Data ubiquity in autonomic wireless mesh networks

Transcription

1 J Ambient Intell Human Comput (2010) 1:3 13 DOI /s ORIGINAL RESEARCH Data ubiquity in autonomic wireless mesh networks Thabo K. R. Nkwe Mieso K. Denko Jason Ernst Received: 10 June 2009 / Accepted: 27 October 2009 / Published online: 4 December 2009 Ó Springer-Verlag 2009 Abstract The ability to compute anywhere anytime is increasingly becoming a necessity. Wireless mesh networks (WMNs) have become a network of choice to provide broadband wireless Internet connectivity where wired infrastructure is uneconomical or impractical to deploy. Their support for inexpensive broadband Internet services has made them even more attractive and increased users demand. WMNs suffer from bottleneck effect at the Internet gateways due to the nature of the traffic pattern which is often towards or away from the Internet. Inspired by the autonomic networking paradigm, we proposed and evaluated a self-optimizing cooperative caching solution for autonomic wireless mesh networks. In cooperative caching, the cache replacement policy is paramount to the performance of the caching scheme used. We also proposed a cacherescue scheme to salvage evicted valid data items. The simulation results show our proposed approach has better performance compared to other cooperative caching solutions. Keywords Autonomic Cross-layering Cooperative caching Cache replacement policy Clustering Wireless mesh networks T. K. R. Nkwe (&) M. K. Denko J. Ernst Department of Computing and Information Science, University of Guelph, Guelph, ON N1G 2W1, Canada tnkwe@uoguelph.ca M. K. Denko mdenko@uoguelph.ca J. Ernst jernst@uoguelph.ca 1 Introduction Wireless mesh networks (WMNs) are often characterized by a semi-static topology with fixed wireless backbone formed by mesh routers (MRs) through which mesh clients (MCs) can access the network and the Internet via the Internet gateways (IGWs) to which most traffic is directed (Akyildiz et al. 2005). WMNs provide redundancy, capable of self-configuration and self-organization thus maintaining reliable network connectivity. These characteristics and their support for inexpensive broadband Internet services have made WMNs attractive, most particularly in regions where wired infrastructure is uneconomical or impractical to deploy (Das et al. 2007). As a result there has been an increase in the use and deployment of WMNs, evident in several cities and developing economies (Wireless Africa et al. 2008; Seattle wireless 2008). In Wireless Africa et al. (2008), researchers have embarked on a mission to explore ways of developing sustainable information communication technology in developing countries, via community owned decentralized mesh networks on open source technology. Even with their abundant computational resources in the form of mesh routers, WMNs suffer from bottleneck effect at the IGW(s) due to the nature of traffic pattern and volume of data it handles. To mitigate this problem, most research proposals focus on routing, scheduling and load balancing, disregarding data caching techniques. Data caching strategies have been proposed for both wired and wireless networks in the form of web caching (Dimokas et al. 2007) and cooperative caching (Chand et al. 2006; Zhao et al. 2008; Chu et al. 2007; Chand et al. 2006; Denko and Tian 2006; Yin and Cao 2006). In cooperative caching, nodes share cached data to improve data accessibility and network throughput by exploiting

2 4 T. K. R. Nkwe et al. the Internet access locality. Moreover data caching provides backup information to the user and can conceal temporary Internet connection loss and go undetected by the user as it may never affect them. User perceived delays and Internet/network traffic are also reduced. Cooperative caching strategies are able to deal with the mobility and their resource limitations to improve network performance. In WMNs, the idea of cooperative caching has not received much attention as compared to other problems in the area. After conducting a study (Das et al. 2007), which determined that there is substantial locality in WMNs Internet traffic, they proposed the idea of cooperative caching by enabling MRs to cooperate in sharing and coordinating cached data to extenuate the IGW(s) bottleneck effect. In this proposal, caching the data at every mesh router along the path to the requester may result in the routers being flooded with redundant data and in turn incur higher overhead. There is still an immense room for better solutions by overcoming the limitations on existing solutions. One of the techniques to adopt is network clustering, by taking advantage of often static nature of MRs in WMNs and designating them as cluster heads (CHs) of their respective clusters. In doing so, we deal with the limitation that causes an overhead and thereby provide scalability. Moreover, the existing solutions do not employ the autonomic properties for self-optimization. Autonomic networking concepts and cross-layer design optimizations can be used to improve network performance and ensure network survivability. Autonomic networking supports self-configuration, self-healing, self-optimization and selfprotection (Bash et al. 2007). So by introducing the autonomic behavior in the WMNs, we contribute to building autonomic wireless mesh networks (AWMNs). In our preliminary work (Nkwe and Denko 2009) on AWMNS and data management, we defined AWMN as a wireless mesh network that possesses the core self-* characteristics and utilizes its abundant resources and other advantages for progressively improving its reliability, usability, fault resistance, availability and performance with minimal or no human effort. In cooperative caching nodes have to commit their resources in coordinating data sharing and employ cache management techniques to get the best use of their cache spaces. Of the three cache management and control techniques, namely, cache admission, cache consistency and cache replacement, the cache replacement and control has a significant bearing in determining the caches performance dependent on the algorithm used. Predominately the cache replacement schemes employed by most proposals are always compelled to replace a valid data item that could be useful in the future and improve network performance. In this paper, we propose a novel self-optimizing cooperative caching solution that aims at exploiting the locality in WMNs Internet access traffic. The solution also employs cross-layer information exchange to improve data accessibility providing data ubiquity and relieving the IGWs of the bottleneck effect. The main contributions of this paper are: (1) we use autonomic mesh cache (AMC), a novel self-optimizing autonomic cooperative caching solution. (2) we propose MeshSynch module that uses cross-layer design optimizations to enable the autonomic cooperation process by the MRs. (3) We propose a cacherescue scheme to salvage all the evicted valid data items from the cluster member local cache; this to complement the AMC and bolster its performance. The rest of this paper is organized as follows: Sect. 2 discusses background and related work. Section 3 describes in detail the proposed system model and the system architecture. Section 4 discusses the performance evaluation, while Sect. 5 concludes the paper and suggests possible future work. 2 Background and related work Cooperative caching ensued from the idea of web caching that uses proxy servers in wired networks characterized by fixed network topology. The idea of a proxy server used in wired networks cannot be directly applied to wireless networks since wireless networks introduce new challenges such as mobility, link failure and resource constraints. The node mobility makes it practically impossible to position a proxy server since the topology may change. A number of solutions have been sought and proposed to implement cooperative caching in wireless networks (Dimokas et al. 2007; Chand et al. 2006; Yin and Cao 2006; Tian and Denko 2007; Shen et al. 2004; Du and Gupta 2005; Artail et al. 2008). One of the initial works (Yin and Cao 2006) proposes three cooperative caching techniques that support data access in ad hoc networks, namely; CacheData (Caches the actual data), CachePath (caches the routing path) and HybridCache. HybridCache combines the strong capabilities of both the CachePath and CacheData while avoiding their weaknesses. The TCP/IP strict-layering protocol stack attainments have been ensued by its conformed structure which provides standardization, modularity, transparency, stability and continuity especially for wired networks. Clearly while this structure provides simplicity by keeping layers independent from each other, prohibiting information sharing and communication among non-adjacent layers. For wireless networks such interactions between layers are vital,

3 Data ubiquity in autonomic wireless mesh networks 5 particularly for optimization purposes (Razzaque et al. 2008). To enable autonomic adaptation and multi-layer communication, there is a need to be aware of available resources and network conditions. Hence, contextual information exchange is essential to allow the realization of self-* systems. One of the other main objectives is to lessen or eliminate dependence on human intervention. This has sparked the research on the capabilities of the current strict layering architecture, which has proven to be inadequate to the full implementation of autonomic computing. In Tian and Denko (2007), a cluster-based cooperative caching scheme for mobile ad hoc networks (MANETs) complemented by a prefetching scheme was proposed. The use of clustering approach is useful for reducing the communication overhead and easing the cache management processes while also providing network scalability. A cross-layer design approach has also been shown to foster the performance of both schemes. In Chu et al. (2007), authors use the Grid technology, applying it to cooperative caching to share memory of free nodes with others, remotely. Their solution uses space merging and time distribution techniques as two forms of autonomic cooperation mechanisms among the caching nodes. In Das et al. (2007), a Mesh Cache, a transparent cooperative caching system was proposed to alleviate IGW(s) bottleneck effect in WMNs caused by the overwhelming traffic from clients, which subsequently reduces throughput. The cooperative data caching solutions in various networks have demonstrated that data caching techniques are capable of improving the network performance and throughput by improving data accessibility, availability or reducing IGW(s) congestion and user perceived delays. For more improved performance these bring into question, issues of cache admission, cache replacement and cache consistency policies which form part of cache management schemes. The most prevalent cache replacement algorithms used in most proposals (Das et al. 2007; Dimokas et al. 2007; Zhao et al. 2008; Chand et al. 2006; Yin and Cao 2006; Shen et al. 2004; Du and Gupta 2005) includes least recently used (LRU), least frequently used (LFU) and least frequently recently used (LFRU). Other proposals Chand et al. (2006; Denko and Tian 2006; Tian and Denko 2007) incorporate more parameters into those algorithms, such as data size, node distance, popularity, coherency for a more robust caching effect. In Yin and Cao (2006), a combination of two parameters are used, size and order. Order is basically the access frequency of a data item, and so the less frequently used item also taking into consideration the size will be replaced. Even then a valid data item still gets evicted from the cache space. The fact that it was cached indicates some degree of interest, so salvaging it could improve the overall caching performance and data accessibility. Some proposals (Denko and Tian 2006, 2008; Tian and Denko 2007) use prefetching to reinforce the caching performance, while others (Artail et al. 2008) send the evicted valid data item to a node within the cluster that has more free cache space. Both approaches require a node to have free space so that in most cases large data items are usually removed first, and it is the same space they are caching into that they have to prefetch into as well. The second approach requires calculations and generation of extra messages to locate a node with more free space than others. This will require more computing power from the node, increase latency, and populate the network with extra messages hence negatively affecting the network performance. The idea of autonomic networking, cross layering and caching should be further exploited to avoid dependence on physical human interventions. Table 1 shows areas of focus for the existing cooperative caching proposals, the cache replacement algorithm used and whether they Table 1 Existing cooperative caching proposals Network type Performance objective Replacement algorithm ANC or CL Reference Wide area networks (WANs) Maximize the caching performance LRU ANC (Chu et al. 2007) WMNs Network throughput and gateway LRU CL (Das et al. 2007) congestion MANETs Query delay and message complexity SXO N/A (Yin and Cao 2006) MANETs Data accessibility, reduce query delays and network traffic overhead LRFU, LRU-MIN CL (Denko and Tian 2006; Tian and Denko 2007) Hybrid wireless networks (HWNs) Wireless multimedia sensor networks (WMSNs) Network life, query delay, throughput, load balancing LRU N/A (Shen et al. 2004) Network quality of service (QoS) Four-step policy N/A (Dimokas et al. 2007) MANETs Data availability and access efficiency LRU N/A (Du and Gupta 2005) MANETs Cache hit ratio and average query latency LUV-Mi N/A (Chand et al. 2006)

4 6 T. K. R. Nkwe et al. employ cross layer optimizations (CL) or autonomic networking/computing (ANC) concepts. These schemes strive to improve network performance including increased data access and reduced query delays. However, they do not exploit cross-layer optimizations and autonomic networking properties. Moreover the conventional LRU cache replacement algorithm is prevalent among most proposals. 3 The proposed approach Inspired by the autonomic networking paradigm, we proposed an Autonomic Mesh Cache, a self-optimizing cooperative caching solution that provides data ubiquity in WMNs to improve data accessibility and availability to the MCs. In doing so, we alleviate the IGW(s) bottleneck effect by leveraging on the MRs vested computational resources. This is achieved by utilizing the cross layer information through a module called MeshSynch and by adopting a clustering architecture to ultimately increase the network performance. We also noted that the cache replacement algorithms used in most schemes inevitably have to replace a valid data item. So we also proposed the cacherescue scheme to rescue those data items for servicing future requests and complement the cooperative caching system. Our system model conforms to the WMNs architecture, which has a hierarchy of four levels, namely: the Internet (first-layer), IGW(s) (second-layer), MRs (third-layer) and the MCs (fourth-layer). To model this architecture, we will have the Internet server which will be the original data source, IGW(s), MRs, and MCs. When accessing the Internet, MCs relay their requests via the MRs, then from the MRs through the IGW(s) to the Internet and the requested data is delivered in the reverse order. MCs that are within a range of each other can directly communicate in a peer to peer fashion. 3.1 Clustering architecture Clustering in MANETS is mainly used to provide network scalability and minimize communication overhead by first restricting the communication to the cluster before extending to the entire or other sections of the network. However, the cluster formation and maintenance can be a burden to the mobile nodes. This is mainly due to limited computational resources and energy as they have to assume the role of the CH at some point in time, determined by a clustering algorithm (Tian and Denko 2007). The role of the CH includes managing the cached information of its cluster members and inter-cluster communications. When the role of CH is changed, it may result in an overhead as Fig. 1 AWMNs architecture the item list of CMs is handed over to incoming CH and deleted from the outgoing CH. To overcome such limitations and challenges, WMNs with their composition and the fact that they often span a small geographical area can capitalize on clustering techniques to improve network performance. WMNs allow more stable clustering topology with low maintenance and low resource demands from the MCs. In the proposed approach, MRs will be designated as CHs taking advantage of their static positioning, good computational and energy resources. These benefits of mesh routers and the mesh networking architecture provide stable network topology reducing the high maintenance required in cluster-based solutions proposed for mobile ad hoc networks and wireless sensor networks. Figure 1 shows the proposed clustering architecture consisting of multiple gateways, mesh routers and mesh clients. The IGWs connect the mesh routers to the Internet allowing the mesh clients to gain connectivity to the Internet services through multihop communication. Our clustering architecture is anchored by the MRs, which are also the CH, consisting of CMs which are only 1 hop away from MRs. The IGW(s) are directly connected to the Internet. In comparison to the clustering approaches commonly used in MANETs, whereby the CH role is transferable among CMs, resulting in high maintenance, our proposed approach is more stable and reduces maintenance overhead. Mesh networks are robust and fault tolerant because of their capability to continuously adjust to their environment. In an application scenario where a mesh network spans the entire university campus, it is possible to route around a

5 Data ubiquity in autonomic wireless mesh networks 7 failed MR; so the MR cannot be a central failure for the entire network. For cluster maintenance, our system relies on the AWMNs self-* characteristics of self-configuration and self-organization. The MCs will always become CMs of the MRs they are newly connected to. The specific benefits of the proposed clustering architecture are: (a) It enables controlled management of cached content in the cluster. (b) It also enables MRs to be knowledgeable of what is cached network wide via the MeshSynch autonomic synchronization process. (c) It extends communication among MCs. Typically if no MRs can provide a direct path between communicating MCs, data will be forwarded to another MR in the network and the process is repeated until a path can be found. (d) It distributes the load network wide, all of which results in alleviating the bottleneck effect at the IGW(s). 3.2 Overview of AMC and cacherescue scheme Self optimization is the capability of a network to efficiently maximize the allocation and use of its resources, thus improving performance, balance energy and work load. By enabling MRs to autonomously exchange their cluster cached content to synchronize their network cached content; the AMC gradually increases data availability, data accessibility and resource utilization. This is, a selfoptimizing strategy employed by our proposed approach. Resource utilization and workload management are two important aspects necessitated for autonomic management. Figure 2 shows the proposed AMC architecture. As shown in the figure, the AMC-Tier consists of multiple components to support caching services. AMC is cluster based and most significantly the MRs play the role of the CH and also keep cache content of other clusters. This is performed in cooperation with other CHs and then Protocol Suite DB Profile Network Traffic status Cross-layer interaction Strict-layer interaction Information Search Application Layer Cache Management Clustering AMC-Tier Network Layer Data link Layer Fig. 2 AMC architecture (Nkwe and Denko 2009) Mesh Sync. periodically synchronizes through cross layer design optimizations. The cross layer design applied in our proposed solution is adopted from solution proposed in (Tian and Denko 2007) to self-optimize the caching system performance. The main components of the AMC architecture are described below. Information search: the information search component is responsible for locating the requested data items from the caching system or from the original data source. Cache management: this component is composed of the cache admission control, cache replacement and cache consistency mechanisms. They are responsible for establishing which data items to cache, remove from cache when a particular cache is full or keep the cached items refreshed with the original data source. MeshSynch: this is an integral part of the AMC and ensures that global mesh network cached content is available and synched in all clusters courtesy of the MRs. Clustering: this component is responsible for cluster formation and management. The number of clusters is equal to the number of MRs. Protocol DB Suite Profile (PDbSP): the systems profile interfaces all the protocol stack layers to enable cross- layer information exchange, and in this case it keeps track of the network traffic information. In the following section we briefly discuss the cacherescue scheme and the implementation of MeshSynch. 3.3 The cacherescue scheme The cacherescue is a novel data caching scheme that complements cooperative caching in WMNs, for improved data accessibility, reduced latency and overall network performance by reducing the IGW(s) load. Having studied and observed data caching schemes in both wired and wireless networks; the approaches have varied as much as network have evolved hence exhibiting different characteristics requiring different solutions. As computing devices became smaller in size and began supporting mobility, it has sacrificed the resource availability and computational power for portability and affordability. This paradigm shift was due to the need of what is referred to as computing anywhere, anytime. As a result, there are increased network devices and other technological complexities hence network performance degradation due to increased network traffic and heterogeneity. As previously stated, the efficacy of a caching scheme lies in its cache replacement policy. From existing literature in the area, all the policies inevitably still have to replace a valid data item from the local cache space of the caching MC. The fact that the item was cached shows that there is indeed interest in that particular item and it may be requested again, maybe in the near future. To minimize the negative impacts that maybe

6 8 T. K. R. Nkwe et al. caused, some researchers propose the prefetching technique, but even then they are still bound by the cache size limitations. The idea of cacherescue is also to take advantage of the MRs expandable storage capacity, since MRs are the CHs of the clusters they form with the MCs connecting to it. In CacheRescue approach, every valid data item that is replaced from the MC s local cache is sent to its corresponding CH, where it will be cached in the CacheRescue database (CRDB), until its originally assigned time-to-live (TTL) elapses, then it can be purged. When the CRDB becomes full, it does not accept anymore data items, until there is enough space freed to accommodate the incoming data item for salvation, as a result of the data item expiring. One option was to renew the TTL of every item coming into the CRDB, but that will cause increased communication overhead, congest the network and choke the IGW(s) since the original data source will have to be contacted for renewal hence lower the network performance. 3.4 Implementation details of MeshSynch MeshSynch is a module in AMC that enables CHs to autonomously exchange and synchronize their cluster database with each other to make up a network database(netdb), thus increasing data availability, data accessibility and resource utilization. In Fig. 3, the Mesh- Synch module continuously monitors the traffic state obtained from the PDbSP and invokes the MeshSynch process when the traffic is low. During this process, MRs autonomously exchange and synchronize their cluster cached list (CCL) from the cluster database (CDB) with each other to update the NetDB. First if the CCL has updated entries, it is allocated to the MeshSynch packet and sent to other MRS as multicast messages. If there are no updates, a notification will be sent but it will still receive updates from other CHs; then on receiving the CCL, each MR updates its NetDB. The MRs form a group of destinations that simultaneously receive and send information using our implemented multicasting strategy to deliver messages. MeshSynch leverages on the fact that MRs which are also the CHs have direct links to each other or virtually make up a network of their own in a peer to peer type of setup. MRs maintains both the cluster cache-list and network cachelist, without major concerns for space or computational capabilities. In our implementation of the caching, we used a Map data structure. Access to Map is fast, guaranteeing O (log (N)) insertion and lookup time (Musser et al. 2001). This is due to the fact that the map keys are stored in order for use by iterators. 3.5 Information search operation The information search operation is one of the main components of our caching system. Its main aim is to look for and serve the requesting MC with a copy of the data item requested from the local, cluster or network cached content before going to the data source or Internet. The search operation traverses the mesh network from a lower level going up to the Internet. As shown in Fig. 4, when a MC (requestor node) makes a request, it first looks up the requested data item in its Fig. 3 Propagation & Handling of MeshSynch Packets Fig. 4 Requests Service Process

7 Data ubiquity in autonomic wireless mesh networks 9 local cache; on a miss, it then forwards the request to its CH to search from the CRDB. Since the CH is part of the cluster, that data item will not be cached by the MC if it is serviced from the CRDB. When the cacherescue scheme is used in conjunction with the AMC scheme; if the requested item is not found in the CRDB and the CDB, then it is also searched in the NetDB (all of which are contained in the MR) before eventually being forwarded to the Internet in a case of the cooperative caching schemes misses. In the proposed scheme, information search does not require flooding. This is because in our WMNs architecture each node is directly connected to the CH which contains the network-wide cached content. Therefore unicasting a request to the CH will be much more efficient and conserve the MC s resources. The search operation is carried out through the following entities: 1. Local cache: MCs are often small for portability and so are their cache sizes. When a mobile client application makes a data request, it first searches in its local cache. On a hit, the reply is almost instant with no significant delays and very low communication overhead. On a miss, the node will send the request to the CH, where the search begins in the cacherescue storage. 2. CRDB: The cacherescue storage resides in the CH; its purpose is to complement the MCs local caches, by providing extra storage space and salvaging valid data items evicted from the MCs local cache in its cluster. On a hit, the data item is returned to the requestor node. The cacherescue has the second lowest delay after the MC s local cache. On a miss, the search moves on to the CDB. 3. CDB: The CDB contains information or references of what is cached and where in the cluster. When the requested data item id and source is located in the CDB, the request will be forwarded to the cluster member (CM) caching that data item. When the identified source node receives the request, it searches its local cache to retrieve the data item. On a hit the item will be returned to the requesting node. The CDB/ CM request resolution in most cases provides the third lowest query delay after the MC s local cache and the cacherescue storage. On a miss the CM will forward the request to the Internet. In a case where the data item was not located in the CDB, the search process proceeds to the NetDB. 4. NetDB: The NetDB contains information or references of what is cached and where in the network or other clusters. When the requested data item id and source is located in the NetDB, the request will be forwarded to CM caching that data item. When the identified source node receives the request, it searches its local cache to retrieve the data item. On a hit the item will be returned to the requesting node. The NetDB request resolution often provides the longest query delay after the Internet and often slower than MC s local cache and the cacherescue storage. On a miss, the network node will forward the request to the Internet. In a case where the data item was not located in the NetDB, the request is still sent to the Internet. 5. Original data source (Internet): When the original data source receives the data item requests, it assigns the requested data item a TTL value and sends the reply to the requestor node. Given that the Internet in most cases yields the most delay of all the replies. It is essentially the furthest from other network devices, so replies from the Internet do not always reach their destinations especially when there is high mobility in the network, in which case by the time it responds the node might have moved. In the midst of this search procedure, to prevent an indefinite wait by the requestor node in a case of network or link failure, after the requestor node unicasts the request to the CH, it schedules a waiting time on the timer. The timer will be reset if the request is replied before the waiting time expires. Otherwise if the waiting period expires before the requestor node receives a reply, it will now address the request to the original data source. 4 Performance evaluation In this section, we evaluate the performance and effectiveness of our proposed cooperative caching, and cacherescue schemes. Cooperative caching with MeshSynch and cacherescue (CMSCR) is compared against the Cooperative caching with cacherescue (CCCR) and Cooperative caching without MeshSynch and cacherescue (CNMSCR) in WMNs of fixed network size and number of MRs. 4.1 Simulation parameters Our performance evaluation was carried out in ns-2 environment Network Simulator-NS2 nsnam/ns/, accessed June 2009, with main configuration parameters shown in Table 2. The Random Way Point mobility model was used in the simulation. We used 4 to 22 MRs and 90 to 200 MCs in the simulation. A modified ad hoc on-demand distance vector routing (AODV) protocol was used (Chakeres and Belding-Royer 2004). Each MC can generate several requests at varying intervals following the commonly used Zipf-like distribution (Breslau et al. 1999). All the clients have the same cache sizes while the data items vary in size.

8 10 T. K. R. Nkwe et al. Table 2 Simulation parameters Parameter Default Value Range Simulation area 3000 m m N/A Transmission range 200 m N/A Number of MCs Number of MRs Node speed (m/s) Data item size (KB) Node cache size (KB) Zipf-like parameter (h) Request frequency (s) Simulation time (s) 2000 N/A 4.2 Performance metrics and simulations parameters Four performance metrics were used in the experiments discussed in this paper. The first metric is the packet delivery ratio. The packet delivery ratio is the percentage of successfully serviced requests to the total number of requests generated. The second metric is the average gateway load. The average gateway load is the number of request sent through the gateway to the original data source or the Internet divided by the number of total requests made by all mesh nodes. The third metric is Coop hit-ratio and includes the local cache (Local Hit Ratio), the cacherescue storage (cacherescue hit ratio), the cluster member(s) replies (Cluster Hit Ratio) from CDB, and the network wide cached (Global Hit Ratio) content provided by the NetDB. Fourth, the average network delay, which is the latency time from the time when the query was made to when it was successfully serviced. Two simulation parameters were used: the MCs cache size and client query access pattern (Zipf-like parameter). We varied the cache sizes to gauge the impacts of the cacherescue storage and MeshSynch. We also varied the Zipf-like parameter to represent the client access pattern and gauge the effects of the client access Internet locality and how the proposed systems can capitalize on it to better performance. 4.3 Discussion of simulation results In this section, we will provide discussion of the simulation results based on the performance metrics and simulation parameters discussed in the preceding section. We conducted a series of experiments to compare the CMSCR, CCCR and the CNMSCR. Figure 5 shows the packet delivery ratio as a function of the cache size. The figure shows that all the three schemes produce the packet delivery ratio over 72%. The CMSCR produces the best Fig. 5 Average packet delivery ratios under varying cache sizes average of 85%, over 10% more than CCCR and CNMSCR and remains constant as the cache size increases. This is largely because most requests are serviced either from the cacherescue or NetDB, but as the cache size increases, there is more space for more items to be stored, hence increased number of local cache hits and network wide information. CCCR has a packet delivery ratio of 85% between 50 to 250 KB of cache size. Then from 300 to 500 KB, the packet delivery ratio is constant at an average of 74%. This occurs partly because small cache sizes are fill up more quickly hence more valid data items being evicted regularly and rescued by the cacherescue scheme and in turn the cacherescue will service future requests. As the cache size increases beyond 250 KB, the CCCR performs the same way as CNMSCR. This is because MCs have bigger cache spaces and there are not many valid requests evicted to make use of the cacherescue. Figure 6 shows the cooperative caching hit ratio as a function of the cache size. The CMSCR hit ratio which includes the cacherescue and NetDB has the highest average hit ratio of over 80%. The reason is that CMSCR has the benefit of the cacherescue and network wide cached content. The second caching scheme with better performance is the CCCR which manages a hit ratio of above 40%. This is because the cacherescue scheme was most effective when the cache size was smaller resulting in a lot of valid items being evicted. The CNMSCR performs poorly when the cache size is small because a lot of valid items were being replaced due to limited cache space. The performance gradually improves and catches up to the CCCR as the cache size increases. Figure 7 shows the average gateway load as a function of the cache size. The figure shows that as a direct result of high packet delivery ratio by CMSCR, it is able to keep the gateway load at an average of 17, 25 and 37% lower than

9 Data ubiquity in autonomic wireless mesh networks 11 Fig. 6 Cooperative caching hit-ratios under varying cache sizes Fig. 8 Cooperative caching hit-ratios under varying Zipf-like parameter Fig. 7 Average gateway loads under varying cache sizes CCCR and CNMSCR, respectively. The CCCR achieves gateway load lower than 50% at an average of 42%. 12% lower than CNMSCR. This shows the effectiveness of the cacherescue scheme when it is combined with MeshSynch. From the experiments using the cache size, we observed that the packet delivery ratio remains relatively high for all schemes and even higher for the proposed schemes when the cache size is small. This is due to that the fact that the cacherescue storage which is able to salvage the valid evicted data items because of the small cache size, and is able to resolve other requests. We observe that, from the gateway load which is under 50% for the proposed schemes and up to 80% for the CNMSCR schemes meaning more requests are sent to the Internet resulting in a longer delay too. As the cache size increase, the CCCR and CNMSCR perform the same way since few data items are evicted because the nodes have bigger storages to cache more items. But for the CMSCR, it produces the best results as it benefits from both the cacherescue storage, CDB and NetDB. Figure 8 shows the cooperative cache hit ratio as a function of the Zipf-like parameter in a fixed network size of 120 MCs with 10 MRs. The CMSCR maintains a coop hit ratio of above 80% at all values of the h and increases as the value h increases too. This is because it has the benefit of all cache levels (local, cacherescue, CMs, and NetDB) hence most requests are being serviced from the network. For CCCR and CNMSCR their coop hit ratios are 10 and 28% respectively when the h is small (between 0.1 and 0.5), because with a smaller h value, requests are made from a wider variety of data items, as a result more requests were serviced from the Internet. But as the value of h increases, the coop hit ratio for both schemes also increases to over 50%, as the requests are made from a smaller data range meaning that most of them are already cached and are reused. Figure 9 shows the average gateway load ratio as a function of the Zipf-like parameter in a network size of 120 MCs with the same number of 10 MRs. The CMSCR has the lowest and gateway load ratio of less than 20%. The CCCR and CNMSCR have a very high gateway load ratio of over 58 and 70%, respectively, at smaller values of h, but they both drop down to below 40% as the increases. This result agrees with Fig. 8. Figure 10 shows the average network delay as a function of the Zipf-like parameter in a network size of 120 MCs with the same number of 10 MRs. The figure shows a similar behavior as in Fig. 9, that the higher the gateway load, the higher the delay and the lower the gateway load, then the lower the delay. When the gateway load is high, it

10 12 T. K. R. Nkwe et al. exploiting MRs and exploring cross layer design optimizations along with the autonomic networking principles. By rescuing the evicted data items and using the network wide cached contents, the proposed cooperative caching schemes improve data accessibility, availability, packet delivery ratio and reduce IGW(s) along with the clients query delays. 5 Conclusions and future work Fig. 9 Average gateway loads under varying Zipf-like parameters Fig. 10 Average network delays under varying Zipf-like parameter means that many requests are sent to the Internet and in most cases bears the longest delay after all the cache levels, which is why the average delays for the CNMSCR and CCCR are higher than CMSCR. From the effects of the Zipf-like parameter results, we observed that when the h is smaller, which is when the whole data set is taken into consideration, more requests are serviced at the Internet, hence higher initial gateway load and delay. When the h is bigger, the requests are concentrated on a smaller data set and more requests are serviced from the local cache(s) and other caches, increasing the coop hit ratio and decrease the gateway load along with the query delay. We also observe that the CMSCR can be just effective without the cacherescue scheme at all values of the h. From all the performance results presented in this paper, we conclude that significant gains can be achieved by In this paper, we addressed the problem of gateway bottleneck in WMNs by using data management techniques. We proposed a self-optimizing cooperative caching solution for autonomic wireless mesh networks. The scheme is able to gradually improve the data caching performance providing data ubiquity, by utilizing the vested MRs computational resources and cross layer design optimizations enabling the mesh synchronization through the MeshSynch operation. The MeshSynch enables cooperation between MRs by synchronizing their clusters cached content, without any human administration to schedule the synchronization process in anticipation for network idle time. We also proposed the cacherescue scheme to complement the cooperative caching process. As demonstrated by the experimental results, our proposed solutions achieve better results than the standard cooperative caching by providing higher packet delivery ratio, cooperative caching hit ratio, lower query latency and gateway load. For future work, it is important to investigate the selfprotection characteristic as security is an important feature in a shared information environment. Our results are very encouraging, suggesting more work to be done in dealing with WMNs problems by using data management strategies and focusing more on the autonomic networking concepts and properties. The cache replacement techniques have also proven to be at the core of any caching systems performance. Having observed that most if not all cache replacement algorithms invariably replaces a valid data item, which could be useful in future, we recommend more research on those and also explore other avenues like utilizing MRs abundant resources to develop more robust caching solutions. Finally one way of dealing with the bottleneck effect at the gateway can be by deploying multiple gateways in WMNs and still employ data management techniques. We also find it highly necessary to carry out this experiments or simulations in a test bed environment for more explorative and real-life experiments, as simulation tools can be limiting to some degree. Acknowledgments This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant no: and the Botswana

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