Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97% infrastructure (hardware) and 95% services (software).

The optimal placement of virtual network functions (VNFs) improves the overall performance of service function chains (SFCs) and decreases the operational costs for mobile network operators. To cope with changes in demands, VNF instances may be added or removed dynamically, resource allocations may be adjusted, and servers may be consolidated. To maintain an optimal placement of SFCs when conditions change, SFC reconfiguration is required, including the migration of VNFs and the rerouting of service-flows. However, such reconfigurations may lead to stress on the VNF infrastructure, which may cause service degradation. On the other hand, not changing the placement may lead to suboptimal operation, and servers and links may become congested or underutilized, leading to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of their new placement and service-flow routing. We develop a multi-objective genetic algorithm that explores the Pareto front by balancing the optimality of the new placement and the cost to achieve it. Our numerical evaluations show that a small number of reconfigurations can significantly reduce the operational cost of the VNF infrastructure. In contrast, too much reconfiguration may not pay off due to high costs. We believe that our work provides an important tool that helps network providers to plan a good reconfiguration strategy for their service chains.

The emergence of Network Functions Virtualization (NFV) is being heralded as an enabler of the recent technologies such as 5G/6G, IoT and heterogeneous networks. Existing NFV monitoring frameworks either do not have the capabilities to express the range of telemetry items needed to perform management or do not scale to large traffic volumes and rates. We present IntOpt, a scalable and expressive telemetry system designed for flexible NFV monitoring using active probing and P4. IntOpt allows us to specify monitoring requirements for individual service chain, which are mapped to telemetry item collection jobs that fetch the required telemetry items from P4 programmable data-plane elements. We propose mixed integer linear program (MILP) as well as a simulated annealing based random greedy (SARG) meta-heuristic approach to minimize the overhead due to active probing and collection of telemetry items. Using P4-FPGA, we benchmark the overhead for telemetry collection. Our numerical evaluation shows that the proposed approach can reduce monitoring overheads by 39% and monitoring delays by 57%. Such optimization may as well enable existing expressive monitoring frameworks to scale for larger real-time networks.

The tactile internet will enable a new range of capabilities to enable immersive remote operations and interactions with a physical world. Tactile internet use cases span over many fields, from remote operation of industrial applications in, e.g., hazardous environments, via remote-controlled driving in a fully automated intelligent transport system, to remote surgery where the unique expert skills can be delivered to different locations in the world. Fifth-generation (5G) communication will play a fundamental part in this tactile internet vision, as it will provide necessary capabilities for the demanding communication needs in terms of reliability and low latency, for operators or teleoperated systems that are connected wirelessly. This paper provides an overview of tactile internet services and haptic interactions and communication. The 5G functionality for ultrareliable and low-latency services is described in depth and it is shown how 5G new radio (NR) and the evolved long-term evolution (LTE) radio interface can achieve guaranteed low-latency wireless transmission. The costs for providing reliable and low-latency wireless transmission in terms of reduced spectral efficiency and coverage are discussed. The 5G system architecture with a software-based network design based on a distributed cloud platform is presented. It is shown how the 5G network is configured for tactile internet services via multidomain orchestration.

Cloud applications are offered to users with high availability and minimal data loss. Any (hardware or software) failure must be detected and recovered quickly, in order to maintain customer trust and avoid financial losses. When we are dealing with multi-tier and stateful applications, the failure recovery process is a big challenge because the whole state of the failed application must be retrieved and restored in a new instance. This process is named as failover; it can be performed by a checkpoint service at application-level or at system-level. Depending on the location of the checkpoint data storage, it can be classified as non-collocated, collocated warm, or collocated hot. This work presents an evaluation between these two checkpoint services in both virtualized and physical environments, considering a multi-tier and stateful application.

With cloud computing consolidation, Platform-as-a-Service (PaaS) has been used as a solution for developing applications with low cost and maximum flexibility. However, an open challenge related to PaaS is the proper handling of multi-tier and stateful applications with support for high availability (HA); and scalability can be considered an essential feature for HA. However, dealing with several instances of the same application that access its state in a common area is not a simple task. This paper presents a novel PaaS framework, named NoPaaS, that supports the deployment of multi-tier and stateful applications assuring their availability according to the Service Availability Forum (SAF) redundancy model. The primary goal of this work is to present NoPaaS framework and prototype, and highlight challenges and open issues when providing multi-tier and stateful applications in high availability clouds.

Cloud outages, no matter how long, are responsible for large financial losses. Cloud providers look for solutions that provide high availability even in failure cases. Redundancy has been often used as an alternative; however it has a cost, and depending on the redundancy model, we can have different levels of availability. Beyond that, adding more resources also means adding more complexity and more point of failures. In this paper, we propose a risk-based model for estimating the availability of a redundancy setup. In order to analyze our model, we conduct Monte Carlo simulations and results show that for all models, the number of applications we deploy in a Cloud environment impacts on its availability.

This article proposes a scheme for bandwidth allocation in wireless ad hoc networks. The quality of service (QoS) levels for each end-to-end flow are expressed using resource-utility functions, and our algorithms aim to maximize aggregated utility. The shared channel is modeled as bandwidth resources defined by maximal cliques of mutual interfering links. We propose an entirely novel resource allocation algorithm that employs auction mechanisms where flows are bidding for resources. The bids depend both on the flow's utility function and the intrinsically derived shadow prices. Then we combine it with a utility-aware on-demand shortest path routing algorithm where shadow prices are used as a natural distance metric. We also show that the problem can be formulated as a linear programming problem. Thus we can compare the performance of our scheme to the centralized optimal LP solution, registering results very close to the optimum. We isolate the performance of the price-based routing and show its advantages in hotspot scenarios, and also propose an asynchronous version that is more feasible for ad hoc environments. Experimental results of a comparison with the state-of-the-art approach based on Kelly's utility maximization framework show that our approach exhibits superior performance for networks with both increased mobility or increased allocation period.

As mobile ad hoc networks provide a wide range of possibly critical services, providing quality of service guarantees becomes an essential element. Yet there is a limited understanding of the performance characteristics of different resource allocation algorithms. In particular, there is little work that comparatively studies different algorithms in the same traffic environment. Therefore we study two algorithms, ad hoc-TARA and an algorithm based on the gradient projection method, for optimised bandwidth allocation in ad hoc networks under overload situations. The focus is on convergence properties and performance measured in terms of accumulated utility. The simulation results show that the gradient projection algorithm converges to an optimal solution even in large, dynamic networks, but that in such dynamic environments the convergence time can significantly influence the overall performance. In comparison, the near-optimal algorithm ad hoc-TARA, which quickly adapts to changes in the state of the network, can exhibit superior performance. Further we illustrate how different parameter settings influence the performance of the algorithms. We conclude that finding an optimal allocation comes at a high price in the rapidly changing environments of ad hoc networks and that near-optimal allocation can be an ample alternative.

Service availability in wireless networks is highly dependent on efficient resource allocation and guaranteed quality of service (QoS) amid overloads and failures. This paper addresses optimal bandwidth allocation in a hybrid network (cellular and ad hoc), where added reach through an ad hoc overlay is combined with the stability and essential services of a cellular network. The paper builds on a near optimal approach in which resource-utility functions are used as a means of adaptive delivery of QoS, user differentiation, and maximisation of system level utility. It distinguishes between non-adaptive, semi-adaptive, and fully adaptive applications. First, the global cellular bandwidth allocation (in the presence of multiple routes through ad hoc relays) is cast in terms of a linear programming problem. Second, a heuristic algorithm that has far lower computational overhead and accrues at worse 12% less than the utility of the optimal solution is presented. Both algorithms are implemented within a model of a hybrid network on top of the J-Sim simulation environment. Comparative studies are made to show effective load balancing and crash tolerance in the presence of a high traffic overload

Abstract — This paper proposes a scheme for bandwidth allocation in wireless ad hoc networks. The Quality of Service (QoS) levels for each end-to-end flow are expressed using resource-utility functions, and our algorithms aim to maximise the aggregated utility of the flows. The scheme differentiates between applications with flexible resource requirements and rigid (real-time) requirements. As an abstract notion of resource, we use maximal cliques of mutual interfering links. Using concave piece-wise linear utility functions we present a linear programming (LP) formulation of the problem that can serve as an optimal though unrealistic solution. Then we replace this centralised approach with a distributed low complexity solution. A key concept, borrowed from the dual of the optimal allocation problem, is the shadow price of a resource. The contributions of the paper are twofold: (1) a distributed algorithm that allocates the bandwidth based on bids that are calculated using the shadow price of the resources and the flow’s utility function, (2) a utility-aware on-demand ”shortest ” path routing algorithm in which the shadow prices are used a natural distance metric. We compare the performance of the distributed allocation scheme with the centralised, optimal linear programming solution. We also compare with a non-utility-based QoS allocation scheme, that uses hop-based shortest path routing followed by highest possible bandwidth accommodation of the flow. I.

This paper presents a time-aware admission control and resource allocation scheme in wireless networks in the context of a future generation cellular network. The quality levels (and their respective utility) of different connections are specified using discrete resource-utility (R-U) functions. The scheme uses these R-U functions for allocating and reallocating bandwidth to connections, aiming to maximize the accumulated utility of the system. However, different applications react differently to resource reallocations. Therefore, at each allocation time point, the following factors are taken into account: the age of the connection, a disconnection (drop) penalty, and the sensitiveness to reallocation frequency. The evaluation of our approach shows a superior performance compared to a recent adaptive bandwidth allocation scheme (RBBS). In addition, we have studied the overhead that performing a reallocation imposes on the infrastructure. To minimize this overhead, we present an algorithm that efficiently reduces the number of reallocations while remaining within a given utility bound.

This paper presents a time-aware admission control and resource allocation scheme in the context of a future generation mobile network. The quality levels (and their respective utility) of the different connections are specified using discrete resource-utility (R-U) functions. The scheme uses these R-U functions for allocating and reallocating bandwidth to connections, aiming to maximize the accumulated utility of the system. However, different applications react differently to resource reallocations Therefore at each allocation timepoint we take into account the following factors: the age of the connection, a drop (disconnection) penalty and the sensitiveness to reallocation frequency. Finally, we show the superior performance of our approach compared to a recent adaptive bandwidth allocation scheme.

In this paper we present strategies to deal with inherent load uncertainties in future generation mobile networks. We address the interplay between user differentiation and resource allocation, and specifically the problem of CPU load control in a radio network controller (RNC).The algorithms we present distinguish between two types of uncertainty: the resource needs for arriving requests and the variation over time with respect to user service policies. We use feedback mechanisms inspired by automatic control techniques for the first type, and policy-dependent deterministic algorithms for the second type. We test alternative strategies in overload situations. One approach combines feedback control with a pool allocation mechanism, and a second is based on a rejection-ratio-minimising algorithm together with a state estimator.A simulation environment and traffic models for users with voice, mail, SMS and web browsing sessions were built for the purpose of evaluation of the above strategies. Our load control architectures were tested in comparison with an existing algorithm based on the leaky bucket principle. The studies show a superior behaviour with respect to load control in presence of relative user priorities, and minimal rejection criteria. Moreover, our architecture can be tuned to future developments with respect to user differentiation policy.