Approvisionnement axé sur le profit des chaines de service réseau

La virtualisation des fonctions réseau (Network Function Virtualization - NFV) est un paradigme émergeant qui est en train de transformer la manière avec laquelle les services réseau sont approvisionnés et gérés. L'idée principale du NFV est de découpler les fonctions réseau des équipements réseau qui les exécutent. Ainsi, un service réseau peut être approvisionné à la demande comme étant une chaine de fonctions réseau virtuelles. Cela permettrait d'améliorer la flexibilité et l'évolutivité des services réseau et éventuellement de réduire les coûts de déploiement. Dans ce contexte, l’un des principaux defies des fournisseurs de nuage qui restent à résoudre est d’allouer efficacement les ressources pour les services réseau de manière à réduire les coûts opérationnels et qui maximize leurs profits. Dans ce travail, nous abordons ce défi en proposant un système d’approvisionnement de service réseaux conçu pour les infrastructures à grande échelle couvrant différents sites géographiquement distribués. Nous proposons trois algorithms qui maximisent le profit du fournisseur de nuage en tenant compte de la consummation d'énergie de l'infrastructure et de la variabilité des prix de l'énergie dans les différentes régions. Nous montrons ensuite grâce à des simulations que ces algorithmes sont capables de trouver efficacement des allocations de ressources quasi-optimales avec un minimum de complexité de calcul et de maximiser le profit du fournisseur

Research challenges in nextgen service orchestration

Fog/edge computing, function as a service, and programmable infrastructures, like software-defined networking or network function virtualisation, are becoming ubiquitously used in modern Information Technology infrastructures. These technologies change the characteristics and capabilities of the underlying computational substrate where services run (e.g. higher volatility, scarcer computational power, or programmability). As a consequence, the nature of the services that can be run on them changes too (smaller codebases, more fragmented state, etc.). These changes bring new requirements for service orchestrators, which need to evolve so as to support new scenarios where a close interaction between service and infrastructure becomes essential to deliver a seamless user experience. Here, we present the challenges brought forward by this new breed of technologies and where current orchestration techniques stand with regards to the new challenges. We also present a set of promising technologies that can help tame this brave new world

Efficient Virtualized Network Service Provisioning in Mobile Edge Computing

There is a substantial growth in the usage of mobile devices. These devices, including smartphones, sensors, and wearables, are limited by their computational and energy capacities, due to their portable size. Mobile edge computing (MEC), which provides cloud resources at the edge of mobile network in close proximity to mobile users, is a promising technology to reduce response delays, ensure network operation efficiency, and improve user service satisfaction. Mobile edge computing is a promising technology to leverage the capability of mobile devices to offload tasks to nearby edge-clouds (cloudlets) for processing. Furthermore, Network Function Virtualization (NFV) is another promising technique that implements various network functions for many applications as pieces of software in servers or cloudlets in MEC networks. The provisioning of virtualized network services in MEC can improve user service experiences, simplify network service deployment, and ease network resource management. In this thesis, we will focus on the efficient virtualized network service provisioning in MEC networks by judicious resource allocations and request admissions to maximize network throughput and minimize request admission cost in different application scenarios. We firstly address dynamic request admissions with service function chain requirements in MEC with the objective to maximize the profit collected by the network service provider, assuming that the cloudlets are located at different geographical locations and electricity prices at different locations are different. We formulate an integer linear programming (ILP) solution to the offline problem, and devise an online algorithm with a provable competitive ratio for the online problem when requests arrive one by one without the knowledge of future request arrivals. We then study NFV-enabled multicasting that is a fundamental routing problem in an MEC network, subject to resource capacities on both its cloudlets and links. We devise an admission framework for single NFV-enabled multicast request admission with the aim to minimize request admission cost. We then develop an efficient algorithm for the throughput maximization problem for the admissions of a given set of NFV-enabled multicast requests. We also devise an online algorithm with a provable competitive ratio for the online NFV-enabled multicast request admissions. We thirdly investigate virtualized network function service provisioning for mobile users in MEC that takes into account user mobility and service delay requirements. We formulate two novel optimization problems of user service request admissions with the aims to maximize the accumulative network utility and accumulative network throughput for a given time horizon, respectively, where network utility is a submodular function that can be used to tradeoff between individual user service satisfaction and accumulative network throughput. We then devise a constant approximation algorithm for the utility maximization problem. We also develop an online algorithm for the accumulative throughput maximization problem. We fourthly explore a non-trivial tradeoff between different types of resources in NFV-enabled request scheduling in MEC with an objective to minimize request admission cost, through introducing a novel concept - load factor. We formulate the cost minimization problem that admits all requests by assuming that there is sufficient computing resource in MEC to accommodate the requested VNF instances of all requests, for which we formulate an ILP solution and two efficient heuristic algorithms. We also deal with the problem under the computing resource constraint, for which we formulate an ILP solution when the problem size is small; otherwise, we devise efficient algorithms for it. We finally summarize the thesis and explore several potential research topics that are based on the work in this thesis