Entering the cloud storage market requires a high upfront investment, thus it is dominated by a few players with existing capacity. Decentralized cloud storage solutions can disrupt the status quo by allowing businesses and individuals to sell their unused storage capacity, reducing the need for large upfront investments in service infrastructure. We show that network operators providing such service can significantly decrease the traffic volume carried on the transport network, which is essential when serving mobile users, while maintaining high data security by implementing our proposed solution, of leveraging controlled replication inside the core network. Upon data uploads encryption and erasure encoding are separated, with the latter moved inside the network, enabling the arbitrary replication of storable data pieces without straining the access network. We present simulation results, showing that the proposed method reduces traffic by 20% compared to the out-of-the-box solution. Moreover, we elaborate on optimal multi-proxy placements and even optimal storage node choosings in complex ISP networks, where deep data penetration is desired, by giving ILP optimization methods and results, achieving minimal overall network load and maximum data security. • New, proxied architecture for separating encryption and erasure coding in DCS systems. • Present improved resource preservation in proxied settings for telcos via simulations. • Show traffic engineering methods with intelligent proxy placement and data regeneration. • Optimizing proxy placements and data flows in multi-operator scenarios, using ILP. • Simulating optimal distribution path setups, ”diamond paths”, on Internet-like graphs.

To implement a Network Service (NS) within a Network Function Virtualization (NFV) environment, it is essential to create a sequence of connected Virtual Network Functions (VNFs), known as a VNF Forwarding Graph (VNF-FG), and then embed it onto the substrate network. The emergence of VNF decomposition as a new functional architecture allows VNFs to be broken down into smaller sub-functions, offering enhanced flexibility, resource sharing, and scalability. VNF decomposition can significantly reduce VNF embedding costs since different sub-functions can be efficiently reused by multiple network requests. However, when VNFs are decomposed into multiple sub-functions, selecting the appropriate decomposition option for each VNF and constructing the VNF-FG to embed onto the substrate network poses a significant challenge in NFV resource allocation (NFV-RA). A key challenge is identifying the optimal decomposition option among all possible choices for VNF embedding. In this paper, we introduce a cost-aware algorithm designed to address the topological decomposition of VNF-FGs, focusing on minimizing embedding costs while meeting specified service requirements. We formulate the VNF topology decomposition problem using Integer Linear Programming (ILP) to select the best decomposition option and minimize the embedding cost. Furthermore, we propose four efficient heuristics for different topologies to identify the optimal decomposition options for network embedding. Simulation results demonstrate that our proposed algorithm outperforms existing benchmarks in terms of embedding costs and achieves execution times that are up to 95% better than the SE approach.

Network Function Virtualization (NFV) is a network architecture that separates network functions from dedicated hardware, implementing them as software modules known as Virtual Network Functions (VNFs), which are executed in virtual machines or containers. NFV increases the deployment flexibility and agility within operator networks and reduces the operating and capital expenditures significantly. In NFV, migration of VNFs can significantly reduce the embedding cost. However, stringent latency requirements between VNFs can make them tightly coupled, thus hindering each VNF from being migrated individually, and resulting in poor performance. One of the main challenges in an NFV environment is therefore to migrate a cluster of VNFs to minimize the embedding cost. In this paper, we aim to solve the problem of cluster VNF migration by considering the given inter-VNF latency requirements. We formulate the VNF migration problem as an Integer Linear Programming (ILP) and present two scalable and efficient algorithms for migrating a cluster of VNFs. Through extensive experiments, we show that our proposed algorithms are highly effective. They reduce the total embedding cost by 14% compared to the existing heuristics, while being much more scalable in terms of execution time compared to the brute-force approach.

Network Function Virtualization (NFV) enables the decoupling of network functions from dedicated hardware to run them as software instances on commodity servers through virtualization, replacing hardware-based network functions with software-based Virtual Network Functions (VNFs). In this paper, we study the joint problem of VNF decomposition and migration to address VNF embedding in NFV resource allocation (NFV-RA). More specifically, we investigate how VNF migration and VNF decomposition can be mutually beneficial to minimize the embedding cost of network services. After presenting a novel formulation of the problem as an integer linear programming (ILP), we validate it by CPLEX and show that our joint VNF decomposition and migration approach can outperform the VNF decomposition-only approach by 20% in terms of embedding cost.

To realize a Network Service (NS) in a Network Function Virtualization (NFV) network, it is needed to form an ordered set of connected Virtual Network Functions (VNFs), commonly referred to as VNF Forwarding Graph (VNF-FG), and then embed it onto the substrate network. Forming a VNF-FG is a challenging step of NFV resource allocation (NFV-RA), especially when the VNFs can be further decomposed into different sub-functions. In this paper, we propose a cost-aware algorithm to solve the problem of topological decomposition of VNF-FGs with the main objective of minimizing the embedding cost while satisfying the given service requirements. The simulation results indicate that our proposed algorithm outperforms the existing benchmark in terms of embedding cost, while being significantly scalable compared to the brute-force approach.

Dynamic and zero-touch management is expected to be the key feature of next-generation 6G networks. Network Function Virtualization (NFV) is one of the key technologies for realizing such management through software-based networks. Despite great benefits offered by NFV, deploying network services (NSs) in NFV ecosystems remains a challenge, especially for latency-sensitive NSs, as they demand stringent latency requirements and fast service provisioning. Specifically, service graphs should be embedded into an infrastructure such that these requirements are satisfied while optimizing network operator’s objectives. To cope with the scalability of optimization-based approaches, heuristic methods are known as promising alternatives to find a satisfactory solution within an acceptable execution time. However, existing VNF embedding heuristics still suffer from the so-called causality issue, which may degrade the embedding solution quality. The causality issue means that embedding decisions cannot be optimally determined before all neighboring dependencies are known. To this end, we introduce our${h}$-horizon sequential look-ahead greedy embedding framework, which provides efficient embedding and re-embedding strategies to alleviate the impact of the causality issue. The simulation results indicate that our proposed algorithm significantly improves embedding cost, compared to the existing heuristic algorithms while being much more scalable than an optimization-based approach.

Nowadays, online applications are moving to the cloud, and for delay-sensitive ones, the cloud is being extended with edge/fog domains. Emerging cloud platforms that tightly integrate compute and network resources enable novel services, such as versatile IoT (Internet of Things), augmented reality or Tactile Internet applications. Virtual infrastructure managers (VIMs), network controllers and upper-level orchestrators are in charge of managing these distributed resources. A key and challenging task of these orchestrators is to find the proper placement for software components of the services. As the basic variant of the related theoretical problem (Virtual Network Embedding) is known to be NP-hard, heuristic solutions and approximations can be addressed. In this paper, we propose two architecture options together with proof-of-concept prototypes and corresponding embedding algorithms, which enable the provisioning of delay-sensitive IoT applications. On the one hand, we extend the VIM itself with network-awareness, typically not available in today's VIMs. On the other hand, we propose a multi-layer orchestration system where an orchestrator is added on top of VIMs and network controllers to integrate different resource domains. We argue that the large-scale performance and feasibility of the proposals can only be evaluated with complete prototypes, including all relevant components. Therefore, we implemented fully-fledged solutions and conducted large-scale experiments to reveal the scalability characteristics of both approaches. We found that our VIM extension can be a valid option for single-provider setups encompassing even 100 edge domains (Points of Presence equipped with multiple servers) and serving a few hundreds of customers. Whereas, our multi-layer orchestration system showed better scaling characteristics in a wider range of scenarios at the cost of a more complex control plane including additional entities and novel APIs (Application Programming Interfaces).

Envisioned 5G applications and services, such as Tactile Internet, Industry 4.0 use-cases, remote control of drone swarms, pose serious challenges to the underlying networks and cloud platforms. On the one hand, evolved cloud infrastructures provide the IT basis for future applications. On the other hand, networking is in the middle of a momentous revolution and important changes are mainly driven by Network Function Virtualization (NFV) and Software Defined Networking (SDN). A diverse set of cloud and network resources, controlled by different technologies and owned by cooperating or competing providers, should be coordinated and orchestrated in a novel way in order to enable future applications and fulfill application level requirements. In this paper, we propose a novel cross domain orchestration system which provides wholesale XaaS (Anything as a Service) services over multiple administrative and technology domains. Our goal is threefold. First, we design a novel orchestration system exploiting a powerful information model and propose a versatile embedding algorithm with advanced capabilities as a key enabler. The main features of the architecture include i) efficient and multi-purpose service embedding algorithms which can be implemented based on graph models, ii) inherent multidomain support, iii) programmable aggregation of different resources, iv) information hiding together with flexible delegation of certain requirements enabling multi-operator use-cases, and v) support for legacy technologies. Second, we present our proof-of-concept prototype implementing the proposed system. Third, we establish a dedicated test environment spanning across multiple European sites encompassing sandbox environments from both operators and the academia in order to evaluate the operation of the system. Dedicated experiments confirm the feasibility and good scalability of the whole framework.

Supporting end-to-end network slices and services across operators has become an important use case of study for 5G networks as can be seen by 5G use cases published in 3GPP, ETSI as well as NGMN. This paper presents the in- depth architecture, implementation and experiment on a multi-domain orchestration framework that is ab le to deploy such multi-operator service as well as monitor the service for SLA compliance. Our implemented architecture allows operators to abstract their sensitive details while exposing the relevant amount of information to support inter-operator slice creation. Our experiment shows that the implemented framework is capable of creating services across operators while fulfilling the respective service requirements.

Cloud networks provide various services on top of virtualized compute and storage resources. The flexible operation and optimal usage of the underlying infrastructure are realized by resource orchestration methods and virtualization techniques developed during the recent years. In contrast, service deployment and service provisioning in carrier networks have several limitations in terms of flexibility, scalability or optimal resource usage as the built-in mechanisms are strongly coupled to the physical topology and special purpose hardware elements. Network Function Virtualization (NFV) opens the door between cloud and carrier networks by providing software-based telecommunication services which can run in virtualized environment on general purpose hardwares. Our main goal is to unify software and network resources in a common framework. In this paper, we propose a novel architecture supporting automated, dynamic service creation based on a fine-granular service chaining model, SDN and cloud virtualization techniques. First, we introduce the architecture with the main components. Second, the most important benefits are highlighted and compared to other state-of-the-art approaches. Finally, preliminary experiences with our proof-of-concept prototypes are presented.

End-to-end service delivery often includes transparently inserted Network Functions (NFs) in the path. Flexible service chaining will require dynamic instantiation of both NFs and traffic forwarding overlays. Virtualization techniques in compute and networking, like cloud and Software Defined Networking (SDN), promise such flexibility for service providers. However, patching together existing cloud and network control mechanisms necessarily puts one over the above, e.g., OpenDaylight under an OpenStack controller. We designed and implemented a joint cloud and network resource virtualization and programming API. In this demonstration, we show that our abstraction is capable for flexible service chaining control over any technology domains.

Existing research work on Distributed Hash Tables (DHTs) assume that, prior to actually establishing a DHT, large number potential member nodes would come together at one point and at one place, and would all agree on the characteristics of the DHT to-be-established. This paper presents DHT bootstrapping, which is a novel approach to enable secure, efficient and scalable deployment of DHTs in wireless networks, in particular in wireless networks with time-varying topology. Our solution is not restricted to a particular type of DHT implementation.

Scalable TCP is a simple sender-side alteration to the TCP congestion window update algorithm. It offers a robust mechanism to improve performance in high speed wide area networks using traditional TCP receivers. Scalable TCP uses fixed increase and decrease parameters for updating its congestion window. The performance of Scalable TCP suffers from achieving full utilization of the bandwidth when it has a long-delay connection (e.g. greater than 200msec). Also at small bandwidth delay products Scalable TCP cannot achieve full utilization whereas NewReno TCP performance is better. To overcome these limitation we propose a method to dynamically adapt the increase and decrease parameters of Scalable TCP based on the measured round trip time. Our primarily goals in design were to improve link utilization for high bandwidth and delay product networks; to improve fairness among flows with different round trip times; and to improve friendliness to NewReno for small bandwidth-delay product networks. To achieve this, we proposed a heuristic formula for the increase and decrease parameters as a function of the round trip time; we numerically evaluated and found that our proposed modifications meet our expectations.

Summary form only given. TCP congestion control can perform badly in highspeed wide area networks because of its slow response with large congestion window. The challenge for any alternative protocol is to better utilize networks with high bandwidth-delay products in a simple and robust manner without interacting badly with existing traffic. Scalable TCP is a simple sender side alteration to the TCP congestion window update algorithm. In this paper we numerically evaluate the congestion control method of scalable TCPs and its impact on other existing TCP versions. The general purpose of this work is to study the behavior of scalable TCP not only in situations where it was reportedly performing good but also under more extreme conditions. We also revealed whether scalable TCP maintains an acceptable fairness towards highspeed and/or regular TCP versions. Fairness of scalable TCPs with different round trip times are also investigated.

Recently, a new working group - Next Steps In Signalling (NSIS) - was established within the IETF to develop a general signalling protocol primarily to support QoS resource reservation. Two main operation modes have been clarified: stateful and reduced state. The first mode relies on per flow-state information in all interior nodes while the other uses aggregated states. Stateful operation is based on the resource reservation protocol (RSVP), while reduced state operation will be similar to the resource management in a Diffserv (RMD) framework. With the use of aggregated states instead of per-flow granularity, one looses fine control over resources but derives a scalable and more efficient protocol. However, some problems need new handling mechanisms. In this paper, we discuss the problem of re-routing from the aspect of reduced state resource reservation. We show how quickly and easily RSVP handles such situations but reduced state solutions like RMD require new mechanisms for a quick reaction. We propose and analyse several solutions.

Dynamic traffic engineering requires the operation of load balanced routing. Load sharing, however, implies a thus far neglected, currently hidden problem: insufficient path integrity. Since load balancing requires changing the routes of traffic in order to achieve proper load distribution, path changes of ongoing communications go hand in hand with dynamic traffic engineering. Frequent path changes will not only degrade the performance of current TCP based applications but they will also dramatically influence the grade of service experienced by future QoS critical applications. To overcome this problem, we present a two level hashing based mechanism for route pinning under load balancing. We analytically formalise the path integrity and convergence time achievable for our routing model, and use these formulas for dimensioning path integrity aware load balancing.

This paper proposes several call admission control (CAC) algorithms for tandem networks that employ the generalized processor sharing (GPS) scheduling discipline, and also points out important network issues that are crucial in the design of network level CAC algorithms for GPS. Sessions are considered to be leaky bucket constrained and are regulated by traffic shapers at each network node. The end-to-end service curve approach of Barta et al. (2001) is used to carry out the analytical framework of our algorithms. Different CAC algorithms are developed to support different session treatment strategies and diverse server capacities. The proposed algorithms follow different end-to-end delay provisioning strategies whose performance is compared through numerical examples.

This work is motivated by the increasing need of end-to-end performance bound provisioning in multi-service networks. Providing performance bounds in a networking environment is a difficult problem as traffic is bursty and its dynamics is far more complex than in a single server environment. As end-to-end bounds are basically based on local switch guarantees, computation of local performance bounds requires the knowledge of traffic conditions at inputs of each switch. This paper investigates the problem of traffic characterization of arbitrary topology networks that employ the generalized processor sharing (GPS) scheduling discipline at each node. The study focuses on the derivation of internal (at inputs of switches) traffic descriptors of sessions which are originally leaky bucket constrained at the network edge. Allowing arbitrary weight assignment of sessions the analysis classifies GPS networks into different sets by means of various stability conditions. Methods are presented for the determination of internal traffic parameters in cyclic, acyclic networks with and without virtual feedback. Numerical examples are shown for the illustration of our algorithmic approach for the above mentioned scenarios as well as for end-to-end delay computation of sessions.

We propose a solution to the problem of calculating deterministic end-to-end delay bounds in arbitrary topology networks, which employ the generalized processor sharing (GPS) discipline at each node. We give a general framework under network calculus by extending the service curve model of GPS and constructing the end-to-end service curve for each session, from which exact values of delay and backlog bounds can be computed. Network internal traffic characterization and traffic shaping are applied to support local performance bound computation. Sessions are allowed to be arbitrary weighted and virtual feedback is handled by the iterative approach of Barta, Nemeth, Szabo and Bird (see ICC2001). Numerical examples are shown to illustrate our algorithmic approach for per-session end-to-end delay bound computation.

Generalized processor sharing (GPS) is an ideal fluid scheduling discipline that supports well defined delay and loss bounds on leaky-bucket constrained traffic. Its packetized versions (WFQ, WF2Q, PGPS etc.) are considered as the packet scheduler of choice in IP routers and ATM switches of the future. The currently accepted approach for the design of GPS schedulers is based on deterministic QoS guarantees, which is overly conservative due to the applied loose bounds and leads to limitations on capacity. We developed a framework for the computation of tighter delay bounds, bandwidth and delay de-coupling in GPS systems. In this paper, we propose several effective call admission control (CAC) algorithms that work in the bandwidth and delay de-coupled system while using the tighter delay bounds presented herein. One of the proposed CAC algorithms also handles the best-effort service class beside the QoS guaranteed service classes. Performance evaluation of several CAC algorithms are presented.