Offline and online power aware resource allocation algorithms with migration and delay constraints

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In order to handle advanced mobile broadband services and Internet of Things (IoT), future Internet and 5G networks are expected to leverage the use of network virtualization, be much faster, have greater capacities, provide lower latencies, and significantly be power efficient than current mobile technologies. Therefore, this paper proposes three power aware algorithms for offline, online, and migration applications, solving the resource allocation problem within the frameworks of network function virtualization (NFV) environments in fractions of a second. The proposed algorithms target minimizing the total costs and power consumptions in the physical network through sufficiently allocating the least physical resources to host the demands of the virtual network services, and put into saving mode all other not utilized physical components. Simulations and evaluations of the offline algorithm compared to the state-of-art resulted on lower total costs by 32%. In addition to that, the online algorithm was tested through four different experiments, and the results argued that the overall power consumption of the physical network was highly dependent on the demands’ lifetimes, and the strictness of the required end-to-end delay. Regarding migrations during online, the results concluded that the proposed algorithms would be most effective when applied for maintenance and emergency conditions.Peer ReviewedPreprin

Resource Allocation in SDN/NFV-Enabled Core Networks

For next generation core networks, it is anticipated to integrate communication, storage and computing resources into one unified, programmable and flexible infrastructure. Software-defined networking (SDN) and network function virtualization (NFV) become two enablers. SDN decouples the network control and forwarding functions, which facilitates network management and enables network programmability. NFV allows the network functions to be virtualized and placed on high capacity servers located anywhere in the network, not only on dedicated devices in current networks. Driven by SDN and NFV platforms, the future network architecture is expected to feature centralized network management, virtualized function chaining, reduced capital and operational costs, and enhanced service quality. The combination of SDN and NFV provides a potential technical route to promote the future communication networks. It is imperative to efficiently manage, allocate and optimize the heterogeneous resources, including computing, storage, and communication resources, to the customized services to achieve better quality-of-service (QoS) provisioning. This thesis makes some in-depth researches on efficient resource allocation for SDN/NFV-enabled core networks in multiple aspects and dimensionality. Typically, the resource allocation task is implemented in three aspects. Given the traffic metrics, QoS requirements, and resource constraints of the substrate network, we first need to compose a virtual network function (VNF) chain to form a virtual network (VN) topology. Then, virtual resources allocated to each VNF or virtual link need to be optimized in order to minimize the provisioning cost while satisfying the QoS requirements. Next, we need to embed the virtual network (i.e., VNF chain) onto the substrate network, in which we need to assign the physical resources in an economical way to meet the resource demands of VNFs and links. This involves determining the locations of NFV nodes to host the VNFs and the routing from source to destination. Finally, we need to schedule the VNFs for multiple services to minimize the service completion time and maximize the network performance. In this thesis, we study resource allocation in SDN/NFV-enabled core networks from the aforementioned three aspects. First, we jointly study how to design the topology of a VN and embed the resultant VN onto a substrate network with the objective of minimizing the embedding cost while satisfying the QoS requirements. In VN topology design, optimizing the resource requirement for each virtual node and link is necessary. Without topology optimization, the resources assigned to the virtual network may be insufficient or redundant, leading to degraded service quality or increased embedding cost. The joint problem is formulated as a Mixed Integer Nonlinear Programming (MINLP), where queueing theory is utilized as the methodology to analyze the network delay and help to define the optimal set of physical resource requirements at network elements. Two algorithms are proposed to obtain the optimal/near-optimal solutions of the MINLP model. Second, we address the multi-SFC embedding problem by a game theoretical approach, considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. In the proposed resource constrained multi-SFC embedding game (RC-MSEG), each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all its NFV nodes. Due to processing-resource sharing, additional delay is incurred and integrated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We first prove that the proposed game RC-MSEG is an exact potential game admitting at least one pure Nash Equilibrium (NE) and has the finite improvement property (FIP). Then, we design two iterative algorithms, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE of the proposed game. Third, the VNF scheduling problem is investigated to minimize the makespan (i.e., overall completion time) of all services, while satisfying their different end-to-end (E2E) delay requirements. The problem is formulated as a mixed integer linear program (MILP) which is NP-hard with exponentially increasing computational complexity as the network size expands. To solve the MILP with high efficiency and accuracy, the original problem is reformulated as a Markov decision process (MDP) problem with variable action set. Then, a reinforcement learning (RL) algorithm is developed to learn the best scheduling policy by continuously interacting with the network environment. The proposed learning algorithm determines the variable action set at each decision-making state and accommodates different execution time of the actions. The reward function in the proposed algorithm is carefully designed to realize delay-aware VNF scheduling. To sum up, it is of great importance to integrate SDN and NFV in the same network to accelerate the evolution toward software-enabled network services. We have studied VN topology design, multi-VNF chain embedding, and delay-aware VNF scheduling to achieve efficient resource allocation in different dimensions. The proposed approaches pave the way for exploiting network slicing to improve resource utilization and facilitate QoS-guaranteed service provisioning in SDN/NFV-enabled networks

Network Function Virtualization Service Delivery In Future Internet

This dissertation investigates the Network Function Virtualization (NFV) service delivery problems in the future Internet. With the emerging Internet of everything, 5G communication and multi-access edge computing techniques, tremendous end-user devices are connected to the Internet. The massive quantity of end-user devices facilitates various services between the end-user devices and the cloud/edge servers. To improve the service quality and agility, NFV is applied. In NFV, the customer\u27s data from these services will go through multiple Service Functions (SFs) for processing or analysis. Unlike traditional point-to-point data transmission, a particular set of SFs and customized service requirements are needed to be applied to the customer\u27s traffic flow, which makes the traditional point-to-point data transmission methods not directly used. As the traditional point-to-point data transmission methods cannot be directly applied, there should be a body of novel mechanisms that effectively deliver the NFV services with customized~requirements. As a result, this dissertation proposes a series of mechanisms for delivering NFV services with diverse requirements. First, we study how to deliver the traditional NFV service with a provable boundary in unique function networks. Secondly, considering both forward and backward traffic, we investigate how to effectively deliver the NFV service when the SFs required in forward and backward traffic is not the same. Thirdly, we investigate how to efficiently deliver the NFV service when the required SFs have specific executing order constraints. We also provide detailed analysis and discussion for proposed mechanisms and validate their performance via extensive simulations. The results demonstrate that the proposed mechanisms can efficiently and effectively deliver the NFV services under different requirements and networking conditions. At last, we also propose two future research topics for further investigation. The first topic focuses on parallelism-aware service function chaining and embedding. The second topic investigates the survivability of NFV services

Enabling multicast slices in edge networks

Telecommunication networks are undergoing a disruptive transition towards distributed mobile edge networks with virtualized network functions (VNFs) (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) within the proximity of users. This transition will enable network services, especially IoT applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this paper we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into Integer Linear Programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a non-trivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on Reinforcement Learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real test-bed. Experimental results demonstrate that the proposed algorithms is promising

Clustering algorithms for dynamic adaptation of service function chains

Network function virtualization is a pillar-stone of today’s network architectures as it offers better management and elasticity and allows also a flexible maintenance of services running on shared resources over cloud environments. Network functions traditionally hosted on dedicated hardware are now provided over software based components that might run either on virtual machines or on containers. The major advantage of this transition is that it makes the deployment of new services easier while optimizing the management and administration of network architectures. It is much easier to spin up a new virtual machine/container hosting a network function or a specific application described as a service function chain, than to deploy a new hardware based equipment and checking its compatibility with the rest of the architecture. With all the advantages that this new paradigm offers comes a set of challenges related mainly to: 1) optimizing the resource consumption on the shared infrastructure 2) making the best decision of placing the virtual functions that respects at the same time clients’ requirements and also leverages the available resources on the substrate network in terms of different metrics (e.g., CPU, memory, latency, bandwidth). This aspect of Network Function Virtualization-NFV and Service Function Chains-SFC placement have been treated in so many research works that propose approaches ensuring optimal placement and chaining of VNFs in virtualized networks, but as the adoption of these technologies gets more important in real network setups, and given the strict restrictions of today’s’ applications (e.g. latency highly-sensitive applications, or availability highly-sensitive service, etc.), it is always important to consider all the parameters impacting the network management in cloud environments. In this research project, we develop new approaches for placement and chaining of virtual network functions in cloud-based environments. The first approach allows forming on demand clusters of servers deployed in a physical infrastructure. These servers are grouped according to their similar attributes (e.g., CPU-intensive server, energy-efficient server, etc). This process is a proactive measure to ensure that SFCs are hosted in servers that meet their specific metrics requirements (CPU, memory, disk, etc.). It employs a meta-heuristic called CRO (Chemical Reaction Optimization) to decide of the best VNF placement guaranteeing optimal resource consumption in terms of CPU / memory. We employ CRO also to ensure the lowest latencies during the routing between the different VNFs. In fact, the E2E delay is an important aspect to consider, as most current applications require low latencies and shortest run times. In the second approach, the clusters are formed using algorithms based on meta-heuristics, including the CRO, allowing to improve the quality of clusters formed in terms of similarity, density and modularity

Cost-effective low-delay cloud video conferencing

The cloud computing paradigm has been advocated in recent video conferencing system design, which exploits the rich on-demand resources spanning multiple geographic regions of a distributed cloud, for better conferencing experience. A typical architectural design in cloud environment is to create video conferencing agents, i.e., virtual machines, in each cloud site, assign users to the agents, and enable inter-user communication through the agents. Given the diversity of devices and network connectivities of the users, the agents may also transcode the conferencing streams to the best formats and bitrates. In this architecture, two key issues exist on how to effectively assign users to agents and how to identify the best agent to perform a transcoding task, which are nontrivial due to the following: (1) the existing proximity-based assignment may not be optimal in terms of inter-user delay, which fails to consider the whereabouts of the other users in a conferencing session; (2) the agents may have heterogeneous bandwidth and processing availability, such that the best transcoding agents should be carefully identified, for cost minimization while best serving all the users requiring the transcoded streams. To address these challenges, we formulate the user-to-agent assignment and transcoding-agent selection problems, which targets at minimizing the operational cost of the conferencing provider while keeping the conferencing delay low. The optimization problem is combinatorial in nature and difficult to solve. Using Markov approximation framework, we design a decentralized algorithm that provably converges to a bounded neighborhood of the optimal solution. An agent ranking scheme is also proposed to properly initialize our algorithm so as to improve its convergence. The results from a prototype system implementation show that our design in a set of Internet-scale scenarios reduces the operational cost by 77% as compared to a commonly-adopted alternative, while simultaneously yielding lower conferencing delays.published_or_final_versio

Contribution to multi-domain network slicing : resource orchestration framework and algorithms

5G/6G services and applications, in the context of the eMBB, mMTC and uRLLC network slicing framework, whose network infrastructure requirements may span beyond the coverage area of a single Infrastructure Provider (InP), are envisaged to be supported by leasing resources from multiple InPs. A challenging aspect for a Service Provider (SP) is how to obtain an optimal set of InPs on which to provision the requests and the particular substrate nodes and links within each InP on which to map the different VNFs and virtual links of the service requests, respectively, for a seamless, reliable and cost-effective orchestration of service requests. Existing works in this area either perform service mapping in uncoordinated manner, do not incorporate service reliability or do so from the perspective of stateless VNFs. Also they assume full information disclosure, or are based on exact approaches, which considerations are not well suited for future network scenarios characterized by delay sensitive mission critical applications and resource constrained networks. This thesis contributes to the above challenge by breaking the multi-domain service orchestration problem into two interlinked sub-problems that are solved in a coordinated manner: (1) Request splitting/partitioning (sub-problem 1), involving obtaining a subset of InPs and the corresponding inter-domain links on which to provision the different VNFs and virtual links of the service request; (2) Intra-domain VNF orchestration (sub-problem 2), involving obtaining the intra-domain nodes and links to provision the VNFs and virtual links of the sub-SFC associated with each InP. In this way, the thesis sets out four key targets that are necessary to align with the mission critical and delay sensitive use-cases envisaged in 5G and future networks in terms of service deployment cost and QoS: (1) coordinated mapping of service requests, with a view of realizing better utilization of the substrate resources; (2) survivability and fault-tolerant orchestration of service requests, to tame both QoS violations and the penalties from such violations; (3) limited disclosure of InP internal information, in order adhere to the privacy requirements InPs, and (4) achieving all the above targets in polynomial time. In order to realize the above targets, the thesis sought for solution techniques that are: (1) able to incorporate information learned in the previous solutions search space and historical mapping decisions, hence, resulting in acceptable performance even in scenarios of limited information exposure and fuzzy environments; (2) robust and less problem specific, hence, can be tailored to different optimization objectives, network topologies and service request constraints, thus enabling to deal with requests with either chained topologies or with bifurcated paths; (3) capable of dealing with an optimization problem that is jointly affected by multiple attributes, since in practice, the service deployment cost is jointly affected by multiple conflicting costs; (4) able to realize near-optimal solutions in practical run-times, thus rendering well suited approaches for delay sensitive and resource constrained scenarios. Three different algorithms namely, an RL, Genetic Algorithm (GA) and a fully distributed multi-stage graph-based algorithms are proposed for sub-problem 1. In addition, five different algorithms based on GA, Harmony search, RL, and multi-stage graph approach are proposed for sub-problem 2. Finally, in order to guide the implementation and adherence of the thesis proposals to the four main targets of the thesis, an architectural framework is proposed, aligned with the ETSI NFV-MANO architectural framework. Overall, the simulations results proved that the thesis proposals are optimized in terms of request acceptance ratios, mapping cost and execution time, hence, rendering such proposals well suited for 5G and future scenarios.Els serveis que es poden presentar en el marc de la tecnologia de “slicing” de xarxa de 5G/6G, com ara eMBB, mMTC o uRLLC, es possible que no els pugui oferir un sol proveïdor d’infraestructura (InP) degut a les limitacions que pot tenir la seva xarxa, i per tant que faci necessària la cooperació de múltiples InPs. En aquest cas, el primer repte que afronta el Proveïdor de Servei (SP) que rep la sol·licitud de desplegament es determinar el conjunt òptim de InPs que hi han d’intervenir i en concret els nodes i enllaços de cada un d’ells que s’han d’utilitzar per al mapatge de les diferents VNFs i enllaços virtuals de la sol·licitud. Els treballs que existeixen en aquesta àrea duen a terme el mapatge del servei be sigui de manera no coordinada, o no incorporen la fiabilitat, o ho fan des de la perspectiva de VNFs sense estat. També, pressuposen la divulgació total de la informació, o estan basats en metodologies exactes que fa que no siguin idonis per a escenaris de xarxes del futur, caracteritzats per aplicacions de missió critica, sensibles al retard i sobre xarxes amb recursos limitats. Aquesta tesi contribueix a afrontar aquests reptes dividint el problema d’orquestració de serveis multi domini en dos subproblemes relacionats, que es resolen de manera coordinada. (1) Divisió / partició de la sol·licitud de servei (sub-problema 1), que implica l'obtenció d'un subconjunt d'InPs i els enllaços interdomini corresponents sobre els quals proporcionar les diferents VNF i enllaços virtuals de la sol·licitud de servei; (2) Orquestració VNF intradomini (sub-problema 2), que implica l'obtenció dels nodes i enllaços intradomini per aprovisionar les VNF i enllaços virtuals dels sub-SFC associats a cada InP. D'aquesta manera, la tesi estableix quatre objectius clau que són necessaris per alinear-se amb els casos d'ús de missió crítica i sensibles al retard previstos en 5G i xarxes futures en termes de cost de desplegament del servei i QoS: (1) mapatge coordinat de les sol·licituds de servei, amb l'objectiu de realitzar una millor utilització dels recursos del substrat; (2) orquestració de les sol·licituds de servei contemplant la supervivència del servei en situacions de fallides, minimitzant les violacions de la QoS i les sancions derivades d'aquestes violacions; (3) divulgació limitada de la informació interna de l’InP, per tal d'adherir-se als requisits de privadesa dels InPs, i (4) aconseguir tots els objectius anteriors en temps polinòmic. Per tal de realitzar els objectius anteriors, la tesi busca solucions que siguin: (1) capaces d'incorporar informació apresa en les solucions anteriors de l'espai de cerca i decisions de mapatge històric, donant lloc a un rendiment acceptable fins i tot en escenaris d'exposició limitada a la informació i entorns difusos; (2) robustes i menys dependents dels problemes específics, i per tant, que es poden adaptar a diferents objectius d'optimització, topologies de xarxa i restriccions de sol·licitud de servei, permetent així fer front a sol·licituds amb cadenes de funcions de topologies molt diverses; (3) capaces de fer front a un problema d'optimització de múltiples atributs, ja que a la pràctica, el cost de desplegament del servei depèn de múltiples costos; (4) capaces de trobar solucions gairebé òptimes en temps suficientment breus, resultant així adequades a escenaris sensibles al retard i amb limitació de recursos. La tesi proposa tres algorismes diferents per al sub-problema 1: un algorisme de RL, un algorisme genètic (GA) i un algorisme multi etapa basat en grafs i completament distribuït. A més, es proposen cinc algorismes diferents basats en l'enfocament de grafs, un algorisme GA, un algorisme de cerca d’harmonia, un algorisme de RL i un algorisme multi-etapa per al sub-problema 2. Finalment, per tal de guiar la implementació i l'adhesió de les propostes als quatre objectius principals de la tesi, es proposa...Postprint (published version