* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Path Splicing with Network Slicing
Survey
Document related concepts
Distributed operating system wikipedia , lookup
Network tap wikipedia , lookup
Cracking of wireless networks wikipedia , lookup
Recursive InterNetwork Architecture (RINA) wikipedia , lookup
Deep packet inspection wikipedia , lookup
Multiprotocol Label Switching wikipedia , lookup
Airborne Networking wikipedia , lookup
List of wireless community networks by region wikipedia , lookup
Dijkstra's algorithm wikipedia , lookup
Transcript
Improving Internet Availability with Path Splicing Nick Feamster Georgia Tech Can the Internet be “Always On”? “It is not difficult to create a list of desired characteristics for a new Internet. Deciding how to design and deploy a network that achieves these goals is much harder. Over time, our list will evolve. It should be: 1. Robust and available. The network should be as robust, fault-tolerant and available as the wire-line telephone network is today. 2. … • Various studies (Paxson, etc.) show the Internet is at about 2.5 “nines” • More “critical” (or at least availabilitycentric) applications on the Internet • At the same time, the Internet is getting more difficult to debug – Scale, complexity, disconnection, etc. 2 Availability of Other Services • Carrier Airlines (2002 FAA Fact Book) – 41 accidents, 6.7M departures – 99.9993% availability • 911 Phone service (1993 NRIC report +) – 29 minutes per year per line – 99.994% availability • Std. Phone service (various sources) – 53+ minutes per line per year – 99.99+% availability 3 Threats to Availability • Natural disasters • Physical device failures (node, link) – Drunk network administrators 4 Threats to Availability • Natural disasters • Physical failures (node, link) – Drunk network administrators – Cisco bugs • • • • • Misconfiguration Mis-coordination Denial-of-service (DoS) attacks Changes in traffic patterns (e.g., flash crowd) … 5 Availability: Two Aspects • Reliability: Connectivity in the routing tables should approach the that of the underlying graph – If two nodes s and t remain connected in the underlying graph, there is some sequence of hops in the routing tables that will result in traffic • Recovery: In case of failure (i.e., link or node removal), nodes should quickly be able to discover a new path 6 Where Today’s Protocols Stand • Reliability – Approach: Compute backup paths – Challenge: Many possible failure scenarios! • Recovery – Approach: Switch to a backup when a failure occurs – Challenge: Must quickly discover a new working path – Meanwhile, packets are dropped, reordered, etc. 7 Multipath: Promise and Problems s t • Bad: If any link fails on both paths, s is disconnected from t • Want: End systems remain connected unless the underlying graph has a cut 8 Path Splicing: Main Idea Compute multiple forwarding trees per destination. Allow packets to switch slices midstream. s t • Step 1 (Perturbations): Run multiple instances of the routing protocol, each with slightly perturbed versions of the configuration • Step 2 (Slicing): Allow traffic to switch between instances at any node in the protocol 9 Outline • Path Splicing – Achieving Reliabile Connectivity • Generating slices • Constructing paths – Forwarding – Recovery • Properties – High Reliability – Bounded Stretch – Fast recovery • Ongoing Work 10 Generating Slices • Goal: Each instance provides different paths • Mechanism: Each edge is given a weight that is a slightly perturbed version of the original weight – Two schemes: Uniform and degree-based “Base” Graph Perturbed Graph 3 s 3 1.5 t 3 s 5 1.25 4 1.5 t 3.5 11 How to Perturb the Link Weights? • Uniform: Perturbation is a function of the initial weight of the link • Degree-based: Perturbation is a linear function of the degrees of the incident nodes – Intuition: Deflect traffic away from nodes where traffic might tend to pass through by default 12 Constructing Paths • Goal: Allow multiple instances to co-exist • Mechanism: Virtual forwarding tables a s dst b t next-hop Slice 1 t a Slice 2 t c c 13 Forwarding Traffic • Packet has shim header with forwarding bits • Routers use lg(k) bits to index forwarding tables – Shift bits after inspection • To access different (or multiple) paths, end systems simply change the forwarding bits – Incremental deployment is trivial – Persistent loops cannot occur • Various optimizations are possible 14 Putting It Together • End system sets forwarding bits in packet header • Forwarding bits specify slice to be used at any hop • Router: examines/shifts forwarding bits, and forwards s t 15 Evaluation • Defining reliability • Does path splicing improve reliability? – How close can splicing get to the best possible reliability (i.e., that of the underlying graph)? • Can path splicing enable fast recovery? – Can end systems (or intermediate nodes) find alternate paths fast enough? 16 Defining Reliability • Reliability: the probability that, upon failing each edge with probability p, the graph remains connected • Reliability curve: the fraction of sourcedestination pairs that remain connected for various link failure probabilities p • The underlying graph has an underlying reliability (and reliability curve) – Goal: Reliability of routing system should approach that of the underlying graph. 17 Reliability Curve: Illustration Fraction of source-dest pairs disconnected Better reliability Probability of link failure (p) More edges available to end systems -> Better reliability 18 Reliability Approaches Optimal • Sprint (Rocketfuel) topology • 1,000 trials • p indicates probability edge was removed from base graph Reliability approaches optimal Average stretch is only 1.3 Sprint topology, degree-based perturbations 19 Recovery: Two Mechanisms • End-system recovery – Switch slices at every hop with probability 0.5 • Network-based recovery – Router switches to a random slice if next hop is unreachable – Continue for a fixed number of hops till destination is reached 20 20 Simple Recovery Strategies Work Well • Which paths can be recovered within 5 trials? – Sequential trials: 5 round-trip times – …but trials could also be made in parallel Recovery approaches maximum possible Adding a few more slices improves recovery beyond best possible reliability with fewer slices. 21 What About Loops? • Persistent loops are avoidable – In the simple scheme, path bits are exhausted from the header – Never switching back to the same • Transient loops can still be a problem because they increase end-to-end delay (“stretch”) – Longer end-to-end paths – Wasted capacity 22 Significant Novelty for Modest Stretch • Novelty: difference in nodes in a perturbed shortest path from the original shortest path Fraction of edges on short path shared with long path Example s d Novelty: 1 – (1/3) = 2/3 23 Splicing Improves Availability • Reliability: Connectivity in the routing tables should approach the that of the underlying graph – Approach: Overlay trees generated using random link-weight perturbations. Allow traffic to switch between them. – Result: Splicing ~ 10 trees achieves near-optimal reliability • Recovery: In case of failure, nodes should quickly be able to discover a new path – Approach: End nodes randomly select new bits. – Result: Recovery within 5 trials approaches best possible. 24 Extension: Interdomain Paths • Observation: Many routers already learn multiple alternate routes to each destination. • Idea: Use the forwarding bits to index into these alternate routes at an AS’s ingress and egress routers. default d alternate Splice paths at ingress and egress routers Required new functionality • Storing multiple entries per prefix • Indexing into them based on packet headers • Selecting the “best” k routes for each destination 25 Open Questions and Ongoing Work • How does splicing interact with traffic engineering? Sources controlling traffic? • What are the best mechanisms for generating slices and recovering paths? • Can splicing eliminate dynamic routing? 26 Conclusion • Simple: Forwarding bits provide access to different paths through the network • Scalable: Exponential increase in available paths, linear increase in state • Stable: Fast recovery does not require fast routing protocols http://www.cc.gatech.edu/~feamster/papers/splicing-hotnets.pdf 27 Network-Level Spam Filtering Spam: More Than Just a Nuisance • 75-90% of all email traffic – PDF Spam: ~11% and growing – Content filters cannot catch! • As of August 2007, one in every 87 emails constituted a phishing attack • Targeted attacks on the rise – 20k-30k unique phishing attacks per month – Spam targeted at CEOs, social networks on the rise 29 Problem #1: Content-Based Filtering is Malleable • Low cost to evasion: Spammers can easily alter features of an email’s content can be easily adjusted and changed • Customized emails are easy to generate: Contentbased filters need fuzzy hashes over content, etc. • High cost to filter maintainers: Filters must be continually updated as content-changing techniques become more sophistocated 30 Problem #2: IPs are Ephemeral • Spammers use various techniques to change the IP addresses from which they send traffic • Humans must notice changing behavior • Existing blacklists cannot stay up to date One technique: BGP Spectrum Ability ~ 10 minutes • Hijack IP address space • Send spam • Withdraw route 31 Our Approach: Network-Based Filtering • Filter email based on how it is sent, in addition to simply what is sent. • Network-level properties are more fixed – – – – Hosting or upstream ISP (AS number) Botnet membership Location in the network IP address block • Challenge: Which properties are most useful for distinguishing spam traffic from legitimate email? 32 SpamTracker: Main Idea and Intuition • Idea: Blacklist sending behavior (“Behavioral Blacklisting”) – Identify sending patterns that are commonly used by spammers • Intuition: Much more difficult for a spammer to change the technique by which mail is sent than it is to change the content 33 SpamTracker Design Approach • For each sender, construct a behavioral fingerprint • Cluster senders with similar fingerprints • Filter new senders that map to existing clusters Email IP x domain x time Lookup Score Collapse Cluster Classify 34 Building the Classifier: Clustering • Feature: Distribution of email sending volumes across recipient domains • Clustering Approach – Build initial seed list of bad IP addresses – For each IP address, compute feature vector: volume per domain per time interval – Collapse into a single IP x domain matrix: – Compute clusters 35 Clustering: Output and Fingerprint • For each cluster, compute fingerprint vector: • New IPs will be compared to this “fingerprint” IP x IP Matrix: Intensity indicates pairwise similarity 36 Classifying IP Addresses • Given “new” IP address, build a feature vector based on its sending pattern across domains • Compute the similarity of this sending pattern to that of each known spam cluster – Normalized dot product of the two feature vectors – Spam score is maximum similarity to any cluster 37 Summary • Spam is on the rise and becoming more clever – 12% of spam now PDF spam. Content filters are falling behind. Also becoming more targeted • IP-Based blacklists are evadable – Up to 30% of spam not listed in common blacklists at receipt. ~20% remains unlisted after a month – Spammers commonly steal IP addresses • New approach: Behavioral blacklisting – Blacklist how the mail was sent, not what was sent – SpamTracker being deployed and evaluated by a large spam filtering company 38