Patent Publication Number: US-8121043-B2

Title: Approach for managing the consumption of resources using adaptive random sampling

Description:
FIELD OF THE INVENTION 
     This invention relates generally to networking, and more specifically, to an approach for managing the resources consumed by flow based traffic monitoring using adaptive random packet sampling. 
     BACKGROUND 
     The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, the approaches described in this section may not be prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. 
     There are several definitions of the term “flow” being used by the Internet community. Within the context of the IETF&#39;s Internet Protocol Information eXport (IPFIX) Working Group, a flow is defined as a set of IP packets passing an observation point in the network during a certain time interval. All packets belonging to a particular flow share a set of common properties. Each property is defined as the result of applying a function to the values of: (1) one or more packet header fields (e.g. destination IP address), transport header fields (e.g. destination port number), or application header fields (e.g. RTP header fields); (2) one or more characteristics of the packet itself (e.g. number of MPLS labels, etc.); or (3) one or more fields derived from packet treatment (e.g. next hop IP address, the output interface, etc.). A packet belongs to a flow if the packet completely satisfies all the defined properties of the flow. This definition covers the range from a flow containing all packets observed at a network interface to a flow consisting of just a single packet between two applications. It includes packets selected by a sampling mechanism. 
     A variety of flow monitoring tools currently exist to monitor the flow of packets in networks. Flow monitoring tools provide valuable information that can be used in a variety of ways. For example, flow monitoring tools may be used to perform network traffic engineering and to provide network security services, e.g., to detect and address denial of service attacks. As yet another example, flow monitoring tools can be used to support usage-based network billing services. 
     Flow monitoring tools are conventionally implemented as flow monitoring processes executing on a network element, such as a router. The flow monitoring processes are configured to examine and classify packets passing through a particular observation point in a network. The flow monitoring processes are also configured to generate flow statistical data that indicates, for example, the number of packets in each flow, the number of bytes in each flow and the protocol of each flow. 
     One of the issues with flow monitoring tools is how to manage the consumption of resources attributable to generating and maintaining flow statistical data. Generating flow statistical data consumes processing resources and storing flow statistical data consumes storage resources. The amount of resources consumed by flow statistical data can be considerable in networks with high traffic volume, which can adversely affect other processes. Furthermore, the amount of resources consumed by flow statistical data can fluctuate dramatically, as network traffic patterns change. 
     One solution to this problem has been to use sampling to collect flow statistical data for less than all of the packets that pass through an observation point. For example, a percentage of packets are sampled, e.g., every n th  packet is sampled, and then the exported flow statistical data is later adjusted to account for the percentage of packets that was sampled. As another example, a fixed probability may be used to determine whether to sample packets. One problem with these approaches is that they do not take into consideration the characteristics of traffic flow. Because of this, it is difficult to select a sampling percentage or probability that works well for both large and small flows. For example, a small sampling probability may work well for large flows but may not be effective for small flows because there may be too few packets to be sampled. 
     A conventional scheme to control storage consumption is to place a limit on the amount of memory used for storing flow statistical data. The limits are typically expressed as percentages of available resources or as absolute amounts. 
     One problem with this solution is that it can have significant unintended consequences on processing resource consumption. For example, when new flows arrive at an extremely high rate, because of the memory usage limitation, the existing flow statistical records would have to be removed at a very high rate in order to free up memory space for the new flows. Because export consumes processing resources, this causes processing consumption to surge, which is undesirable. Therefore, this scheme does not address the trade-off between memory and processing resource consumption. 
     Based on the foregoing, there is a need for an approach for managing the consumption of resources that does not suffer from limitations of prior approaches. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the figures of the accompanying drawings like reference numerals refer to similar elements. 
         FIG. 1  is a block diagram that depicts an arrangement for managing the consumption of resources using adaptive sampling, according to an embodiment of the invention. 
         FIG. 2  is a table that depicts an example of flow statistical data for five flows. 
         FIG. 3  is a graph that depicts memory consumption behavior using adaptive random sampling without export. 
         FIG. 4  is a graph of sampling probabilities. 
         FIG. 5  is a graph of memory consumption over time using adaptive random sampling. 
         FIG. 6  is a flow diagram that depicts an approach for managing the consumption of resources using adaptive sampling according to an embodiment of the invention. 
         FIG. 7  is a block diagram of a computer system on which embodiments of the invention may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention. Various aspects of the invention are described hereinafter in the following sections: 
     I. OVERVIEW 
     II. ARCHITECTURE 
     III. ADAPTIVE RANDOM SAMPLING 
     IV. OPERATIONAL EXAMPLE 
     V. IMPLEMENTATION MECHANISMS 
     I. Overview 
     An approach for managing the consumption of resources uses adaptive sampling to decrease the collection of flow statistical data as the consumption of resources increases. According to the approach, when a packet is received from a network, a determination is made whether the packet belongs to an existing flow, for which flow statistical data is being collected, or to a new flow. If the packet belongs to an existing flow, then the packet is always sampled, and the statistical data for the existing flow is updated to reflect the packet. If the packet belongs to a new flow, then a sampling probability is used to determine whether the new flow is to be sampled, i.e., whether statistical data is to be collected for the new flow. The sampling probability is determined, at least in part, upon a current usage of resources. If, based upon the sampling probability, statistical data is to be collected for the new flow, then statistical data that reflects the packet is generated and stored. From then on, the statistical data for the new flow is updated to reflect all additional packets that are received for that flow. A key assumption for this approach is that updating an existing piece of statistical data consumes much less resources than creating a new piece of statistical data. This assumption is generally true in conventional computing environments. 
     The approach provides control over the amount of resources consumed by flow statistical data, while ensuring that the export of flow statistical data occurs in a deterministic manner. For purposes of explanation, embodiments of the invention are described hereinafter in the context of managing the consumption of memory resources. The approach is not limited to the memory resource context however, and is applicable to any type of resource. For example, the approach is applicable to managing the consumption of processing resources. 
     II. Architecture 
       FIG. 1  is a block diagram that depicts an arrangement  100  for managing the consumption of resources using adaptive sampling, according to an embodiment of the invention. Arrangement  100  includes a router  102  that may be communicatively coupled to other network elements via communications links  104 ,  106 . Communications links  104 ,  106  may be implemented by any mechanism or medium that provides for the exchange of data between router  102  and other network elements. Examples include, without limitation, a network such as a Local Area Network (LAN), Wide Area Network (WAN), Ethernet or the Internet, or one or more terrestrial, satellite or wireless links. For purposes of explanation, embodiments of the invention are described hereinafter in the context of managing the consumption of memory resources on router  102 . The approach is not limited to the router context however, and is applicable to any type of network device or element. Examples of such devices include, without limitation, gateways, Web servers, switches and any other type of network device or element. 
     Router  102  is configured with a flow monitor  108 , a resource monitor  110  and a memory  112  with flow statistical data  114 . Flow monitor  108  monitors packets passing through router  102  and generates flow statistical data  114 . Flow monitor  108  is also configured to export flow statistical data  114  to other network elements (not depicted), such as a flow collector. Resource monitor  110  monitors the consumption of resources on router  102 , such as memory  112 , CPU resources, or bandwidth, and makes this information available to other elements and processes, such as flow monitor  108 . Flow monitor  108  and resource monitor  110  are typically implemented as processes executing on router  102 , but may be implemented by hardware or any combination of hardware and software processes. Memory  112  may be any type or combination of mechanisms and media, volatile or non-volatile, that provide for the storage of data. Examples of memory  112  include, without limitation, a random access memory (RAM), a cache, one or more disks, or any combination thereof. 
     Flow statistical data  114  may include flow statistical data for any number of flows passing through router  102 . In practice, flow statistical data  114  may include flow statistical data spanning many orders of magnitude depending upon the amount of traffic router  102  can process. The particular statistical data included in flow statistical data  114  may vary widely, depending upon the requirements of a particular implementation, and the approach is not limited to any particular statistical data. The particular types of data included in flow statistical data  114  may be specific to router  102  and thus different from flow statistical data at other network elements and devices. Router  102  may be configured with other elements and processes, depending upon a particular implementation, that are not depicted in the figures and described herein for purposes of explanation. 
       FIG. 2  is a table  200  that depicts example flow statistical data  114  for five flows, identified as flows  1 - 5 . Although embodiments of the invention are described herein in the context of flow statistical data  114  being maintained in a table, the approach is not limited to this context and flow statistical data may be stored in any type of data structure or format. Flow monitor  108  generates the data for table  200  by examining the contents of packets passing through router  102 . The flow attributes in table  200  include, for each flow, a source address, a destination address, a start time, an end time, a number of packets, a number of bytes, a protocol and a sampling probability. The source address is the source IP address of the flow. The destination address is the destination IP address of the flow. The start time is the time at which the first packet in the flow was received at router  102 . The end time is the time at which the latest packet in the flow was received at router  102 . The number of packets is the number of packets seen in the flow. The number of bytes is the number of bytes seen in the flow. The protocol is the IP protocol of the flow. Example protocols include, without limitation, at the application layer, hypertext transfer protocol (HTTP); simple mail transfer protocol (SMTP); file transfer protocol (FTP); at the transport layer, transmission control protocol (TCP) and user datagram protocol (UDP); at the network layer, Internet Protocol (IP) and Internet control message protocol (ICMP). Note that only one protocol layer may apply at once. The sampling probability is the probability that was used to determine whether to generate flow statistical data for the flow and is described in more detail hereinafter. 
     III. Adaptive Random Sampling 
     As previously described herein, adaptive random sampling is used to manage the consumption of resources. According to this approach, when it is determined that a received packet does not belong to an existing flow, then the packet belongs to a new flow. In this situation, a sampling probability is determined for the new flow. The sampling probability indicates a probability that the new flow will be sampled, i.e., that collection of flow statistical data will be initiated for the new flow. The sampling probability is used to determine whether to generate flow statistical data for a given flow, and is an attribute assigned to and exported with each flow as described in more detail hereinafter. 
     According to one embodiment of the invention, the sampling probability is dynamically adjusted to prevent all available resources from being consumed.  FIG. 3  is a graph  300  that depicts the memory consumption behavior using adaptive random sampling without export. The x-axis  302  represents time and the y-axis  304  represents memory usage. The memory usage curve  306  depicts how memory usage asymptotically approaches the total available memory as time approaches infinity. The threshold amount of memory usage is when the adaptive random sampling approach begins to slow the consumption of memory by reducing the sampling probability for packets that belong to new flows. 
     According to one embodiment of the invention, the sampling probability for a new flow is determined based upon the current consumption of memory.  FIG. 4  is a graph  400  of sampling probabilities. The x-axis  402  represents the current available memory and the y-axis  404  represents the sampling probability. Line  406  indicates how the sampling probability decreases as the current available memory decreases. The value of P is the base sampling probability and may be any value between zero and one, for example, one. M is the maximum available memory. 
     In this example, line  406  represents a step function where the sampling probability is reduced by 50% for each 50% reduction in available memory. Thus, the first 50% reduction in available memory (to ½ of the available memory) corresponds to a 50% reduction in sampling probability to P/2. The next 50% reduction in available memory (to ¼ of the available memory) corresponds to another 50% reduction in sampling probability to P/4, and so on. Starting with a full available memory and assuming a base probability of P=1 (the base probability may be any value between 0 and 1), a sampling probability of one is used to determine whether to initiate collection of flow statistical data for new flows. In this situation, flow statistical data collection is initiated for all new flows until the available memory is reduced by one half. At that point, the sampling probability used for new flows is reduced by 50%, making it less likely that flow statistical data collection will be initiated for new flows. This process continues with a 50% reduction being made in sampling probability for each 50% reduction in available memory. 
     Once the sampling probability has been determined for a packet that belongs to a new flow, then a determination is made, based upon the sampling probability, whether statistical data collection should be initiated for the new flow. For example, a random number generator may be used to determine whether the packet should be sampled and the collection of statistical data should be initiated for the new flow, given the sampling probability determined for the packet. 
     The result of using adaptive random sampling as described herein is that as the available memory decreases, the number of new flows for which collection of statistical data is initiated decreases. This behavior may be characterized by the memory consumption asymptotically approaching, but never reaching, the total available memory, as depicted in  FIG. 3 . The greater the number of packets in a new flow, the better the chances that statistical data will be collected for the new flow. Eventually however, as the memory consumption approaches the total available memory, even new flows with large numbers of packets will not be selected for sampling, because the sampling probabilities will be so low. Based upon statistics and probability theory, where each packet is independently sampled with probability p, the probability (Q) that the collection of flow statistical data will be initiated for a flow of N packets is defined by: Q{k&gt;=1}=1−(1−p) N , where k is the total number of sampled packets in the flow. 
     At some point, flow statistical data is exported from the memory, which increases the amount of available memory. Assuming all of the flow statistical data is exported, then the base sampling probability is assigned to the next new flow, since all of the memory will be available.  FIG. 5  is a graph  500  of memory usage, i.e., the amount of memory consumed, over time using the adaptive random sampling approach described herein, with export of all flow statistical data at equal time intervals T 0 -T 3 . The x-axis  502  represents time and the y-axis  504  represents the memory usage. Line  506  indicates how, using the adaptive random sampling approach described herein, the memory usage approaches a threshold level and then begins asymptotically heading towards the total available memory until an export occurs at the end of each time window. The use of the adaptive random sampling approach provides predictable behavior with well-defined windows that include complete data, which allows proper statistical analysis of flow statistical data. Although  FIG. 5  depicts an example where all flow statistical data is exported at once, the approach is not limited to this context and is applicable to any export scenario. This includes export scenarios where less than all of the flow statistical data is exported during an export. For example, the approach is applicable to implementations that selectively export flow statistical data on a per flow basis. In this situation, the sampling probability assigned to the next new flow may, or may not be, the base sampling probability, depending upon the amount of memory available after the export and the function used to determine the sampling probability. The invention is not limited to the step function depicted in  FIG. 4  and described herein to determine sampling probabilities. A wide variety of functions with varying characteristics and curves may be used depending upon the implementation. Furthermore, although  FIG. 5  depicts the export of flow statistical data occurring at equal time intervals, the approach is not limited to this context and is applicable to exports occurring at any time for any reason. 
     IV. Operational Example 
       FIG. 6  is a flow diagram  600  that depicts an approach for managing the consumption of resources using adaptive sampling according to an embodiment of the invention. In step  602 , router  102  receives a packet. In step  604 , flow monitor  108  examines the packet and determines whether the packet belongs to an existing flow or a new flow. Flow monitor  108  makes this determination by comparing attributes of the packet to attributes of existing flows for which flow statistical data is currently being collected. For example, flow monitor  108  may compare the source and destination address of the packet to source and destination addresses for existing flows in table  200 . If the source and destination address of the packet match the source and destination address of a flow, then the packet belongs to that flow. Other attributes may also be used to make the determination. 
     In step  606 , if the packet belongs to an existing flow, then in step  608 , the statistical data for the existing flow is updated to reflect the packet. For example, flow monitor  108  updates the flow attributes in table  200  for the existing flow to reflect the packet. This may include, for example, updating the end time, number of packets and the number of bytes to reflect the packet. Updating flow statistical data incurs no storage penalty since additional packets only affect the flow attribute values for a flow entry in table  200 . 
     If, in step  606 , the packet does not belong to an existing flow and instead belongs to a new flow, then in step  610 , a sampling probability is determined for the packet based upon the current consumption of resources. In the present example, a sampling probability is determined for the packet based upon the current consumption of memory  112 , as indicated by resource monitor  110 . 
     In step  612 , a determination is made whether, based upon the determined sampling probability, the packet is to be sampled, i.e., that collection of statistical data for the new flow is to be initiated. Suppose that in step  610 , resource monitor  110  indicated that the amount of available memory  112  was between ¼ and ½ and the sampling probability was therefore determined to be ½ (for a base sampling probability of P=1). A random number generator may be used to generate a number between zero and ten. If the result is between zero and five, then the new flow is sampled. If the result is between five and ten, then the new flow is not sampled. Other ranges may be used, depending upon a particular implementation. Furthermore, other techniques may be used to determine whether a packet should be sampled, given a sampling probability for the packet. 
     In step  614 , if the packet belonging to a new flow is to be sampled, then statistical data is generated for the new flow. For example, a new entry may be added to table  200  for the new flow and the corresponding flow attributes determined based upon the packet. From then on, all subsequent packets that belong to the flow will be sampled. According to one embodiment of the invention, the attributes include the sampling probability that will also be exported along with the statistical data for each flow. The per flow sampling probability allows external applications to normalize the flow statistical data in order to compare across flows that have different sampling probabilities attached. 
     If, in step  612 , a determination is made that the packet is not to be sampled, then flow statistical data is not generated for the flow. When the next packet that belongs to the same flow is received, a new sampling probability is determined for this packet, based upon the then current consumption of resources. This sampling probability may be the same or different than the sampling probability for the first packet that was not sampled. 
     V. Implementation Mechanisms 
     As previously mentioned herein, the approach is applicable to any type of resources, such as memory resources and processing resources. The approach may also be based upon the amount of time left in a current fixed export window until the next export occurs. In this situation, the sampling probability for a new flow is determined based upon an amount of time remaining (percentage or absolute amount) until the next scheduled export of flow statistical data. 
     The approach described herein may be implemented in a variety of mechanisms/processes and contexts and the invention is not limited to any particular mechanism/process or context. The approach may be implemented in any environment where sampling is performed. For example, the approach may be implemented in flow monitor  108 , or some other process executing on router  102 . Furthermore, any portion of the approach may be implemented in hardware, computer software or any combination of hardware and computer software. 
     When implemented in a software process, such as flow monitor  108 , the process may execute on any type of computing architecture or platform.  FIG. 7  is a block diagram that illustrates an example computer system  700  upon which an embodiment of the invention may be implemented. Computer system  700  includes a bus  702  or other communication mechanism for communicating information, and a processor  704  coupled with bus  702  for processing information. Computer system  700  also includes a main memory  706 , such as a random access memory (RAM) or other dynamic storage device, coupled to bus  702  for storing information and instructions to be executed by processor  704 . Main memory  706  also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor  704 . Computer system  700  further includes a read only memory (ROM)  708  or other static storage device coupled to bus  702  for storing static information and instructions for processor  704 . A storage device  710 , such as a magnetic disk or optical disk, is provided and coupled to bus  702  for storing information and instructions. 
     Computer system  700  may be coupled via bus  702  to a display  712 , such as a cathode ray tube (CRT), for displaying information to a computer user. An input device  714 , including alphanumeric and other keys, is coupled to bus  702  for communicating information and command selections to processor  704 . Another type of user input device is cursor control  716 , such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor  704  and for controlling cursor movement on display  712 . This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. 
     The invention is related to the use of computer system  700  for implementing the techniques described herein. According to one embodiment of the invention, those techniques are performed by computer system  700  in response to processor  704  executing one or more sequences of one or more instructions contained in main memory  706 . Such instructions may be read into main memory  706  from another machine-readable medium, such as storage device  710 . Execution of the sequences of instructions contained in main memory  706  causes processor  704  to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware circuitry and software. 
     The term “machine-readable medium” as used herein refers to any medium that participates in providing data that causes a machine to operation in a specific fashion. In an embodiment implemented using computer system  700 , various machine-readable media are involved, for example, in providing instructions to processor  704  for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device  710 . Volatile media includes dynamic memory, such as main memory  706 . Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus  702 . Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications. 
     Common forms of machine-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punchcards, papertape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read. 
     Various forms of machine-readable media may be involved in carrying one or more sequences of one or more instructions to processor  704  for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system  700  can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus  702 . Bus  702  carries the data to main memory  706 , from which processor  704  retrieves and executes the instructions. The instructions received by main memory  706  may optionally be stored on storage device  710  either before or after execution by processor  704 . 
     Computer system  700  also includes a communication interface  718  coupled to bus  702 . Communication interface  718  provides a two-way data communication coupling to a network link  720  that is connected to a local network  722 . For example, communication interface  718  may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface  718  may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface  718  sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information. 
     Network link  720  typically provides data communication through one or more networks to other data devices. For example, network link  720  may provide a connection through local network  722  to a host computer  724  or to data equipment operated by an Internet Service Provider (ISP)  726 . ISP  726  in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet”  728 . Local network  722  and Internet  728  both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link  720  and through communication interface  718 , which carry the digital data to and from computer system  700 , are exemplary forms of carrier waves transporting the information. 
     Computer system  700  can send messages and receive data, including program code, through the network(s), network link  720  and communication interface  718 . In the Internet example, a server  730  might transmit a requested code for an application program through Internet  728 , ISP  726 , local network  722  and communication interface  718 . The received code may be executed by processor  704  as it is received, and/or stored in storage device  710 , or other non-volatile storage for later execution. In this manner, computer system  700  may obtain application code in the form of a carrier wave. 
     In the foregoing specification, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. Thus, the sole and exclusive indicator of what is, and is intended by the applicants to be, the invention is the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Hence, no limitation, element, property, feature, advantage or attribute that is not expressly recited in a claim should limit the scope of such claim in any way. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.