Network Layer Implemented Anycast Load Balancing

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1 Network Layer Implemented Anycast Load Balancing Ricardo Lopes Pereira IST, Inesc-ID Teresa Vazão IST, Inesc-ID Abstract This paper presents a server selection method where the server selection is performed by the network, at the first router from the client. This method, usable in networks under single administrative control, combines network metrics (such as traffic or latency) with common server load balancing metrics (such as load, CPU utilisation or response time), allowing network conditions to be taken into account when selecting the server which will handle the request. The performance of this technique was compared, by means of simulations, to that of widely used techniques such as random, round robin and least loaded server selection. 1 Introduction Despite the advent of the Personal Computers (PC) and distributed computing, the client/server model still plays a major role today. Many applications, such as Database servers and Web Services, remain centralised due to their nature. The ubiquity of the Internet or intranets, coupled with the emergence of the web browser as a human interface, also dictates that many applications are developed using the web server centric paradigm. However, this centralised approach has its weaknesses, as it presents a single point of failure. The concentration of all clients on the same server may also result in slow response times due to server overload, large network delays or congestion along the path from client to server. Replicating the application across several servers will eliminate the single point of failure. Server load can be mitigated by placing the application on a set of servers located in the same Local Area Network (LAN). Network latency and congestion may be avoided by placing servers across a Wide Area Network (WAN), closer to the clients. The two methods can be combined by spreading several clusters across a WAN. For each of these approaches, different load balancing methods are used to distribute the clients requests [1]. WAN load balancing techniques may take into account not only server availability but also the network distance and conditions along the paths from client to server and viceversa. This is also what routing protocols such as Open Shortest Path First (OSPF) do, compute shortest paths. Assigning each server the same anycast address [13] allows each router to know the path towards the nearest server. When clients use the anycast address to reach servers, the network layer will automatically forward packets to the nearest server (according to the routing metrics) [8]. This method is successfully being used today in the Internet to distribute User Datagram Protocol (UDP) queries by the Domain Name System (DNS) root servers [6]. This paper presents a transparent load balancing technique usable on networks under common administrative control, which aims at providing unmodified clients with better reply times while maximising the total number of requests handled by the set of servers. This method, which was named Network layer implemented Anycast Load Balancing (NALB), combines network proximity and server load information in order to achieve dynamic load balancing resorting to anycast addresses and link cost variation in already deployed routing protocols. This technique was validated by means of simulation using the HyperText Transfer Protocol (HTTP) protocol, although it may be applied to other applications such as DNS, Simple Mail Transfer Protocol (SMTP) or proxy servers. Section 2 details the anycast concept, its strengths and limitations. Section 3 presents the proposed load balancing scheme, which was compared with others by means of simulations, whose results are provided in section 4. The conclusions are presented in section 5. 2 IP Anycast The anycast concept was introduced into IP networking in [13], which defines it as: the best effort delivery of a datagram to at least one, and preferably only one, of the servers that accept datagrams for the anycast address. IPv6 is the first version of the IP protocol to support anycasting, albeit with limitations: anycast addresses may only be assigned to routers, not hosts, and may only be used as the destination

2 of a packet, never as the source [7]. Given several servers sharing the same anycast IP address and a proper routing infrastructure in place, clients should be directed to the nearest server (in number of hops or other routing metric). 2.1 Current combined anycast load balancing methods Different research proposals appeared combining anycast load balancing methods. Ticket based probing combines anycast with server load information by sending probes to several anycast servers. They reply indicating their status (load). The status information combined with the reply time provides server and network information which is used to select the server [3]. This method, however, is not transparent, requiring clients to be modified. Most importantly, it requires routers to be modified, for probe packets to reach several anycast servers. These demands complicate its deployment in a legacy network. Instead of leaving to the client the decision of which server to use, [1] proposes to restore it to the network, by using an active network. Active networks use routers capable of running user supplied code on each packet. In this approach, routers communicate with the servers, gathering load information, which may eventually be piggybacked on traffic from the servers. This information is then used to forward packets destined to the anycast server address. The router chooses the best server and changes the packet s target address from the anycast address to the best server s unicast address. When packets arrive in the opposite direction, the source address is changed, from the server s unicast address to the anycast one. This solution requires an active network, the development of a communication protocol between servers and routers and uses anycast addresses as the source of packets, which IPv6 disallows. A different approach consists in the utilisation of routing protocols which determine anycast routes using network metrics as well as server state information [9]. This method requires routers to collect and propagate server s state information. Legacy routers would have to be upgraded to support these routing protocols. 2.2 Anycast addressing and routing Anycast addresses can not be aggregated with other addresses as they may be dispersed by several subnetworks and their worldwide deployments would result in the explosion of the number of routing entries carried by each router. Nevertheless, they can be successfully used under single administrative control and several anycast intra-domain routing protocols have been proposed. Single-path routing anycast (SPRA) directs packets to the nearest server; it can be performed on small or confined networks using traditional routing protocols as OSPF [15]. Multi-path routing distributes packets among all servers within the same distance. Multi-path or global scale deployment of single-path routing requires specialised routing protocols [4, 17]. 2.3 Maintaining TCP connections Stateful connection methods, such as TCP, expect endpoints to remain the same throughout the life-time of the connection, but anycast routing does not ensure that packets from the same connection will always reach the same server. Routing tables may change due to topology changes or due to the introduction or removal of anycast servers. Several proposals address the lack of stateful transport support by anycast, either by adding support applications, modifying Transmission Control Protocol (TCP) or using IPv6 packet headers. A support application may be used to inform the client of the unicast address of an anycast server [17]. A UDP service, running at each anycast server on a well known port would receive UDP probe packets from the clients, which would use the anycast address as destination. The server would respond with a UDP message indicating its unicast address, which the client would then use to establish the TCP connection. Should several anycast servers respond, only the first one would be used. This method requires modifications to the clients and may not be used by legacy clients. Clients will also require a mechanism to know when a target address is an anycast address. This method adds a round-trip to the connection establishment process. A client could also determine the server s unicast address by sending a ping (ICMP ECHO request) to the anycast address. The server would then respond (ICMP ECHO reply) using its unicast address [11]. Modifications to the TCP protocol would enable it to overcome anycast limitations [13]. Just like in standard TCP, clients would send a SYN packet to the server s anycast address. However, the SYN-ACK response would arrive not from the anycast address but from the server s unicast address, which would be used from then on. A different proposal is to extend TCP s three-way handshake to a five-way handshake, creating a first phase where the unicast address of the server is determined [17]. These methods do not require the client to know, beforehand, when the address is an anycast one. Large scale TCP implementation substitution, albeit simpler than legacy applications modification, still represents a monumental endeavour. IPv6 packet extension headers may also be used to achieve the same goal [14]. The SYN-ACK response packet is extended with a header containing the anycast address, while it originates in the server s unicast address. The ex-

3 tension header allows the client to perceive the original address as anycast and allows it to match the SYN-ACK to its TCP connection. The ensuing packets will use the unicast address. This method requires a special TCP implementation. The IPv6 source route option has also been used to provide connection capabilities to anycast [5]. The client sends a normal SYN packet to the anycast address. The server responds with a SYN-ACK using its anycast address as source, but providing a route option header with its unicast address. The client may then, if and only if the packet is properly authenticated, reverse the route header, forwarding the next packets to the server s unicast address [7]. The server, upon receiving the packets at its unicast address, forwards them to the anycast address, according to the route header. The closest interface to the server with the anycast address will always belong to the server. Other than the added size, the use of the source route option will not overload the intermediate routers, as the route option header will only be processed at the hosts. Assuming the IPv6 source route option to be honoured at the TCP layer, this method would only require modifications to the servers software as well as an authentication infrastructure in place. However, this method infringes on IPv6 as the server sends packets originated in its anycast address. 2.4 IP Anycast evaluation In a network under single administrative control, current intra-domain routing protocols, such as OSPF, provide single-path anycast routing, by considering that paths to different servers sharing the same anycast address are different paths to the same server. Routing table dimension is not a problem as the number of anycast addresses used will be limited. This enable anycast use with stateless protocols such as DNS. The deployment of one of the methods described in section 2.3 on a single owner intranet is viable, enabling TCP connections. Strict standard compliance may also be overlooked if the anycast methods are confined to the intranet. In these conditions, SPRA load balancing is possible, directing clients to the nearest server, while also minimising network traffic. The possibility of transparently using anycast without requiring client modifications makes it an attractive option. However, [2] shows that scheduling each client to the nearest server (networkwise) may cause unbalanced servers, resulting in higher response times, due to large variations in each network region s traffic, attributable to population distribution, time zones and hour of day. The load unbalance caused by SPRA will prevent servers from being used to the fullest and will increase the response time for clients assigned to overloaded servers. A better result may be achieved by combining the network metrics used by the routing protocols with server status information. 3 Network layer implemented Anycast Load Balancing 3.1 NALB architecture NALB is an intra-domain architecture that provides load balancing by using a routing protocol that combines both network and server status information. NALB can also be combined with network QoS conditions should the intranet run a QoS enabled routing protocol [16]. Anycast addresses are assigned to the servers of a NALB domain and these servers will run the routing protocol, announcing a route to the anycast address. However, when receiving a packet for this address, a server will process it as his. The announced cost of the link to the anycast address will vary, according to the server state. Should the server be overloaded, the cost will be very high. Should it be idle, the cost would drop. When all the servers are idle, or equally loaded, clients will use their nearest server. When a server becomes overloaded, it will announce a higher cost to the anycast address, causing the routing protocol to consider it to be more distant. This will result in a number of clients now having a different nearest server, effectively being transfered from the overloaded server. Server failure is automatically supported as down servers will stop announcing their routes to the anycast address, being removed from the anycast group by the routing protocol. 3.2 Routing metrics Different types of metrics may be used to measure the server status, namely: CPU usage, run queue length, number of clients, transfer rate or a combination. However, should the load metric used be relative to each server s performance (like CPU usage) and not absolute (such as number of requests being served), the NALB method will automatically allow servers with different capacities to be used. The load metrics used, as well as the link cost variation, should be fine-tuned for best results experimentally. Furthermore, the link cost should only be increased when the load crosses a threshold, as assigning a client to a more distant server to avoid a mildly loaded server might prove a bad move. The periodicity of routing announcements will also have to be fine-tuned for best performance. A trade-off must be achieved between the added network traffic and router processing caused by frequent updating and the inaccurate view provided by making updates sparse. However, the added routing traffic may be compensated by the global traffic diminution granted by having clients use the nearest server.

4 3.3 Application examples Figure 1 presents a situation where two anycast servers provide service to four clients. Servers S1 and S2 each have their own unicast address but both share the anycast address A. Routers are connected by links with a unitary cost. In this situation, both servers are under low load, announcing a cost of one towards the anycast address. The dashed lines encompass the clients closest to each server: clients C1 and C2 will use server S1, clients C3 and C4 will use server S2. Figure 2 portrays a scenario where S1 is under heavier load than S2. S1 reacts by increasing the announced route cost towards the anycast address. As a result, C1 will now be closer to S2 and will use this server for future requests. S1 will reduce the number of potential clients to one whereas S2 will now have to handle three clients. Clients are diverted from the server with the highest load to the least loaded one. 4 Simulation results The proposed method was compared with traditional HTTP load balancing methods via simulation, performed using the 2.. version of the SSFNet network simulator [12]. The simulator was modified to implement CPU processing delays, traditional load balancing algorithms, anycast and the proposed method (including server performance measurement). The load balancing alternatives used were: SRPA, source IP address hashing, random, round robin and least loaded. The utilisation of a single server, with capacity equal to the sum of that of all servers, was also tested in order to provide a baseline for comparison. 4.1 Simulation scenario The core network of a large American ISP, with pointsof-presence in 25 cities, was used. It spans four time zones, Figure 1. Two servers with equal load Figure 2. Left server under heavier load making it a good candidate for a dynamic load balancing technique. To each of the 25 core routers, a network island was attached. Network islands are all identical, each having 2 HTTP clients connected at 1Mb/s, 3 at 1Mb/s and 4 at 1Mb/s. Servers, connected to the core routers using 1Mb/s links, were placed on opposite sides of the network. The OSPFv2 routing protocol was used. A stateful connection mechanism for anycast was assumed to be in place. HTTP 1.1 with persistent connections and no pipelining was used. Whenever a client established a connection, the scheduling algorithm was used by it to choose a server. Round robin state is kept globally. When a client chooses server 1, the next client will choose the second server and vice-versa. The least loaded algorithm is perfect, in the sense that it has instant up-to-date knowledge of the servers status. The least loaded server is considered to be the one with the smallest CPU run queue, to which clients have instant access with no network overhead. As the server selection is performed at the client, no dispatching overhead is included. Traditional dispatching mechanisms such as DNS, server redirection or triangulation will add further overhead to traditional load balancing techniques which does not affect SPRA or NALB. In these simulations, this overhead is not accounted for, providing traditional load balancing techniques with an advantage. The routing protocol uses link costs proportional to the link delay, which in turn is proportional to its distance. With NALB, servers generate new route announcements every 15 minutes. CPU utilisation is measured in 5 minute intervals. CPU s utilisation moving average is used as the server s load metric. The route to the anycast address will present a cost of 1 whenever this average is inferior to 5% and a cost from 1 to 25 when the moving average raises above this figure. The longest core links present a cost of 3. Four adjacent time zones were defined: zone has 6 islands, zone 1 has 2, zone 2 has 7 and zone 3 has 1. Simula-

5 tions were run over a 24 hour period, after a 2 hour warm-up period. The client generated traffic was defined as an office work day, with low traffic during the night and lunch time, and high activity during work hours (9 to 13 and 15 to 19). Clients are simulated to perform as real persons. They access the web site in sessions, which occur from time to time. During each session they will visit several pages, taking some time to read each one before moving to the next one. Each web page will be constituted by an HTML object and several binary objects such as images, each with its own size. For each object request the server will require some CPU time. Servers assign 5ms CPU time slices to each request using a round robin scheduler. Replies are only sent to the clients after the request has received the required CPU time. Clients timeout a request when the reply is not fully received within 2 minute. Simulations were performed with 1Kb/s, 1Mb/s and 1Mb/s links connecting the core routers. No traffic other than OSPF s and HTTP s existed. Links with 1Kb/s are insufficient to respond to the demands of the clients, becoming the bottleneck. In this case, the load balancing mechanism has to overcome the network limitations. Overdimensioned 1Mb/s links guarantee traffic to flow swiftly across the network, making servers the bottleneck. Here the load balancing mechanism has to divert clients to the least loaded server. 1Mb/s links provide a test where both server CPU and network capacity are at a premium. 4.2 Results analysis The existence of different timezones, each with a different number of clients, results in unbalanced traffic generation across the network. Fig. 3 shows how the load is distributed across both servers when using SPRA. The thin lines represent CPU usage while the thick lines represent the moving average. At the beginning of the day, clients in the east start peaking and, being closest to server 2, will cause it to have a higher load than server 1. Server 2 remains the most loaded one during the day as it is closer to a larger number of clients than server 1. Towards the end of the day, the roles are inverted. Server 2 s load starts to decrease as the end of the day approaches. On the other hand, server 1 still has a significant number of clients due to the 3 hour difference from the east to the west of the network, and becomes the most loaded server. On the whole, server 2 has to support a significantly larger load. Using NALB the load gap between servers is reduced as can be observed in Fig. 4. The thin lines at the bottom represent the link cost, which is read against the right vertical axis. At around 8h, server 2 crosses the CPU utilisation threshold, prompting the cost of its link to the anycast address to raise. As a result, the routing algorithm will determine that the shortest path to the anycast address for some CPU usage (%) H 8H S1 CPU S2 CPU 11H 14H Time (s) 17H 23H S1 Link cost S2 Link cost Figure 3. CPU usage using SPRA and 1Mb/s of the network islands now passes through server 1. Consequently, server 1 has an increase in traffic when compared to the previous SPRA scenario. Server 2 s traffic also increases more gradually. The same effect is visible during the afternoon period. This enables the set of servers to service a greater number of requests, as shown in table 1. NALB also fares well when compared to the classical load balancing methods. Table 1 shows the number of requests successfully satisfied by each method during the afternoon peak. Similar results were observed when considering the entire duration of the simulation. When the core network bandwidth is limited to only 1Kb/s, it becomes the bottleneck. Under this scenario,, server capacity is not an issue. NALB and SPRA behave exactly the same as CPU utilisation remains below 5%, preventing servers from announcing higher route costs. Anycast assigns each client to the closest server, limiting each request s traffic to part of the network. Client locality reduces global traffic, enabling a better utilisation of the available bandwidth. As a result, anycast is capable of answering a significantly larger number of requests. CPU usage (%) H 8H S1 CPU S2 CPU 11H 14H Time (s) 17H 23H S1 Link cost S2 Link cost Figure 4. CPU usage and link cost using NALB and 1Mb/s Link cost Link cost

6 Table 1. Successful requests from 14 to 17 Link speed Scheduler 1Kb/s 1Mb/s 1Mb/s NALB SPRA IP hash least loaded random round robin single server Reply time is also better, as shown in Fig. 5. For each load balancing method, the graphic shows the Cumulative Distribution Function (CDF) of the reply time. Due to the bandwidth limitations, no method is able to serve more than 2% of the requests in under 5 seconds. NALB and SPRA, whose lines overlap, show a clear advantage over the other protocols. IP hash, random and round robin present similar results as they all distribute clients over the two servers in almost uniform ways. The selection of the least loaded server will allocate most of the traffic to the first server, as server analysis is always performed in the same order, and run queue length will be zero. As a consequence, the network surrounding server 1 will be more loaded, decreasing its response capacity. Therefore, it will not perform as well as the other methods. The worst performance results from using a single server. As all packets must cross the core links surrounding the server, these will become overloaded. Nevertheless, as the server s CPU has double the capacity of all others, the processing time is shorter, resulting in a large number of successful replies under 1 second. When 1Mb/s links are used in the core, CPU also becomes scarce in the face of the large number of requests. In this situation, although SPRA performs the second best, it is outperformed by NALB. The SPRA method s performance is limited by the CPU shortage during peak hours. The NALB method is capable of serving a larger number of requests by diverting clients from the loaded server to the least loaded one. However, this comes at a price. Fig. 6 shows that the reply time using NALB is slightly higher than that of SPRA. The diverted clients get access to a server which provides faster response times but the path has higher delay. Network bandwidth is still a bottleneck, as can be observed by the relative performance of the other methods, which are similar to those attained using 1Kb/s links. 1Mb/s links allow the network not to obstacle performance. Server CPU speed becomes the single bottleneck. In these simulations, each server represents what, in a real deployment, would be a cluster of servers. Therefore, servers are quick to respond to requests, and are only overloaded during the peak hours of the day, as may be observed in Fig. 3 and 4. This results in very homogeneous results for the several methods used. Reply time is also very similar, as shown in Fig. 7. SPRA no longer performs significantly better than traditional methods with regard to the number of successful replies. NALB, however, by diverting further away clients to a least loaded server is capable of providing nearby clients with a fast response time, while only making far away clients move to the least loaded server. Clients further away from the loaded server may not be much further away from the new server, suffering little impact from the move, while releasing some capacity on the overloaded server. This explains the better response time of NALB. The Least loaded method performs comparably to NALB in terms of number of replies, but presents worse reply times, due to client to server paths. Round robin also performs very well as it provides the most uniform client distribution. Using a single server, with double the capacity, does not turn out to be a problem as the network is capable of forwarding all traffic easily..8.7 Cumulative request ratio.1 Cumulative request ratio NALB SPRA IP hash least loaded random round robin single server NALB SPRA.2 IP hash least loaded random.1 round robin single server Reply time (s) Reply time (s) Figure 5. Request reply time using 1Kb/s Figure 6. Request reply time using 1Mb/s

7 Cumulative request ratio NALB.3 SPRA IP hash.2 least loaded random.1 round robin single server Reply time (s) Figure 7. Request reply time using 1Mb/s 5 Conclusion In this paper a load balancing method that combines network proximity and server load information is presented. This is accomplished by combining anycast mechanisms with varying link costs, forcing the routing protocol to forward clients packets to the best server. This method was compared to ideal implementation of traditional load balancing methods and single path routing anycast using simulation. The results suggest that it performs as well as or better than the other methods under different network conditions. It also proved robust, providing consistent results under the different scenarios. Although this method is not yet applicable to the Internet at large, it could be deployed on large corporate or ISP networks. The method is completely distributed, supports servers with different capacities and can overcome server failure. The proposed load balancing method was used in HTTP load balancing simulations, yet nothing prevents it from being applied to other applications. Though simulations suggest this method to be better than traditional ones, further work could be performed to overcome some of its limitations. Anycast methods allow packets from client to server to travel the shortest path. However, in Web applications, the bulk of the traffic is in the opposite direction. A server which is more distant from the client might present a shorter path in the reverse direction. More complex server load metrics could be used, other than CPU usage. Running queue length, connected clients, bandwidth being consumed or a combination of these could be tested. Currently each server/cluster works independently, altering its link cost without regarding the other servers state. Cooperation between the servers could be established to better balance the load. The simulations were performed using a single server at each location. In a real deployment several servers would be used, in a cluster. Only one of them would run the routing protocol, requiring knowledge of the others state to produce an aggregate link cost. Resilience would have to be introduced into the protocol for this server to be replaced by one of the others should it fail. References [1] V. Cardellini, M. Colajanni, and P. S. Yu. Dynamic load balancing on web-server systems. 3(3):28 39, May-June [2] V. Cardellini, M. Colajanni, and P. S. Yu. Geographic load balancing for scalable distributed Web systems. In Proc. 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pages 2 27, San Francisco, CA, 2. [3] H. Chang, W. Jia, and L. Zhang. Distributed server selection with imprecise state for replicated server group. In Proc. 7th International Symposium on Parallel Architectures, Algorithms and Networks, pages 73 78, May 24. [4] S. Doi, S. Ata, H. Kitamura, and M. Murata. IPv6 anycast for simple and effective service-oriented communications. 42(5): , May 24. [5] R. Engel, V. Peris, and D. Saha. Using IP anycast for load distribution and server location. In Proc. 3rd Global Internet Mini-Conf., pages 27 35, Nov [6] T. Hardie. Distributing authoritative name servers via shared unicast addresses. IETF RFC3258, Apr. 22. [7] R. Hinden and S. Deering. Internet Protocol, Version 6 (IPv6) Specification. IETF RFC246, Dec [8] R. M. Hinden. IP next generation overview. Commun. ACM, 39(6):61 71, [9] C.-Y. Lin, J.-H. Lo, and S.-Y. Kuo. Load-balanced anycast routing. In Proc. Tenth International Conference on Parallel and Distributed Systems, pages 71 78, July 24. [1] H. Miura, M. Yamamoto, K. Nishimura, and H. Ikeda. Server load balancing with network support: Active anycast. In H. Yasuda, editor, IWAN, volume 1942 of Lecture Notes in Computer Science, pages Springer, 2. [11] M. Oe and S. Yamaguchi. Implementation and Evaluation of IPv6 Anycast. In Proc. 1th Annual Internet Soc. Conf., 2. [12] A. Ogielski, D. Nicol, J. Cowie, et al. SSFNet network simulator. [13] C. Partridge, T. Mendez, and W. Milliken. Host anycasting service. IETF RFC1546, Nov [14] S. Shah and D. Sanghi. Host Anycast Support in IPv6. In Proc. of 5th Int l Conf. on Advanced Computing (AD- COMP), Dec [15] K. H. Tan, M. Yaacob, T. C. Ling, and K. K. Phang. IPv6 single-path and multi-path anycast routing protocols performance analysis. In ISICT 3: Proceedings of the 1st international symposium on Information and communication technologies, pages Trinity College Dublin, 23. [16] Z. Wang. Internet QoS: Architectures and Mechanisms for Quality of Service. Morgan Kaufmann, Mar. 21. [17] S. Weber and L. Cheng. A survey of anycast in IPv6 networks. 42(1): , Jan. 24.

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