اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدStratos: A Network-Aware Orchestration Layer for Virtual Middleboxes in Clouds
115
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Enterprises want their in-cloud services to leverage the performance and security benefits that middleboxes offer in traditional deployments. Such virtualized deployments create new opportunities (e.g., flexible scaling) as well as new challenges (e.g., dynamics, multiplexing) for middlebox management tasks such as service composition and provisioning. Unfortunately, enterprises lack systematic tools to efficiently compose and provision in-the-cloud middleboxes and thus fall short of achieving the benefits that cloud-based deployments can offer. To this end, we present the design and implementation of Stratos, an orchestration layer for virtual middleboxes. Stratos provides efficient and correct composition in the presence of dynamic scaling via software-defined networking mechanisms. It ensures efficient and scalable provisioning by combining middlebox-specific traffic engineering, placement, and horizontal scaling strategies. We demonstrate the effectiveness of Stratos using an experimental prototype testbed and large-scale simulations.
قيم البحث
اقرأ أيضاً
With the constant increase in demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while ensuring continual improvements to network performance. Although Network Function V
irtualization (NFV) has been identified as a solution, several challenges must be addressed to ensure its feasibility. In this paper, we present a machine learning-based solution to the Virtual Network Function (VNF) placement problem. This paper proposes the Depth-Optimized Delay-Aware Tree (DO-DAT) model by using the particle swarm optimization technique to optimize decision tree hyper-parameters. Using the Evolved Packet Core (EPC) as a use case, we evaluate the performance of the model and compare it to a previously proposed model and a heuristic placement strategy.
As data traffic volume continues to increase, caching of popular content at strategic network locations closer to the end user can enhance not only user experience but ease the utilization of highly congested links in the network. A key challenge in
the area of proactive caching is finding the optimal locations to host the popular content items under various optimization criteria. These problems are combinatorial in nature and therefore finding optimal and/or near optimal decisions is computationally expensive. In this paper a framework is proposed to reduce the computational complexity of the underlying integer mathematical program by first predicting decision variables related to optimal locations using a deep convolutional neural network (CNN). The CNN is trained in an offline manner with optimal solutions and is then used to feed a much smaller optimization problems which is amenable for real-time decision making. Numerical investigations reveal that the proposed approach can provide in an online manner high quality decision making; a feature which is crucially important for real-world implementations.
Great progress has been made recently in verifying the correctness of router forwarding tables. However, these approaches do not work for networks containing middleboxes such as caches and firewalls whose forwarding behavior depends on previously obs
erved traffic. We explore how to verify isolation properties in networks that include such dynamic datapath elements using model checking. Our work leverages recent advances in SMT solvers, and the main challenge lies in scaling the approach to handle large and complicated networks. While the straightforward application of model checking to this problem can only handle very small networks (if at all), our approach can verify simple realistic invariants on networks containing 30,000 middleboxes in a few minutes.
With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased demand. Although Netwo
rk Function Virtualization (NFV) has been identified as a promising solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) migration problem by developing the VNF Neural Network for Instance Migration (VNNIM), a migration strategy for VNF instances. The performance of VNNIM is further improved through the optimization of the learning rate hyperparameter through particle swarm optimization. Results show that the VNNIM is very effective in predicting the post-migration server exhibiting a binary accuracy of 99.07% and a delay difference distribution that is centered around a mean of zero when compared to the optimization model. The greatest advantage of VNNIM, however, is its run-time efficiency highlighted through a run-time analysis.
Distributed Virtual Private Networks (dVPNs) are new VPN solutions aiming to solve the trust-privacy concern of a VPNs central authority by leveraging a distributed architecture. In this paper, we first review the existing dVPN ecosystem and debate o
n its privacy requirements. Then, we present VPN0, a dVPN with strong privacy guarantees and minimal performance impact on its users. VPN0 guarantees that a dVPN node only carries traffic it has whitelisted, without revealing its whitelist or knowing the traffic it tunnels. This is achieved via three main innovations. First, an attestation mechanism which leverages TLS to certify a user visit to a specific domain. Second, a zero knowledge proof to certify that some incoming traffic is authorized, e.g., falls in a nodes whitelist, without disclosing the target domain. Third, a dynamic chain of VPN tunnels to both increase privacy and guarantee service continuation while traffic certification is in place. The paper demonstrates VPN0 functioning when integrated with several production systems, namely BitTorrent DHT and ProtonVPN.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
