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International Journal of Multidisciplinary Research and Development Online ISSN: 2349-4182 Print ISSN: 2349-5979 www.allsubjectjournal.com Volume 3; Issue 3; March 2016; Page No. 174-175; (Special Issue) Performance Issues in Computer Networks Speed M. Mahalakshmi, Dr. N. Pasupathi, P. Manimegalai Research scholar, department of Electronics, Erode Arts &Science College, Erode Associate professor in Electronics, Erode Arts & science College, Erode Abstract The data path between any two computers involves dozens, sometimes thousands, of hardware and software devices. Any one of these may have a substantial impact on performance. At any given time there is likely to be one factor which most directly limits the maximum speed of a data flow. Identifying the limiting factor for each data flow is vital to improving performance. The limiting factor may change with time or in response to changes in other factors. Changing network conditions may change the relative performance of MTP and TCP. Test under a variety of conditions and learn all you can about your network environment. Keywords: Computer Networks Introduction Performance issues are very important in computer networks. When hundreds of thousands of computers are connected together complex interaction with unforeseen consequences are common. Frequently this complexity Leeds to pore performance. Five Aspects of Network Performance Performance problems Measuring network performance System design for better performance Fast TPDU processing Protocol for future light performance networks Some performance problems such as congestion are caused by temporary resource over loads. If more traffic suddenly arrives at a router then router can handle congestion will build up and performance will suffer. Performance also degrades when there in a structural resource imbalance. For example, if a gigabit communication line is attached to a low progress the incoming packets fast enough some will be lost these packets will eventually be retransmitted adding delay wasting bandwidth and generally reducing performance. Analyzing Network Performance Tests should be conducted in the same real-world environments where you plan to deploy the software. If that is not practical, great care must be taken to ensure that the test environment matches the production environment in equipment, network devices, and traffic patterns. A checklist of factors which may affect performance. Avoid Emulators Network emulators use statistical models that are designed around TCP and TCP-like network traffic. While emulators can be useful, they must be carefully configured using statistics appropriate to the traffic being tested. DEI strongly recommends testing MTP in real-world environments whenever possible. Firewalls, NAT gateways, and other such devices are designed to interfere with network traffic. They may selectively block or degrade MTP/IP applications. They are often configured to interfere with some traffic more than others. For testing purposes, you must disable, or at least be aware of, any "bandwidth management", "packet shaping", "traffic shaping", "stateful security, "dynamic security", or other features which selectively slow down traffic. Disable TCP acceleration devices you may already be using a hardware or software device to "accelerate" your network. Such systems may be configurable to work with MTP, but for test purposes disable third-party acceleration devices. You can re-enable them later once you have established a baseline of performance. Measure Time Independently Many FTP and HTTP clients (especially Internet Explorer) are very "optimistic" when reporting transfer speeds because they only report during bursts of data arrival and don't count the times when TCP is stalled. For the most accurate results, measure the time it takes to transfer data using a stopwatch or a command line utility such as "time". Test Different Times and Different Paths TCP is very sensitive to the type of network being used and the patterns of traffic present. Its performance will vary between different sites and throughout the day and week. For the most complete picture of how MTP and TCP compare, try as wide a variety of tests sites and times as possible. Maximum Path Speed MTP cannot move data faster than the underlying network hardware. When hardware is the limiting factor, MTP will not provide a throughput improvement, but will still provide reliability and transaction speed improvements. Note that the maximum path speed is the actual maximum throughput of the slowest link between two computers. Very often, hardware such as DSL lines and WiFi, or services such as Virtual Private Networks, are rated at speeds much higher than they actually deliver. Determine your actual path speed before evaluating any performance options. Configure Firewalls, NAT, and VPN Devices Network Devices 174 As described above firewalls, emulators, routers, NATs, acceleration appliances, and other devices may selectively limit performance of some traffic flows and not others. 3. 4. TCP Relative Speed TCP's performance may vary substantially due to many factors, including the time of day. If TCP is operating close to the Maximum Path Speed, then MTP may not offer faster throughput than TCP, because no transport protocol can be faster than the network itself. Conclusion Overloads can also be synchronously triggered for example if a TPDU contains a bad parameter in many cases the recovers will thought fully send back an error notification if a bad TPDU is broad cost to 10,000 machines each one Wight send back an error message. The resulting broad cast storm could cripple the network .Even though "speed" or "throughput" is the most commonly talked about network metric, it does not actually exist as a well-defined characteristic. In any packet-switched network, datagrams arrive at fixed points in time. Only the physical and link layers deal with the signaling of individual bits. At the network layer and above, the "speed" at any single moment in time is either zero or infinity. To calculate a meaningful speed statistic, discrete datagram arrival events must be averaged over time. For example if two 1400 byte datagrams arrive 10 milliseconds apart, you could say that data is arriving at a rate of 2800 bytes per 10 milliseconds or 2.24 megabits per second. But if a third 1400 byte datagram arrives 10 milliseconds after that, then this same calculation (4200 bytes in 20 milliseconds) yields a speed of only 1.68 megabits per second. Even though data is arriving at the same rate, we get a radically different result from the same calculation. The speed of data movement heavily depends on how, when, and where you measure it. The interaction of device buffers, signaling rates, and competing traffic flows means that speed will vary not just over time, but also depending on how and where you measure it. For example, data may appear to be moving very quickly as it leaves the source, but will become slowed down as it crosses the network and experiences latency and loss. The same idea even applies at the receiving end. Data may appear to be arriving quickly at the receiving network, but may become delayed in I/O processing such as writing to disk. Speed calculations are further complicated by the distinction between the application-level data that is being transported, versus all the overhead consisting of packet headers, link frames, error correction, lost datagrams, and duplicate datagrams. Thus the bit-rate of a link is only an upper bound on speed. The actual rate of transport of useable data, sometimes called "goodput", will be significantly lower. 5. 6. 7. 8. Yunhong Gu and Robert L. Grossman. UDT: UDPbased data transfer for high-speed wide area networks. Computer Networks, 51(7):1777—1799, 2007. Hbase Development Team. Hbase: Bigtable-like structured storage for hadoop hdfs. http://wiki.apache.org/lucene-hadoop/Hbase, 2007. Hillol Kargupta, editor. Proceedings of Next Generation Data Mining 2007. Taylor and Francis, to appear. D.A. Reed. Grids, the teragrid and beyond. Computer, 36(1):62–68, Jan 2003. I. Stoica, R. Morris, D. Karger, M. F. Kaashoek, and H Balakrishnana. Chord: A scalable peer to peer lookup service for internet applications. In Proceedings of the ACM SIGCOMM ’01, pages 149–160, 2001. Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. Data Mining. Addison-Wesley, 2006. Reference 1. Robert L. Grossman. A review of some analytic architectures for high volume transaction systems. In The 5th International Workshop on Data Mining Standards, Services and Platforms (DM-SSP ’07), pages 23—28. ACM, 2007. 2. Robert L Grossman, Michael Sabala, Yunhong Gu, Anushka Anand, Matt Handley, Rajmonda Sulo, and Lee Wilkinson. Distributed discovery in e- science: Lessons from the angle project. In Next Generation Data Mining (NGDM ’07), page to appear, 2008. 175