, extensive performance evaluations are performed to study the impact of various jamming scenarios in a multi-hop multi-channel wireless network. When dealing with node or link failures in Software Defined Networking (SDN), the network capability to establish an alternative path depends on controller reachability and on the round trip times (RTTs) between controller and involved switches. Moreover, current SDN data plane abstractions for failure detection (e.g. OpenFlow "Fast-failover") do not allow programmers to tweak switches' detection mechanism, thus leaving SDN operators still relying on proprietary management interfaces (when available) to achieve guaranteed detection and recovery delays. We propose SPIDER, an OpenFlow-like pipeline design that provides i) a detection mechanism based on switches' periodic link probing and ii) fast reroute of traffic flows even in case of distant failures, regardless of controller availability. SPIDER can be implemented using stateful data plane abstractions such as OpenState or Open vSwitch, and it offers guaranteed short (i.e. ms) failure detection and recovery delays, with a configurable trade off between overhead and failover responsiveness. We present here the SPIDER pipeline design, behavioral model, and analysis on flow tables' memory impact. We also implemented and experimentally validated SPIDER using OpenState (an OpenFlow 1.3 extension for stateful packet processing), showing numerical results on its performance in terms of recovery latency and packet losses. Many existing data center network (DCN) flow scheduling schemes minimize flow completion times (FCT) based on prior knowledge of flows and custom switch functions, making them superior in performance but hard to use in practice. By contrast, we seek to minimize FCT with no prior knowledge and existing commodity switch hardware. To this end, we present PIAS, a DCN flow scheduling mechanism that aims to minimize FCT by mimicking Shortest Job First (SJF) on the premise that flow size is not known a priori. At its heart, PIAS leverages multiple priority queues available in existing commodity switches to implement a Multiple Level Feedback Queue (MLFQ), in which a PIAS flow is gradually demoted from higher-priority queues to lower-priority queues based on the number of bytes it has sent. As a result, short flows are likely to be finished in the first few high-priority queues and thus be prioritized over long flows in general, which enables PIAS to emulate SJF without knowing flow sizes beforehand. We have implemented a PIAS prototype and evaluated PIAS through both testbed experiments and ns- 2 simulations. We show that PIAS is readily deployable with commodity switches and backward compatible with legacy TCP IP stacks. Our evaluation results show that PIAS significantly outperforms existing information-agnostic schemes. For example, it reduces FCT by up to 50 and 40 over DCTCP [11] and L2DCT [27] respectively; and it only has a 4.9 performance gap to an ideal information-aware scheme, pFabric [13], for short flows under a production DCN workload. We consider the problem of designing an online scheduling scheme for a multi-hop wireless packet network with arbitrary topology and operating under arbitrary scheduling constraints. The objective is to design a scheme that achieves high throughput and low delay simultaneously. We propose a scheduling scheme that - for networks operating under primary interference constraints - guarantees a per-flow end-to-end packet delay bound of 5dj (1-ρj), at a factor 5 loss of throughput, where dj is the path length (number of hops) of flow j and ρj is the effective loading along the route of flow j. Clearly, dj is a universal lower bound on end-to-end packet delay for flow j. Thus, our result is essentially optimal. To the best of our knowledge, our result is the first one to show that it is possible to achieve a per-flow end-to-end delay bound of O(# of hops) in a constrained network. Designing such a scheme comprises two related subproblems: Global Scheduling and Local Scheduling. Global Scheduling involves determining the set of links that will be simultaneously active, without violating the scheduling constraints. While local scheduling involves determining the packets that will be transferred across active edges. We design a local scheduling scheme by adapting the Preemptive Last-In-First-Out (PL) scheme, applied for quasi-reversible continuous time networks, to an unconstrained discrete-time network. A global scheduling scheme will be obtained by using stable marriage algorithms to emulate the unconstrained network with the constrained wireless network. Our scheme can be easily extended to a network operating under general scheduling constraints, such as secondary interference constraints, with the same delay bound and a loss of throughput that depends on scheduling constraints through an intriguing "sub-graph covering" property. In this paper we present pFabric, a minimalistic datacenter transport design that provides near theoretically optimal flow completion times even at the 99th percentile for short flows, while still minimizing average flow completion time for long flows. Moreover, pFabric delivers this performance with a very simple design that is based on a key conceptual insight: datacenter transport should decouple flow scheduling from rate control. For flow scheduling, packets carry a single priority number set independently by each flow; switches have very small buffers and implement a very simple priority-based scheduling dropping mechanism. Rate control is also correspondingly simpler; flows start at line rate and throttle back only under high and persistent packet loss. We provide theoretical intuition and show via extensive simulations that the combination of these two simple mechanisms is sufficient to provide near-optimal performance.