Aiman Erbad

A survey on service function chaining

Cloud computing is gaining significant attention and virtualized datacenters are becoming popular as a cost-effective infrastructure. The network services are transitioning from a host-centric to a data-centric model moving the data and the computational resources closer to the end users. To meet the dynamic user demands, network operators have chosen to use elastic virtual resources to implement network services over static rigid physical model. With the advent of network function virtualization (NFV), network services instances are provisioned across multiple clouds for performance and load balancing purposes. Interconnection of these instances to form a complete end-to-end network service is complex, time consuming and expensive task. Service function chaining (SFC) is a mechanism that allows various service functions to be connected to each to form a service enabling carriers to benefit from virtualized software defined infrastructure. SFC is an enabler for NFV, providing a flexible and economical alternative to todays static environment for Cloud Service providers (CSPs), Application Service Providers (ASPs) and Internet Service Providers (ISPs). This paper provides a closer look at the current SFC architecture and a survey of the recent developments in SFC including its relevance with NFV to help determine the future research directions and the standardization efforts of SFC. Finally, the paper discusses open research topics in relevance with the SFC architecture and demonstrates a need for an analytical model for the SFC architecture to achieve the optimal performance.

Multi-cloud Distribution of Virtual Functions and Dynamic Service Deployment: Open ADN Perspective

Network Function Virtualization (NFV) and Service Chaining (SC) are novel service deployment approaches in the contemporary cloud environments for increased flexibility and cost efficiency to the Application Service Providers and Network Providers. However, NFV and SC are still new and evolving topics. Optimized placement of these virtual functions is necessary for acceptable latency to the end-users. In this work we consider the problem of optimal Virtual Function (VF) placement in a multi-cloud environment to satisfy the client demands so that the total response time is minimized. In addition we consider the problem of dynamic service deployment for OpenADN, a novel multi-cloud application delivery platform.

Optimal virtual network function placement in multi-cloud service function chaining architecture

Service Function Chaining (SFC) is the problem of deploying various network service instances over geographically distributed data centers and providing inter-connectivity among them. The goal is to enable the network traffic to flow smoothly through the underlying network, resulting in an optimal quality of experience to the end-users. Proper chaining of network functions leads to optimal utilization of distributed resources. This has been a de-facto model in the telecom industry with network functions deployed over underlying hardware. Though this model has served the telecom industry well so far, it has been adapted mostly to suit the static behavior of network services and service demands due to the deployment of the services directly over physical resources. This results in network ossification with larger delays to the end-users, especially with the data-centric model in which the computational resources are moving closer to end users. A novel networking paradigm, Network Function Virtualization (NFV), meets the user demands dynamically and reduces operational expenses (OpEx) and capital expenditures (CapEx), by implementing network functions in the software layer known as virtual network functions (VNFs). VNFs are then interconnected to form a complete end-to-end service, also known as service function chains (SFCs). In this work, we study the problem of deploying service function chains over network function virtualized architecture. Specifically, we study virtual network function placement problem for the optimal SFC formation across geographically distributed clouds. We set up the problem of minimizing inter-cloud traffic and response time in a multi-cloud scenario as an ILP optimization problem, along with important constraints such as total deployment costs and service level agreements (SLAs). We consider link delays and computational delays in our model. The link queues are modeled as M/D/1 (single server/Poisson arrival/deterministic service times) and server queues as M/M/1 (single server/Poisson arrival/exponential service times) based on the statistical analysis. In addition, we present a novel affinity-based approach (ABA) to solve the problem for larger networks. We provide a performance comparison between the proposed heuristic and simple greedy approach (SGA) used in the state-of-the-art systems. Greedy approach has already been widely studied in the literature for the VM placement problem. Especially we compare our proposed heuristic with a greedy approach using first-fit decreasing (FFD) method. By observing the results, we conclude that the affinity-based approach for placing the service functions in the network produces better results compared against the simple greedy (FFD) approach in terms of both, total delays and total resource cost. We observe that with a little compromise (gap of less than 10% of the optimal) in the solution quality (total delays and cost), affinity-based heuristic can solve the larger problem more quickly than ILP.

Multi-objective scheduling of micro-services for optimal service function chains

Lately application service providers (ASPs) and Internet service providers (ISPs) are being confronted with the unprecedented challenge of accommodating increasing service and traffic demands from their geographically distributed users. Many ASPs and ISPs, such as Facebook, Netflix, AT&T and others have adopted micro-service architecture to tackle this problem. Instead of building a single, monolithic application, the idea is to split the application into a set of smaller, interconnected services, called micro-services (or simply services). Such services are lightweight and perform distinct tasks independent of each other. Hence, they can be deployed quickly and independently as user demands vary. Nevertheless, scheduling of micro-services is a complex task and is currently under-researched. In this work, we address the problem of scheduling micro-services across multiple clouds, including micro-clouds. We consider different user-level SLAs, such as latency and cost, while scheduling such services. Our aim is to reduce overall turnaround time for the complete end-to-end service in service function chains and reduce the total traffic generated. In this work we present a novel fair weighted affinity-based scheduling heuristic to solve this problem. We also compare the results of proposed solution with standard biased greedy scheduling algorithms presented in the literature and observe significant improvements.

SAIS: Smartphone Augmented Infrastructure Sensing for Public Safety and Sustainability in Smart Cities

We consider the problem of efficiently using smartphone users to augment the stationary infrastructure sensors for better situation awareness in smart cities. We envision a dynamic sensing platform that intelligently assigns sensing tasks to volunteered smartphone users, in order to answer queries by performing sensing tasks at specific locations that may not be covered by in-situ infrastructure sensors. We mathematically formulate the problem into an integer programming problem to minimize the overall energy consumption while satisfying the required query accuracy. We present an optimal algorithm to solve this problem using an existing computationally expensive optimization solver. To reduce the running time, we also propose a more practical heuristic algorithm. Our trace-driven simulation results reveal the benefits of our proposed heuristic algorithm, it: (i) finishes all the tasks, (ii) achieves 6 times shorter response time, and (iii) performs better with more volunteers. In contrast, exclusively using in-situ sensors completes 6% of the tasks, while using in-situ sensors with opportunistic sensing (without user intervention) completes 20% of the tasks. Our prototype system is validated in a user study and receives fairly positive feedback from the smartphone users who utilize it to submit and answer various spatial/temporal dependent queries.

COLAP: A predictive framework for service function chain placement in a multi-cloud environment

Network function virtualization (NFV) over multi-cloud promises network service providers amazing flexibility in service deployment and optimizing cost. Telecommunications applications are, however, sensitive to performance indicators, especially latency, which tend to get degraded by both the virtualization and the multiple cloud requirement for widely distributed coverage. In this work we propose an efficient framework that uses the novel concept of random cloud selection combined with a support vector regression based predictive model for cost optimized latency aware placement (COLAP) of service function chains. Extensive empirical analysis has been carried out with training datasets generated using a queuing-theoretic model. The results show good generalization performance of the predictive algorithm. The proposed framework can place thousands of virtual network functions in less than a minute and has high acceptance ratio.

Crowdsourced Multi-View Live Video Streaming using Cloud Computing

Advances and commoditization of media generation devices enable capturing and sharing of any special event by multiple attendees. We propose a novel system to collect individual video streams (views) captured for the same event by multiple attendees, and combine them into multi-view videos, where viewers can watch the event from various angles, taking crowdsourced media streaming to a new immersive level. The proposed system is called Cloud-based Multi-View Crowdsourced Streaming (CMVCS), and it delivers multiple views of an event to viewers at the best possible video representation based on each viewer’s available bandwidth. The CMVCS is a complex system having many research challenges. In this paper, we focus on resource allocation of the CMVCS system. The objective of the study is to maximize the overall viewer satisfaction by allocating available resources to transcode views in an optimal set of representations, subject to computational and bandwidth constraints. We choose the video representation set to maximize QoE using Mixed Integer Programming. Moreover, we propose a Fairness-Based Representation Selection (FBRS) heuristic algorithm to solve the resource allocation problem efficiently. We compare our results with optimal and Top-N strategies. The simulation results demonstrate that FBRS generates near optimal results and outperforms the state-of-the-art Top-N policy, which is used by a large-scale system (Twitch).

Edge computing for interactive media and video streaming

Video streaming and computer games are among the most popular and highest bandwidth consuming media in the Internet. Video contents consume around 70% of the total bandwidth usage in the Internet today. Advancements in media generation tools, high processing power, and high speed connectivity have enabled generation of live, interactive, multi-view media generation. Cognitive assisted and online multi-player gaming have unlocked new horizons for gaming experience. However, such interactive gaming, and multi-view and 360-degree view videos, are currently limited by delay intolerance and excessive bandwidth usage. Edge computing is the name of a set of new technologies, such as cloudlets, micro data centers, fog, and mobile edge computing. It aims to provide storage and computational resources near to user at the network edge, to minimize latency and response time. Edge computing is foreseen as a significant enabler of Internet of Every Thing (IoET) era by extending the cloud services and resources at the end of the network to deliver very low latency and real-time communication. It can provide significant services to video and gaming applications and enable new stream of interactive multimedia era. In this paper, we highlight some of the potentials and prospects of edge computing for interactive media, and present some preliminary works in the area. We shed light on how edge computing can be used to tackle various challenges faced by todays interactive media application. We also present the benefits of using edge computing to save cost, bandwidth, and energy in multimedia applications, video streaming, and transcoding.

Service Chaining for NFV and Delivery of Other Applications in a Global Multi-cloud Environment

Network Function Virtualization (NFV) allows Internet Service Providers (ISPs) to implement key function modules, such as, BRAS (Broadband Remote Access Server), IMS (Internet Multimedia System), etc. in virtual machines in a cloud environment. One of the key problems in NFV implementation is the placement of virtual machines (VMs) in clouds managed by different cloud service providers each with its own management interface. It would be helpful if the clients can implement their policies in a multi-cloud environment using a single interface. Our proposed solution is a modular multi-cloud management system called OpenADN that provides a common interface for resource allocation in a multi-cloud environment. The solution is also applicable to non-ISP applications, such as, banking, financial, and other sectors that need to use globally distributed multi-cloud resources. This paper presents a brief overview of the OpenADN architecture. The key feature of OpenADN is that multiple tenants can share the resources and all resource owners keep complete control over their resources. The data plane module of OpenADN is called OpenADN (Open Application Delivery Network). OpenADN has been implemented and brief details of implementation are also presented in this paper.

On Optimizing MMVEs in Network-Aware Clouds

Network operators will soon cooperate with traditional cloud providers to offer network-virtualization-based converged cloud services, which are referred to as network-aware clouds. Network-aware clouds allow network operators to share income with Over-The-Top (OTT) providers by providing them with end-to-end network QoS guarantees. For MMVE providers, leveraging the computation, storage, and communication resources offered by network-aware clouds for the best MMVE QoE levels is crucial to their success. In this paper, we point out a main research challenge: optimally placing various fine-grained MMVE tasks across heterogeneous clouds, which provide diverse computation and storage QoS guarantees (in data centers) and communication QoS guarantees (end-to-end). Via real experiments, we demonstrate the potential of network-aware clouds on improving the QoE of MMVEs. Achieving the optimal QoE level, however, is no easy task because of the dynamic nature of networks and virtual environments and the complex interplay between cloud QoS guarantees and MMVE QoE metrics, such as responsiveness, precision, and fairness. Throughly addressing the task placement problem is our current work.