Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
Abstract
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Abstract
1. Introduction
- We model the problem of latency-bounded optimal server placement in multilayer overlay networks and formulate it as an integer linear program problem with the objective of minimizing the deployment costs;
- We develop two algorithms based on the heuristic ideas of inter-layer information transfer (IIT) and server reuse. The IIT-DOWN algorithm passes the server placement information from the high-level layer to the low-level layer. It reuses servers chosen in the high-level layer to provide low-level services as well. The IIT-UP algorithm passes the server placement information as well as detailed latency information from low to high. The network scale shrinks during this procedure, and the execution time reduces greatly;
- We conduct experiments on different scales of simulation networks and a real-world dataset to measure the performance of our algorithms. We compare our algorithms with several approaches to solve the server placement problem, and the experimental results show that our algorithms can find more cost-efficient solutions with a shorter execution time.
2. Related Work
2.1. Name-Resolution System
2.2. Deterministic Latency Name Resolution
2.3. Name-Resolution Server Placement in Information-Centric Networking (ICN)
3. System Model and Problem Statement
4. Proposed Algorithms
4.1. Inter-Layer Information Transfer (IIT)-DOWN Algorithm
Algorithm 1: IIT-DOWN |
Input:, Output:, Hs Parameters: 1: Initialization: , 2: for from to 1 do 3: for each in do 4: generate where , , 5: for in do 6: .color = white 7: if in then .color = black 8: for each , in that do 9: if then 10: add to and add to 11: for in do 12: for in do 13: if in and .color = white then .color = grey 14: remove from 15: = Choose_HM () 16: for each in do 17: if in then .color = black 18: if not .color = black then 19: find with the minimum in and add to 20: add to 21: add to and add to Hs 22: return , Hs |
4.2. IIT-UP Algorithm
Algorithm 2: IIT-UP |
Input:, Output:, Hs Parameters: 1: Initialization: 2: for each in do 3: 4: for from 1 to do 5: generate where , , 6: for each in do 7: .color = white 8: for each , in that do 9: if then 10: add to and add to 11: = Choose_HM () 12: for each in do 13: if in then .color = black 14: if not .color = black then 15: find with the minimum in and add to 16: add to 17: add to and add to Hs 18: return , Hs |
4.3. Computation of Hierarchical Elastic Areas Manager (HM) in a Single Layer
Algorithm 3: Choose_HM |
Input: Output:HMs 1: Initialization: Vars = 2: for each in do 3: add a variable to Vars, 4: for each in do 5: if .color = white then 6: add constraint 7: minimize as optimize objective 8: do optimize 9: for each in Vars do 10: if then add to HMs 11: return HMs |
5. Evaluation and Discussion
5.1. Simulation Network
- LHP: this algorithm was described in [23], and it is a heuristic graph-partitioning algorithm. It divides a physical network into one or more connected subgraphs with the latency level constrained from high to low. It chooses name-resolution nodes with the maximum degree and finds nodes they cover. Partitioning for each layer is achieved one by one;
- MDSM: this is the algorithm that was used to analyze the upper bound of MDS in multilayer networks in [51]. It is an algorithm that removes the neighboring nodes of higher-level dominators before solving the current level layer’s MDS. The original MDSM does not satisfy the latency constraints in multilayer overlay networks. We extended the MDSM appropriately to adapt to the problem;
- Random allocate (RA): in this algorithm, the HM is chosen randomly, and users are allocated to an HEA, depending on which HM is closest to them.
5.1.1. Deployment Costs
5.1.2. HM Count
5.1.3. Execution Time
5.1.4. Average Latency
5.1.5. Cost Parameter
5.2. Coverage in K Placement Algorithms
- Density-based clustering (DBC) [38]: DBC first selects one node which has the largest degree among all the nodes in the network and places one server on this node. Subsequently, all users that have an access delay within this server’s coverage and have not been allocated are allocated to this server. This procedure continues until all K servers are placed in the network, or all users are allocated;
- K-Mediods [45]: this approach is a variant of K-means, which is commonly used to cluster a data set into K groups automatically. In this approach, K initial cluster centers are selected and then iteratively refined. In every iteration, a new cluster center is selected to minimize the within-cluster sum of the access delay. This procedure continues until no cluster center changes, and the last iteration’s cluster centers are placed servers;
- Top-K: this approach places the K servers on k nodes with the largest degree. Users are allocated to their closest servers;
- Random-K: this approach randomly selects K nodes and places servers on them. Users are allocated to their closest servers.
5.3. Real-World Dataset
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Notation | Description |
---|---|
the set of nodes that have the potential to place servers | |
the total count of nodes in | |
the set of links directly connect between nodes | |
the set of one-way transmission latencies of links | |
matrix, the shortest path latencies between every pair of nodes in | |
the link between node and node v | |
the one-way transmission latencies of link | |
the shortest path latency between node and node | |
the set of upper bounds of each level layer’s constraint latencies | |
the count of layer levels | |
the index of a level | |
the count of HEAs in level | |
the index of HEA | |
the m-th cluster in level | |
the cluster head of the m-th HEA in level | |
the cluster member set of the m-th HEA in level | |
binary variable, equal to 1 if node is chosen as an HM in level | |
binary variable, equal to 0 if node is chosen as HM in the level higher than | |
the costs of deploying a server at level | |
the global set of HM | |
the attribute of a HM to record the maximum latency in its HEA |
Parameter | Value |
---|---|
Number of nodes | 100~2000 |
Average degree | 2 |
3 | |
(ms) | 10, 25, 50 |
Latency scope (ms) | (0, 10] |
Algorithm | Execution Time (s) | Level | Cost | ||
---|---|---|---|---|---|
3 (0.2 ms) | 2 (0.05 ms) | 1 (0.01 ms) | |||
LHP | 79.49 | 5 | 34 | 302 | 308 |
Random Allocate (RA) | 101.74 | 6 | 42 | 328 | 342 |
Minimum Dominator Set in Multilayer (MDSM) | 27.31 | 4 | 27 | 286 | 286 |
Inter-layer Information Transfer Down (IIT-DOWN) | 28.98 | 4 | 27 | 283 | 283 |
Inter-layer Information Transfer Up (IIT-UP) | 13.57 | 4 | 37 | 267 | 267 |
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Li, J.; Sheng, Y.; Deng, H. Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking. Appl. Sci. 2020, 10, 3588. https://doi.org/10.3390/app10103588
Li J, Sheng Y, Deng H. Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking. Applied Sciences. 2020; 10(10):3588. https://doi.org/10.3390/app10103588
Chicago/Turabian StyleLi, Jiaqi, Yiqiang Sheng, and Haojiang Deng. 2020. "Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking" Applied Sciences 10, no. 10: 3588. https://doi.org/10.3390/app10103588