A multi-agent algorithm to improve content management in CDN networks

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1 A multi-agent algorithm to improve content management in CDN networks Agostino Forestiero, Carlo Mastroianni, ICAR-CNR Institute for High Performance Computing and Networks Cosenza, Italy IDCS 2014, September 22-24, 2014

2 Application Domain and Objectives P2P content delivery networks A content delivery network (CDN) is a large distributed system of servers deployed in multiple data centers across the Internet. The goal of a CDN is to serve content to end-users with high availability and high performance. While most early CDNs served content using dedicated servers owned and operated by the CDN, there is a recent trend [e.g., Akamai] to use a hybrid model that uses P2P technology. Hybrid architecture of CDN and P2P is a promising network technology enabling effective realtime streaming services. It complements the advantages of quality control and reliability in CDN and the scalability in the P2P system. When the network size increases, they show limits and weaknesses Decentralized algorithms and protocols can be usefully employed to improve their efficiency We propose a self-organizing, decentralized and adaptive approach to improve content management in P2P CDNs.

3 Application Domain and Objectives A multi-agent algorithm biologically inspired for contents management Contents are described through metadata documents/descriptors Metadata descriptors are indexed through binary keys which can represent the presence or the absence of some topics, e.g. in the case that resources are text documents, or be the result of the application of a locality preserving hash function, that maps similar contents into similar keys Ant-like mobile agents travel the network through P2P interconnections Agents replicate/pick/drop metadata descriptors in order to disseminate useful information They also spatially sort metadata descriptors by placing similar descriptors in neighbour hosts

4 Agent operations Agents use probability functions to replicate and move metadata descriptors These functions are based on the definition of a similarity measure among descriptors similarity can be calculated on metadata descriptors because similar descriptors correspond to similar contents agents tend to pick a descriptor from a host when this descriptor is considered different from the others located in the same region agents tend to drop a descriptors in a region that maintains similar descriptors

5 Similarity function It is calculated by an agent each time it tries to pick or drop a metadata descriptor m This function measures the similarity of a metadata binary string m with all the other metadata located in the local region R 1. N m is the overall number of descriptor in R Ham(m, m) is the Hamming distance 2 between the metadata descriptor m under examination and the metadata descriptor m sim(m, R) assumes values comprised between 0 and 1 1) The local region R for each host s includes s and all the hosts reachable from s in a number of hops h. 2) The Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different.

6 P 1 probability function When an unloaded agent gets to a new host, it evaluates the P1 function for each descriptor of this host The agent extracts a random number between 0 and 1. If this number is lower than P 1, the pick operation is performed The probability of picking a metadata document from a server must be inversely proportional to the similarity function sim. The parameter K1 can be tuned to modulate the degree of similarity In this analysis, K1 is set to 0.1

7 P 2 probability function When a loaded agent gets to a new host, it evaluates the P 2 function for each carried metadata descriptor The agent extracts a random number between 0 and 1. If this number is lower than P 2, the drop operation is actually performed P 2 is directly proportional to the average similarity sim The parameter K 2 is set to 0.5 K 2 is higher than K1. This limits the drop probability and allows agents to carry the descriptors for a sufficient number of hops, in order to deposit them into appropriate hosts

8 Operation mode of agents An agent can operate in 2 modes: copy and move Under the copy mode, the agent replicates a metadata descriptor before picking it: one copy is left on the host, the other is carried by the agent Under the move mode, the agent just picks the metadata descriptor, without generating any replica Agents in copy are able to replicate and disseminate the information Agents in move are specialized in the relocation of information The copy mode cannot be maintained for a long time, since eventually every host would store a very large number of metadata of all types, thus weakening the efficacy of spatial reorganization. Each agent autonomously switches from copy to move

9 Mode switch of agents Each agents switches from the copy to the move mode according to a self-organization mechanism inspired by ants and other insects The agent maintains a pheromone level (real value) which increases as its activeness (in terms of pick and drop operations) decreases As the pheromone level exceeds a threshold Th, the agent switches to move Indeed, low activeness means that descriptors have already been reorganized Therefore the generation of more replicas would be damaging The pheromone level at the end of the i-th time interval is: Ev is the evaporation rate and is set to 0.9 The threshold Th is set to 9.0

10 High-level description of the algorithm performed by mobile agents Cyclically, the agents perform a given number of hops among servers and, when they get to a server, they decide which probability function they must use, based on their state. If the agent does not carry metadata it computes P1, otherwise it computes P2. As a network region accumulates metadata descriptors having similar keys, it becomes more and more likely that: outlier metadata descriptors will be picked by agents other similar metadata descriptors will be dropped by other agents in this region

11 Uniformity function The effectiveness of the algorithm has been evaluated by defining the spatial uniformity function, i.e. the average homogeneity of metadata documents stored in neighbor hosts. The overall uniformity function U s for each host we average the Hamming distance between all the couples of descriptors within the visibility region then, we average the result for all the hosts The objective is to increase the Uniformity function as much as possible this would mean that similar metadata descriptors have been aggregated in neighbor hosts, and therefore an effective sorting of metadata descriptors has been achieved

12 Simulation scenario An event-based simulator, written in Java, was implemented to evaluate the performance of the algorithm The P2P scenario is characterized as follows: number of bits of metadata descriptors, dim= 3,4,5,6 number of hosts Ns = 500, 1000, 2000, 4000, 8000 but results are independent from network size and number of bits, meaning that the algorithm is scalable average connection degree = 4 (use of power law networks) probability of agent generation Pgen = 0.5 average number of agents Na = Np * Pgen average number of resources per peer = 15 (Gamma distribution) average interval between two agent movements Tmov = 60 s

13 Graphical description of the sorting process 2,500 hosts are arranged in a grid topology and each metadata is associated to a RGB color with 3 bit descriptors Each host is visualized by means of the RGB color of the metadata with the highest number of elements placed in it e.g., a red color corresponds to a large fraction of (1,0,0) descriptors in that host T = T 10,000 20,000 40,000 5,000 = 0 Time As the process goes on, metadata descriptors are reorganized and sorted, which is proved by the presence of clearly distinguishable (and gradually changing) color spots

14 Uniformity function Uniformity of the whole network when the number of bits of the binary string representing the content ranges from 3 to 6. The logical reorganization is obtained independently of the number of bits.

15 Uniformity function Uniformity, vs. time, for different values of the number of servers. The size of the network has no detectable effect on the overall uniformity index

16 Metadata documents handled by a server Mean number of metadata documents handled by a server when the length of binary strings ranges from 3 to 6 bits. The number of metadata documents maintained by a server increases from an initial value of about 15 to much higher values; The trend of this value undergoes a transient phase, then it becomes stabilized, even if with some fluctuations.

17 Discovery of metadata descriptors The sorting of metadata descriptors can be exploited by an informed resource/content discovery protocol 1. users issue queries for resources/contents having specified metadata descriptors 2. a query is forwarded towards the neighbor host whose metadata descriptors are the closest to the target metadata descriptor the next host in the path is selected through the similarity function 3. the search stops when no better neighbor can be selected, and a queryhit message will return to the requesting host The logical reorganization of the metadata documents improves the rapidity and effectiveness of discovery operations, and enables the execution of range queries Range queries are queries in which some bits of the target binary string are wildcard bits, while other bits are specified (overlapping bits)

18 Mean number of results Mean number of results collected by a range query when the length of the binary string representing the content is set to 4 and the number of overlapping bits ranges from 1 to 4. The number of results decreases with the number of overlapping bits Range queries provide an efficient way to discover (in just one shot) much many results than a single query.

19 Conclusions A nature-inspired algorithm to build an P2P information system for Content Delivery Networks, was presented. Ant-inspired mobile agents use probability functions to replicate and move metadata descriptors so as to cluster similar metadata in neighbor hosts. The logical reorganization of the metadata documents improves the rapidity and effectiveness of discovery operations, and enables the execution of range queries, i.e., requests of content that matches some specified features. Performance analysis, achieved through event-based simulation, confirms the effectiveness of the approach and the increased efficiency of discovery operations, specifically of range queries. 2014

20 Thank you for your attention!!!!

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