11 research outputs found
Dynamic Cloud Network Control under Reconfiguration Delay and Cost
Network virtualization and programmability allow operators to deploy a wide
range of services over a common physical infrastructure and elastically
allocate cloud and network resources according to changing requirements. While
the elastic reconfiguration of virtual resources enables dynamically scaling
capacity in order to support service demands with minimal operational cost,
reconfiguration operations make resources unavailable during a given time
period and may incur additional cost. In this paper, we address the dynamic
cloud network control problem under non-negligible reconfiguration delay and
cost. We show that while the capacity region remains unchanged regardless of
the reconfiguration delay/cost values, a reconfiguration-agnostic policy may
fail to guarantee throughput-optimality and minimum cost under nonzero
reconfiguration delay/cost. We then present an adaptive dynamic cloud network
control policy that allows network nodes to make local flow scheduling and
resource allocation decisions while controlling the frequency of
reconfiguration in order to support any input rate in the capacity region and
achieve arbitrarily close to minimum cost for any finite reconfiguration
delay/cost values.Comment: 15 pages, 7 figure
Optimal Control of Wireless Computing Networks
Augmented information (AgI) services allow users to consume information that
results from the execution of a chain of service functions that process source
information to create real-time augmented value. Applications include real-time
analysis of remote sensing data, real-time computer vision, personalized video
streaming, and augmented reality, among others. We consider the problem of
optimal distribution of AgI services over a wireless computing network, in
which nodes are equipped with both communication and computing resources. We
characterize the wireless computing network capacity region and design a joint
flow scheduling and resource allocation algorithm that stabilizes the
underlying queuing system while achieving a network cost arbitrarily close to
the minimum, with a tradeoff in network delay. Our solution captures the unique
chaining and flow scaling aspects of AgI services, while exploiting the use of
the broadcast approach coding scheme over the wireless channel.Comment: 30 pages, journa
An End-to-End Performance Analysis for Service Chaining in a Virtualized Network
Future mobile networks supporting Internet of Things are expected to provide
both high throughput and low latency to user-specific services. One way to
overcome this challenge is to adopt Network Function Virtualization (NFV) and
Multi-access Edge Computing (MEC). Besides latency constraints, these services
may have strict function chaining requirements. The distribution of network
functions over different hosts and more flexible routing caused by service
function chaining raise new challenges for end-to-end performance analysis. In
this paper, as a first step, we analyze an end-to-end communications system
that consists of both MEC servers and a server at the core network hosting
different types of virtual network functions. We develop a queueing model for
the performance analysis of the system consisting of both processing and
transmission flows. We propose a method in order to derive analytical
expressions of the performance metrics of interest. Then, we show how to apply
the similar method to an extended larger system and derive a stochastic model
for such systems. We observe that the simulation and analytical results
coincide. By evaluating the system under different scenarios, we provide
insights for the decision making on traffic flow control and its impact on
critical performance metrics.Comment: 30 pages. arXiv admin note: substantial text overlap with
arXiv:1811.0233
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
Decentralized Control of Distributed Cloud Networks with Generalized Network Flows
Emerging distributed cloud architectures, e.g., fog and mobile edge
computing, are playing an increasingly important role in the efficient delivery
of real-time stream-processing applications such as augmented reality,
multiplayer gaming, and industrial automation. While such applications require
processed streams to be shared and simultaneously consumed by multiple
users/devices, existing technologies lack efficient mechanisms to deal with
their inherent multicast nature, leading to unnecessary traffic redundancy and
network congestion. In this paper, we establish a unified framework for
distributed cloud network control with generalized (mixed-cast) traffic flows
that allows optimizing the distributed execution of the required packet
processing, forwarding, and replication operations. We first characterize the
enlarged multicast network stability region under the new control framework
(with respect to its unicast counterpart). We then design a novel queuing
system that allows scheduling data packets according to their current
destination sets, and leverage Lyapunov drift-plus-penalty theory to develop
the first fully decentralized, throughput- and cost-optimal algorithm for
multicast cloud network flow control. Numerical experiments validate analytical
results and demonstrate the performance gain of the proposed design over
existing cloud network control techniques
Dynamic Resource Provisioning and Scheduling in SDN/NFV-Enabled Core Networks
The service-oriented fifth-generation (5G) core networks are featured by customized network services with differentiated quality-of-service (QoS) requirements, which can be provisioned through network slicing enabled by the software defined networking (SDN) and network function virtualization (NFV) paradigms. Multiple network services are embedded in a common physical infrastructure, generating service-customized network slices. Each network slice supports a composite service via virtual network function (VNF) chaining, with dedicated packet processing functionality at each VNF. For a network slice with a target traffic load, the end-to-end (E2E) service delivery is enabled by VNF placement at NFV nodes (e.g., data centers and commodity servers) and traffic routing among corresponding NFV nodes, with static resource allocations. To provide continuous QoS performance guarantee over time, it is essential to develop dynamic resource management schemes for the embedded services experiencing traffic dynamics in different time granularities during virtual network operation. In this thesis, we focus on processing resources and investigate three research problems on dynamic processing resource provisioning and scheduling for embedded delay-sensitive services, in presence of both large-timescale traffic statistical changes and bursty traffic dynamics in smaller time granularities.
In problem I, we investigate a dynamic flow migration problem for multiple embedded services, to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. We develop optimization problem formulations and efficient heuristic algorithms, based on a simplified M/M/1 queueing model with Poisson traffic arrivals. Motivated by the limitations of Poisson traffic model, in problem II, we restrict to a local network scenario and study a dynamic VNF scaling problem based on a real-world traffic trace with nonstationary traffic statistics in large timescale. Under the assumption that the nonstationary traffic trace can be partitioned into non-overlapping stationary traffic segments with unknown change points in time, a change point detection driven traffic parameter learning and resource demand prediction scheme is proposed, based on which dynamic VNF migration decisions are made at variable-length decision epochs via deep reinforcement learning. The long-term trade-off between load balancing and migration overhead is studied. A fractional Brownian motion (fBm) traffic model is employed for each detected stationary traffic segment, based on properties of Gaussianity and self-similarity of the real-world traffic. In Problem III, we focus on a sufficiently long time duration with given VNF placement and stationary traffic statistics, and study a delay-aware VNF scheduling problem to coordinate VNF scheduling for multiple services, which achieves network utility maximization with timely throughput guarantee for each service, in presence of bursty and unpredictable small-timescale traffic dynamics, while using a realistic state-of-the-art time quantum (slot) for CPU processing resource scheduling among VNF software processes. Based on the Lyapunov optimization technique, an online distributed VNF scheduling algorithm is derived, which greedily schedules a VNF at each NFV node based on a weight incorporating the backpressure-based weighted differential backlogs with the downstream VNF, the service throughput performance indicated by virtual queue lengths, and the packet delay.
With the proposed dynamic resource management framework, resources can be efficiently and fairly allocated to the embedded services, to avoid congestion and QoS degradation in the presence of traffic dynamics. This research provides some insights in dynamic resource management for delay-sensitive services in a virtualized network environment with CPU processing resources
Optimal dynamic cloud network control
Various exemplary embodiments relate to a network node in a distributed dynamic cloud, the node including: a memory; and a processor configured to: observe a local queue backlog at the beginning of a timeslot, for each of a plurality of commodities; compute a processing utility weight for a first commodity based upon the local queue backlog of the first commodity, the local queue backlog of a second commodity, and a processing cost; where the second commodity may be the succeeding commodity in a service chain; compute an optimal commodity using the processing utility weights; wherein the optimal commodity is the commodity with the highest utility weight; assign the number of processing resource units allocated to the timeslot to zero when the processing utility weight of the optimal commodity is less than or equal to zero; and execute processing resource allocation and processing flow rate assignment decisions based upon the optimal commodity