Survey of Network Coding Based P2P File Sharing in Large Scale Networks
2. BitTorrent-Like Systems and Network Coding Background
2.1. How BitTorrent-Like Systems Work
2.2. Network Coding Overview
2.3. The Butterfly Example
2.4. Detailed Network Coding Tutorial for P2P File Sharing Systems
- Addition and Subtraction: The sum of two elements is computed according to (2):Addition and subtraction modulo 2 are the same. Moreover, addition modulo 2 is simply equivalent to bitwise XOR.
- Multiplication: two elements of are multiplied using the standard polynomial multiplication rule. However, if the product polynomial has a degree higher than , then it has to be reduced. Irreducible polynomials, which are roughly comparable to prime numbers in such that their only factors are 1 and the polynomial itself, are used for the modulo reduction. Let be an irreducible polynomial over ; then, the multiplication of two elements is done as in (3):To find the product of and in , we begin by doing normal polynomial multiplication: . Since , the irreducible polynomial is needed and thus we calculate .
- Inversion: Every Element (except 0) in has an inverse. The inverse of a nonzero element is defined as in (4):For example, in and , is the inverse of since .
- 1st Step: The seeder decomposes the file into two 6-bit pieces (100011, 110110).
- 2nd Step: For encoding over , we should decompose each piece into two 3-bits blocks: (100, 011), and (110, 110).
- 3rd Step: Interpret each block as element (using (1)); then, we have , and .
- 4th Step: Since we have two plain pieces, we need two coded pieces. Randomly draw coefficients from and multiply them by the blocks. Assuming we first draw , , then the first block of the first encoded piece is (011), and the second block is (000). Now, is completely encoded and ready to be shared. Next, to get the second encoded piece, we randomly draw two additional coefficients, say and generate , and (100). Thus, is completely encoded and ready to be shared.
- 5th Step: The sender shares the encoded pieces along with their coefficients , and . This is algebraically represented as:
- 6th Step: Upon receiving the encoded pieces with their coefficients, the receiver should solve the previous matrices system to recover the original blocks and thus the original pieces. This is achieved by using the formula: . First, find the determinant of the coefficients matrix as shown in (8):Since , the system is solvable:This leads to reconstructing the blocks and thus the pieces: and .
- 7th Step: Simply interpret the pieces’ blocks as binary presentation and link the pieces together to reconstruct the original file.
2.5. Network Coding Challenges
3. Related Works
4. Network Coding Based P2P File Sharing Systems
4.1. Full Network Coding
4.2. Sparse Network Coding
4.3. Generation Based Coding
4.4. Combined Network Coding
4.5. Multi-Generation Mixing (MGM) and Overlapping Network Coding
Conflicts of Interest
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|Reference||Approach||Main Findings||Verification Method||Network Topology||PerformanceMetrics|
|Gkantsidis et al. ||Utilizing RNLC on P2P file sharing networks.||- RLNC alleviates the pieces selection problem and churns, and accelerates the downloading process.||Numerical Simulation||Single overlay, and Multi-clusters overlay.||- Download time.|
|Acedanski et al. ||Providing extensively theoretical analysis for P2P RNLC-based method.||- RLNC overhead is about % of the file size.|
- RLNC is highly applicable for uncoordinated P2P networks.
|Numerical Simulation||Star.||- Probability of download completion.|
- Probability of contents availability.
|Deb et al. ||proposing RLNC-based approach for P2P gossip protocols.||- Computational overhead of RNLC to reconstruct the file is rather reasonable for .||Numerical Simulation.||Complete Mesh.||- Download time.|
|Wang et al. ||Proposing DRLNC to mitigate unlucky combination problem.||- Unlucky combination problem could be eliminated even with .||Numerical simulation.||Mesh.||- Rounds to complete download.|
|Yeung ||Analysis Avalanche using graph theory.||- Avalanche can achieve the theoretical lower bound of the file downloading time.||No experimental work.||Mesh.||—–|
|Chiu et al. ||Avalanche is studied by modeling a simple star topology network.||- No advantage of network coding over traditional routing.||No experimental work.||Star.||—–|
|Wang et al. ||Justifying the feasibility of RLNC on P2P systems by realistic application layer implementation.||- RNLC performs worse than any conventional store and forward P2P file sharing system.||High performance C++ implementation.||Mesh||- Average downloadtime.|
- Encoding/ decoding complexity.
|Reference||Approach||Main Findings||Verification Method||Network Topology||PerformanceMetrics|
|M. Guanjun et al. ||Proposing sparse network coding based on stochastic formulas.||- Sparse coding encodes/decodes faster than full coding andslightly downloads faster thanbaseline BitTorrent.|
- Encoding interval anddependency test canminimize the drop rateof dependent coded pieces.
|Implementation||Chord overlay||- Encoding/decoding rate.|
- Download time.
|C. Ortolf et al. ||Proposing sparse coding such that each encoded piece is a combination of only tworandomized original pieces.||- Paircoding relatively decodes pieces as good as BitTorrent, and for some scenarios, it achieves the piece diversity of full coding.||Numerical Simulation||Mesh||- Decoding rate.|
- Content availability.
|C. Ortolf et al. ||Proposing sparse coding such that each encoded piece is a combination of two adjacent original pieces.||- Fixing the choice of the two original pieces yields to faster decoding. yet affects the piece diversity.||No experimental work||—–||—–|
|Q. Cai et al. ||Analyze Avalanche and Paircoding, and propose a Fixed-Paircoding with considering the rarest first scheduling policy.||- Achieves both fast decoding opposed to Avalanche and wide piece diversity opposed to Paircoding.|
- Increases throughput with slightly control overhead.
|Numerical Simulation||Mesh||- Content availability.|
- Control overhead.
- Download time.
|C. Ortolf et al. ||Proposing sparse coding by modeling encoded pieces as full binary tree||- allowing encoding only in the seeder, yields to worse pieces availability. On the other hand, dynamic encoding yields to much complexity.||No experimental work||—–||—–|
|Reference||Approach||Main Findings||Verification Method||Network Topology||Performance Metrics|
|Xu et al. ||Proposing pull-based generation coding system and mixing rarest-first selection policy with generation coding to alleviate the overhead of control messages.||- Swifter can reduce the average download time by 40% compared to push-based generation system with random selection policy.||Implementation over LAN with 30 nodes.||PartiallyMeshed||- Download time.|
|Xu et al. ||Proposing push-based generation coding system promising to reduce the requests overhead.||- I-swifter can reduce the average download time by 4% compared to Swifter.||Implementation over LAN with 30 nodes.||PartiallyMeshed||- Download time|
|Hundeboll et al. ||Implementing generation coding system to study the parameters of generation coding: generation size, GF size, and piece size.||- BRONCO far outperforms HTTP while it performs almost as good as BitTorrent.|
- BRONCO consumes almost quarter the CPU utility Avalanche consumes but with extra 9% redundant pieces.
|C++ Implementation||PartiallyMeshed||- Download time|
|Niu et al. [69,70]||Modeling generation coding system by Markov process and differential equations and study the optimal generation size.||- The optimal generation size to enjoy network coding is 20–30 pieces.||Numerical simulation||Mesh||- Decoding rate.|
- Download time.
|Zhang et al. [71,72]||Proposing a game theory framework to study generation network coding considering a system with many free-riders.||- network coding can enhance the market’s flexibility for urgent peers, but with the high encodingdecoding cost.|
- network coding can improve the peers’ incentive. Only mathematical and analytical model.
|Only mathematical and analytical model||—–||- Robustness to churn.|
|Leu et al. ||Proposing a framework based on simulations to deeply analyze and understand generation network coding.||- Network coding outperforms trivial approaches when (1) DRLNC is used, (2) appropriate coding size is selected and, (3) Gauss-Jordan elimination is applied for early decoding.||C++ P2P simulator ||GIA  overlay||- Encoding/ decoding rates.|
- Download time.
- Network overhead.
|Yang et al. ||Proposing deterministic network coding and utilizing a special network topology “combination network”.||- The overall download time of PPFEED is shorter than Narada and Avalanche by 15-20% and 8-10% respectively.||Simulation||Combination network overlay||- Throughput.|
- Reliability (to churn).
- Link stress (redundancy).
- Download time.
|Braun et al. ||Proposing generation coding P2P file sharing system with backward compatibility with standard BitTorrent.||- In some scenarios, NCME can share a file to the network 20% faster than BitTorrent.|
- The suggested generation size for good level of performance is 43.
|Java Implementation||Partially Meshed||- Download time.|
- Generation size.
|Reference||Approach||Main Findings||Verification Method||Network Topology||Performance Metrics|
|Zeng et al. ||Introducing and adjusting new parameters (generation size and encoding size).||- If parameters are well tuned, network coding outperform other traditional P2P schemes. Otherwise, network coding performs worse.||Simulation on NS-2 platform||Partially Meshed||- Download time.|
- Robustness to churn.
|Yong et al. ||Studying the effect of the generation and encoding sizes on download time and churn.||- The download time is shortened compared with Avalance and BitTorrent by 10% and 20%–30%.|
- Churn resist can be improved by 12.5%.
|Simulation based on CoolStreaming overlay network.||Partially Meshed||- Download time.|
- Effect of churn.
|Kaqian et al. ||Proposing combined coding with adoption of local rarest first policy for generation scheduling.||- Dasher can download faster than Chunker and BitTorrent as well as decode faster than Sparser.||Implementation. Tested both on Planet-Lab  and LAN testbeds.||Mesh||- Download time.|
- Decoding speed.
|Su et al. ||Proposing (1) adaptive encoding window size and upper triangle matrix to speed-up encoding/decoding and (2) postponement and loop self-checking schemes to minimize linear dependency.||- PCLNC is shortened the download time by 3.17% and 21.0% compared to sparse coding and BitTorrent, respectively.|
- PCLNC peer can start sharing a piece faster than BitTorrent by 36.8%, and almost as fast as sparse coding.
|Simulation based on peerSim ||Mesh||- Download time.|
- Start-up time.
- coding degree.
|Reference||Approach||Main Findings||Verification Method||Network Topology||Performance Metrics|
|Silva et al. ||Proposing first P2P overlapping coding scheme by introducing grid and diagonal structures.||- The proposed scheme can mainly reduce network overhead by up to 70%.||Simulation||Mesh||- Decoding complexity overhead trade-off.|
|Heidarzadeh et al. ||Proposing head-to-toeoverlapping scheme over line network topology||- Overlapping can reduce network overhead for line networks.|
- Overlapping is appropriate for multimedia streaming.
|Numerical Simulation||Line networks overlay||- Decoding probability.|
|Li et al. ||Proposing random overlapping by attaching an annex to the base generation.||- The optimal overlap size to achieve the highest throughput is around the half of the generation size.|
- Random-Annex coding outperforms both head-to-toe and disjoint generations coding.
|Numerical Simulation||Point-to-point||- Probability ofDecoding failure.|
|Tang et al. ||Proposing and modelinggenerations overlapping as an expander graph (EOC).||- The best number of generations to overlapped varies between 3 to the generation size.|
- EOC decoder decodes faster than random annex and head-to-toe.
|Numerical simulation||Point-to-point||- Throughput.|
- Decoding rate.
|Joshi et al. ||Proposing deterministicstructures of overlapping and consider round-robin scheduling||- The upper bound limit of the optimal overlapping is .|
- Deterministic overlapping structures avoid network overhead and index complexity of random structure (EOC).
- Proposed schemes minimize the expected download time compared to random annex.
|Numerical Simulation||Mesh||-Download time.|
|Li et al. ||Proposing overlapping with unequal generations’ sizes based on degree distribution and merge both sequential and random scheduling||- Proposed scheme achieves the minimum overhead whereas independent generations codes achieve the minimum decoding complexity.|
- Among the overlapping schemes, the best overhead-complexity trade-off is achieved by the proposed scheme.
|Numerical Simulation||Point-to-point||- Decoding rate.|
|Robustness to Churn||Decoding Speed||Network Overhead|
|Full Coding||Constant: very slow (infeasible)||↑|
|Sparse Coding||Variant: slow at the beginning to quick at the end.||↑|
|Generation Coding||Constant: based on generation size (moderate usually).|
|Combined Coding||Variant: moderate at the beginning to quick at the end.|
|Overlapping Coding||Variant: moderate at the beginning to very quick at the end.|
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AbuDaqa, A.A.; Mahmoud, A.; Abu-Amara, M.; Sheltami, T. Survey of Network Coding Based P2P File Sharing in Large Scale Networks. Appl. Sci. 2020, 10, 2206. https://doi.org/10.3390/app10072206
AbuDaqa AA, Mahmoud A, Abu-Amara M, Sheltami T. Survey of Network Coding Based P2P File Sharing in Large Scale Networks. Applied Sciences. 2020; 10(7):2206. https://doi.org/10.3390/app10072206Chicago/Turabian Style
AbuDaqa, Anas A., Ashraf Mahmoud, Marwan Abu-Amara, and Tarek Sheltami. 2020. "Survey of Network Coding Based P2P File Sharing in Large Scale Networks" Applied Sciences 10, no. 7: 2206. https://doi.org/10.3390/app10072206