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Due to the tremendous amount and its rapid increase of network traffic, the performance of network equipments are becoming an important issue. Network filtering is one of primary functions affecting the performance of the network equipment such as a firewall or a load balancer to process the packet. In this paper, we propose a cache based tri method to improve the performance of the existing tri method of searching for network filtering. When several packets are exchanged at a time between a server and a client, the tri method repeats the same search procedure for network filtering. However, the proposed method can avoid unnecessary repetition of search procedure by exploiting cache so that the performance of network filtering can be improved. We performed network filtering experiments for the existing method and the proposed method. Experimental results showed that the proposed method could process more packets up to 790,000 per second than the existing method. When the size of cache list is 11, the proposed method showed the most outstanding performance improvement (18.08%) with respect to memory usage increase (7.75%).

An SMTP(Simple Mail Transfer Protocol) server was designed for delivering and storing user’s email across a network. An SMTP server can be distributed as multiple servers for large service, huge users or massive emails. An SMTP server can be constructed by multiple servers with separating file system as email storing space, and each SMTP server can usually share each file system by using the NFS(Network File system). However the NFS is originally designed for sharing each file system across a network, and contains all attributes and features of regular file system. Using this NFS for email storing space of SMTP servers, it makes overhead due to unnecessary work of regular file system.In this paper, we propose a method to do directly operation with socket for reducing work overhead caused by the NFS. For doing directly operation with socket, this paper defines information based storing space structure and a protocol for storing emails. We performed experiments using Mailstone made by Netscape. The experimental results show the performance improvement of the proposed method compared to the existing method.Keywords:Email, SMTP Server, Mail Server, Distributed Architecture, NFS(Network File System)

The signature hashing algorithm[9] provides the fast pattern matching speed for network IPS(Intrusion Prevention System) using the hash table. It selects 2 bytes from all signature rules and links to the hash table by the hash value. It has an advantage of performance improvement because it reduces the number of inspecting rules in the pattern matching. However it has a disadvantage of performance drop if the number of rules with the same hash value increases when the number of rules are large and the corelation among rules is strong. In this paper, we propose a method to make all rules distributed evenly to the hash table independent of the number of rules and corelation among rules for overcoming the disadvantage of the signature hashing algorithm. In the proposed method, it checks whether or not there is an already assigned rule linked to the same hash value before a new rule is linked to a hash value in the hash table. If there is no assigned rule, the new rule is linked to the hash value. Otherwise, the proposed method recalculate a hash value to put it in other position. We implemented the proposed method in a PC with a Linux module and performed experiments using Iperf as a network performance measurement tool. The signature hashing method shows performance drop if the number of rules with the same hash value increases when the number of rules are large and the corelation among rules is strong, but the proposed method shows no performance drop independent of the number of rules and corelation among rules.

With increasing multicore system, much effort has been put on the performance improvement of its application. Because multicore system has multiple processing devices in one system, its processing power increases compared to the single core system. However in many cases the advantages of multicore can not be exploited fully because the existing software and hardware were designed to be suitable for single core. When the existing software runs on multicore, its performance improvement is limited by the bottleneck of sharing resources and the inefficient use of cache memory on multicore. Therefore, according as the number of core increases, it doesn`t show performance improvement and shows performance drop in the worst case. In this paper we propose a method of performance improvement of multicore system by applying Flow-Level Parallelism to the existing TCP/IP network application and operating system. The proposed method sets up the execution environment so that each core unit operates independently as much as possible in network application, TCP/IP stack on operating system, device driver, and network interface. Moreover it distributes network traffics to each core unit through L2 switch. The proposed method allows to minimize the sharing of application data, data structure, socket, device driver, and network interface between each core. Also it allows to minimize the competition among cores to take resources and increase the hit ratio of cache. We implemented the proposed methods with 8 core system and performed experiment. Experimental results show that network access speed and bandwidth increase linearly according to the number of core.

A Clustering based wireless internet proxy server needs a layer-7 load balancer with URL hashing methods to reduce the total storage space for servers. Layer-4 load balancer located in front of server cluster is to distribute client requests to the servers with the same contents at transport layer, such as TCP or UDP, without looking at the content of the request. Layer-7 load balancer located in front of server cluster is to parse client requests in application layer and distribute them to servers based on different types of request contents. Layer 7 load balancer allows servers to have different contents in an exclusive way so that it can minimize the total storage space for servers and improve overall cluster performance. However, its scalability is limited due to the high overhead of parsing requests in application layer as different from layer-4 load balancer. In order to overcome its scalability limitation, in this paper, we propose a distributed layer-7 load balancer by replacing a single layer-7 load balancer in the conventional scheme by a single layer-4 load balancer located in front of server cluster and a set of layer-7 load balancers located at server cluster. In a clustering based wireless internet proxy server, we implemented the conventional scheme by using KTCPVS(Kernel TCP Virtual Server), a linux based layer-7 load balancer. Also, we implemented the proposed scheme by using IPVS(IP Virtual Server), a linux-based layer-4 load balancer, installing KTCPVS in each server, and making them work together. We performed experiments using 16 PCs. Experimental results show scalability and high performance of the proposed scheme, as the number of servers grows, compared to the conventional scheme.Keyword:Clustering, Hashing, Layer-4 Load Balancer, Layer-7 Load Balancer, Scalability

Performance scalability and storage scalability become important in a large scale cluster of wireless internet proxy cache servers. Performance scalability means that the whole performance of the cluster increases linearly according as servers are added. Storage scalability means that the total size of cache storage in the cluster is constant, regardless of the number of cache servers used, if the whole cache data are partitioned and each partition is stored in each server, respectively. The Round-Robin based load balancing method generally used in a large scale server cluster shows the performance scalability but no storage scalability because all the requested URL data need to be stored in each server. The hashing based load balancing method shows storage scalability because all the requested URL data are partitioned and each partition is stored in each server, respectively. but, it shows no performance scalability in case of uneven pattern of client requests or Hot-Spot. In this paper, we propose a novel dynamic hashing method with performance and storage scalability. In a time interval, the proposed scheme keeps to find some of requested URLs allocated to overloaded servers and dynamically reallocate them to other less-loaded servers. We performed experiments using 16 PCs and experimental results show that the proposed method has the performance and storage scalability as different from the existing hashing method. Key Words:Duplication, Storage, Scalability, Request pattern, Hot-Spot, Dynamic hashing

Caching in a cache sorrel cluster environment has an advantage that minimizes the request and response tine of internet traffic and web user. Then, one of the methods that increases the hit ratio of cache is using the hash function with cooperative caching. It is keeping a fixed size of the total cache memory regardless of the number of cache servers. On the contrary, if there is no cooperative caching, the total size of cache memory increases proportional to the number of cache sowers since each cache server should keep all the cache data. The disadvantage of hashing method is that clients` requests stress a few servers in all the cache servers due to the characteristics of hashing md the overall performance of a cache server cluster depends on a few servers. In this paper, we propose the method that distributes uniformly client requests between cache servers using dynamic server information. We performed experiments using 16 PCs. Experimental results show the uniform distribution o

Caching in a Wireless Internet Proxy Server Cluster Environment has an effect that minimizes the time on the request and response of Internet traffic and Web user As a way to increase the hit ratio of cache, we can use a hash function to make the same request URLs to be assigned to the same cache server. The disadvantage of the hashing scheme is that client requests cannot be well-distributed to all cache servers so that the performance of the whole system can depend on only a few busy servers. In this paper, we propose an improved load balancing scheme using hashing and Round Robin scheme that distributes client requests evenly to cache servers. In the existing hashing scheme, if a hashing value for a request URL is calculated, the server number is statically fixed at compile time while in the proposed scheme it is dynamically fixed at run time using round robin method. We implemented the proposed scheme in a Wireless Internet Proxy Server Cluster Environment and performed experiments using 16 PCs. Experimental results show the even distribution of client requests and the 52% to 112% performance improvement compared to the existing hashing method.

This paper presents (1) a quantitative performance comparison of the features used in off-line handwritten Korean alphabet recognition, and (2) some experimental results based on the feature combination approach. We classified the features into three: geometrical/topological, statistical and global. For each class, we selected four or five features and performed recognition experiments with PE92 database using neural network classifiers. We compared their recognition performances and selected the top four among those thirteen features: Up Down Left Right Hole, Gradient, Structure, and Mesh. With the combination of some or all of the four features, we repeated recognition experiments. Experimental results show that (1) as a whole, geometrical/topological features outperform the other two feature classes in terms of the recognition rate, and (2) UDLRH and Gradient features in geometrical/topological feature class outperform the other features, (3) the feature combination approach can result in performance improvement.

Service providers and enterprises capitalize on evolving cloud computing technologies when building datacenters for their own computing infrastructure and/or for providing services to others. Virtual machines and their migration are an important underlying technology of Infrastructure as a Service (IaaS) for building efficient cloud computing infrastructure on a cluster of servers. Autonomous migration of virtual machines is designed to increase the overall resource utilization on a cluster of servers. If a particular computing pattern caused imbalance that triggers migration, this pattern will be remembered or "learned" with its corresponding migration details for future use. In this paper, we use a proactive learning methodology that not only accumulates the past history of computing patterns and resulting migration decisions but more importantly searches all predefined possibilities for the most suitable decisions. We set up an experimental environment that consists of extensive real world benchmarking problems and a cluster of 16 physical machines each of which has on average eight virtual machines. We demonstrate through experimental results that our self regulated autonomous VM migration increases resource utilization of the servers on which cloud computing IaaS is currently running.

This paper presents a new paradigm for constructing a partially ordered plan network, referred to as "resource-oriented parallel planning (RP2)". In resource-oriented parallel planning, goal states are grouped into a collection of goal state subsets, each of which consists of those goal states associated with a particular resource. A subplan is generated for each goal state subset by planning the flow of the corresponding resource, and thus satisfying the constraints specified by each of the goal state subsets. A complete plan is then constructed by synthesizing individual subplans based on the synchronization among subplans. A distinctive feature of RP2 is that each subplanner generates a conflict-free subplan by controlling the flow of a particular resource while synthesizing a complete plan in cooperation with other subplanners. This can be compared with the conventional state-oriented parallel planning, where each subplanner generates a subplan achieving a goal state while resolving conflicts among subplans in cooperation with other subplanners. RP2 makes use of resource reasoning in distinguishing important and unimportant resources associated with a goal. This allows each subplanner to hierarchically construct a resource-flow plan and to dynamically coordinate among subplanners in achieving a goal associated with more than one resource. The proposed scheme not only makes it possible to generate an optimal plan in terms of the maximum parallelism or the minimum depth of a plan network, but also makes it easier to implement the concurrent generation of parallel plans in a parallel and distributed processing environment.

Dynamic migration of virtual machines on a cluster of physical machines is designed to maximize resource utilization by balancing loads across the cluster. When the utilization of a physical machine is beyond a fixed threshold, the machine is deemed overloaded. A virtual machine is then selected within the overloaded physical machine for migration to a lightly loaded physical machine. Key to such threshold-based VM migration is to determine when to move which VM to what physical machine, since wrong or inadequate decisions can cause unnecessary migrations that would adversely affect the overall performance. We present in this paper a learning framework that autonomously finds and adjusts thresholds at runtime for different computing requirements. Central to our approach is the previous history of migrations and their effects before and after each migration in terms of standard deviation of utilization. We set up an experimental environment that consists of extensive real world benchmarking problems and a cluster of 16 physical machines each of which has on average eight virtual machines. We demonstrate through experimental results that our approach autonomously finds thresholds close to the optimal ones for different computing scenarios and that such varying thresholds yield an optimal number of VM migrations for maximizing resource utilization.

This paper proposes a load sharing scheme to maximize network bandwidth utilization in IEEE 802.11 WLAN networks using the SSED (Service Set Identifier) hiding. For this purpose, the proposed scheme keeps checking the available bandwidths of a group of wireless routers; selects the most bandwidth-optimal one; makes it visible to clients. Such that, whenever a client connects to a wireless router, only the selected one is visible to it while others are hiding. We implemented the proposed scheme with modifying the firmware of ASUS WL- 500 G wireless router and performed experiments. Experimental results show 35.6% performance increase in the bandwidth utilization compared to the conventional scheme.

A 3D vertex processor with a floating-point 4-threaded and 4-issue VLIW architecture and a TnL vertex cache is implemented for mobile multimedia applications in a 0.18mum 4M CMOS process. The proposed architecture efficiently reduces the total energy consumption and achieves 120Mvertices/s with a 2.5GFLOPS datapath using 157mW when operating at 100MHz

A new paradigm for constructing a partially ordered plan network, referred to as resource-oriented parallel planning (RP/sup 2/), is presented. In resource-oriented parallel planning, goal states are grouped into a collection of goal state subsets, each of which consists of those goal states associated with a particular resource. A subplan is generated for each goal state subset by planning the flow of the corresponding resource and thus satisfying the constraints specified by each of the goal state subsets. A complete plan is then constructed by synthesizing individual subplans based on the synchronization among subplans. A distinctive feature of RP/sup 2/ is that each subplanner generates a conflict-free subplan by controlling the flow of a particular resource while synthesizing a complete plan in cooperation with other subplanners. The proposed scheme not only makes it possible to generate an optimal plan in terms of the maximum parallelism or the minimum depth of a plan network but also makes it easier to implement the concurrent generation of parallel plans in a parallel and distributed processing environment. >

Much research has been done on combining multiple classifiers for handwritten character recognition to improve the performance of the classifier. Given a fixed set of classifiers using the same or different kinds of feature set, they focus on a methodology to combine all of the classifiers. In this paper, given a variable set of classifiers, we focus on a methodology to determine which subset of classifiers achieves the optimal combination results. In order to evaluate the dependency between classifiers, we propose a similarity measure between them which can be calculated from the errors generated by each classifier. This similarity measure allows us to compare the performance of one combination case relative to those of the other cases without performing any experiments. Using five individual neural net classifiers with different feature sets [gradient, structural, UDLRH (up-down left-right hole), mesh and LSF (large stroke feature)], we perform handwritten digit recognition experiments. With three combination methods [majority voting, Borda count and LCA (linear confidence accumulation)], we perform combination experiments for all possible cases of three classifiers selected from among the above five. Then, we compare their rankings in terms of the recognition rate with that in terms of the similarity measure. This comparison shows the effectiveness of the proposed method.

For improved recognition of videotexts, we have focused on image enhancement techniques. Since the videotexts are low-resolution and mixed with complex backgrounds, image enhancement is a key to successful recognition of the videotexts. Especially in Hangul characters, several consonants cannot be distinguished without sophisticated image enhancement techniques. In the paper, after multiple videotext frames containing the same captions are detected and the caption area in each frame is extracted, five different image enhancement techniques are serially applied to the image: multi-frame integration, resolution enhancement, contrast enhancement, advanced binarization, and morphological smoothing operations. We have tested the proposed techniques with the video caption images containing both Hangul and English characters from various video sources such as cinema, news, sports, etc. The character recognition results are greatly improved by using enhanced images in the experiment.

This paper presents a resource-level conflict detection and conflict resolution scheme which is combined with a state-level backward planning algorithm and provides efficient conflict detection and global conflict resolution for nonlinear planning. The scheme keeps track of the usage of individual resources during planning, and constructs a Resource-Usage Flow (RUF) structure (based on which conflict detection and resolution is accomplished). The RUF structure allows the system to perform minimal and nonredundant operations for conflict detection and resolution. Furthermore, resource-level conflict detection and resolution facilitates problem decomposition in terms of resources, thereby providing easy implementation in a parallel and distributed processing environment. Performance analysis indicates that the proposed architecture has a speed-up factor of the average depth of a plan network, D(Na), compared to the distributed NOAH, where Na (the total number of action nodes at the completion of planning) and D(Na) are considerably larger than the number of resources involved in planning as well as the number of initial goal states.