Archive for the 'data center' Category

David vs. Goliath, UCSD vs. Microsoft?

In high school, I took journalism and worked on the school newspaper.  This means that I know the value of a good headline, that the headline may not reflect reality, and that the author of an article often does not write the headline.  In fact, the headline may not even reflect the contents of the article.  Still, I was surprised to see the headline to a recent Network World article, It’s Microsoft vs. the professors with competing data center architectures.  The article invokes an image of one side or the other throwing down the gauntlet and declaring war. In fact, nothing could be further from the truth.  If it were true, I would definitely feel worried about taking on Microsoft.

My group has been active in data center research.  The article does a very nice job of describing the architecture of PortLand, our recent work on a layer 2 network fabric designed to scale to very large data centers.  The article also describes recent work on VL2 from my colleagues at Microsoft Research.  Both papers appeared at SIGCOMM 2009 and were presented back-to-back in the same session at the conference.

I will leave detailed comparison between the two approaches to the papers themselves.  However, at a high level, both efforts start with a similar premise: the data center networking fabric, at the scale of 10k-100k ports, should be managed as a single network fabric.  One desirable goal here is to manage the netwrok as a single layer 2 domain.  However, conventional wisdom dictates that you cannot go beyond a few 100’s of ports for a layer 2 domain because of scalability and performance problems with traditional layer 2 protocols.  I described one such scalability limitation, limited switch state for forwarding tables, in an earlier post.  There are other challenges including spanning tree protocols, and broadcast overhead of ARP.

So the main takeaway is that we cannot scale a layer 2 network to target levels without changing some of the underlying protocols, at least a bit.  With perfect hindsight, the key difference between PortLand and VL2 is one of philosophy.  Both groups agree that the network should consist of unmodified switch hardware.  However, we believe that the end hosts should also remain unmodified, instead implementing new functionality by modifying switch software.  All switch hardware vendors export some API for programming switch forwarding tables and recently, systems such as OpenFlow export standard APIs for programming switch forwarding tables.  In fact, we implemented our prototype of PortLand using OpenFlow with the goal of maintaining the boundary between system and network administration.  VL2, on the other hand, prefers to leave the switch software unmodified and instead introduces its new functionality by modifying the end hosts themselves.  This leads to different architectural techniques and different designs.

One of our overriding goals is to reduce management burden, so we further introduce a decentralized Location Discovery Protocol (LDP) to automatically assign hierarchical prefixes to switches and end hosts.  These prefixes are the basis for compact forwarding tables in intermediate switches.  Both VL2 and PortLand leverage a directory service to essentially find an efficient path between a source and destination without resorting to broadcast (as would be required by default with ARP).

I consider the VL2 paper to be excellent.  I certainly learned a lot from reading the paper.  Perhaps the ultimate complement I can give is that I plan to assign it to my class in the spring when I teach graduate computer networks again.

Still, it is true that one of the best things about research is that we live in a marketplace of ideas and hence there must be some implicit competition.  We can only get better knowing that the folks at Microsoft are working on similar problems and certainly the “truth” as ascertained with 20/20 hindsight in 5-10 years will consist of some mixture of the competing techniques.  That way, everyone can declare victory.

“When 640KB is Still a Lot of Memory” or “Another Reason Scaling Layer 2 Networks is Hard”

In one apocryphal part of computing lore, Bill Gates famously explained the 640KB main memory limit in DOS back in 1981 by stating that 640KB should be sufficient for any program.  (According to at least Wikipedia, Bill Gates never made such a statement.)  Nearly 30 years later, computers routinely ship with gigabytes of memory.  We recently installed some machines 256GB of memory here at UCSD.  So we have all become desensitized to memory limitations in many settings.

Recently, we have been considering building large-scale Layer 2 networks for the data center environment.  In a Layer 2 network, each switch performs packet forwarding based on flat MAC addresses.  For any possible destination, the switch must match the destination MAC address in a packet in a lookup table and determine the output port for that destination.  High end switches today typically allocate 32k-64k entries in their MAC forwarding tables.  This means, assuming the potential for (eventual) all-to-all communication, the switches can scale to networks with up to 64k communicating end points.

Let’s assume that a forwarding table entry consists of 10 bytes, 6 bytes for the 48-bit MAC address and 4 bytes for the output port and any other bookkeeping information.  The resulting forwarding tables for such a high end switch would consist of 640KB of memory.

Initially, supporting 64k MAC entries may seem like it should be sufficient for just about any situation.  However, today we are starting to see data centers with hundreds of thousands of hosts.  Further, with the advent of virtualization, we often see 10+ virtual machines, each with their own unique MAC address, multiplexed onto individual physical machines.  So, let’s consider an extreme scenario where we would like to enable potentially all-to-all communication in a data center with 10 million virtual Layer 2 end points for communication (e.g., 500k hosts each with 20 virtual machines).

Clearly, in the short term at least, we will not have applications running on 10 million hosts simultaneously (I won’t make any pronouncements about never needing such application support!).  However, for maximum flexibility, a switch has to be at least be prepared for any directly-connected host to wish to an arbitrary host somewhere else in the data center.  Otherwise, the switch would have to run a reactive routing protocol to find an appropriate path to a destination for a given packet, introducing unnecessary and perhaps intolerable delays in establishing communication with a new destination.

One way to deal with this limitation is to partition the network into individual Layer 3 zones and require Layer 3/IP routing for hosts in different zones.  Employing Layer 3 routing in the data center decreases flexibility and increases administrative costs, as further discussed in our SIGCOMM 2009 paper on PortLand.

So let’s consider scaling a switch to support Layer 2 forwarding for 10 million end points.  Again assuming 10 bytes per forwarding table entry, this would require a forwarding table with 100 MB of memory.  For someone like me coming from an operating systems/application background, 100 MB of memory sounds like a tiny amount of memory.  After all, today I can buy 2GB of DRAM for about $25.  So what’s the problem?  We can scale switches to support the largest data centers imaginable by just adding a few dollars of memory.

Unfortunately, the lookups have to take place on the fast path of packet forwarding.  Switches operating today at 10 Gb/s have a few nanoseconds to perform such a lookup and determine the appropriate output port for a switch.  This requirement by itself eliminates the possibility of employing DRAM, it is simply not fast enough.  Still 100 MB of fast SRAM should still be affordable.  Unfortunately, the forwarding latency and the required bandwidth means that the forwarding tables have to be on-chip, i.e., on the same physical die as the switch ASIC.  At least for commodity switches, all functionality has to be on a single chip.  Otherwise, the cost for engineering hardware architecture that deliver sufficient bandwidth between a switch ASIC and off-chip SRAM (or TCAM) is prohibitive and eliminates the possibility of leveraging commodity hardware.  After all, one cannot expect commodity switch designers to target scenarios with 10 million potentially communicating end points as their target market while still maintaining their cost structure.

By analogy, even high-end processors from Intel/ACM using the very latest manufacturing technology (commodity switch hardware typically lags processor manufacturing by a generation or two) have Layer 1 caches with only a few MB of capacity.  Putting 100 MB of Layer 1 cache on a processor would be prohibitively expensive.  Similarly, having 640KB of fast forwarding table memory for commodity switches is at the high end (especially considering the significant amount of on-chip memory that must be allocated for packet buffering).

The bottom line is that getting 10’s or 100’s of MB of memory onto a switch ASIC just for forwarding tables is prohibitively expensive.  If we want to scale Layer 2 networks to potentially hundreds of thousands or millions of end hosts in the near future, we will require techniques to avoid having a single entry for each possible destination in switch forwarding tables.  This is one of the goals of our PortLand work: essentially, how to introduce hierarchy into Layer 2 addresses internally within the switch infrastructure to enable hierarchical (and much more compact) entries in forwarding tables.  With appropriate organization of the MAC address space, we should be able to support essentially arbitrary-sized data centers with a few hundred or a few thousand forwarding table entries, well within the bounds of commodity switch hardware.

Yahoo!’s Geo-Replication Service, PNUTS

A few weeks ago, I had the chance to visit Yahoo! Research.  I had nice conversations with Brian Cooper and Raghu Ramakrishnan regarding their new storage infrastructure, PNUTS.  I had a great time during my visit and wanted to write a bit about PNUTS after going through their paper in more detail.  Their work is addressing what I consider to be an increasingly important problem, delivering applications to a global audience from data centers spread all across the planet.

Such geo-replication of application data is required because no single data center can provide requisite levels of availability to clients and because speed of light delays and wide-area network congestion make it impossible to deliver interactive response times for clients potentially half ways across the planet.

PNUTS goal is to provide a hosted storage infrastructure exporting a record-based API.  Clients may insert records into tables following a loose scheme (not all columns have to be specified for all records).  Each record has a primary key and an assigned owner, used to deliver PNUTS’s consistency guarantees.  A table’s primary keys may be ordered or hashed, with ordering more naturally supporting range queries and hashing lending itself to load balancing.

Perhaps the primary question for any wide-area replication service is the consistency model.  Because the Yahoo! services leveraging PNUTS have strict performance requirements, the PNUTS designers deemed the overhead of providing strong consistency to be too high.  Instead, individual individual records export “timeline consistency.”  Essentially, all updates are forwarded to a per-record master.  Once the write is applied at the master, the synchronous portion of a write completes and success is returned to the client.  PNUTS then propagates the writes asynchronously to the other data centers replicating the record.  While reads to remote data centers may return stale data, updates will be ordered at the master (hence no conflicts) and pushed to remote replicas in order.

PNUTS aims to scale to ten+ wide-area data centers, each with 1,000 storage machines (petabyte-scale storage).  PNUTS targets record-based storage for online access and hence is complementary to storage systems such as HDFS that target batch-based analysis or other storage systems that target large “blob” storage (video, audio, etc.).

I find this space to be extremely interesting and really in its infancy.  Kudos to the folks at Yahoo! for being among the first to tackle this important space. There are a number of alternative techniques.  Dynamo from Amazon targets single data-center storage and exports an eventual consistency model that may leave updates applied “out of order” from the perspective of a client.  BigTable/GFS provide stronger consistency guarantees but synchronously apply updates to multiple replicas within the data center, making them less appropriate for geo-replication.

In my own group, we are also building a system targeting geo-replication across multiple wide-area data centers.  Our goal is to quantify the exact costs of strong consistency is for web services leveraging data replicated across multiple data centers.  We feel that there are applications that would benefit from strong consistency and sacrifice it in terms of significant additional complexity.  From Yahoo!’s internal measurements from the paper, we see that 85% of writes to a record originate from the same data center.  This certainly justifies locating the master at this location.  However, with timeline consistency, the remaining writes must go across the wide-area anyway to the master, making it difficult to enforce SLAs that are often set at the 99 or even 99.9%.  Further, unavailability of the master makes a record either unavailable or imposes the need to “fork” the timeline, requiring the client application to potentially reconcile conflicting updates.

Can we architect a system that enforces strong consistency, delivers acceptable performance, and maintains availability in the face of any single replica failure?  Clearly, there will be cases where the answer will be a resounding “No!”  We want to understand the scenarios where achieving these properties is possible and, with the appropriate architecture and design, expand the space.

Update: I recently found another nice writeup on PNUTS here.

The Push Toward Cloud Computing and Mega Data Centers

The computing industry is evolving toward a world where an increasing fraction of global computation and storage will be delivered from a relatively small number of dense data centers.  I think that is yet another very exciting to be in computing as we are once again in the process of reinventing what the computing model will look like ten years out.  Here I will talk about some of the drivers behind this trend.  A comprehensive discussion of these issues and more can be found in James Hamilton’s excellent Perspectives.

The driving forces behind this evolution are economics and convenience.  From an economic perspective, energy costs, rather than initial capital outlay, can dominate the cost of operating computing and storage equipment.  In this environment, operating large numbers of computers in regions of the world with cheap and clean power can incur an order of magnitude less cost while simultaneously making computing more environmentally friendly. Similarly, a well-run, dense data center may require an order of magnitude fewer people to operate than similar amounts of computing spread across multiple organizations and physical locations.

Finally, most computers operate at less than 10% (and often less than 1%) overall utilization when measured over long time scales. The advent of virtualization technologies allows for the multiplexing of multiple logical virtual machines onto individual physical machines, allowing for more efficient hardware utilization and also enabling end users and organization to only pay for the resources that they actually require at fine granularity.  Using 100 computers for 1 hour costs the same as using 1 computer for 100 hours without the need to procure and manage 100 computers for the long term.

Much of the computation running in data centers today run in parallel on hundreds, thousands, or even tens of thousands of processors.  A simple search request to Google, Yahoo!, etc. runs in parallel across a multi-petabyte dataset on thousands of computers.  Results must be returned interactively (e.g., less than 300 ms) for queries that require significant computation and communication.  At the other end of the interactivity spectrum, companies wish to run very large-scale data processing and data mining on their own petabyte-scale datasets.  For example, consider queries running over all the items stocked and sold by Walmart.

My group has become very interested in some of the issues in:

  • building out large-scale data centers, particularly the data center network infrastructure;
  • programming and managing applications running across multiple wide area data centers.

I will summarize some of our work in these areas in subsequent posts.

Facebook’s Network Requirements

At the IEEE LEOS meeting, I had the chance to hear an excellent presentation by Donn Lee from Facebook on their network infrastructure and pain points. I first met Donn when I was speaking at Stanford on our own Data Center networking project.  He had a lot of great questions and feedback based on his experience at Facebook (not to mention Google, Cisco, etc.).

One interesting thing that came out of the presentation is the rate at which switch capacity has increased relative to the size and bandwidth requirements of data centers over the last decade or so.  Today, the biggest switch that one can buy is approximately a 128-port 10 Gigabit Ethernet switch.  However, data centers with 100,000’s of thousands of ports are not unheard of today and individual distributed applications can run on tens of thousands of machines.

A significant challenge is interconnecting all of these machines.  Donn mentioned that his ideal switch from an operational perspective at Facebook.  Would have 1500 ports.  The switch would have 1000 10GbE ports facing downward to end hosts (either 10k at 1 GbE each or 1k at 10 GbE each) and 50 100GbE ports facing up to another switch to allow communication with other logical clusters.  This suggests a requirement of nonblocking bandwidth at the granularity of 1-10k hosts and an oversubscription ratio of 2 in talking to other clusters.  This also suggests a switch that has 15 Terabits/sec of aggregate capacity.  The fact that this represents about a factor of 15 more bandwidth than what is available from commercial switches (not to mention the fact that there is no standard for 100 Gigabit Ethernet yet) means that Facebook has to build out complex meshes, presumably with some performance-limiting hashing to map flows to paths.

Given that doubling single switch capacity roughly requires a factor of 4 more logic, and the continued buildout of ever denser data centers, the communication fabric for these data centers is likely to form a top of increasing interest.

My group remains quite interested in this space.  In fact, our upcoming Merchant Silicon paper at Hot Interconnects this year considers the design of multi-stage 34 Tbps switch.

UPDATE: The slides from Donn’s talk are now available here.

Amin Vahdat is a Professor in Computer Science and Engineering at UC San Diego.

May 2020