Improving Queuing System Throughput Using Distributed Mean Value Analysis to Control Network Congestion

In this paper, we have used the distributed mean value analysis (DMVA) technique with the help of random observe property (ROP) and palm probabilities to improve the network queuing system throughput. In such networks, where finding the complete communication path from source to destination, especially when these nodes are not in the same region while sending data between two nodes. So, an algorithm is developed for single and multi-server centers which give more interesting and successful results. The network is designed by a closed queuing network model and we will use mean value analysis to determine the network throughput (b) for its different values. For certain chosen values of parameters involved in this model, we found that the maximum network throughput for β≥0.7?remains consistent in a single server case, while in multi-server case for β≥ 0.5?throughput surpass the Marko chain queuing system.

Improving Network Efficiency by Selecting and Modifying Congestion Control Constraints

Congestion in wired networks not only causes severe information loss but also degrades overall network performance. To cope with the issue of network efficiency, in this paper we have pro- posed and investigated an efficient mechanism for congestion control by the selection of appropri- ate congestion window size and proactive congestion avoidance, which improves system overall performance and efficiency. The main objective of this work is to choose the accurate size of con- gestion window based on available link bandwidth and round trip time (RTT) in cross and grid topologies, instead of choosing number of hops (Previous researches), we have achieved significant improvement in the overall performance of the network. General simulation results under distinctive congestion scenarios are presented to illuminate the distinguished performance of the proposed mechanism.

A remotely sensed tracking of forest cover and associatedtemperature change in Margalla hills

Worldwide, forest degradation is a serious environmental issue, and inPakistan, forest wealth is depleting at the highest rate in South Asia. Toensure sustainable development goals of environmental stewardship,social development and economic growth, a sound monitoring andregulatory mechanism is essential for tracking forest cover changes. Thisstudy aims to quantify the decline of forest reserves and associatedtemperature variations in a relatively unexplored biodiversity hotspot ofIslamabad, Margalla Hills National Park (MHNP). Imagery acquired byLandsat TM (Thematic Mapper) for the year 1992, 2000 and 2011 areused to assess the spatial and temporal changes occurred over the lasttwo decades (from 1992 to 2011). A robust hybrid-classification routineis implemented to monitor the changes in forest cover and ANOVAalong with Tukey’s HSD (Honestly Significant Difference) test is used totest the significance of temperature variation associated with a shift inland cover classes. The results showed a significant growth insettlements, agricultural area and barren soil whereas water body, lowervegetation, scrub and pine forest are diminishing. In both decades, thetemperature alteration associated with a change in land cover classesare statistically significant (confirmed by ANOVA and Tukey’s HSD tests)for most of the land use/land cover classes. Based on these findings, thisstudy concludes that forests are dwindling at MHNP and the degradingcondition of the forest is below par and necessitates the promotion ofconservation practices to minimize ecological disturbances.