Delivering cloud-like computing facilities at the network edge provides computing services with ultra-low-latency access, yielding highly responsive computing services to application requests. The concept of fog computing has emerged as a computing paradigm that adds layers of computing nodes between the edge and the cloud, also known asmicro data centers,cloudlets, orfog nodes. Based on this premise, this article proposes a component-based service scheduler in a cloud-fog computing infrastructure comprising several layers of fog nodes between the edge and the cloud. The proposed scheduler aims to satisfy the application’s latency requirements by deciding which services components should be moved upwards in the fog-cloud hierarchy to alleviate computing workloads at the network edge. One communication-aware policy is introduced for resource allocation to enforce resource access prioritization among applications. We evaluate the proposal using the well-known iFogSim simulator. Results suggest that the proposed component-based scheduling algorithm can reduce average delays for application services with stricter latency requirements while still reducing the total network usage when applications exchange data between the components. Results have shown that our policy was able to, on average, reduce the overload impact on the network usage by approximately 11 percent compared to the best allocation policy in the literature while maintaining acceptable delays for latency-sensitive applications.

In hybrid clouds, inter-cloud links play a key role in the execution of jobs with data dependencies. Insufficient available bandwidth in inter-cloud links can increase the makespan and the monetary cost to execute the application on public clouds. Imprecise information about the available bandwidth can lead to inefficient scheduling decisions. This paper attempts to evaluate the impact of imprecise information about the available bandwidth in inter-cloud links on workflow schedules, and it proposes a mechanism to cope with imprecise information about the available bandwidth and its impact on the makespan and cost estimates. The proposed mechanism applies a deflating factor on the available bandwidth value furnished as input to the scheduler. Simulation results showed that the mechanism is able to increase the number of solutions with makespans that are shorter than the defined deadline and reduce the underestimations of the makespan and cost provided by workflow schedulers.

To guarantee an efficient and high-performance environment for mobile devices to perform offloading with low end-to-end delay, it is important to ensure no network policies are violated. In this paper, we explore the simultaneous, dynamic virtual machine (VM) and policy consolidation, and formulate the Policy-VM Latency-aware Consolidation problem for Mobile Edge Computing, which is shown to be NP-Hard. We propose the PL-Edge, an efficient scheme to jointly consolidate network policies and virtual machines for mobile edge computing to reduce communication end-to-end delays among devices and virtual machines. Our simulation results demonstrate that the proposed PL-Edge can significantly reduces policy-flows end-to-end delay by nearly 45% while adhering strictly to the requirements of network policies.

Network Function Virtualisation (NFV) enables rapid deployment of new services in networks on an on-demand basis using general purpose servers. Multiple virtual network functions (VNFs) can be dynamically chained in an ordered sequence for the delivery of end-to-end services. Nevertheless, network latency caused by the sequential order of packet processing on every VNF can hurt the performance of latency-sensitive applications. To reduce such network latency, existing solutions only consider the maximum capacity of individual virtual network functions (VNFs) and do not take into account the fact that performance of VNFs, as with any software applications, is bottlenecked by either CPU or I/O peripheral capacity of the server they run on and their underneath implementation such as singleor multi-threaded.By exploiting this knowledge, we can better determine the number of required VNF instances and distribute the network traffic among them for any given VNF chain. In this paper, we formulate the VNF Scaling and Traffic Distribution problem and prove that it is NP-hard. We then present the design and implementation of Natif, an efficient VNF-Aware VNF insTantIation and traFfic distribution scheme. Through our OpenStack-based testbed evaluations, we demonstrate that Natif can significantly improve the network latency by 188% on average as compared to other approaches. As a chain composition scheme, Natif can effectively work with any VNF chaining algorithms.

Imprecise input data imposes special challenges to workflow scheduling. This paper introduces a robust scheduler based on particle swarm optimisation, called RobWE, which considers uncertainties of available bandwidth when producing schedules for workflow ensembles. The proposed scheduler is also a flexible scheduler since it allows the replacement of its objective function according to the user's needs. The effectiveness of the proposed RobWE scheduler is compared to a non-robust scheduler that does not consider the presence of such uncertainties. Results of simulations considering diverse scenarios, based on several degrees of uncertainty in available bandwidth estimates, characteristics of bandwidth estimations and workflow applications, demonstrate the advantages of the proposed RobWE scheduler.

In hybrid clouds, the available bandwidth in inter-cloud links is quite variable. Overestimating the available bandwidth on theses channels at scheduling time can enlarge the makespan and cause deadline misses. In this paper, we propose a procedure for deflating the estimated available bandwidth used as input to cloud schedulers since schedulers are not usually designed to cope with inaccurate information on available bandwidth. The procedure is based on a multiple linear regression procedure which utilizes historical information of previous executions of workflows. Results showed that the proposed procedure can increase the number of valid schedules without increasing the makespan and cost estimations, regardless the variability in the available bandwidth during the execution of an application workflow.

Cloud computing is being used to avoid maintenance costs and upfront investment, while providing elasticity to the available computational power in a pay-per-use basis. Customers can make use of the cloud as a software (SaaS), platform (PaaS), or infrastructure (IaaS) provider. When one customer utilizes an environment provided by a SaaS cloud, she is unaware of any details about the computational infrastructure where her requests are being processed. Therefore, such infrastructure can be composed of computational resources from a datacenter owned by the SaaS or its resources can be leased from a cloud infrastructure provider. In this paper we present an integer linear program (ILP) formulation for the problem of scheduling SaaS customer's workflows into multiple IaaS providers where SLA exists at two levels. In addition, we present heuristics to solve the relaxed version of the presented ILP. Simulation results show that the proposed ILP is able to find low-cost solutions for short deadlines, while the proposed heuristics are effective when deadlines are larger.