Search

Emerging edge computing has seen latency- sensitive services moving rapidly from cloud to the edge to take advantage of its close vicinity to end users, while cloud is retained for carrying out latency-insensitive and computation-intensive tasks. When more edge computing service providers come to the market, the network will become increasingly more fragmented because of their proprietary services and policies deployed in the network. This means that the Internet can become more cumbersome and riskier as there will be more tiers and potential vulnerabilities that could be exploited. To tackle this issue, we envisage a federated service chaining paradigm in which operators can share and put service functions in other participants' networks so as to improve resource utilization, collaboratively mitigate cyber threats, and enable service innovations. In this position article, building on our past experience in enabling federated cloud infrastructure and heterogeneous service chaining, we present a Federated Service Chaining architecture followed by discussions of its key components. Several key research challenges are described for the successful realization of such architecture. We hope this article can open a discussion in the research community and generate enough research interest to significantly advance this field. [ABSTRACT FROM AUTHOR]

Today’s data center operators deploy network policies in both physical (e.g., middleboxes, switches) and virtualized (e.g., virtual machines on general purpose servers) network function boxes (NFBs), which reside in different points of the network, to exploit their efficiency and agility respectively. Nevertheless, such heterogeneity has resulted in a great number of independent network nodes that can dynamically generate and implement inconsistent and conflicting network policies, making correct policy implementation a difficult problem to solve. Since these nodes have varying capabilities, services running atop are also faced with profound performance unpredictability. In this paper, we propose a Heterogeneous netwOrk Policy Enforcement (HOPE) scheme to overcome these challenges. HOPE guarantees that network functions (NFs) that implement a policy chain are optimally placed onto heterogeneous NFBs such that the network cost of the policy is minimized. We first experimentally demonstrate that the processing capacity of NFBs is the dominant performance factor. This observation is then used to formulate the Heterogeneous Network Policy Placement problem, which is shown to be NP-Hard. To solve the problem efficiently, an online algorithm is proposed. Our experimental results demonstrate that HOPE achieves the same optimality as Branch-and-bound optimization but is 3 orders of magnitude more efficient. [ABSTRACT FROM AUTHOR]

Policies play an important role in network configuration and therefore in offering secure and high performance services especially over multi-tenant Cloud Data Center (DC) environments. At the same time, elastic resource provisioning through virtualization often disregards policy requirements, assuming that the policy implementation is handled by the underlying network infrastructure. This can result in policy violations, performance degradation and security vulnerabilities. In this paper, we define PLAN, a PoLicy-Aware and Network-aware VM management scheme to jointly consider DC communication cost reduction through Virtual Machine (VM) migration while meeting network policy requirements. We show that the problem is NP-hard and derive an efficient approximate algorithm to reduce communication cost while adhering to policy constraints. Through extensive evaluation, we show that PLAN can reduce topology-wide communication cost by 38 percent over diverse aggregate traffic and configuration policies. [ABSTRACT FROM AUTHOR]

Equal cost multiple path (ECMP) forwarding is the most prevalent multipath routing used in data center (DC) networks today. However, it fails to exploit increased path diversity that can be provided by traffic engineering techniques through the assignment of nonuniform link weights to optimize network resource usage. To this extent, constructing a routing algorithm that provides path diversity over nonuniform link weights (i.e., unequal cost links), simplicity in path discovery and optimality in minimizing maximum link utilization (MLU) is nontrivial. In this paper, we have implemented and evaluated the Penalizing Exponential Flow-spliTing (PEFT) algorithm in a cloud DC environment based on two dominant topologies, canonical and fat tree. In addition, we have proposed a new cloud DC topology which, with only a marginal modification of the current canonical tree DC architecture, can further reduce MLU and increase overall network capacity utilization through PEFT routing. [ABSTRACT FROM AUTHOR]

This paper presents an empirical study on the performance of mobile High Speed Packet Access (a 3.5G cellular standard usually abbreviated as HSPA) networks in Hong Kong via extensive field tests. Our study, from the viewpoint of end users, covers virtually all possible mobile scenarios in urban areas, including subways, trains, off-shore ferries, and city buses. We have confirmed that mobility has largely negative impacts on the performance of HSPA networks, as fast-changing wireless environment causes serious service deterioration or even interruption. Meanwhile, our field experiment results have shown unexpected new findings and thereby exposed new features of the mobile HSPA networks, which contradict commonly held views. We surprisingly find out that mobility can improve fairness of bandwidth sharing among users and traffic flows. Also, the triggering and final results of handoffs in mobile HSPA networks are unpredictable and often inappropriate, thus calling for fast reacting fallover mechanisms. Moreover, we find that throughput performance does not monotonically decrease with increased mobility level. We have conducted in-depth research to furnish detailed analysis and explanations to what we have observed. We conclude that mobility is a double-edged sword for HSPA networks. To the best of our knowledge, this is the first public report on a large-scale empirical study on the performance of commercial mobile HSPA networks. [ABSTRACT FROM AUTHOR]

Multi-access Edge Computing (MEC) offers cloud computing capabilities at the edge of the network. Growing demand for low-latency services requires Service Function Chains (SFCs) to be scaled up beyond MEC network to core network. To adapt to network dynamics and provide low-latency services, being able to migrate SFCs when needed is of paramount importance. However, migration of SFCs among edge and core networks such that average latency is optimized as well as considering resource consumption is an intractable challenge because improper migration of Virtual Network Functions (VNFs) results in failure of meeting the requirements of network policies. In this paper, we investigate SFCs in edge-core networks and model the Latency-aware Edge-Core SFCs Migration problem based on open Jackson networks. Two SFC migration algorithms, i.e., Profit-driven Heuristic Search (PHS) and Average Utilization Based (AUB), are proposed to efficiently optimize average latency of all SFCs in edge-core networks. Extensive evaluation results show that PHS optimizes average latency by 19.5%, while AUB can further reduce average latency by up to 36.9% by allowing a marginally higher number of VNF migrations. [ABSTRACT FROM AUTHOR]

This paper investigates a dynamic 3D trajectory design of multiple cache-enabled unmanned aerial vehicles (UAVs) in a wireless device-to-device (D2D) caching network with the goal of maximizing the long-term network throughput. By storing popular content at the nearby mobile user devices, D2D caching is an efficient method to improve network throughput and alleviate backhaul burden. With the attractive features of high mobility and flexible deployment, UAVs have recently attracted significant attention as cache-enabled flying base stations. The use of cache-enabled UAVs opens up the possibility of tracking the mobility pattern of the corresponding users and serving them under limited cache storage capacity. However, it is challenging to determine the optimal UAV trajectory due to the dynamic environment with frequently changing network topology and the coexistence of aerial and terrestrial caching nodes. In response, we propose a novel multi-agent reinforcement learning based framework to determine the optimal 3D trajectory of each UAV in a distributed manner without a central coordinator. In the proposed method, multiple UAVs can cooperatively make flight decisions by sharing the gained experiences within a certain proximity to each other. Simulation results reveal that our algorithm outperforms the traditional single- and multi-agent Q-learning algorithms. This work confirms the feasibility and effectiveness of cache-enabled UAVs which serve as an important complement to terrestrial D2D caching nodes. [ABSTRACT FROM AUTHOR]

The cloud has become integral to most Internet-based applications and user gadgets. This article provides a brief history of the cloud and presents a researcher's view of the prospects for innovating at the infrastructure, middleware, and applications and delivery levels of the already crowded cloud computing stack. [ABSTRACT FROM AUTHOR]