Network Substate

Random Concepts and Terminology

• DC = "Data Center"
• TOR = "Top of Rack"
  • TOR switches connect all machines within a rack and provide uplinks to higher network levels in the topology
    • TOR switches may use 1 or more ports to uplink. The ports may be of the same bandwidth as the hosts (e.g. gigabit) or higher (e.g. 10GigE)
  • DCs organize machines (~20-40) into racks, which all connect to a TOR switch, which in turn connects to some aggregation switch
• EOR = "End of Row"
  • switch connecting a row of racks
  • appears to describe both one big switch handling all hosts for several racks, or an aggregation switch connecting to TORs of several racks.
• hierarchical vs. fat-tree topologies:
  • hierarchical => different switches and bandwidths at the core than toward leaves
  • fat-tree => same switches everywhere, all at same bandwidth

• Oversubscription - networks can be oversubscribed at various points.
  • Switches generally have enough backplane bandwidth to saturate all internal ports
  • But uplinks to other switches may be a fraction of this, reducing total bandwidth between hosts on different switches
    • using multiple ports and/or higher speed uplinks can mitigate this
  • Oversubscription often described as a ration (n:m), e.g. for every n megabits of bandwidth at a host port, only m megabits of bandwidth exist between it and the most distant machine in the network
• cut-through vs. store-and-forward
  • switches can either buffer entire frames and retransmit out a port (store-and-forward) or stream them without fully buffering in-between (cut-through)
  • cut-through can provide better latency

Data Center Networks

• Current data centers purported to be highly specialized
  • hierarchical network topologies with higher bandwidth aggregation and core switches/routers
    • that is, data rates increase up the tree to handle accumulation of bandwidth used by many, slower leaves further down
  • requires big, specialised switches to maintain reasonable bandwidth
    • e.g. 100+ 10GigE switches with >100 ports each, at the core
      • pricey... Woven Systems 144 ports 10GigE switch debuted at $1500/port in mid-2007
        • current cost unknown
  • oversubscription is purportedly common
    • mainly affects bi-section bandwidth (the data center isn't uniform - locality important, else lower bandwidth expectations)
    • implies congestion is possible, adding overhead for reliable protocols and packet latency
    • 2.5:1 to 8:1 ratios quoted by Al-Fares, et al ('08 SIGCOMM)
      • 2.5:1 means for every 2.5Gb at the end hosts, only 1Gb is allocated at the core
      • a saturated network, therefore, cannot run all hosts at full rates
• Current hot trend is commoditisation
  • Google does this internally, Microsoft/Yahoo/Amazon probably similarly smart about it
    • they've solved it, but either find it too important to share, or don't yet need SIGCOMM papers
  • Nothing is standard. Requires modifications to routing and/or address resolution protocols
    • hacks to L2 or L3 routing
      • L4 protocols generally oblivious
      • need to be careful about not excessively reordering packets
    • non-standard is reasonable for DC's, since internal network open to innovation
  • Main idea is to follow in footsteps of commodity servers
    • From fewer, big, less hackable Sun/IBM/etc boxen to many smaller, hackable i386/amd64 machines running Linux/FreeBSD/something Microsofty
    • Clear win for servers (~45% of DC budget), less so for networks (~15%) [%s from: greenberg, jan '09 ccr]
      • Is 15% large enough to care that much about optimisation (Amdahl strikes again)?
      • Alternatively, is 15% small enough that we can increase it to get features we want (iWARP, full, non-blocking 10GigE bi-section bandwidth, lower latencies, etc)?
  • Similarly, Network Commoditisation => lots of similar, cheaper, simpler building blocks
    • i.e. many cheaper, (near-)identical switches with a single, common data rate
      • Favours Clos (Charles Clos) topologies such as the fashionable "fat-tree", i.e.:
        • Multi-rooted, wide trees with lots of redundancies to spread bandwidth of # of links
        • large number of equal throughput paths between distant nodes
        • switches with equivalent #'s of ports used throughout
        • 6 maximum hops from anywhere to anywhere in the system
        • scales massively
        • does not necessitate faster data rates further up the tree to avoid oversubscription

Fat-Trees

• Size is defined by a factor k, the number of ports per identical switch in the network
• 3-level heirarchy:
  • core level ((k/2)^2 = k^2/4 switches)
    • each core switch uses all k ports to connect to k switches in first layer of the pod level
  • pod level (k pods)
    • each pod has 2 internal layers with (k/2 switches/layer => k switches/pod)
    • upper level switches (k/2 of them) connect k/2 of their ports to core level switches
      • other k/2 ports connect to each of the k/2 lower pod level switches
    • lower level switches (k/2 of them) connect to k/2 hosts each
  • end host level (k^3/4 total hosts)

• k	# hosts	# switches	# ports	host:switch ratio	host:port ratio
  4	16	20	80	0.8	0.2
  8	128	80	640	1.6	0.2
  16	1,024	320	5,120	3.2	0.2
  32	8,192	1,280	40,960	6.4	0.2
  48	27,648	2,880	138,240	9.6	0.2
  64	65,536	5,120	327,680	12.8	0.2
  96	221,184	11,520	1,105,920	19.2	0.2
  128	524,288	20,480	2,621,440	25.6	0.2

• Fat-tree will have no oversubscription if network resources can be properly exploited ("rearrangeably non-blocking")
  • i.e. for a network of 1GigE switches, there will always be 1Gbit available between two arbitrary hosts if the interconnects between them can be properly scheduled
    • ways of handling this include recomputation of routes based on load, randomizing core switch hops, etc
  • take away: can max out all ports, but only if we're smart
  • Al-Fare's SIGCOMM '08 paper shows > 80% utilisation under worst-case conditions

Fat-Tree vs. Hierarchical

• Hierarchical limited by fastest core switch speed, Fat-tree is not so limited
  • => Cannot get 10GigE bi-section bandwidth with hierarchical today, need a Fat-tree with 10-GigE switches
• Example: MSR's Monsoon vs. UCSD's Fat-Tree commodity system
  • Want network connecting many 1GigE nodes with no oversubscription
  • MSR uses a hierarchical configuration (10GigE aggregation and core switches, 1GigE TOR switches)
  • UCSD's uses identical, 24-port, commodity 1GigE switches (i.e. k = 48)
  • Both theoretically capable of 1:1 oversubscription (i.e. no oversubscription)

• 	Hierarchical	Fat-tree
  # hosts	25,920	27,648
  # switches	108 x 144-port 10GigE 1,296 x 20-port 1GigE w/ 2x10GigE uplinks	2,880 x 48-port 1GigE
  # wires	57,024 (~91% GigE, ~9% 10GigE)	82,944
  # unique paths	144 (36 via core with 2x dual uplinks in each subtree)	572

  • Notes:
    • 48-port 1GigE switches cost ~$2.5-3k
      • 2,880 * $2500 = $7M
    • 20-port 1GigE switches w/ 10GigE uplinks probably cost about the same (~2.5-3k) [uplinks not commodity]
      • 1,296 * $2500 = $3.24M
    • 144-port 10GigE switches advertised as $1500/port ($216k/switch) in mid-2007
      • to be competitive with fat-tree on per-port cost, price per port must drop 6.25x to $241.76 ($34.8k/switch)
      • 6.25x drop seems pretty close to the common 2-year drop in price
    • If we were to make a 10GigE Fat-tree similar to the above, today it would cost (MSRP) about $20k/switch x 2,880 switches = $57.6M

Alternative Network Topologies

• Ideas from supercomputing:
  • Hypercubes
    • nCUBE hypercube machines, probably many others
  • Torus's (?Tori?)
    • IBM Blue Gene connects tens of thousands of CPUs with high bandwidth (e.g. 380MB/sec with 4.5usec avg. ping-pongs - link- IBM says 6.4usec latency)
• Hosts connect to n neighbours and route amongst themselves
  • Requires hosts to route frames
    • => higher latencies, unless we can do it on the NIC (NetFPGA?)
  • High wiring complexity
    • no idea how this compares to already high complexity of hierarchical and, especially, fat-tree topologies
  • May impose greater constraints on cluster geometry to appropriately establish links??
  • No dedicated switching elements, simpler (electrically) point-to-point links

L2 vs. L3 switching

• L2 switches are cheaper and simpler, but L2 doesn't scale by default
  • Broadcast domains cannot get too large, else performance suffers
  • switches have MAC forwarding tables, but are often <16k entries in size and we want to scale beyond that
• L3 switches are more expensive, but L3 scales more clearly
  • Set up localized subnets to restrict broadcast domains, or use VLANs
  • Route between subnets intelligently
• To be workable in fat-trees, L3 needs work:
  • Need custom/non-standard software/algorithms to update routes to achieve maximum bandwidth and balance of links
• So long as we're hacking, why not see if L2 can be made to work?
  • Ditch ARP, restrict broadcasts (bcasts can be specially handled out of the fast-path)
  • Use L2 source-routing and L2-in-L2 encapsulation - hosts transmit frames wrapped with destination MAC header of the TOR switch for the destination host
    • => switches need only know MACs of other switches, not all hosts - overcomes 16k MAC entry table limit
    • requires a directory service and host stack modifications
    • use VLB (Valiant Load Balancing) to maximise bandwidth
  • advantage of a flat address space and simpler, lower-level protocol
  • disadvantage of pushing changes into host protocol stacks (though IP is oblivious)

Importance of Programmability

• Clear that to use commodity parts, at either L2 or L3, some modifications must be made
  • e.g. L3 routing to achieve maximum bandwidth, L2 stack changes to avoid broadcast issues, L2 VLB to maximize link utilisation
  • OpenFlow may be an important part of this
    • unknown how programmability of switches affects their 'commodity' status

RAMCloud Requirements and Network Effects

• latency
  • Arista 48-port 10GigE switches advertise a minimum of 600nsec latency (no idea what the distribution looks like)
    • across 6 hops, that's > 3.6 usec
  • Woven System's 144-port 10GigE switches advertise 1.6usec port-to-port latency (almost 3x Arista's minimum)
    • => > 3.2usec in first two levels of hierarchy
  • Force-10 networks cut-through 10GigE latencies:

    • hops	64byte pkt	1500byte pkt
      1	351 nsec	1500 nsec
      3	951 nsec	2100 nsec
      5	1550 nsec	2700 nsec

• • take away: sub-5usec is probably not currently possible across a data center
  • how important is RDMA (iWARP) for our goals?
    • if it necessitates 10GigE, it'd be costly (today)
    • unclear if custom (i.e. non-commodity) hardware required
      • Force-10 forsees iWARP done on NIC, like TOE (TCP Offload Engine) -link
• bandwidth
  • 128 bytes / object * 1.0e6 objects/second = 122MBytes/sec (not including any packet overhead)
    • this is gigabit range... 10GigE vs. GigE may be a significant question:
      • Arista 48-port 10GigE's not commodity (~$20k/switch, vs. $2-3k/switch of commodity 1GigE)
      • But what if we have much bigger, hot objects on a machine?
        • Do we want to assume a single machine can always handle requests?
          • e.g. 10KByte object => max. ~12,500 requests/sec on gigabit
  • Going beyond gigabit is still very costly
    • ~25k cluster with full 10GigE bi-section bandwidth would be ~$57M for switches (~7x the cost of gigabit)
    • if 10GigE not needed, but gigabit not enough, may be cheaper to dual-home machines and increase total # of ports
• load balancing
  • RAMCloud is expected to deal with many small, well-distributed objects
    • this may aid obtaining maximum bandwidth utilisation, since we can bounce packets randomly across potential paths
    • no idea if random is even close to optimal

Congestion

• congestion results in:
  • packet loss if buffers overflow
  • else, increased latency from waiting in line
  • which is worse?
    • if RAMCloud fast enough, occasional packet loss may not be horrible
    • buffering may cause undesired latencies/variability in latency
• even if no oversubscription, congestion is still an issue
  • e.g. any time multiple flows funnel from several ports to one port (within the network) or host (at the leaves)
    • conceivable in RAMCloud, as we expect to communicate with many different systems
      • e.g.: could be a problem if client issues enough sufficiently large requests to a large set of servers
• UDP has no congestion control mechanisms
  • connectionless, unreliable protocol probably essential for latency and throughput goals
  • need to avoid congestion how?
    • rely on user to stagger queries/reduce parallelism? [c.f. Facebook]
    • if we're sufficiently fast, will we run into these problems anyhow?
• balaji's points: buffers don't scale with bandwidth increases
  • simply can't get 2x buffers with similar increase in bandwidth at high end
  • further, adding more bandwidth and keeping a reservation for temporary congestion is better than adding buffers
    • especially for RAMCloud - reduces latency
    • is this an argument against commodity (at least, against a pure commodity fat-tree)?
• ECN - Explicit Congestion Notification
  • already done by switches - set bit in IP TOS header if nearing congestion, with greater probability as we approach saturation
  • mostly for sustained flow traffic
    • RAMCloud expects lots of small datagrams, rather than flows

"Data Center Ethernet"

• Cisco: "collection of standards-based extensions to classical Ethernet that allows data center architects to create a data center transport layer that is:"
  • stable
  • lossless
  • efficient
• Purpose is apparently to buck the trend of building multiple application-specific networks (IP, SAN, Infiniband, etc)
  • how? better multi-tenacy (traffic class isolation/prioritisation), guaranteed delivery (lossless transmission), layer-2 multipath (higher bisectional bandwidth) 
• A series of additional standards:
  • "Class-based flow control" (CBFC)
    • for multi-tenancy
  • Enhanced transmission selection (ETS)
    • for multi-tenancy
  • Data center bridging exchange protocol (DCBCXP)
  • Lossless Ethernet
    • for guaranteed delivery
  • Congestion notification
    • end-to-end congestion management to avoid dropped frames (i.e. work around TCP congestion collapse, retrofit non-congestion-aware protocols to not cause trouble )

In the field

• Google
  • Use long-lived TCP connections
    • pre-established and left open to avoid handshake overhead
    • unclear how TCP has been tweaked for low-latency environment (retransmit timeouts, etc)

Misc. Thoughts

• If networking costs only small part of total DC cost, why is there oversubscription currently?
  • it's possible to pay more and reduce oversubscription - cost doesn't seem the major factor
  • but people argue that oversubscription leads to significant bottlenecks in real DCs
    • but, then, why aren't they reducing oversubscription from the get go?