14 research outputs found
Multi-Commodity Flow with In-Network Processing
Modern networks run "middleboxes" that offer services ranging from network
address translation and server load balancing to firewalls, encryption, and
compression. In an industry trend known as Network Functions Virtualization
(NFV), these middleboxes run as virtual machines on any commodity server, and
the switches steer traffic through the relevant chain of services. Network
administrators must decide how many middleboxes to run, where to place them,
and how to direct traffic through them, based on the traffic load and the
server and network capacity. Rather than placing specific kinds of middleboxes
on each processing node, we argue that server virtualization allows each server
node to host all middlebox functions, and simply vary the fraction of resources
devoted to each one. This extra flexibility fundamentally changes the
optimization problem the network administrators must solve to a new kind of
multi-commodity flow problem, where the traffic flows consume bandwidth on the
links as well as processing resources on the nodes. We show that allocating
resources to maximize the processed flow can be optimized exactly via a linear
programming formulation, and to arbitrary accuracy via an efficient
combinatorial algorithm. Our experiments with real traffic and topologies show
that a joint optimization of node and link resources leads to an efficient use
of bandwidth and processing capacity. We also study a class of design problems
that decide where to provide node capacity to best process and route a given
set of demands, and demonstrate both approximation algorithms and hardness
results for these problems
A Multi-commodity network flow model for cloud service environments
Next-generation systems, such as the big data cloud, have to cope with several challenges, e.g., move of excessive amount of data at a dictated speed, and thus, require the investigation of concepts additional to security in order to ensure their orderly function. Resilience is such a concept, which when ensured by systems or networks they are able to provide and maintain an acceptable level of service in the face of various faults and challenges. In this paper, we investigate the multi-commodity flows problem, as a task within our D 2 R 2 +DR resilience strategy, and in the context of big data cloud systems. Specifically, proximal gradient optimization is proposed for determining optimal computation flows since such algorithms are highly attractive for solving big data problems. Many such problems can be formulated as the global consensus optimization ones, and can be solved in a distributed manner by the alternating direction method of multipliers (ADMM) algorithm. Numerical evaluation of the proposed model is carried out in the context of specific deployments of a situation-aware information infrastructure
Optimal Control of Distributed Computing Networks with Mixed-Cast Traffic Flows
Distributed computing networks, tasked with both packet transmission and
processing, require the joint optimization of communication and computation
resources. We develop a dynamic control policy that determines both routes and
processing locations for packets upon their arrival at a distributed computing
network. The proposed policy, referred to as Universal Computing Network
Control (UCNC), guarantees that packets i) are processed by a specified chain
of service functions, ii) follow cycle-free routes between consecutive
functions, and iii) are delivered to their corresponding set of destinations
via proper packet duplications. UCNC is shown to be throughput-optimal for any
mix of unicast and multicast traffic, and is the first throughput-optimal
policy for non-unicast traffic in distributed computing networks with both
communication and computation constraints. Moreover, simulation results suggest
that UCNC yields substantially lower average packet delay compared with
existing control policies for unicast traffic
Service placement and request routing in MEC networks with storage, computation, and communication constraints
The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers