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We revisit the long-standing problem of providing network QoS to applications, and propose the concept of judicious QoS -- combining the cheaper, best effort IP service with the cloud, which offers a highly reliable infrastructure and the ability to add in-network services, albeit at higher cost. Our proposed J-QoS framework offers a range of reliability services with different cost vs. delay trade-offs, including: i) a forwarding service that forwards packets over the cloud overlay, ii) a caching service, which stores packets inside the cloud and allows them to be pulled in case of packet loss or disruption on the Internet, and iii) a novel coding service that provides the least expensive packet recovery option by combining packets of multiple application streams and sending a small number of coded packets across the more expensive cloud paths. We demonstrate the feasibility of these services using measurements from RIPE Atlas and a live deployment on PlanetLab. We also consider case studies on how J-QoS works with services up and down the network stack, including Skype video conferencing, TCP-based web transfers, and cellular access networks.
The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To ensure robust communication against jamming, an interference-aware routing protocol is developed that allows nodes to avoid communication holes created by jamming attacks. Then, a distributed cooperation framework, based on deep reinforcement learning, is proposed that allows nodes to assess network conditions and make deep learning-driven, distributed, and real-time decisions on whether to participate in data communications, defend the network against jamming and eavesdropping attacks, or jam other transmissions. The objective is to maximize the network performance that incorporates throughput, energy efficiency, delay, and security metrics. Simulation results show that the proposed jamming-aware routing approach is robust against jamming and when throughput is prioritized, the proposed deep reinforcement learning approach can achieve significant (measured as three-fold) increase in throughput, compared to a benchmark policy with fixed roles assigned to nodes.
Scalability and efficient global search in unstructured peer-to-peer overlays have been extensively studied in the literature. The global search comes at the expense of local interactions between peers. Most of the unstructured peer-to-peer overlays do not provide any performance guarantee. In this work we propose a novel Quality of Service enabled lookup for unstructured peer-to-peer overlays that will allow the users query to traverse only those overlay links which satisfy the given constraints. Additionally, it also improves the scalability by judiciously using the overlay resources. Our approach selectively forwards the queries using QoS metrics like latency, bandwidth, and overlay link status so as to ensure improved performance in a scenario where the degree of peer joins and leaves are high. User is given only those results which can be downloaded with the given constraints. Also, the protocol aims at minimizing the message overhead over the overlay network.
In wireless communication systems, Quality of Service (QoS) is one of the most important issues from both the users and operators point of view. All the parameters related to QoS are not same important for all users and applications. The satisfaction level of different users also does not depend on same QoS parameters. In this paper, we discuss the QoS parameters and then propose a priority order of QoS parameters based on protocol layers and service applications. We present the relation among the QoS parameters those influence the performance of other QoS parameters and, finally, we demonstrate the numerical analysis results for our proposed soft-QoS scheme to reduce the dropped call rate which is the most important QoS parameter for all types of services
This paper considers the problem of service placement and task scheduling on a three-tiered edge-to-cloud platform when user requests must be met by a certain deadline. Time-sensitive applications (e.g., augmented reality, gaming, real-time video analysis) have tight constraints that must be met. With multiple possible computation centers, the where and when of solving these requests becomes paramount when meeting their deadlines. We formulate the problem of meeting users deadlines while minimizing the total cost of the edge-to-cloud service provider as an Integer Linear Programming (ILP) problem. We show the NP-hardness of this problem, and propose two heuristics based on making decisions on a local vs global scale. We vary the number of users, the QoS constraint, and the cost difference between remote cloud and cloudlets(edge clouds), and run multiple Monte-Carlo runs for each case. Our simulation results show that the proposed heuristics are performing close to optimal while reducing the complexity.
The area of quality of service (QoS) in communications networks has been the target of research for already several decades with tens of thousands of published journal and conference papers. However, the practical introduction of QoS systems in commercial networks has been limited (with a preference for simple overprovisioning). Despite this dissonance, most influential QoS papers do not discuss this lack of penetration or challenge any of the common assumptions used to argue for QoS systems. So far, the few critical QoS papers have had only a minor effect on QoS research and standardization. Therefore, there is a serious risk that QoS will remain an academic research topic without significant practical relevance. To help elucidate these issues, in this work, we first perform a comprehensive review of QoS including a general overview and an analysis of both influential and critical work from the past 30 years. We examine properties such as citations, keywords, and author traits to show that QoS has passed through several distinct phases with different topics while maintaining the overall attitude towards the role and objective of QoS systems. We then discuss QoS as a social phenomenon and in the context of current networking standards. Finally, we propose a QoS scheme based on incentives that avoids some of the problems identified in critical work, and we provide simple recommendations for network operators. Overall, we hope to spark the community to take a fresh look at QoS.