Resource allocation in wireless access networks has been an intensively researched topic recently: many proposed solutions tackle radio channel access and dynamic spectrum allocation, but traditional issues of queuing, bandwidth sharing and packet processing at wireless access points have been targeted as well. In most of the related work the competition for high quality of service is usually solved by central coordination among users via optimizing a specific target aspect of the overall communication. In this paper we take a turn and provide users with the possibility of resource allocation suggestions. We propose a wireless access sharing framework, in which users have a say in optimizing their quality of service on the long term, and we tackle its analysis with the tool set of stochastic game theory. Our findings show that greedy users become polite against their counterparts when the load is relatively low with the goal of preparing for situations with high load.

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.

Data analysis is widely used in all domains of the economy. While the amount of data to process grows, the time criteria and the resource consumption constraints get stricter. These phenomena call for advanced resource orchestration for the big data applications. The challenge is actually even greater at the advent of edge computing: orchestration of big data resources in a hybrid edge-cloud infrastructure is challenging. The difficulty stems from the fact that wide-area networking and all its well-known issues come into play and affect the performance of the application. In this paper we present the steps we made towards network-aware big data application design over such distributed systems. We propose a HDFS block placement algorithm for the network reliability problem we identify in geographically distributed topologies. The heuristic algorithm we propose provides better big data application performance compared to the default block placement method. We implement our solution in our simulation environment and show the improved quality of big data applications.

Efficient and highly available packet classification is fundamental for various security primitives. In this paper, we evaluate whether the de facto Tuple Space Search (TSS) packet classification algorithm used in popular software networking stacks such as the Open vSwitch is robust against low-rate denial-of-service attacks. We present the Tuple Space Explosion (TSE) attack that exploits the fundamental space/time complexity of the TSS algorithm.

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.