Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining

Service Function Chaining (SFC) allows the forwarding of a traffic flow along a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT). Software Defined Networking (SDN) solutions can be used to support SFC reducing 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 impact to the quality of services. In this paper, we propose a novel resource (re)allocation architecture which enables energy-aware SFC for SDN-based networks. To this end, we model the problems of VNF placement, allocation of VNFs to flows, and flow routing as optimization problems. Thereafter, heuristic algorithms are proposed for the different optimization problems, in order find near-optimal solutions in acceptable times. The performance of the proposed algorithms are numerically evaluated over a real-world topology and various network traffic patterns. The results confirm that the proposed heuristic algorithms provide near optimal solutions while their execution time is applicable for real-life networks.Comment: Extended version of submitted paper - v7 - July 201

Soluble ST2 Levels Are Associated with Bleeding in Patients with Severe Leptospirosis

Leptospirosis is a bacterial disease that is mainly spread by rodents and other small mammals. Transmission frequently occurs in (sub-) tropical countries, where environmental circumstances are most favourable. Severe leptospirosis can cause bleeding and vital organ dysfunction. An exaggerated immune response is thought to play an important role in the pathophysiology of leptospirosis. Soluble ST2 (sST2) is thought to inhibit negative regulatory pathways of this response. Soluble ST2 is produced by cells that surround, for example, blood vessels, and several of these blood cells play an important part in the host immune response. In an observational study, we measured the extent of sST2 release in patients suffering from severe leptospirosis. We found that patients that died from leptospirosis displayed higher levels of sST2. Moreover, from this study we have seen that sST2 levels were associated with bleeding, whereas other markers of infection were not. In an experiment, we showed that (white) blood cells did not seem to be the source of sST2 production. Damage to blood vessels is likely to cause bleeding in leptospirosis patients, exposing sST2 producing cells like fibroblasts to the blood stream. Hence, we believe that sST2 may be used as a marker for tissue damage in patients suffering from severe leptospirosis

SDN‐based resource allocation in MPLS networks: A hybrid approach

The highly dynamic nature of the current network traffics makes the network managers to exploit the flexibility of the state-of-the-art paradigm called SDN. In this way, there has been an increasing interest in hybrid networks of SDN-MPLS. In this paper, a new traffic engineering architecture for SDN-MPLS network is proposed. To this end, OpenFlow-enabled switches are applied over the edge of the network to improve flow-level management flexibility while MPLS routers are considered as the core of the network to make the scheme applicable for existing MPLS networks. The proposed scheme re-assigns flows to the Label-Switched Paths (LSPs) to highly utilize the network resources. In the cases that the flow-level re-routing is insufficient, the proposed scheme re-computes and re-creates the undergoing LSPs. To this end, we mathematically formulate two optimization problems, ie, i) flow re-routing and ii) LSP re-creation, and propose a heuristic algorithm to improve the performance of the scheme. Our experimental results show the efficiency of the proposed hybrid SDN-MPLS architecture in traffic engineering superiors traditionally deployed MPLS networks

SDN-based resource allocation in MPLS networks: A hybrid approach

The highly dynamic nature of the current network traffics makes the network managers to exploit the flexibility of the state-of-the-art paradigm called SDN. In this way, there has been an increasing interest in hybrid networks of SDN-MPLS. In this paper, a new traffic engineering architecture for SDN-MPLS network is proposed. To this end, OpenFlow-enabled switches are applied over the edge of the network to improve flow-level management flexibility while MPLS routers are considered as the core of the network to make the scheme applicable for existing MPLS networks. The proposed scheme re-assigns flows to the Label-Switched Paths (LSPs) to highly utilize the network resources. In the cases that the flow-level re-routing is insufficient, the proposed scheme re-computes and re-creates the undergoing LSPs. To this end, we mathematically formulate two optimization problems, ie, i) flow re-routing and ii) LSP re-creation, and propose a heuristic algorithm to improve the performance of the scheme. Our experimental results show the efficiency of the proposed hybrid SDN-MPLS architecture in traffic engineering superiors traditionally deployed MPLS networks

Joint Energy Efficient and QoS-Aware Path Allocation and VNF Placement for Service Function Chaining

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

Software defined service function chaining with failure consideration for fog computing

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&nbsp;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 in an SFC context. First, we present an architecture for Failure Recovery 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 Fast Failure Recovery algorithm (OFFR) and propose a near‐optimal heuristic called Heuristic HFFR to solve the corresponding problem in polynomial time. In this way, the reliability of the selected paths are optimized, while the network congestion is minimized.</p

Joint failure recovery, fault prevention, and energy-efficient resource management for real-time SFC in fog-supported SDN

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. (C) 2019 Elsevier B.V. All rights reserved

Optimal Estimation of Link Delays Based on End-to-End Active Measurements

Current IP-based networks support a wide range of delay-sensitive applications such as live video streaming of network gaming. Providing an adequate quality of experience to these applications is of paramount importance for a network provider. The offered services are often regulated by tight Service Level Agreements (SLAs) that needs to be continuously monitored. Since the first step to guarantee a metric is to measure it, delay measurement becomes a fundamental operation for a network provider. In many cases, the operator needs to measure the delay on all network links. We refer to the collection of all link delays as the Link Delay Vector (LDV). Typical solutions to collect the LDV impose a substantial overhead on the network. In this paper, we propose a solution to measure the LDV in real-time with a low-overhead approach. In particular, we inject some flows into the network and infer the LDV based on the delay of those flows. To this end, the monitoring flows and their paths should be selected minimizing the network monitoring overhead. In this respect, the challenging issue is to select a proper combination of flows such that by knowing their delay it is possible to solve a set of linear equations and obtain a unique LDV. This combination of monitoring flows should be optimal according to some criteria and should meet some feasibility constraints. We first propose a mathematical formulation to select the optimal combination of flows, in form of an Integer Linear Programming (ILP) problem. Then we develop a heuristic algorithm to overcome the high computational complexity of existing ILP solvers. As a further step, we propose a meta-heuristic algorithm to solve the above-mentioned equations and infer the LDV. The challenging part of this step is the volatility of link delays. The proposed solution is evaluated over real-world emulated network topologies using the Mininet network emulator. Emulation results show the accuracy of the proposed solution with a negligible networking overhead in a real-time manner