20. Membership Library

20.1. Introduction

The DPDK Membership Library provides an API for DPDK applications to insert a new member, delete an existing member, or query the existence of a member in a given set, or a group of sets. For the case of a group of sets, the library will return not only whether the element has been inserted before in one of the sets but also which set it belongs to. The Membership Library is an extension and generalization of a traditional filter structure (for example Bloom Filter [Member-bloom]) that has multiple usages in a wide variety of workloads and applications. In general, the Membership Library is a data structure that provides a “set-summary” on whether a member belongs to a set, and as discussed in detail later, there are two advantages of using such a set-summary rather than operating on a “full-blown” complete list of elements: first, it has a much smaller storage requirement than storing the whole list of elements themselves, and secondly checking an element membership (or other operations) in this set-summary is much faster than checking it for the original full-blown complete list of elements.

We use the term “Set-Summary” in this guide to refer to the space-efficient, probabilistic membership data structure that is provided by the library. A membership test for an element will return the set this element belongs to or that the element is “not-found” with very high probability of accuracy. Set-summary is a fundamental data aggregation component that can be used in many network (and other) applications. It is a crucial structure to address performance and scalability issues of diverse network applications including overlay networks, data-centric networks, flow table summaries, network statistics and traffic monitoring. A set-summary is useful for applications who need to include a list of elements while a complete list requires too much space and/or too much processing cost. In these situations, the set-summary works as a lossy hash-based representation of a set of members. It can dramatically reduce space requirement and significantly improve the performance of set membership queries at the cost of introducing a very small membership test error probability.

Fig. 20.1 Example Usages of Membership Library

There are various usages for a Membership Library in a very large set of applications and workloads. Interested readers can refer to [Member-survey] for a survey of possible networking usages. The above figure provide a small set of examples of using the Membership Library:

• Sub-figure (a) depicts a distributed web cache architecture where a collection of proxies attempt to share their web caches (cached from a set of back-end web servers) to provide faster responses to clients, and the proxies use the Membership Library to share summaries of what web pages/objects they are caching. With the Membership Library, a proxy receiving an http request will inquire the set-summary to find its location and quickly determine whether to retrieve the requested web page from a nearby proxy or from a back-end web server.
• Sub-figure (b) depicts another example for using the Membership Library to prevent routing loops which is typically done using slow TTL countdown and dropping packets when TTL expires. As shown in Sub-figure (b), an embedded set-summary in the packet header itself can be used to summarize the set of nodes a packet has gone through, and each node upon receiving a packet can check whether its id is a member of the set of visited nodes, and if it is, then a routing loop is detected.
• Sub-Figure (c) presents another usage of the Membership Library to load-balance flows to worker threads with in-order guarantee where a set-summary is used to query if a packet belongs to an existing flow or a new flow. Packets belonging to a new flow are forwarded to the current least loaded worker thread, while those belonging to an existing flow are forwarded to the pre-assigned thread to guarantee in-order processing.
• Sub-figure (d) highlights yet another usage example in the database domain where a set-summary is used to determine joins between sets instead of creating a join by comparing each element of a set against the other elements in a different set, a join is done on the summaries since they can efficiently encode members of a given set.

Membership Library is a configurable library that is optimized to cover set membership functionality for both a single set and multi-set scenarios. Two set-summary schemes are presented including (a) vector of Bloom Filters and (b) Hash-Table based set-summary schemes with and without false negative probability. This guide first briefly describes these different types of set-summaries, usage examples for each, and then it highlights the Membership Library API.

20.2. Vector of Bloom Filters

Bloom Filter (BF) [Member-bloom] is a well-known space-efficient probabilistic data structure that answers set membership queries (test whether an element is a member of a set) with some probability of false positives and zero false negatives; a query for an element returns either it is “possibly in a set” (with very high probability) or “definitely not in a set”.

The BF is a method for representing a set of n elements (for example flow keys in network applications domain) to support membership queries. The idea of BF is to allocate a bit-vector v with m bits, which are initially all set to 0. Then it chooses k independent hash functions h1, h2, ... hk with hash values range from 0 to m-1 to perform hashing calculations on each element to be inserted. Every time when an element X being inserted into the set, the bits at positions h1(X), h2(X), ... hk(X) in v are set to 1 (any particular bit might be set to 1 multiple times for multiple different inserted elements). Given a query for any element Y, the bits at positions h1(Y), h2(Y), ... hk(Y) are checked. If any of them is 0, then Y is definitely not in the set. Otherwise there is a high probability that Y is a member of the set with certain false positive probability. As shown in the next equation, the false positive probability can be made arbitrarily small by changing the number of hash functions (k) and the vector length (m).

Fig. 20.2 Bloom Filter False Positive Probability

Without BF, an accurate membership testing could involve a costly hash table lookup and full element comparison. The advantage of using a BF is to simplify the membership test into a series of hash calculations and memory accesses for a small bit-vector, which can be easily optimized. Hence the lookup throughput (set membership test) can be significantly faster than a normal hash table lookup with element comparison.

Fig. 20.3 Detecting Routing Loops Using BF

BF is used for applications that need only one set, and the membership of elements is checked against the BF. The example discussed in the above figure is one example of potential applications that uses only one set to capture the node IDs that have been visited so far by the packet. Each node will then check this embedded BF in the packet header for its own id, and if the BF indicates that the current node is definitely not in the set then a loop-free route is guaranteed.

Fig. 20.4 Vector Bloom Filter (vBF) Overview

To support membership test for both multiple sets and a single set, the library implements a Vector Bloom Filter (vBF) scheme. vBF basically composes multiple bloom filters into a vector of bloom filers. The membership test is conducted on all of the bloom filters concurrently to determine which set(s) it belongs to or none of them. The basic idea of vBF is shown in the above figure where an element is used to address multiple bloom filters concurrently and the bloom filter index(es) with a hit is returned.

Fig. 20.5 vBF for Flow Scheduling to Worker Thread

As previously mentioned, there are many usages of such structures. vBF is used for applications that need to check membership against multiple sets simultaneously. The example shown in the above figure uses a set to capture all flows being assigned for processing at a given worker thread. Upon receiving a packet the vBF is used to quickly figure out if this packet belongs to a new flow so as to be forwarded to the current least loaded worker thread, or otherwise it should be queued for an existing thread to guarantee in-order processing (i.e. the property of vBF to indicate right away that a given flow is a new one or not is critical to minimize response time latency).

It should be noted that vBF can be implemented using a set of single bloom filters with sequential lookup of each BF. However, being able to concurrently search all set-summaries is a big throughput advantage. In the library, certain parallelism is realized by the implementation of checking all bloom filters together.

20.3. Hash-Table based Set-Summaries

Hash-table based set-summary (HTSS) is another scheme in the membership library. Cuckoo filter [Member-cfilter] is an example of HTSS. HTSS supports multi-set membership testing like vBF does. However, while vBF is better for a small number of targets, HTSS is more suitable and can easily outperform vBF when the number of sets is large, since HTSS uses a single hash table for membership testing while vBF requires testing a series of Bloom Filters each corresponding to one set. As a result, generally speaking vBF is more adequate for the case of a small limited number of sets while HTSS should be used with a larger number of sets.

Fig. 20.6 Using HTSS for Attack Signature Matching

As shown in the above figure, attack signature matching where each set represents a certain signature length (for correctness of this example, an attack signature should not be a subset of another one) in the payload is a good example for using HTSS with 0% false negative (i.e., when an element returns not found, it has a 100% certainty that it is not a member of any set). The packet inspection application benefits from knowing right away that the current payload does not match any attack signatures in the database to establish its legitimacy, otherwise a deep inspection of the packet is needed.

HTSS employs a similar but simpler data structure to a traditional hash table, and the major difference is that HTSS stores only the signatures but not the full keys/elements which can significantly reduce the footprint of the table. Along with the signature, HTSS also stores a value to indicate the target set. When looking up an element, the element is hashed and the HTSS is addressed to retrieve the signature stored. If the signature matches then the value is retrieved corresponding to the index of the target set which the element belongs to. Because signatures can collide, HTSS can still has false positive probability. Furthermore, if elements are allowed to be overwritten or evicted when the hash table becomes full, it will also have a false negative probability. We discuss this case in the next section.

20.3.1. Set-Summaries with False Negative Probability

As previously mentioned, traditional set-summaries (e.g. Bloom Filters) do not have a false negative probability, i.e., it is 100% certain when an element returns “not to be present” for a given set. However, the Membership Library also supports a set-summary probabilistic data structure based on HTSS which allows for false negative probability.

In HTSS, when the hash table becomes full, keys/elements will fail to be added into the table and the hash table has to be resized to accommodate for these new elements, which can be expensive. However, if we allow new elements to overwrite or evict existing elements (as a cache typically does), then the resulting set-summary will begin to have false negative probability. This is because the element that was evicted from the set-summary may still be present in the target set. For subsequent inquiries the set-summary will falsely report the element not being in the set, hence having a false negative probability.

The major usage of HTSS with false negative is to use it as a cache for distributing elements to different target sets. By allowing HTSS to evict old elements, the set-summary can keep track of the most recent elements (i.e. active) as a cache typically does. Old inactive elements (infrequently used elements) will automatically and eventually get evicted from the set-summary. It is worth noting that the set-summary still has false positive probability, which means the application either can tolerate certain false positive or it has fall-back path when false positive happens.

Fig. 20.7 Using HTSS with False Negatives for Wild Card Classification

HTSS with false negative (i.e. a cache) also has its wide set of applications. For example wild card flow classification (e.g. ACL rules) highlighted in the above figure is an example of such application. In that case each target set represents a sub-table with rules defined by a certain flow mask. The flow masks are non-overlapping, and for flows matching more than one rule only the highest priority one is inserted in the corresponding sub-table (interested readers can refer to the Open vSwitch (OvS) design of Mega Flow Cache (MFC) [Member-OvS] for further details). Typically the rules will have a large number of distinct unique masks and hence, a large number of target sets each corresponding to one mask. Because the active set of flows varies widely based on the network traffic, HTSS with false negative will act as a cache for <flowid, target ACL sub-table> pair for the current active set of flows. When a miss occurs (as shown in red in the above figure) the sub-tables will be searched sequentially one by one for a possible match, and when found the flow key and target sub-table will be inserted into the set-summary (i.e. cache insertion) so subsequent packets from the same flow don’t incur the overhead of the sequential search of sub-tables.

20.4. Library API Overview

The design goal of the Membership Library API is to be as generic as possible to support all the different types of set-summaries we discussed in previous sections and beyond. Fundamentally, the APIs need to include creation, insertion, deletion, and lookup.

20.5. References

[Member-bloom] B H Bloom, “Space/Time Trade-offs in Hash Coding with Allowable Errors,” Communications of the ACM, 1970.

[Member-survey] A Broder and M Mitzenmacher, “Network Applications of Bloom Filters: A Survey,” in Internet Mathematics, 2005.

[Member-cfilter] B Fan, D G Andersen and M Kaminsky, “Cuckoo Filter: Practically Better Than Bloom,” in Conference on emerging Networking Experiments and Technologies, 2014.

[Member-OvS] B Pfaff, “The Design and Implementation of Open vSwitch,” in NSDI, 2015.