Download presentation
Presentation is loading. Please wait.
Published byJewel Blair Modified over 9 years ago
1
Resource Allocation in Network Virtualization Jie Wu Computer and Information Sciences Temple University
2
Road Map 1.Motivation and Applications 2.Tracing Back: Embedding 3.Basic Models 4.Extensions 1.Hose model 2.Virtual backbone 5.Looking Forward: Other Fields 6.Conclusions
3
1. Motivation Network virtualization (Peterson, Shenker, and Turner’04) A number of virtual networks (VNs) co-exist over the same physical network (PN) (substrate network) VN: a group of nodes that are connected, with bandwidth reserved in the underlying network Implementation: RSVP and MPLS
4
Applications Coexistence Flexibility Manageability Scalability Isolation Heterogeneity ISP = SP + InP SP: Service Provider InP: Infrastructure Provider SDN Programmable switches and routers than (using virtualization) can process packets for multiple isolated networks Virtualization Data center networks (DCNs)
5
2. Tracing Back: Embedding Embedding (E) of tasks (G) in processors (G’) Dilation of an edge of G is the length of the path in G’ onto which an edge of G is mapped. Dilation of E is the maximum edge dilation of G. Expansion of G is the ratio of the number of nodes in G to the number of nodes in G’. Congestion of E is the maximum number of paths containing an edge in G’, where every path represents an edge in G. Load of an E is the maximum number of tasks of G assigned to any processor of G’.
6
Embedding Examples
7
Virtualization Examples
8
3. Basic Models Embed VNs in PN Subject to CPU (node) and bandwidth (link) constraints General VN embedding NP-hard (multiway separator problem) Special VN embedding (fixed nodes) Multicommodity flow problem
9
Minimum Cost Multicommodity Flow Multicommodity flow Capacity constraints, flow conservation, demand satisfaction Minimum cost Sum of a(u, v) f(u, v) on edge (u, v) Integer flow: hard Fractional flows: solvable (Yu et al 06) Path split Path migration
10
Scheduling of Network Updates Dionysus (Jin et al’14) Loop freedom Congestion freedom Special constraint A link must occur after an update that removes an existing flow Dynamic scheduling Dependency graph (Resource allocation graphs)
11
Scheduling of Network Updates Schedulability Extension Introducing intermediate steps
12
4. Extensions: Hose Model (Duffield, Goyal, and Greenberg’99) Hose: aggregate traffic to and from endpoints in a VN Routing structures Pipe Ingree (Egree) tree Shared tree Mesh E.g. X (in 3), Y (out 2), and Z (out 2) using a Steiner tree
13
Extensions: Virtual Backbone Mapping VNs onto a shared substrate (Lu and Turner’06) Backbone-star, a complete graph, a ring or a star Connected dominating set (CDS) (Wu and Li’99) A subset (V) of nodes such that all other nodes not in V have at least one neighbor in V Resilience (Dai and Wu’05) K-covered CDS: each node has k CDS nodes in its 1-hop neighborhood (including itself) K-connected CDS: can tolerate k-1 faults and still connected
14
Challenges Different models Static Dynamic (long-term statistical guarantees) QoS Different provisioning models Different measurements Minimization of weighted sum of maximum values of node and link stress Minimization of long term average value of the weighted sum of bandwidth and CPU revenue
15
QoS-based Slice Provisioning Safe vs. Unsafe In terms of available network resource QoS-based slice provisioning Slice reservation in unsafe areas Other extensions K-hop CDS: A subset V such that each node not in V can reach a node in V within k hops K-spanner: A spanning subgraph S in which every two vertices are at most k times as far apart in S than on G
16
6. Looking Forward: Other Fields Virtualization in data center networks Virtual machines (VMs) assignment in physical machines (PMs) Subject to CPU and network bandwidth constraints Virtualization in DSN Hadoop scheduling: map, shuffle, and reduce
17
Virtualization in SDNs Virtualization of controller in SDNs Multiple controllers Disjointed Overlapped (token-based access control) Controller placement
18
Hose Model in DCNs Elasticity (Li, Wu, and Blaisse’12) The CPU / bandwidth utilization is the ratio of the used CPU / bandwidth among all PMs / links The combined utilization is the maximal one of the CPU and bandwidth utilizations (bottleneck) Minimizing the combined utilization To provide flexibilities for new VM requests (elasticity)
19
Hose Model in DCNs (cont’d) Iterative stack up Layer by layer recursive placement CPU bottleneck: load balancing placement Link bottleneck: load unbalancing placement
20
Conclusions Allocation centralized vs. distributed Reconfiguration migration and dynamic scheduling Survivability and Flexibility resource overprovisioning and controlled slicing Other Applications SDNs and DCNs
21
Future Challenges Performance guarantee Deterministic vs. statistic Resource discovery and allocation Cooperation and competition between IPs Heterogeneity and diversity of infrastructure
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.