Emerging services with demand for high perfor¬mance and resource availability call for network management solutions that enable distributed, flexible and adaptive im¬plementation of their components. Software-based networks, characterized by modular structures, are key in supporting management functionality with the desired level of agility. An important challenge in realizing a modular management plane is the handling of interactions between independent decision¬making applications operating in parallel, which can compromise service delivery. This paper presents LogiSig, a novel approach to automatically detect and classify interactions manifesting in the management plane of software-based networks. LogiSig builds upon a fine-grained taxonomy of possible interaction types and simple system-level operations to realize its functionality. Our evaluation results show that LogiSig can efficiently determine interactions between application pairs.

Network operators have recently been developing multi-Gbps traffic monitoring tools on commodity hardware, as part of the packet-processing pipelines realizing software dataplanes. These solutions allow the execution of sophisticated per-packet monitoring using the processing power available on servers. Although advances in packet capture have enabled the interception of packets at high rates, bottlenecks can still arise in the monitoring process as a result of concurrent access to shared processor resources, variations of the traffic skew, and unbalanced packet-rate spikes. In this paper we present an adaptive monitoring framework, MONA, which is resilient to bottlenecks while maintaining the accuracy of monitoring reports above a user-specified threshold. MONA dynamically reduces the measurement task sets under adverse conditions, and reconfigures them to recover potential accuracy degradations. To quantify the monitoring accuracy at run time, MONA adopts a novel task-independent technique that generates accuracy estimates according to recently observed traffic characteristics. With a prototype implementation based on a generic packet-processing pipeline, and using well-known measurements tasks, we show that MONA achieves lossless traffic monitoring for a wide range of conditions, significantly enhances the level of monitoring accuracy, and performs adaptations at the time scale of milliseconds with limited overhead.

The software-defined networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDN-based management architectures, it is of paramount importance to design a monitoring system that can provide timely and consistent updates to heterogeneous management applications. To support such applications operating with low latency requirements, the monitoring system should scale with increasing network size and provide precise network views with minimum overhead on the available resources. In this paper, we present a novel, self-adaptive, decentralized framework for resource monitoring in SDN. Our framework enables accurate statistics to be collected with limited burden on the network resources. This is realized through a self-tuning, adaptive monitoring mechanism that automatically adjusts its settings based on the traffic dynamics. We evaluate our proposal based on a realistic use case scenario, where a content distribution service and an on-demand gaming platform are deployed within an ISP network. The results show that reduced monitoring latencies are obtained with the proposed framework, thus enabling shorter reconfiguration control loops. In addition, the proposed adaptive monitoring method achieves significant gain in terms of monitoring overhead, while preserving the performance of the services considered.

Network operators have recently been developing multi-Gbps traffic monitoring tools that execute on commodity hardware and are part of the packet-processing pipelines realizing software dataplanes. These solutions allow sophisticated tasks to be performed on a per-packet basis, without relying on sampling or passive trace analysis, by leveraging the processing power available on servers. Although advances in packet capture have enabled intercepting packets from network cards at high rates, bottlenecks can still arise in the monitoring process as a result of concurrent access to shared processor resources, variations of the traffic skew, and unbalanced packet-rate spikes. In this paper we present an adaptive traffic monitoring approach that copes with emerging bottlenecks by timely detecting changes in the operational conditions and reconfiguring monitoring-related operations for subsets of traffic flows. Our solution performs responsive adaptations at the time scale of milliseconds and does not require a significant amount of resources. To demonstrate the capabilities of our approach we implemented it as part of a generic packet-processing pipeline and show that lossless traffic monitoring can be achieved for a wide range of conditions.

It is our great pleasure to welcome you to the 15th IFIP/IEEE International Symposium on Integrated Network Management (IM 2017), sponsored by the IEEE Communications Society and IFIP Working Group 6.6. Held in odd-numbered years since 1989, IM 2017 will follow the 28-year tradition of IM as the primary IEEE Communications Society's forum for technical exchange on management of information and communication technology focusing on research, development, integration, standards, services, and user communities. IM 2017 focuses on the theme “Integrated Management in the Cloud and 5G Era”, that aims at capturing the emerging approaches and technical solutions for dealing with 5G and cloud infrastructures, as well as associated services and applications.

The Software-Defined Networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDN-based management architectures, it is of paramount importance to design a monitoring system that can provide frequent and consistent updates to heterogeneous management applications. For the monitoring functionality to scale according to the requirements of large-scale networks a distributed monitoring approach is required. In this paper we present a decentralized approach for resource monitoring in SDN, which is designed to support a wide range of measurement tasks and requirements in terms of monitoring rates and information granularity levels. Our solution leverages effective processing of the monitoring requests to reduce the consumption of limited resources, such as the control plane bandwidth of OpenFlow switches. To demonstrate the benefits of the proposed approach, our evaluation is based on a realistic and demanding use case, where a distributed management application coordinates a content distribution service in an ISP network.

Recent approaches have proposed to empower Internet Service Providers (ISPs) with caching capabilities that can allow them to implement their own cache management strategies and as such have better control over the utilization of their resources. In this demo paper, we present CacheMAsT (Cache Management Analysis and Visualization Tool), a decision support tool to visualize the configuration and performance of in-network cache management approaches. CacheMAsT is aimed at assisting researchers and engineers in analyzing and evaluating the different factors that can affect the performance of a cache management strategy and ultimately decide on the optimal approach to apply.

The management of network infrastructures has become increasingly complex over time, which is mainly attributed to the introduction of new functionality to support emerging services and applications. To address this important issue, research efforts in the last few years focused on developing software-defined networking solutions. While initial work proposed centralized architectures, their scalability limitations have led researchers to investigate a distributed control plane. Controller placement algorithms and mechanisms for building a logically centralized network view are some examples of challenges addressed in this context. A critical issue that requires specific attention concerns the communication between distributed entities involved in decision-making processes. To this end, we propose SigMA, a signaling framework that supports communication between the different entities of a decentralized management and control system. We also define the communication primitives and interfaces involved in such a decentralized environment. The benefits of SigMA are illustrated through three realistic network resource management use cases with different communication requirements. Based on simulation, we demonstrate the flexibility and extensibility of our solution in satisfying these requirements, thus effectively supporting advanced decentralized decision-making processes.

The management of network infrastructures has become increasingly complex over time, which is mainly attributed to the introduction of new functionality to support emerging services and applications. To address this important issue, research efforts in the last few years focused on developing Software-Defined Networking solutions. While initial work proposed centralized architectures, their scalability limitations have led researchers to investigate a distributed control plane, with controller placement algorithms and mechanisms for building a logically centralized network view, being examples of challenges addressed. A critical issue that has not been adequately addressed concerns the communication between distributed decision-making entities to ensure configuration consistency. To this end, this paper proposes a signaling framework that can allow the exchange of information in distributed management and control scenarios. The benefits of the proposed framework are illustrated through a realistic network resource management use case. Based on simulation, we demonstrate the flexibility and extensibility of our solution in meeting the requirements of distributed decision-making processes.

Content delivery networks (CDNs) have been the prevalent method for the efficient delivery of content across the Internet. Management operations performed by CDNs are usually applied only based on limited information about Internet Service Provider (ISP) networks, which can have a negative impact on the utilization of ISP resources. To overcome these issues, previous research efforts have been investigating ISP-operated content delivery services, by which an ISP can deploy its own in-network caching infrastructure and implement its own cache management strategies. In this paper, we extend our previous work on ISP-operated content distribution and develop a novel scalable and efficient distributed approach to control the placement of content in the available caching points. The proposed approach relies on parallelizing the decision-making process and the use of network partitioning to cluster the distributed decision-making points, which enables fast reconfiguration and limits the volume of information required to take reconfiguration decisions. We evaluate the performance of our approach based on a wide range of parameters. The results demonstrate that the proposed solution can outperform previous approaches in terms of management overhead and complexity while offering similar network and caching performance.

Cloud computing has revolutionized service delivery by providing on-demand invocation and elasticity. To reap these benefits, computation has been displaced from client devices and into data centers. This partial centralization is undesirable for applications that have stringent locality requirements, e.g., low latency. This problem could be addressed with large numbers of smaller cloud resources closer to users. However, as cloud computing diffuses from within data centers and into the network, there will be a need for cloud resource allocation algorithms that operate on resource-constrained computational units that serve localized subsets of customers. In this paper, we present a mechanism for service provisioning in distributed clouds where applications compete for resources. The mechanism operates by enabling execution zones to assign resources based on Vickrey auctions and provides high-quality probabilistic models that applications can use to predict the outcomes of such auctions. This allows applications to use knowledge of the locality distribution of their clients to accurately select the number of bids to be sent to each execution zone and their value. The proposed mechanism is highly scalable, efficient, and validated by extensive simulations.

Traffic engineering (TE) functionality aims to control and fine-tune the routing configuration and bandwidth allocation in order to optimize the use of network resources and avoid the build-up of congestion. The performance of a given TE scheme is, however, strongly influenced by the degree of flexibility offered in distributing the traffic load. Multipath routing coupled with arbitrary traffic splitting are two essential ingredients for achieving the desired flexibility. Current proposals for multipath routing in OpenFlow have mostly focused on equal splitting solutions, which impose limitations in terms of the level of control that can be achieved. In this paper, we investigate a new approach, which exploits the properties of bit-masking operations to enable flexible TE in OpenFlow networks. The proposed solution relies on the matching entry feature and the multiple table pipeline capability of OpenFlow, and as such, is in line with the current standard. Based on empirical evaluation, we illustrate the influence of the considered masking parameters and how these can be configured to achieve the desired traffic splitting ratios. The results demonstrate that our solution can achieve a similar level of splitting accuracy as the one obtained with a hash-based approach. However, in contrast to current proposals, it does not require complex extensions to the OpenFlow protocol and can be easily implemented in an OpenFlow environment.

In order to support reactive and adaptive operations, Software-Defined Networking (SDN)-based management and control frameworks call for decentralized solutions. A key challenge to consider when deploying such solutions is to decide on the degree of distribution of the management and control functionality. In this paper, we develop an approach to determine the allocation of management and control entities by designing two algorithms to compute their placement. The algorithms rely on a set of input parameters which can be tuned to take into account the requirements of both the network infrastructure and the management applications to execute in the network. We evaluate the influence of these parameters on the configuration of the resulting management and control planes based on real network topologies and provide guidelines regarding the settings of the proposed algorithms.

The heterogeneous nature of the applications, technologies and equipment that today's networks have to support has made the management of such infrastructures a complex task. The Software-Defined Networking (SDN) paradigm has emerged as a promising solution to reduce this complexity through the creation of a unified control plane independent of specific vendor equipment. However, designing a SDN-based solution for network resource management raises several challenges as it should exhibit flexibility, scalability and adaptability. In this paper, we present a new SDN-based management and control framework for fixed backbone networks, which provides support for both static and dynamic resource management applications. The framework consists of three layers which interact with each other through a set of interfaces. We develop a placement algorithm to determine the allocation of managers and controllers in the proposed distributed management and control layer. We then show how this layer can satisfy the requirements of two specific applications for adaptive load-balancing and energy management purposes.

Centralized and offline network management functionality, traditionally deployed by operators, cannot easily deal with the traffic patterns of emerging services, which are becoming more dynamic and unpredictable. As such, decentralized solutions that are flexible and adaptive to traffic and network dynamics are of paramount importance. To this end, we have been developing an in-network management approach in which an intelligent substrate allows the dynamic reconfiguration of resources according to network conditions. The set of nodes forming this logical structure are able to communicate with each other to coordinate their decisions. While in previous work we investigated the use of full-mesh and ring structures to connect the substrate nodes, we consider here a hybrid approach that combines the benefits of the other two. We describe algorithms that can be practically used to compute this hybrid structure and that take into account important criteria such as minimizing the latency and the communication overhead among the substrate nodes. We evaluate the impact of key parameters associated with the construction process.

Management operations performed by Content Delivery Network (CDN) providers consist mainly in controlling the placement of contents at different storage locations and deciding where to serve client requests from. Configuration decisions are usually taken by using only limited information about the carrier networks, and this can adversely affect network usage. In this work we propose an approach by which ISPs can have more control over their resources. This involves the deployment of caching points within their network, which can allow them to implement their own content placement strategies. The work presented in this paper investigates lightweight strategies that can be used by the ISPs to manage the placement of contents in the various network caching locations according to user demand characteristics. The proposed strategies differ in terms of the volume and nature of the information required to determine the new caching configurations. We evaluate the performance of the proposed strategies, in terms of network resource utilization, based on a wide range of user demand profiles and we compare the obtained performance according to metrics we define to characterize the demand. The results demonstrate that the proposed metrics can provide useful indications regarding the performance one strategy can achieve over another and, as such, can be used by the ISP to improve the utilization of network resources.

In order to meet the requirements of emerging demanding services, network resource management functionality that is decentralized, flexible and adaptive to traffic and network dynamics is of paramount importance. In this paper we describe the main mechanisms of DACoRM, a new intra-domain adaptive resource management approach for IP networks. Based on path diversity provided by multi-topology routing, our approach controls the distribution of traffic load in the network in an adaptive manner through periodical re-configurations that uses real-time monitoring information. The re-configuration actions performed are decided in a coordinated fashion between a set of source nodes that form an in-network overlay. We evaluate the overall performance of our approach using realistic network topologies. Results show that near-optimal network performance in terms of resource utilization can be achieved in scalable manner.

There has recently been an increasing research interest in network management infrastructures that autonomously adapt to the dynamics of the environment. In this paper, we present an information management platform, called the Information Management Overlay (IMO), which is an infrastructure that regulates information flow based on the properties and the state of the network environment. We discuss the design, implementation and optimization issues of this platform and we investigate experimentally how the IMO can adapt to different settings and optimization requirements.

Several intra- and inter-domain quality of service (QoS) provisioning mechanisms for IP networks have been researched and developed using Differentiated Services (DiffServ) technology. However, the incremental efforts needed to manage DiffServ networks from a business-oriented viewpoint have received relatively little attention. This paper addresses this gap and presents a framework for achieving business-driven QoS provisioning in DiffServ over MPLS networks. We provide a rich model of SLA and business indicators as well as reasonable mapping functions that define, with key degrees of importance, the impact of business indicators over service management policies. Business and service indicators are used to control static and dynamic admission of services. The paper advances the state of the art by considering the influence of business-level objectives on the policy refinement process. We evaluate and discuss the effectiveness of our approach through a simulation environment that we developed over OPNET.

Policy-based management provides the ability to (re-)configure differentiated services networks so that desired Quality of Service (QoS) goals are achieved. This requires implementing network provisioning decisions, performing admission control, and adapting bandwidth allocation to emerging traffic demands. A policy-based approach facilitates flexibility and adaptability as policies can be dynamically changed without modifying the underlying implementation. However, inconsistencies may arise in the policy specification. In this paper we provide a comprehensive set of QoS policies for managing Differentiated Services (DiffServ) networks, and classify the possible conflicts that can arise between them. We demonstrate the use of Event Calculus and formal reasoning for the analysis of both static and dynamic conflicts in a semi-automated fashion. In addition, we present a conflict analysis tool that provides network administrators with a user-friendly environment for determining and resolving potential inconsistencies. The tool has been extensively tested with large numbers of policies over a range of conflict types.

Modern cities are becoming increasingly networked environments and a plethora of computing equipment interacts with today's urban citizens. We refer to these ubiquitous networked environments as "urban spaces" and attempt to manage these under a unified framework based on policies. Since users actively participate in urban spaces with their owned devices and demand more control and privacy, we introduce a scheme to protect user privacy and respect their preferences. We adopt a multiple manager paradigm to enable more entities to offer their services and cooperatively shape management logic based on their objectives. Detailed policy examples illustrate the concepts and simulation results measure the effect of policies on network performance.

Policy-based dynamic resource management may involve interaction between independent decision-making components which can lead to conflicts. For example, conflicts can occur between the policies for allocating resources and those setting quotas for users or classes of service. These policy conflicts cannot be detected by static analysis of the policies at specification-time as the conflicts arise from the current state of the resources within the system and so can only be detected at run-time. In this paper we use policies related to quality of service (QoS) provisioning for configuring differentiated services (DiffServ) networks to illustrate techniques for the dynamic detection and resolution of conflicts. Configuration includes implementing network provisioning decisions, performing admission control, and adapting bandwidth allocation dynamically according to emerging traffic demands. We identify possible conflicts between policies that manage the allocation of resources, and we also investigate conflicts that may arise between these policies and higher-level directives refined at the dynamic resource management level, acting as constraints. The paper shows how event calculus can be used to detect conflicts, focusing on the ones that emerge at run-time, and provides an approach for specifying policies to automate conflict resolution. The latter is demonstrated through our initial implementation of a dynamic conflict analysis tool

Goal-oriented requirements engineering methodologies have been suggested as an alternative to address the policy refinement paradigm. Moreover, practical approaches that capture the administrative and technical requirements to make policy refinement a systematic process are still missing although such integrated solutions are rather convenient to make policy-based management systems really useful. In this paper, we present GOREMOCH, a goal-oriented policy refinement environment grounded in goal-oriented requirements engineering methodologies, linear temporal logic and reactive systems analysis techniques. We describe the rationale of this integrated solution and the necessary mechanisms to achieve policy refinement in a systematic manner

Policy-based management provides the ability to dynamically re-configure DiffServ networks such that desired Quality of Service (QoS) goals are achieved. This includes network provisioning decisions, performing admission control, and adapting bandwidth allocation dynamically. QoS management aims to satisfy the Service Level Agreements (SLAs) contracted by the provider and therefore QoS policies are derived from SLA specifications and the provider's business goals. This policy refinement is usually performed manually with no means of verifying that the policies written are supported by the network devices and actually achieve the desired QoS goals. Tool support is lacking and policy refinement has rarely been addressed in the literature. This paper extends our previous approach to policy refinement and shows how to apply it to the domain of DiffServ QoS management. We make use of goal elaboration and abductive reasoning to derive strategies that will achieve a given high-level goal. By combining these strategies with events and constraints, we show how policies can be refined, and what tool support can be provided for the refinement process using examples from the QoS management domain. The approach presented here can be used in other application domains such as storage area networks or security management.

Policy-based management provides the ability to dynamically re-configure DiffServ networks such that desired quality of service (QoS) goals are achieved. This includes network provisioning decisions, performing admission control, and adapting bandwidth allocation dynamically. QoS management aims to satisfy the service level agreements (SLAs) contracted by the provider and therefore QoS policies are derived from SLA specifications and the provider's business goals. This policy refinement is usually performed manually with no means of verifying that the policies written are supported by the network devices and actually achieve the desired QoS goals. Tool support is lacking and policy refinement has rarely been addressed in the literature. This paper extends our previous approach to policy refinement and shows how to apply it to the domain of DiffServ QoS management. We make use of goal elaboration and abductive reasoning to derive strategies that achieves a given high-level goal. By combining these strategies with events and constraints, we show how policies can be refined, and what tool support can be provided for the refinement process using examples from the QoS management domain. However, the approach presented here can be used in other application domains such as storage area networks or security management.