Online service provisioning involves two main entities, i.e., cloud providers renting cloud resources to service providers. In this process, the service provider would like to minimize its costs, while the cloud providers seek ways to increase their profit. Novel container orchestration platforms like Kubernetes allow deploying services on the same physical or virtual (e.g., virtual machines) infrastructure while delivering both hard and soft resource isolation. When the soft resource isolation is allowed, guaranteed (or request) resources of one service can be used (if idle) by another one as limit resources, for short time intervals in a best-effort manner. The use of limit resources represents an extra font of revenue for the cloud provider to charge different service providers for the same resources. At the same time, soft isolation allows service providers to decrease the number of request resources and rely on more limit resources, paid only when accessed, reducing the overall resources needed. Therefore, soft resource isolation has potential benefits for both cloud and service providers. To enable these benefits, the price of limit resources should be carefully set by the cloud provider to generate, on one hand, extra profits and, on the other, be appealing to service providers. This paper proposes a framework for evaluating the pricing window for the limit resources under which it is possible to reduce the cost for service providers and increase the profits of cloud providers. Results in a sample simulated scenario show that by pricing limit resources within six to twelve times the request resources, cloud and service providers can achieve financial gains in the order of 10%-20%.

Existing container orchestration platforms provide frameworks capable of automatically scaling resources to evolving traffic conditions based on resource usage. Service providers rely on these automatic scaling capabilities to improve the traffic adaptability of their cloud native applications while reducing costs. However, this may lead to service degradation related to the delayed response to the traffic changes. Traditionally, Pod dimensioning has been performed considering guaranteed (or request) resources. Recently, container orchestration platforms included the possibility to allow Pods to use idle resources that can be withheld for a short period of time in a best-effort fashion (known as limit resources). This paper analyzes the potential of limit resources as a way to mitigate degradation while reducing the amount of request resources. Results for a sample case show that a strategy relying on limit resources achieves the same level of degradation as a conventional strategy that uses only request resources, while reducing requested CPU by 25%.

Answering a key question from operators, the paper compares the techno-economic performance of fiber and microwave-based 5G transport deployments using vendor's inventories and real-life field deployment scenarios. Results highlight how microwave gains vary based on the geo-types, the fiber trenching, and microwave equipment costs.

An exponential growth of bandwidth demand, spurred by emerging network services, often with diverse characteristics and stringent performance requirements, drive the need for more dynamic operation of optical networks, efficient use of spectral resources, and automation. Spectrum fragmentation is one of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs). Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of incoming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. DeepDefrag outperforms the well-known Oldest-First FirstFit (OF-FF) defragmentation heuristic, substantially reducing blocking probability and defragmentation overhead.

One of the challenges for network operators is to design and deploy cost-efficient transport networks (TNs) to meet the high capacity and strict latency/reliability requirements of today’s emerging services. Therefore, they need to consider different aspects, including the appropriate technology, the level of reconfigurability, and the functional split option. A crucial aspect of network design is assessing the impact of these aspects against the total cost of ownership (TCO), latency, and reliability performance of a given solution. For this reason, this paper proposes a framework to investigate the TCO, latency, and reliability performance of a set of fiber and microwave-based TN architectures. They are categorized based on their baseband functional split option and the reconfigurability capabilities of the equipment used. The results, based on real data from a non-incumbent operator, show that in most of the considered scenarios, a microwave-based TN exhibits lower TCO than a fiber-based one. The TCO gain may vary with the choice of the functional split option, geo-type, reconfigurability features, fiber trenching costs, and cost of microwave equipment, with a more significant impact in a dense urban geo-type, where for a low layer functional split option the fiber- and microwave-based architectures have a comparable TCO. Finally, it was found that the considered fiber and microwave architectures have almost similar average latency and connection availability performance. Both are suitable to meet the service requirements of 5G and beyond 5G services in most of the considered scenarios. Only in extreme latency-critical scenarios, a small number of the cells might not fully satisfy the latency requirements of a low layer split option due to multiple microwave hops in the microwave-based architecture.