Automata Based Cooperative Caching Scheme to ...

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Dr.S.J.K. Jagadeesh Kumar, Professor and Head, Computer. Science and Engineering, Sri Krishna College of Engineering and. Technology, Coimbatore.
11th International Conference on Science Engineering & Technology

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Automata Based Cooperative Caching Scheme to Improve Data Access in Disruption Tolerant Networks S. Manju, Dr.S.J.K. Jagadeesh Kumar and V.R. Azhaguramyaa Abstract--- Disruption Tolerant Networks (DTN’s) are the opportunistic networks that are characterized by higher and unpredictable node Mobility, irregular network connectivity, long delay and low node density. Data access is thecommon issue inDTN because of its opportunistic contacts. The common technique used for improving the data access is Caching. In this paper we propose a Cooperative Caching scheme based on Learning Automata (LA) that will reduce the data access delay by caching data at multiple nodes. The underlying idea is to select a set of nodes called as Network Central Locations (NCL) or Caching Node (CN)that cache data and coordinate among themselves to provide data access to the requestor. The challenge lies in two facts like determining the caching location among many nodesand in generating the probabilistic response to the requestor in order to avert resource wastage there by providing efficient data access. Keywords--- Disruption Tolerant Networks, Learning Automata, Cooperative Caching, Connected Dominating Set, Network Central Location, Relay Node, and Cache Replacement.

I.

INTRODUCTION

D

ISRUPTION tolerant networks (DTN) [3] are featured by long latency, variable delays measured in days, intermittent connectivity, lack of end to end connectivity and unstable network topology.The mobile nodes in DTN contacts each other opportunistically.Due to intermittent connectivity it is mandatory to use “Store, Carry and Forward” techniques for data transmission.DTN is widely applied in challenging networks like disaster recovery, satellite communications, military adhoc networks, terrestrial mobile networks and

underwater communication where connectivity is of major concern [4].

data

Although there exists more data forwarding schemes like proactive and reactive routing protocols, these cannot be deployed for DTNs [3] because of their frequent disconnections and lack of global network knowledge. Hence data forwarding is an issue.To resolve this issue new routing protocols like Epidemic Routing [7], PROPHET, Direct Delivery routing and geography based routing ideas were introduced that works based on flooding. The epidemic routing being the first routing protocol for DTN architecture allows nodes to exchange information when they come into each other’s transmission range resulting in an opportunistic contact. This may greatly impair the data access performance. One common technique to resolve this issue is Caching. Caching is done at the mobile nodes to store the frequently accessed information.In this paper we deal with a new cooperative caching scheme in which data are cached at multiple nodes based on the query history that reduces delay [1]. The challenge lies in making a decision of where to cache, how to cache and how much to cache. In this paper we propose a new cooperative caching that works based on learning automata [2] and Connected Dominating Set (CDS) which is a group of nodes accessed easily by other nodes in the network. Finally data will be cached at Final Caching Set (FCS), yet another set of nodes selected from CDS which has the highest probability value of being a cache node. The main objectives of this paper include • • •

S. Manju, PG Student (M.E.) - Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore. Dr.S.J.K. Jagadeesh Kumar, Professor and Head, Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore. V.R. Azhaguramyaa, Assistant Professor - Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore

achieving

Selecting caching nodes based on learning automata to provide efficient data access Probabilistically coordinating the caching nodes to respond the query Implementing the cache replacement scheme based on query history

The rest of the paper is organized as follows. In Section II existing caching schemes of DTN’s are briefly explained. Section III explains the concepts regarding the proposed system. Section IV deals with the simulation

ISBN 978-93-85477-73-7

11th International Conference on Science Engineering & Technology

parameters in ONE simulator. Section V concludes the paper. II.

LITERATURE SURVEY

In this section we present the various caching schemes deployed in DTNs. This survey describes the three different ways of selecting caching nodes andsome special parametersto be considered upon selection [6]. A. Cooperative Caching The basic idea followedin this scheme is intentional caching. It is a technique by which the pass by data is cached only at some specific nodes (NCLs) based on the query history[1] [8]. Each NCL is represented by a central node. The significance of the scheme lies in the selection of Network Central Location (NCL), which is based on the Hypoexponential Probability Distribution. This selection is based on the inter contact time between nodes which follows exponential distribution. Hence the time required to transmit data from source to destination is a series of exponential distribution i.e. Hypoexponential Distribution.A metricCi called selection metric (1) will be calculated for each and every node in the network based on number of neighbor nodes. Ci is obtained as degree of centrality. This metric depends on the number of nodes in the network and summation of all inter contact time between nodes that fall between source and destination [1]. 1 𝐶𝑖 = . ∑𝑗∈𝑉 𝑝𝑖𝑗 (𝑇) (1) |𝑉|

After calculatingCi for all nodes, the K nodes having the highest selection metricare selected as Central Nodes or Caching Nodes. As selection of caching nodes is performed at every nodei.e. in a distributed manner, a particular node may choose one node as central node while other node in the network may choose another node as central node. This results in inconsistency [1] [2] where one node’s selection is unaware of other. B. Adaptive Caching using Learning Automata To handle the inconsistency caused by above scheme, a new adaptive learning automata technique [5] is used here to choose caching nodes. In this scheme the Initial Caching Set (ICS) is selected based on the ICS algorithm [2]. ICS consists of nodes that could provide services to all the other nodes in the network. Then each node of ICS is given as input to the Learning Automata (LA) [5]. LA outputs either as 0 (reward) or as 1(penalty).Depending

on

the

output

the

selection

probabilities of nodes are calculated based on the

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Finally the K number of nodes having the highest selection probability is chosen as FCS [2]. C. Social Based Duration Aware Caching Protocol This is one and only form of cooperative caching based on social relation between nodes in which a new parameter called contact duration is considered [6]. Contact Duration is a time interval at which the nodes are in each other’s transmission range. The need of this consideration lies in the following assumption. What if the contact duration between nodes is too small such that the entire data requested cannot be delivered within the short interval?Either the data will not be delivered or data delivery will take more time. Hence the parameter Contact Duration should also be dealt when speaking about Data access in DTN’s. Due to the limited contact duration the nodes may not be able to transmit complete data. In such cases we must be aware of transferring entire data within the time. Hence the idea is to fragment data and to store it at different caching nodes. When requestor passes along the caching nodes it can collect the data packets stored at each node and can rearrange them later.This resembles the common coupon collector problem. In this scheme nodes are organized as communities by k- clique algorithm and each node in the community has a community information table which includes ID of node, centrality metric, caching bound, inventory and timestamp [6]. Depending on the decisions of passive and active caching schemes in community the caching efficiency is improved. III.

PROPOSED SYSTEM - ADAPTIVE CACHING SCHEME

In this section we propose a learning automata based cache node selection to cache data for efficient data forwarding. This also deals with relay node selection and cache replacement techniques. A.

Network Model and Learning Automata Let G (N, E) be the DTN where N is the number of nodes and E is the opportunistic contact between nodes.Let S be a dominating set i.e. the subset of network G such that each node in S has at least one adjacent node in network. From the dominated set [2] Initial Caching Set is chosen based on the number of neighboring nodes stated in Algorithm 1 ICS ( ). Then each node in ICS is given as input to the learning automata which is selfoperating machine that responds according to the environment shown in Fig.1. The LA is described by 5 tuples {IC, FC, Q, F, G} where,

forwarding ratio.

IC–Initial Set of caching nodes

𝑝𝑖 (𝑛 + 1) = 𝑝𝑖 (𝑛) + 𝜆𝑖 (1 − 𝑝𝑖 (𝑛))

FC–Final set of caching nodes

𝑝𝑗 (𝑛 + 1) = �1 − 𝜆𝑗 �𝑝𝑗 (𝑛)For all j ≠ i

Q–Set of selection probability values

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11th International Conference on Science Engineering & Technology

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F–Mapping between the selection probability

10. Repeat steps 3to 10until one of the probabilities is

and ICS

close to 1

G–Function determining the automata’s output.

11. Select highest Kselection probabilities nodes as

Environment is the medium depending on which the automaton functions. It is described as {C, S, P} where C is set of input given to automaton, S is the set of output and P is the selection probability vector of ICS to become FCS.

Fig. 1: Working of Learning Automata based on Environment The selection probability is updated according to Linear Reward-inaction scheme which depends on the parameter S. From ICS the K (Number of Caching nodes) FCS nodes are chosen based on the following algorithm ACLA() [2]. Algorithm 1ACLA (): Selection of FCS based on LA Input: A set of nICS nodes output from ICS () Algorithm [2]. Output: A set of K final caching nodes. 1. For each node in ICS 2. Initialize LA 3. Select a node from ICS = C1, C2,...,Cni.eCi 4. β= Environment(Ci) 5. If β = 0 6. Update selection probabilities for becoming caching node: PCi (n + 1) = PCi(n) + λr(1 − PCi (n)) PCj (n + 1) = (1 − λr)PCj (n)

7. Else

∀j ≠ i

8. No change in selection probabilities 9. End if

caching nodes B.

Caching Scheme Nodes are classified in the network as Normal node and Caching node. After selecting the CN’s the data can be stored and forwarded at caching nodeswhile it is only forwarded at normal nodes. Whenever the data source generates the data it is sent to the all CN’s for storage. When a query arises requesting the data then each and every caching nodes respondthe query wastingnetwork resource. Hence more replies are sent. However the requestor accepts only the first response and ignores other’s. There are two issues to be handled at this point.They are, i) Avoiding multiple response to requestor by generating probabilistic response ii) Providing a cache replacement scheme and relay selection methodology while cache is full. C.

Probabilistic Response When caching node receives the query it doesn’t immediately transmits the data. Instead it decides probabilistically whether to return cached data to requestor or not. The probabilistic function follows the equation (10) of [1].This probabilistic response can reduce the traffic and also can avert the wastage of network resource. D.

Relay Selection This sectiondeals with selection of new nodes when cache node becomes full. Since caching nodes or Central Nodes (CN) have only limited buffer space it might get full at any case. Hence an alternate solution has to be provided to cache data when CN is full. To resolve this issue we propose a new idea to choose some new node called as Relay Nodes (RN) that are exactly one hop neighbor nodes of the CN. With the help of these nodes we may cache data at the nearest neighbor of the CN (together termed as NCL) only whenever CN is full. The overhead of this technique is that the caching node should be aware of RN and the data RN has. Compared to NCL and RN selection of [1], this technique of choosing 1 hop neighbor alone as RN decreases transmission delay because RN selection in [1] may choose farthest nodes also as RN while the proposed scheme chooses only nearest nodes as RN. Cache Replacement When RNs buffer and CNs buffer are fullthen alternate solution of cache replacement techniques like Least Recently Used Scheme (LRU) is used. When two E.

ISBN 978-93-85477-73-7

11th International Conference on Science Engineering & Technology

caching nodes come into contact they exchange their cached data. The nodes should now analyze which query has the maximum request and should update its cache accordingly. When cache becomes full, least used data must be overwritten with recently accessed data in a way that the queries which have the highest frequency of occurrence should be cached in Central node for reducing delay and others to be cached in RN. The popularity or the frequency of query occurrence in the network can be found by query history which give us the frequency count of each query [1]. IV.

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[2]

[3] [4]

[5]

[6]

[7]

SIMULATION

The simulation scenario to be created depends on the DTN’s features. Hence ONE simulator is chosen. Our network is modeled as collection of mobile nodes in an area of 3000x3000 m2.

[8]

Reisha Ali and Rashmi Rout, “An Adaptive caching Technique using Learning Automata in Disruption Tolerant Networks”, IEEE, 2014 Long Xiang Gao, Shui Yu, Tom H. Luan, Wanlei Zhou, “Delay Tolerant Networks”, Springer, 2015 K. Fall, “A Delay-Tolerant Network Architecture for Challenged Internets,” Proc. ACM SIGCOMM Conf. Applications, Technologies, Architectures, and Protocols for Computer Comm., pp. 27-34, 2003. S. K. Narendra andM. A. L. Thathachar, “Learning Automata:A Survey,” IEEE Trans. On Systems, Man andCybernetics, Vol. 4, No. 4, Pp. 323–334, 1974. XuejunZhuo, Quinghua Li, Guohong Cao, Yigi Dai, Boleslaw Szymanski, Tom La Porta, “Social-Based Cooperative Caching in DTNs : A Contact duration Aware Approach”, IEEE, 2011. A.Vahdat, D. Becker, “Epidemic Routing for PartiallyConnected Ad Hoc Networks”, Duke Tech Report CS-2000-06, 2000. W. Gao, G. Cao, A. Iyengar, and M. Srivatsa, “Supporting Cooperative Caching in Disruption Tolerant Networks”, Proc. Int’l Conf. Distributed Computing Systems (ICDCS), 2011.

Table 1: Simulation Parameters Parameters

Values

Number of nodes

50

Number of Caching Nodes (K)

5

Mobility Model

Random Way Point model

Node Speed (Km/hr)

30

Transmission Range (m)

250

Message Size (KB)

10-50

Buffer Size (MB)

100

The two performance importance in DTN are,

metric

• •

having

higher

Packet Delay – Time taken to deliver the data. Data Delivery Ratio – The ratio of number of packets delivered to the number of packets generated. V.

CONCLUSION

In this paper we propose a novel method of selecting CN’s based on LAand RN’sbased on one hop neighboring scheme. Whenever data are cached at NCL it could be accessed easily at less delay. Hence this scheme of caching data at central location of the network (NCL) will reduce delay thereby transmitting data efficiently. REFERENCES [1]

Wei Gao, Guohong Cao, Arun Iyengar and Mudhakar Srivatsa, “Cooperative Caching For Efficient Data Access in Disruption Tolerant Networks”, IEEE Vol. 13, No.3, March 2014.

ISBN 978-93-85477-73-7