Disaster Recovery Backup System for P2P Based VoIP Application
|
|
- Lauren Richard
- 8 years ago
- Views:
Transcription
1 Journal of Computational Information Systems 9: 20 (2013) Available at Disaster Recovery System for P2P Based VoIP Application Kai SHUANG, Jing XIE State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing , China Abstract Using P2P network as the distributed data storage network for VoIP system is an Internet hotpot research. How to realize a data backup strategy to reply to data disaster in a VoIP system based P2P network under the premise of carrier-grade demands has not been mentioned in previous researches. This paper analyzed the characteristics of VoIP system. A suitable data backup strategy reply to data disaster is designed and the design proposal for data disaster recovery backup system is described in detail. Finally, the experiments were given to assess the performance of this strategy. Keywords: Data Storage and ; VoIP; P2P; DHT Algorithm 1 Introduction For the past few years, many distributed storage systems emerged. However, there is no existing storage system or strategy mentioned user node as serving node to provide storage. Since user node always has shorter online time and higher probability of crash and abnormally exit than service provided nodes, existing system is not suitable for this scene. The disaster recovery backup strategy discussed in this paper can provide the basic function, such as reliable storage and rapid recovery, in a storage network, which has various nodes as serving nodes including user nodes. Besides that, our strategy ensured the high availability and reliability for data and can meet the five nines carrier-grade demand. This paper is organized as follows. After introduction part, the related works are given. In the third part, the analysis and design of the storage strategy is described, including background analysis, performance objectives and control scheme for data storage. Control scheme for data storage is introduced as three aspects, such as data storage and backup, data recovery and data update. Next is performance assessment for this strategy. The main concerning factors are reliability, recovery latency and bandwidth consumption. Finally, we summarize this paper and give a conclusion. Corresponding author. address: shuangk@bupt.edu.cn (Kai SHUANG) / Copyright 2013 Binary Information Press DOI: /jcis6851 October 15, 2013
2 8100 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) Related Works There are many distributed storage systems at present, such as GFS [1], Bit-Value [2], Dynamo [3] and SandStone [4]. GFS deals meta data with primary server and realizes read/write operation using block data server. Bit-Value is a content-addressable retention platform for large volumes of reference data - seldom changing information that needs to be retained for a long time. It uses smart bricks as the building block to lower the hardware cost. Dynamo is a highly available key-value storage system that some of Amazon s core services use to provide an always-on experience. It is used to manage the state of services that have very high reliability requirements and need tight control over the tradeoffs between availability, consistency, cost-effectiveness and performance. SandStone is another reliable storage system with traffic localization, strong consistency, high availability and scalability. These storage systems all adopt DHT algorithm of key based routing. The first three systems only guarantee the basic performance demand for Internet application, while the solution of SandStone lists the carrier-grade demand as the important factor. However, SandStone mainly used for distributed core network of telecommunication. The storage nodes in that system are stable which are all deployed by service provider. Data storage mechanism has been widely discussed. But there is no strategy applicable for all the scenes. Therefore, we put forward a new data storage and backup strategy used in our specific P2P based VoIP system. 3 Design of Storage and Strategy 3.1 Applicable scenario The disaster recovery backup strategy studied in this paper mainly applies to P2P based VoIP application. All data are stored in distributed network. In the storage network, the serving node in the network contains not only peer, but also client which upgrades as peer. Peer is supposed to be the stable nodes deployed by service provider, such as giant server or ordinary PC. Meanwhile, client is just common user node in VoIP system which usually uses service rather than provides service. Users maybe never have a long online time like nodes deployed by service provider. Besides, since the upgraded peer is actually a user node, it still has many restraining factors on network environment. What s more, the case of crash or exit abnormally for upgraded peers is more likely to happen. Thus, we design a new system for data storage. Considering the geographic distribution pattern of call sessions and service provider s full knowledge in his IP networks, we believe that the layered DHT [5, 6] is the simple but effective way to adopt. The overlay of our system is typically deployed as a two layered DHT, including a global DHT and several regional DHTs. For each region, at least one boundary peer should be chosen to route update messages between regions. They must be deployed by service providers and pre-configured. Every peer in the same region must be aware of this BP (bootstrap peer) while BP is also aware of every peer s current situation in its region. 3.2 System features The disaster recovery backup strategy used in P2P based VoIP application differs from traditional telecom application or common P2P application. It combines the features of both, but has the
3 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) distinction which is embodied in the following two aspects. On the one hand, the serving nodes in the network are distributed, eliminating the centralized control. Compared with traditional telecom network, the network of the system in this paper is organized with structured P2P mode. It gets ride of HSS core storage server. In this method, the investment in high performance equipment can reduce and the bottleneck effect on core server can be solved. It makes the serving network more flexible and easier to expand. On the other hand, the logic role of the client changes dynamically. In comparison with the applications in flat P2P network, the client in our system may change to a storage node providing service to achieve lower contributed capital and better network adaption. But the upgrade peer has no guarantee on online time and network connectivity. Therefore, there should be an upper limit for the number of upgrade peers to the number of all serving peers ratio. This requirement can avoid network disruption which may cost more on network maintenance of stability. Considering these two characteristics above, the fault model of disaster recovery backup system is established as follows to ensure reliable data storage. There are 3 kinds of failure model: single node failure model, nonlocal batch nodes failure model and local batch nodes failure model. In single node failure model case, exponential distribution is adopted to set up model which is usually used for reliability analysis. Suppose the failure density function is exponentially distributed. Using P 11 represents the failure rate, which is equal to 1/MT BF. China enforces MT BF of PC is 4000 hours. We assume MT BF equals 1000 hours to leave a sufficient margin for the situations that the system upgrades and bug fix in the actual scene. Thus, the failure rate is calculated as below. P 11 = 1/MT BF = 1/1000 = 0.1% As to another case for single node failure model, upgrade peer exit abnormally, we build the model as follows. Average online time and the proportion of the network need to be considered for the upgrade peer to estimate the probability of abnormal offline. The research data show that ordinary P2P user s online time is about 2.9 hours [7]. With the variable P 12 indicates the probability of abnormal offline for upgrade peer, P 12 = 1/2.9 = % Suppose the upper limit for the number of upgrade peers to the number of all serving peers ratio is M/N. Thus, the single node failure rate is calculated below. P 1 = 1 (1 P 11 ) [1 (UP/SP ) P 12 ] We subjectively assume that the possibility for nonlocal batch nodes failure case is exponentially distributed, that means MT BF equals hours. Using P 2 represents nonlocal batch nodes failure rate. Therefore, P 2 can be calculated as below. P 2 = 1/MT BF = 1/20000 = 0.005% It shows that this situation is extremely rare to happen. Local batch nodes failure often happens in the case of the earthquake or human misconfiguration. In these cases, the closed analysis has little significance. At present, Huawei s HSS use offsite backup method and put them in this province and another province. This method is adopted by our paper. 3.3 Performance goals This disaster recovery backup strategy should meet the basic function of data storage. In addition, there are four performance requirements as follows.
4 8102 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) (1) High Availability: Although this system is used in distributed VoIP system, it still has to meet the requirements of carrier-grade five nines. That means the availability should reach %. Our system will use ordinary PC to achieve the same effect. (2) Scalability: The system should be easy to expand. When the amount of VoIP user data become more and more, the system needs to support the ability to smoothly expand. (3) Cost-effectiveness: Input costs of this system must be lower than the current telecommunications systems and other P2P-based VoIP systems while providing the same performance. This system uses ordinary PCs to provide service. Meanwhile, it allows the user node to upgrade to be a serving node. In this way, it can achieve the lower input and higher dynamic scalability. (4) Self-recovery: When an exception occurs, the self-recovery function of this system will work. The topology recovery is implemented by DHT algorithm. Data recovery relays on the data recovery strategy in this paper. 3.4 Strategy design For high data availability, network coding is used to encode the source data and distribute encoded fragments with original data pieces [8]. And we use data storage strategy ensures reliable data storage and rapidly effective recovery. The key technologies contain the following three parts: the data storage and backup strategy, data recovery mechanism and data update mechanism. The following described these three aspects Data replica analysis The primary means of data disaster recovery rely on data redundancy. To ensure reliable data storage, we explain the data storage and backup strategy from two sides: the number and the placement of data replications. First, consider the number of data duplicates. Assume that the failure rate is P. According to the node failure model discussed in section 3.2, we can get the following equation: P = P 1 + P 2 = 1 (1 P 11 ) [1 (UP/SP ) P 12 ] + P 2 (1) Where P 11 = 0.1%, P 12 = 34.48%, P 2 = 0.005%, UP and SP are variable and 0 < UP/SP < 1. The formula above shows the calculation of the total probability of the single node failure and nonlocal batch nodes failure. In these two cases, assuming the number of the data replications is x. Considering the reliability requirements of the system discussed in section 3.1, it should satisfy the following inequality: 1 P x > % (2) In the former two fault model scenes, the number of replications, x, can be calculated according to the values and the range of the parameters in formula 1, inequality 2. In addition, in the case that the local batch nodes crash, we add a backup outside the domain as the strategy. To sum up, the desired total number of data replications is x + 1. The placement of the data replications is discussed below. In our system, the data is stored in the form of key-value to facilitate the storage and retrieval of user data. The specific way of
5 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) placement is as follows: According to the rules of structured P2P resource storage, the primary storage node stores a complete data, x 1 redundancies are stored in the domain, and another one copy of data is placed in another domain. The redundant replications in the domain are placed randomly to deal with the case of simultaneously continuous nodes failure. The replications in another domain are to deal with the local large quantities nodes failure Data recovery mechanism To achieve fast and reliable data recovery, this system use parallel recovery mechanism. Under this mechanism, the data in the primary storage node can be recovered by multiple backup nodes in the meantime. It can accelerate the speed of data recovery in the way that multiple backup nodes simultaneously transmit part of data to primary storage node to restore the complete data. The Dynamo system is used for key-value pairs stored in the system. The system used in a distributed network with the annular structure. There is a coordinating node to place a copy of the data on the primary storage node and its N 1 consecutive successor nodes. In order to reduce the influence brought about by the limited recovery resources, Dynamo nodes can join the network many times as the virtual node. At the same time, it will bring the problem of the integration of data traffic. SandStone system proposed by Huawei uses the N:N copy of the replacement policy [4]. N refers to the parallel recovery factor. With the preconfigured number of backups B (e.g. 2), each primary peer will divide its stored data into S = N/B slices, and every slice will be backup by different B peers. The primary peer determines the B backup positions for its i th (starts from 1) slice by the following criteria: A slice + H size ((i 1) B + j)/(n + 1)(i = 1,, S, j = 1,, B) (3) Where, A slice is the begin address of the slice, and H size is the size of hash space. The backup replicas are stored in the successors after each position. Each backup node is only responsible for providing part of the data. The N backup data restore the data in the primary storage node simultaneously, unlike the Dynamo system which provides all data from only one backup node. Our system uses the same mechanism with SandStone for backup data placement to achieve good parallel recovery efficiency. This placement way increases the efficiency of the parallel recovery, and avoids the problem of backup data integration bandwidth wasted. Compare with 1:1 replica placement, every node to provide recovery data in our N:N replica placement method only need to have the capacity of 1/N of the original way. In this way, we can achieve the high cost performance described in section Data update mechanism Data update mechanism is critical to achieve data consistency maintenance. Data update trigger principle contains two ways: the backup node within the real-time data updates, the Outland backup node data is updated regularly. Efficient Remote Data Synchronization Algorithm is used to compare the remote replica and the crucial data, then update the remote replica using the bit difference [9]. Suppose that the parallel recovery factor is L and the replica number is x + 1 in total. The specific update strategy follows as shown in Fig. 1 to Fig. 3, including indexes keep-alive and data update flows.
6 8104 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) PrimaryStorageNode (ResponsibleID:0-k) 1_1 No.1inDomain 1_M 1_L x-1_1 No.x-1inDomain x-1_m x-1_l inanother Domain Calculaterandombackup nodesinthisregionandin anotherregion Setupindexfor databackupnodes Startindexheartbeat tokeepalive Ping Ping Ping Fig. 1: The primary storage node keep alive with the backup nodes regularly PrimaryStorageNode (ResponsibleID:0-k) No.1inthesameregion 1_M 1_1 1_L No.x-1inthesameregion x-1_1 x-1_m x-1_l Newdatastoragerequest /Existingdataupdaterequest Store/UpdateSucces Localstorage/update Markadded/updateddata Lookupindexofbackupnodes newdata/updateddata CalculateNode-IDthatshould storethisnew/updateddata Succes newdata/updateddata The Mth segmentdata Localstorage/update Succes Succes CalculateNode-IDthatshould storethisnew/updateddata The Mth segmentdata Localstorage/update Succes Fig. 2: The storage and backup procedure for new data or updated data As shown above, the primary storage node stores the index identity for every backup data replica. And primary storage node keeps alive with all its index nodes by heartbeat. When the index node fails, the timer for keep-alive will be up. Then the primary storage node will select a new index node, and transfer data to the new index to backup data. After that, it updates index information about the backup replicas and keeps alive with the new index node by heartbeat. When the data on the primary storage node is updated, the primary storage node should transmit the updated data the backup nodes to ensure data consistency. Since it is less likely to happen that all the nodes in one region crash at the same time, the backup replica in another region is updated by timer not in real time and only the changed data can be updated. 3.5 Routing To meet the real-time response requirement, the O(logN) lookup performance of traditional DHT must be revisited. Our system use improved one-hop routing method. One-hop routing is based on DHT algorithm. It has great advantages on quick query. But the cost for maintaining rout table is expensive. It should maintain two tables: one is the ordinary DHT routing table (Leafset,
7 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) PrimaryStorageNode (ResponsibleID:0-k) inanotherdomain Dataupdateregularlyinotherregion Lookupdataadded/updatedflags Lookupindexofbackupnodesinotherregion New/updateddata Succes Resetdataadded/updatedflags Localstorage/update Fig. 3: The backup procedure for new data or updated data in other region regularly FingerTable), the other is WRT (Whole Routing Table). The later one is for all peers in the network. It seems obviously that the main problem is how to update WRT. Since the WRT will become larger and larger with the expansion of the network, the update for WRT is costly. Thus, we take some enhancements for the original method. For the neighboring successors, sequential monitoring and notification has been proved an efficient tactic. Apart from that, our system determines which remote peers should get notified, because they have the failed peer in their finger table. Meanwhile, regarding the geographic distribution pattern of call sessions, we use two layered DHT overlay [10]. BPs play important role in two layered DHT overlay. BP can divide the whole overlay into several parts. Every region can only exchange update messages inside. Each BP communicates with each other to get information about other regions. Every peer in the region knows the BP in its region. If any peer joins the overlay or its direct successor crashes, this peer will notify BP in its region. The BP notification mechanism tremendously reduces the maintenance traffic demand for backbone network because one update event only transfers once in backbone links. BPs don t deal with actual application requests so that they unlikely become the bottle-neck of the performance even with the increasing of application request. In this way, the cost of WRT update can be reduced. 4 Simulation Experiments and Assessment We implemented our disaster recovery backup system in C++, atop the OMNET++ and OverSim [11] simulation tool which is an extensible, modular, component-based C++ simulation library and framework, primarily for building network simulators. In the simulation experiment, each node mainly consists of three parts: data storage controller, one-hop routing and TLS transmission which is based on security assurances. The topology of experiments environment includes 4 regions and some servers to monitor the numbers and flows of messages in the system, such as globleobserver, MessageObserver and so on. Some of the servers are used for experiment statistics to estimate the performance of the system. In the process of building and deploying our system, we have experienced a variety of issues, some operational and technical. We built a prototype of 600 peers located in four regions separately. There is one toppeer in each region. The system supports client upgrading. Thus, the real number of serving nodes is more than 600. We configure the number of upgrade peers to be 400. The total number of serving peers is actually In the system, we use enhanced one-hop overlay algorithm.
8 8106 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) Fig. 4: Time for data recovery 4.1 Reliability The system takes three kinds of fault models. The experiment data is used to consider data recovery success rate. Suppose that the proportion of the number of upgrade peers and all servicing peers is M / N and the system need to meet the recovery success rate of % or more. According to formula (1) and inequality (2) listed in section 3.4.1, we get the theoretical calculation of the minimum amount of data replicas for each ratio of the number of upgrade peers to the number of serving peers: 4 for 15%, 5 for 30%, 6 for 45%, 8 for 60% and 9 for 75%. The number of data replicas will be more and more with the ratio growing. But the maintenance of data consistency will be costly in this way. Considering the cost performance, we set the number of data replicas in the intra-region have to be four. In other words, where will be one replica in the main storage node, three backup data in the intra-region nodes and one backup data in the extra-region node. Totally, five data replicas are necessary. We set the replicas number as five in our experiments to see the reliability. The experiments were made under 3 kinds of failure model talked above. Besides, 3 kinds of application models were set for each different failure model as follows. Firstly, write once and read 10 times for each node per second. Then, write 10 times and read 50 times for each node per second. Next, write 50 times and read 100 times for each node per second. For each application model, the number of upgrade peers to the number of serving peers ratio were set to be 15%, 30%, 45%, 60% and 75%. From the results, we can see that the success rate of recovery is all more than 99.5% when we set the upper limit to the number of replicas. It is an adoptable method to achieve our goal in system reliability. 4.2 Recovery time The recovery time discussed in this paper begins with the time of the main storage node failed and ends with the time that all data completely restored in the new main storage node, including the time that consume on backup nodes detects the main storage node s failure by heartbeat and transmit data to new main storage data. The pre-condition of the experiment is that each node stored approximately 10,000 user data and the interval of heartbeats set to 0.5 minutes. The experimental results are as shown in Fig. 4. From Fig. 4, with the growth of the parallel recovery factor, the time for recovery becomes
9 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) shorter. It means that the more recovery nodes can bring less recovery time. This kind of recovery time in the Carrier Grade network is totally acceptable. The recovery time relays on the parallel factor and processing capacity of the main storage node. Although the parallel factor can become big enough, the processing capacity of the peer is limited. Thus, the recovery time will be not declining linearly with the growing of the parallel factor. We can see from the figure, after parallel factor 6, the recovery time declines slowly. Even after 10, it doesn t change any more. It may be the bound value limited by the peer s processing capacity. 4.3 Bandwidth consumption In this paper, the design of disaster recovery backup system should achieve high data reliability and recoverability taking the bandwidth as the loss. Bandwidth consumption is used for main storage node send heartbeat messages with the node that stored the first segment backup data in the same region and the node that have backup data in another region. Thus, the main storage node can detect backup nodes failure to achieve the data high availability and timely recovery. Another main consumption is cost in data recovery. The bandwidth consumption for heartbeat is very small. We just take the bandwidth consumption for data recovery as the main point. The specific experimental data is shown in Table 1. Table 1: Bandwidth consumption for single node failed in different parallel recovery factor The parallel recovery factor Bandwidth consumption (KB/s) From Table 1, we can see that the bandwidth consumption becomes more and more. But it changes tiny. It varies just because the number of parallel recovery nodes is different. Since the messages for recovery contains a light weighted header, the more parallel recovery nodes, the more messages will be transmit. Thus, the bandwidth consumption will be a little more. The change is almost linear. Regarding this aspect, the parallel recovery factor should be as small as it can be. Considering Fig. 4 and Table 1, we can summarize that the recovery time has a boundary value because of the processing capacity of serving node and the bandwidth consumption will become a little more with the increase of the parallel recovery factor. We assume the benefit equals the decline of the recovery time minus the increase of the bandwidth consumption with the increase of the parallel recovery factor. There is a yield curve as Fig. 5. From Fig. 5, we can see that when the parallel recovery factor is six, the benefit reach the biggest value. Thus, the best parallel recovery factor in our simulation environment is six. And the Table 2 shows the bandwidth consumption in different failure models while the parallel recovery factor sets six. It shows that the bandwidth consumption in the three kinds of Failure Model is very tiny. Even in nonlocal batch nodes failure model, the bandwidth consumption is just KB/s. It can be almost ignored. The simulate experiments above prove that our disaster recovery backup strategy is feasible. The targets listed in section 3.3 can be achieved.
10 8108 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) Fig. 5: The yield with the change of parallel recovery factor Table 2: The theoretical calculation of the minimum amount of data replicas Failure Model Failure ratio Bandwidth consumption (KB/s) 1% 1.76 single node failure model 5% % fails nonlocal batch nodes failure model 100 fails fails fails local batch nodes failure mode 100% Conclusion This paper describes the disaster recovery backup system based on VoIP business in P2P network. The system implements reliable storage and rapid recovery for VoIP users data with high availability, scalability, and carrier-class guarantee. After analyzing the characteristics of specific VoIP business, the flexibility of the role for the common client in the system and the small amount of each user information data which changes frequently, we put forward the design of the storage system, including the core data storage controller strategy, the routing mechanism and TLS transmission mode. Its innovation lies in the determination of the number of data replicas, N:N data placement strategy and the principle of multi-node parallel recovery mechanism. Finally, we simulate and run the prototype in an experimental environment (thousands of nodes). The results show that the design of the system can meet the expected target. The design and implementation in this paper have practical significance. Acknowledgements Important national science & technology specific projects:next-generation broadband wireless mobile communications network (2010ZX ), Innovative Research Groups of the National Natural Science Foundation of China ( ), National Key Basic Research Program of China (973 Program)(2009CB320504).
11 K. Shuang et al. /Journal of Computational Information Systems 9: 20 (2013) References [1] Ghemawat, S., Gobioff, H., and Leung. The Google file system. In Proc of ACM SOSP, [2] Zheng Zhang, Qiao Lian,Shiding Lin et al. BitVault: a Highly Reliable Distributed Data Retention Platform [J]. Operating systems review, 2007, 41(2): DOI: / [3] Giuseppe DeCandia, Deniz Hastorun,Madan Jampani et al. Dynamo: Amazon s Highly Available Key-value Store [J]. Operating systems review, 2007, 41(6): DOI: / [4] Guangyu Shi, Jian Chen,Hao Gong et al. SandStone: A DHT based Carrier Grade Distributed Storage System [C] th International Conference on Parallel Processing (ICPP 2009). 2009: [5] Cheng Lan, Luo Jian. ActiveSuper Node Based Layered DHT P2P Model Research [J]. Aeronautical Computing Technique, 2012, 42(5): [6] Yu Zhang, Yuanda Cao, Baodong Cheng. A Layered P2P Network Topology Based on Physical Network Topology [C]. The 4th International Conference on Wireless Communications, Networking and Mobile Computing. 2008: 1-4. [7] S. Saroiu, P.K. Gummadi, and S.D. Gribble. A measurement study of peer-to-peer file sharing systems [C]. Proceedings of the Multimedia Computing and Networking (MMCN), San Jose, January, [8] Zeng, Rongfei, Jiang, Yixin,Lin, Chuang et al. A Distributed Fault/Intrusion-Tolerant Sensor Data Storage Scheme Based on Network Coding and Homomorphic Fingerprinting [J]. IEEE Transactions on Parallel and Distributed Systems, 2012, 23(10): [9] Zhonghua Li. Network Data Disaster Recovery Scheme for Bulk Data of E-Commerce and E- Government [J]. Journal of Computational Information Systems, 2009, 5 (2): [10] Ye Ping, Li Yizhong, Xia Qin. Strategy Research on Delay Optimizing oriented Overlay Routing [J]. Chinese Journal of Computers, 2010, 33(1): DOI: / SP.J [11] Cui Jianqun, Lai Mincai, Jiang Wenbin. OverSim: A Scalable Application Layer Multicast Network Simulation Framework [J]. Computer Engineering and Science, 2012, 34(10): 1-5. DOI: /j.issn X
An Optimization Model of Load Balancing in P2P SIP Architecture
An Optimization Model of Load Balancing in P2P SIP Architecture 1 Kai Shuang, 2 Liying Chen *1, First Author, Corresponding Author Beijing University of Posts and Telecommunications, shuangk@bupt.edu.cn
More informationP2P VoIP for Today s Premium Voice Service 1
1 P2P VoIP for Today s Premium Voice Service 1 Ayaskant Rath, Stevan Leiden, Yong Liu, Shivendra S. Panwar, Keith W. Ross ARath01@students.poly.edu, {YongLiu, Panwar, Ross}@poly.edu, Steve.Leiden@verizon.com
More informationA Topology-Aware Relay Lookup Scheme for P2P VoIP System
Int. J. Communications, Network and System Sciences, 2010, 3, 119-125 doi:10.4236/ijcns.2010.32018 Published Online February 2010 (http://www.scirp.org/journal/ijcns/). A Topology-Aware Relay Lookup Scheme
More informationbcp for a large scale carrier level VoIP system
bcp for a large scale carrier level VoIP system using p2psip draft zhang p2psip bcp 04 Yunfei.Zhang Gang.Li Jin.Peng Baohong.He Shihui.Duan Wei.Zhu {zhangyunfei,ligangyf,pengjin}@chinamobile.com {hebaohong,duanshihui,zhuwei}@catr.cn
More informationhttp://www.paper.edu.cn
5 10 15 20 25 30 35 A platform for massive railway information data storage # SHAN Xu 1, WANG Genying 1, LIU Lin 2** (1. Key Laboratory of Communication and Information Systems, Beijing Municipal Commission
More informationBig Data Storage Architecture Design in Cloud Computing
Big Data Storage Architecture Design in Cloud Computing Xuebin Chen 1, Shi Wang 1( ), Yanyan Dong 1, and Xu Wang 2 1 College of Science, North China University of Science and Technology, Tangshan, Hebei,
More informationMemory Database Application in the Processing of Huge Amounts of Data Daqiang Xiao 1, Qi Qian 2, Jianhua Yang 3, Guang Chen 4
5th International Conference on Advanced Materials and Computer Science (ICAMCS 2016) Memory Database Application in the Processing of Huge Amounts of Data Daqiang Xiao 1, Qi Qian 2, Jianhua Yang 3, Guang
More informationA Brief Analysis on Architecture and Reliability of Cloud Based Data Storage
Volume 2, No.4, July August 2013 International Journal of Information Systems and Computer Sciences ISSN 2319 7595 Tejaswini S L Jayanthy et al., Available International Online Journal at http://warse.org/pdfs/ijiscs03242013.pdf
More informationFault-Tolerant Framework for Load Balancing System
Fault-Tolerant Framework for Load Balancing System Y. K. LIU, L.M. CHENG, L.L.CHENG Department of Electronic Engineering City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong SAR HONG KONG Abstract:
More informationScalable Multiple NameNodes Hadoop Cloud Storage System
Vol.8, No.1 (2015), pp.105-110 http://dx.doi.org/10.14257/ijdta.2015.8.1.12 Scalable Multiple NameNodes Hadoop Cloud Storage System Kun Bi 1 and Dezhi Han 1,2 1 College of Information Engineering, Shanghai
More informationA Load Balancing Method in SiCo Hierarchical DHT-based P2P Network
1 Shuang Kai, 2 Qu Zheng *1, Shuang Kai Beijing University of Posts and Telecommunications, shuangk@bupt.edu.cn 2, Qu Zheng Beijing University of Posts and Telecommunications, buptquzheng@gmail.com Abstract
More informationInternational Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 349 ISSN 2229-5518
International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 349 Load Balancing Heterogeneous Request in DHT-based P2P Systems Mrs. Yogita A. Dalvi Dr. R. Shankar Mr. Atesh
More informationAdapting Distributed Hash Tables for Mobile Ad Hoc Networks
University of Tübingen Chair for Computer Networks and Internet Adapting Distributed Hash Tables for Mobile Ad Hoc Networks Tobias Heer, Stefan Götz, Simon Rieche, Klaus Wehrle Protocol Engineering and
More informationA Slow-sTart Exponential and Linear Algorithm for Energy Saving in Wireless Networks
1 A Slow-sTart Exponential and Linear Algorithm for Energy Saving in Wireless Networks Yang Song, Bogdan Ciubotaru, Member, IEEE, and Gabriel-Miro Muntean, Member, IEEE Abstract Limited battery capacity
More informationCloud Computing Disaster Recovery (DR)
Cloud Computing Disaster Recovery (DR) Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF Need for Disaster Recovery (DR) What happens when you
More informationA Deduplication-based Data Archiving System
2012 International Conference on Image, Vision and Computing (ICIVC 2012) IPCSIT vol. 50 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V50.20 A Deduplication-based Data Archiving System
More informationA Network Simulation Experiment of WAN Based on OPNET
A Network Simulation Experiment of WAN Based on OPNET 1 Yao Lin, 2 Zhang Bo, 3 Liu Puyu 1, Modern Education Technology Center, Liaoning Medical University, Jinzhou, Liaoning, China,yaolin111@sina.com *2
More informationPerformance Evaluation of AODV, OLSR Routing Protocol in VOIP Over Ad Hoc
(International Journal of Computer Science & Management Studies) Vol. 17, Issue 01 Performance Evaluation of AODV, OLSR Routing Protocol in VOIP Over Ad Hoc Dr. Khalid Hamid Bilal Khartoum, Sudan dr.khalidbilal@hotmail.com
More informationTOPOLOGIES NETWORK SECURITY SERVICES
TOPOLOGIES NETWORK SECURITY SERVICES 1 R.DEEPA 1 Assitant Professor, Dept.of.Computer science, Raja s college of Tamil Studies & Sanskrit,Thiruvaiyaru ABSTRACT--In the paper propose about topology security
More informationResearch and Application of Redundant Data Deleting Algorithm Based on the Cloud Storage Platform
Send Orders for Reprints to reprints@benthamscience.ae 50 The Open Cybernetics & Systemics Journal, 2015, 9, 50-54 Open Access Research and Application of Redundant Data Deleting Algorithm Based on the
More informationMulticast vs. P2P for content distribution
Multicast vs. P2P for content distribution Abstract Many different service architectures, ranging from centralized client-server to fully distributed are available in today s world for Content Distribution
More informationDistributed Consistency Method and Two-Phase Locking in Cloud Storage over Multiple Data Centers
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 15, No 6 Special Issue on Logistics, Informatics and Service Science Sofia 2015 Print ISSN: 1311-9702; Online ISSN: 1314-4081
More informationInternational journal of Engineering Research-Online A Peer Reviewed International Journal Articles available online http://www.ijoer.
RESEARCH ARTICLE ISSN: 2321-7758 GLOBAL LOAD DISTRIBUTION USING SKIP GRAPH, BATON AND CHORD J.K.JEEVITHA, B.KARTHIKA* Information Technology,PSNA College of Engineering & Technology, Dindigul, India Article
More informationDesign of Remote data acquisition system based on Internet of Things
, pp.32-36 http://dx.doi.org/10.14257/astl.214.79.07 Design of Remote data acquisition system based on Internet of Things NIU Ling Zhou Kou Normal University, Zhoukou 466001,China; Niuling@zknu.edu.cn
More informationSAN Conceptual and Design Basics
TECHNICAL NOTE VMware Infrastructure 3 SAN Conceptual and Design Basics VMware ESX Server can be used in conjunction with a SAN (storage area network), a specialized high speed network that connects computer
More informationNew Cloud Computing Network Architecture Directed At Multimedia
2012 2 nd International Conference on Information Communication and Management (ICICM 2012) IPCSIT vol. 55 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V55.16 New Cloud Computing Network
More informationCLOUDDMSS: CLOUD-BASED DISTRIBUTED MULTIMEDIA STREAMING SERVICE SYSTEM FOR HETEROGENEOUS DEVICES
CLOUDDMSS: CLOUD-BASED DISTRIBUTED MULTIMEDIA STREAMING SERVICE SYSTEM FOR HETEROGENEOUS DEVICES 1 MYOUNGJIN KIM, 2 CUI YUN, 3 SEUNGHO HAN, 4 HANKU LEE 1,2,3,4 Department of Internet & Multimedia Engineering,
More informationA SWOT ANALYSIS ON CISCO HIGH AVAILABILITY VIRTUALIZATION CLUSTERS DISASTER RECOVERY PLAN
A SWOT ANALYSIS ON CISCO HIGH AVAILABILITY VIRTUALIZATION CLUSTERS DISASTER RECOVERY PLAN Eman Al-Harbi 431920472@student.ksa.edu.sa Soha S. Zaghloul smekki@ksu.edu.sa Faculty of Computer and Information
More informationResearch on P2P-SIP based VoIP system enhanced by UPnP technology
December 2010, 17(Suppl. 2): 36 40 www.sciencedirect.com/science/journal/10058885 The Journal of China Universities of Posts and Telecommunications http://www.jcupt.com Research on P2P-SIP based VoIP system
More informationLoad Balancing in Structured Peer to Peer Systems
Load Balancing in Structured Peer to Peer Systems DR.K.P.KALIYAMURTHIE 1, D.PARAMESWARI 2 Professor and Head, Dept. of IT, Bharath University, Chennai-600 073 1 Asst. Prof. (SG), Dept. of Computer Applications,
More informationOptimization of Distributed Crawler under Hadoop
MATEC Web of Conferences 22, 0202 9 ( 2015) DOI: 10.1051/ matecconf/ 2015220202 9 C Owned by the authors, published by EDP Sciences, 2015 Optimization of Distributed Crawler under Hadoop Xiaochen Zhang*
More informationA Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster
, pp.11-20 http://dx.doi.org/10.14257/ ijgdc.2014.7.2.02 A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster Kehe Wu 1, Long Chen 2, Shichao Ye 2 and Yi Li 2 1 Beijing
More informationSmart Queue Scheduling for QoS Spring 2001 Final Report
ENSC 833-3: NETWORK PROTOCOLS AND PERFORMANCE CMPT 885-3: SPECIAL TOPICS: HIGH-PERFORMANCE NETWORKS Smart Queue Scheduling for QoS Spring 2001 Final Report By Haijing Fang(hfanga@sfu.ca) & Liu Tang(llt@sfu.ca)
More informationResearch on Job Scheduling Algorithm in Hadoop
Journal of Computational Information Systems 7: 6 () 5769-5775 Available at http://www.jofcis.com Research on Job Scheduling Algorithm in Hadoop Yang XIA, Lei WANG, Qiang ZHAO, Gongxuan ZHANG School of
More informationStudy on Redundant Strategies in Peer to Peer Cloud Storage Systems
Applied Mathematics & Information Sciences An International Journal 2011 NSP 5 (2) (2011), 235S-242S Study on Redundant Strategies in Peer to Peer Cloud Storage Systems Wu Ji-yi 1, Zhang Jian-lin 1, Wang
More informationInternational Journal of Advanced Research in Computer Science and Software Engineering
Volume 2, Issue 9, September 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com An Experimental
More informationMPLS: Key Factors to Consider When Selecting Your MPLS Provider Whitepaper
MPLS: Key Factors to Consider When Selecting Your MPLS Provider Whitepaper 2006-20011 EarthLink Business Page 1 EXECUTIVE SUMMARY Multiprotocol Label Switching (MPLS), once the sole domain of major corporations
More informationDisaster Recovery Design Ehab Ashary University of Colorado at Colorado Springs
Disaster Recovery Design Ehab Ashary University of Colorado at Colorado Springs As a head of the campus network department in the Deanship of Information Technology at King Abdulaziz University for more
More informationSafety in Numbers. Using Multiple WAN Links to Secure Your Network. Roger J. Ruby Sr. Product Manager August 2002. Intelligent WAN Access Solutions
Copyright 2002 Quick Eagle Networks Inc. All rights reserved. The White Paper Series Safety in Numbers Using Multiple WAN Links to Secure Your Network Roger J. Ruby Sr. Product Manager August 2002 Executive
More informationHow To Protect Data On Network Attached Storage (Nas) From Disaster
White Paper EMC FOR NETWORK ATTACHED STORAGE (NAS) BACKUP AND RECOVERY Abstract This white paper provides an overview of EMC s industry leading backup and recovery solutions for NAS systems. It also explains
More informationA Distributed Architecture of Video Conference Using P2P Technology
1852 JOURNAL OF NETWORKS, VOL. 7, NO. 11, NOVEMBER 2012 A Distributed Architecture of Video Conference Using P2P Technology Xiaoyan Yu State Key Laboratory of Networking & Switching Technology Beijing
More informationNQA Technology White Paper
NQA Technology White Paper Keywords: NQA, test, probe, collaboration, scheduling Abstract: Network Quality Analyzer (NQA) is a network performance probe and statistics technology used to collect statistics
More informationDistributed file system in cloud based on load rebalancing algorithm
Distributed file system in cloud based on load rebalancing algorithm B.Mamatha(M.Tech) Computer Science & Engineering Boga.mamatha@gmail.com K Sandeep(M.Tech) Assistant Professor PRRM Engineering College
More informationMobile Storage and Search Engine of Information Oriented to Food Cloud
Advance Journal of Food Science and Technology 5(10): 1331-1336, 2013 ISSN: 2042-4868; e-issn: 2042-4876 Maxwell Scientific Organization, 2013 Submitted: May 29, 2013 Accepted: July 04, 2013 Published:
More informationService Quality Assurance Mechanisms for P2P SIP VoIP
Service Quality Assurance Mechanisms for P2P SIP VoIP Xiaofei Liao, Fengjiang Guo, Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology
More informationAutonomous Fault Detection and Recovery System in Large-scale Networks
Autonomous Fault Detection and Recovery System in Large-scale Networks Raheel Ahmed Memon 1, Yeonseung Ryu, Abdul Qadir Rahimoo Abstract In networks like Combat ship data network, the Ethernet is required
More informationMagnus: Peer to Peer Backup System
Magnus: Peer to Peer Backup System Naveen Gattu, Richard Huang, John Lynn, Huaxia Xia Department of Computer Science University of California, San Diego Abstract Magnus is a peer-to-peer backup system
More informationCisco WAAS for Isilon IQ
Cisco WAAS for Isilon IQ Integrating Cisco WAAS with Isilon IQ Clustered Storage to Enable the Next-Generation Data Center An Isilon Systems/Cisco Systems Whitepaper January 2008 1 Table of Contents 1.
More informationHigh Availability and Clustering
High Availability and Clustering AdvOSS-HA is a software application that enables High Availability and Clustering; a critical requirement for any carrier grade solution. It implements multiple redundancy
More informationLoad Balancing in Structured Peer to Peer Systems
Load Balancing in Structured Peer to Peer Systems Dr.K.P.Kaliyamurthie 1, D.Parameswari 2 1.Professor and Head, Dept. of IT, Bharath University, Chennai-600 073. 2.Asst. Prof.(SG), Dept. of Computer Applications,
More informationBackup with synchronization/ replication
Backup with synchronization/ replication Peer-to-peer synchronization and replication software can augment and simplify existing data backup and retrieval systems. BY PAUL MARSALA May, 2001 According to
More informationOptimizing Congestion in Peer-to-Peer File Sharing Based on Network Coding
International Journal of Emerging Trends in Engineering Research (IJETER), Vol. 3 No.6, Pages : 151-156 (2015) ABSTRACT Optimizing Congestion in Peer-to-Peer File Sharing Based on Network Coding E.ShyamSundhar
More informationHardware Configuration Guide
Hardware Configuration Guide Contents Contents... 1 Annotation... 1 Factors to consider... 2 Machine Count... 2 Data Size... 2 Data Size Total... 2 Daily Backup Data Size... 2 Unique Data Percentage...
More informationArchitecture of distributed network processors: specifics of application in information security systems
Architecture of distributed network processors: specifics of application in information security systems V.Zaborovsky, Politechnical University, Sait-Petersburg, Russia vlad@neva.ru 1. Introduction Modern
More informationSCADA. Supervisory Control and Data Acquisition. www.newtec.eu. How to monitor and control your business operation in the most cost-effective way.
Supervisory Control and Data Acquisition The ability to remotely monitor, control and report on the results of distributed assets is key to achieving maximum operational efficiency. The abundance of measuring
More informationIndex Terms : Load rebalance, distributed file systems, clouds, movement cost, load imbalance, chunk.
Load Rebalancing for Distributed File Systems in Clouds. Smita Salunkhe, S. S. Sannakki Department of Computer Science and Engineering KLS Gogte Institute of Technology, Belgaum, Karnataka, India Affiliated
More informationConventionally, software testing has aimed at verifying functionality but the testing paradigm has changed for software services.
1 Conventionally, software testing has aimed at verifying functionality but the testing paradigm has changed for software services. Developing a full-featured and functioning software service is necessary;
More informationEthernet. Ethernet. Network Devices
Ethernet Babak Kia Adjunct Professor Boston University College of Engineering ENG SC757 - Advanced Microprocessor Design Ethernet Ethernet is a term used to refer to a diverse set of frame based networking
More informationDefinition. A Historical Example
Overlay Networks This lecture contains slides created by Ion Stoica (UC Berkeley). Slides used with permission from author. All rights remain with author. Definition Network defines addressing, routing,
More informationCDMA-based network video surveillance System Solutions
1 Contact:Peter Zhang Email: sales10@caimore.com CDMA-based network video surveillance System Solutions Introduction In recent years, mobile communication, video surveillance for its intuitive, easy to
More informationMulti Protocol Label Switching (MPLS) is a core networking technology that
MPLS and MPLS VPNs: Basics for Beginners Christopher Brandon Johnson Abstract Multi Protocol Label Switching (MPLS) is a core networking technology that operates essentially in between Layers 2 and 3 of
More informationTrace Driven Analysis of the Long Term Evolution of Gnutella Peer-to-Peer Traffic
Trace Driven Analysis of the Long Term Evolution of Gnutella Peer-to-Peer Traffic William Acosta and Surendar Chandra University of Notre Dame, Notre Dame IN, 46556, USA {wacosta,surendar}@cse.nd.edu Abstract.
More informationResearch Article ISSN 2277 9140 Copyright by the authors - Licensee IJACIT- Under Creative Commons license 3.0
INTERNATIONAL JOURNAL OF ADVANCES IN COMPUTING AND INFORMATION TECHNOLOGY An international, online, open access, peer reviewed journal Volume 2 Issue 2 April 2013 Research Article ISSN 2277 9140 Copyright
More informationConstructing High Quality IP Core Network
Constructing High Quality IP Core Network What we need is not only a network, but also the services that network can provide. ---------Huawei-3Com Constructing networks is much like building bridges or
More informationStudy of Different Types of Attacks on Multicast in Mobile Ad Hoc Networks
Study of Different Types of Attacks on Multicast in Mobile Ad Hoc Networks Hoang Lan Nguyen and Uyen Trang Nguyen Department of Computer Science and Engineering, York University 47 Keele Street, Toronto,
More informationVoIP QoS. Version 1.0. September 4, 2006. AdvancedVoIP.com. sales@advancedvoip.com support@advancedvoip.com. Phone: +1 213 341 1431
VoIP QoS Version 1.0 September 4, 2006 AdvancedVoIP.com sales@advancedvoip.com support@advancedvoip.com Phone: +1 213 341 1431 Copyright AdvancedVoIP.com, 1999-2006. All Rights Reserved. No part of this
More informationKeywords Wimax,Voip,Mobility Patterns, Codes,opnet
Volume 5, Issue 8, August 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Effect of Mobility
More informationInternational Journal of Applied Science and Technology Vol. 2 No. 3; March 2012. Green WSUS
International Journal of Applied Science and Technology Vol. 2 No. 3; March 2012 Abstract 112 Green WSUS Seifedine Kadry, Chibli Joumaa American University of the Middle East Kuwait The new era of information
More informationTechnical White Paper for the Oceanspace VTL6000
Document No. Technical White Paper for the Oceanspace VTL6000 Issue V2.1 Date 2010-05-18 Huawei Symantec Technologies Co., Ltd. Copyright Huawei Symantec Technologies Co., Ltd. 2010. All rights reserved.
More informationDistributed Software Development with Perforce Perforce Consulting Guide
Distributed Software Development with Perforce Perforce Consulting Guide Get an overview of Perforce s simple and scalable software version management solution for supporting distributed development teams.
More informationMonitoring Large Flows in Network
Monitoring Large Flows in Network Jing Li, Chengchen Hu, Bin Liu Department of Computer Science and Technology, Tsinghua University Beijing, P. R. China, 100084 { l-j02, hucc03 }@mails.tsinghua.edu.cn,
More informationSystem Infrastructure Non-Functional Requirements Related Item List
System Infrastructure Non-Functional Requirements Related Item List April 2013 Information-Technology Promotion Agency, Japan Software Engineering Center Copyright 2010 IPA [Usage conditions] 1. The copyright
More informationA Scheme for Implementing Load Balancing of Web Server
Journal of Information & Computational Science 7: 3 (2010) 759 765 Available at http://www.joics.com A Scheme for Implementing Load Balancing of Web Server Jianwu Wu School of Politics and Law and Public
More informationDistributed File Systems
Distributed File Systems Mauro Fruet University of Trento - Italy 2011/12/19 Mauro Fruet (UniTN) Distributed File Systems 2011/12/19 1 / 39 Outline 1 Distributed File Systems 2 The Google File System (GFS)
More informationVMware vsphere Data Protection
VMware vsphere Data Protection Replication Target TECHNICAL WHITEPAPER 1 Table of Contents Executive Summary... 3 VDP Identities... 3 vsphere Data Protection Replication Target Identity (VDP-RT)... 3 Replication
More informationHigh Throughput Computing on P2P Networks. Carlos Pérez Miguel carlos.perezm@ehu.es
High Throughput Computing on P2P Networks Carlos Pérez Miguel carlos.perezm@ehu.es Overview High Throughput Computing Motivation All things distributed: Peer-to-peer Non structured overlays Structured
More informationA NOVEL RESOURCE EFFICIENT DMMS APPROACH
A NOVEL RESOURCE EFFICIENT DMMS APPROACH FOR NETWORK MONITORING AND CONTROLLING FUNCTIONS Golam R. Khan 1, Sharmistha Khan 2, Dhadesugoor R. Vaman 3, and Suxia Cui 4 Department of Electrical and Computer
More informationIPv4 and IPv6: Connecting NAT-PT to Network Address Pool
Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(5):547-553 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 Intercommunication Strategy about IPv4/IPv6 coexistence
More informationThe Google File System
The Google File System By Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung (Presented at SOSP 2003) Introduction Google search engine. Applications process lots of data. Need good file system. Solution:
More informationCloud Computing for Agent-based Traffic Management Systems
Cloud Computing for Agent-based Traffic Management Systems Manoj A Patil Asst.Prof. IT Dept. Khyamling A Parane Asst.Prof. CSE Dept. D. Rajesh Asst.Prof. IT Dept. ABSTRACT Increased traffic congestion
More informationDeploying VSaaS and Hosted Solutions Using CompleteView
SALIENT SYSTEMS WHITE PAPER Deploying VSaaS and Hosted Solutions Using CompleteView Understanding the benefits of CompleteView for hosted solutions and successful deployment architecture Salient Systems
More informationEudemon8000 High-End Security Gateway HUAWEI TECHNOLOGIES CO., LTD.
Eudemon8000 High-End Security Gateway HUAWEI TECHNOLOGIES CO., LTD. Product Overview Faced with increasingly serious network threats and dramatically increased network traffic, carriers' backbone networks,
More informationHow To Make A Vpc More Secure With A Cloud Network Overlay (Network) On A Vlan) On An Openstack Vlan On A Server On A Network On A 2D (Vlan) (Vpn) On Your Vlan
Centec s SDN Switch Built from the Ground Up to Deliver an Optimal Virtual Private Cloud Table of Contents Virtualization Fueling New Possibilities Virtual Private Cloud Offerings... 2 Current Approaches
More informationWHITEPAPER MPLS: Key Factors to Consider When Selecting Your MPLS Provider
WHITEPAPER MPLS: Key Factors to Consider When Selecting Your MPLS Provider INTRODUCTION Multiprotocol Label Switching (MPLS), once the sole domain of major corporations and telecom carriers, has gone mainstream
More informationIMPLEMENTATION OF SOURCE DEDUPLICATION FOR CLOUD BACKUP SERVICES BY EXPLOITING APPLICATION AWARENESS
IMPLEMENTATION OF SOURCE DEDUPLICATION FOR CLOUD BACKUP SERVICES BY EXPLOITING APPLICATION AWARENESS Nehal Markandeya 1, Sandip Khillare 2, Rekha Bagate 3, Sayali Badave 4 Vaishali Barkade 5 12 3 4 5 (Department
More informationInfluence of Load Balancing on Quality of Real Time Data Transmission*
SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol. 6, No. 3, December 2009, 515-524 UDK: 004.738.2 Influence of Load Balancing on Quality of Real Time Data Transmission* Nataša Maksić 1,a, Petar Knežević 2,
More informationPortable Wireless Mesh Networks: Competitive Differentiation
Portable Wireless Mesh Networks: Competitive Differentiation Rajant Corporation s kinetic mesh networking solutions combine specialized command and control software with ruggedized, high-performance hardware.
More informationDynamic Adaptive Feedback of Load Balancing Strategy
Journal of Information & Computational Science 8: 10 (2011) 1901 1908 Available at http://www.joics.com Dynamic Adaptive Feedback of Load Balancing Strategy Hongbin Wang a,b, Zhiyi Fang a,, Shuang Cui
More informationDisaster Recovery Solutions for Oracle Database Standard Edition RAC. A Dbvisit White Paper
Disaster Recovery Solutions for Oracle Database Standard Edition RAC A Dbvisit White Paper Copyright 2011-2012 Dbvisit Software Limited. All Rights Reserved v2, Mar 2012 Contents Executive Summary... 1
More informationResearch on Operation Management under the Environment of Cloud Computing Data Center
, pp.185-192 http://dx.doi.org/10.14257/ijdta.2015.8.2.17 Research on Operation Management under the Environment of Cloud Computing Data Center Wei Bai and Wenli Geng Computer and information engineering
More informationReal-time Protection for Hyper-V
1-888-674-9495 www.doubletake.com Real-time Protection for Hyper-V Real-Time Protection for Hyper-V Computer virtualization has come a long way in a very short time, triggered primarily by the rapid rate
More informationQuestions to be responded to by the firm submitting the application
Questions to be responded to by the firm submitting the application Why do you think this project should receive an award? How does it demonstrate: innovation, quality, and professional excellence transparency
More informationHigh Availability and Disaster Recovery for Exchange Servers Through a Mailbox Replication Approach
High Availability and Disaster Recovery for Exchange Servers Through a Mailbox Replication Approach Introduction Email is becoming ubiquitous and has become the standard tool for communication in many
More informationStorage Systems Autumn 2009. Chapter 6: Distributed Hash Tables and their Applications André Brinkmann
Storage Systems Autumn 2009 Chapter 6: Distributed Hash Tables and their Applications André Brinkmann Scaling RAID architectures Using traditional RAID architecture does not scale Adding news disk implies
More informationLecture 5: GFS & HDFS! Claudia Hauff (Web Information Systems)! ti2736b-ewi@tudelft.nl
Big Data Processing, 2014/15 Lecture 5: GFS & HDFS!! Claudia Hauff (Web Information Systems)! ti2736b-ewi@tudelft.nl 1 Course content Introduction Data streams 1 & 2 The MapReduce paradigm Looking behind
More informationAnalysis of Effect of Handoff on Audio Streaming in VOIP Networks
Beyond Limits... Volume: 2 Issue: 1 International Journal Of Advance Innovations, Thoughts & Ideas Analysis of Effect of Handoff on Audio Streaming in VOIP Networks Shivani Koul* shivanikoul2@gmail.com
More informationCS2510 Computer Operating Systems
CS2510 Computer Operating Systems HADOOP Distributed File System Dr. Taieb Znati Computer Science Department University of Pittsburgh Outline HDF Design Issues HDFS Application Profile Block Abstraction
More informationCS2510 Computer Operating Systems
CS2510 Computer Operating Systems HADOOP Distributed File System Dr. Taieb Znati Computer Science Department University of Pittsburgh Outline HDF Design Issues HDFS Application Profile Block Abstraction
More information