Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical hos

Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical hos

PERFORMANCE ANALYSIS OFADAPTIVE MULTIPLE QUEUING DISCIPLINES (AMQD) FORVOIPROUTING IN RANDOM WAY POINT MOBILITY MODEL OVER MANET SCENARIO

In Mobile Ad hoc Network (MANET), QOS (Quality of Services) in VOIP application plays anextremelysignificantresponsibility. Queuing disciplinesis anissue of concentratedconversation and research in the wireless network field for development of packets from dissimilar traffic flow for dispensation at anexactnode.Hence Mobility takes an important rolein networks to evaluate the presentation of AMQD with different Codec's for voice Over Internet. VOIPis arisingaccepted Internet application toprovidehigh-quality services through Mobile Adhoc Network (MANET).Based on the analysis and assessment of different mobility models such as Random Waypoint Models, Reference point group models,Manhattan Mobility models , it is pointing out that this network also facea lot of challenges on QOS issueupon the node movement of different mobility. The QoS issues such as packet loss, less throughput, more delay, jitter issues and high energy consumption, Combine these issues together with mobility models, in this paper the researcherestimate the performance of various VOIP codec with Adaptive Multiple Queuing Disciplines (AMQD) namely, IAE3, DBPQ, CBCRTQ over MANET. Simulation and GUI experiments demonstrate thecomparative analysis of different queuing in quality of services parameter

Differential Effects of Hepatocyte Nuclear Factor 4α Isoforms on Tumor Growth and T-Cell Factor 4/AP-1 Interactions in Human Colorectal Cancer Cells

The nuclear receptor hepatocyte nuclear factor 4α (HNF4α) is tumor suppressive in the liver but amplified in colon cancer, suggesting that it also might be oncogenic. To investigate whether this discrepancy is due to different HNF4α isoforms derived from its two promoters (P1 and P2), we generated Tet-On-inducible human colon cancer (HCT116) cell lines that express either the P1-driven (HNF4α2) or P2-driven (HNF4α8) isoform and analyzed them for tumor growth and global changes in gene expression (transcriptome sequencing [RNA-seq] and chromatin immunoprecipitation sequencing [ChIP-seq]). The results show that while HNF4α2 acts as a tumor suppressor in the HCT116 tumor xenograft model, HNF4α8 does not. Each isoform regulates the expression of distinct sets of genes and recruits, colocalizes, and competes in a distinct fashion with the Wnt/β-catenin mediator T-cell factor 4 (TCF4) at CTTTG motifs as well as at AP-1 motifs (TGAXTCA). Protein binding microarrays (PBMs) show that HNF4α and TCF4 share some but not all binding motifs and that single nucleotide polymorphisms (SNPs) in sites bound by both HNF4α and TCF4 can alter binding affinity in vitro, suggesting that they could play a role in cancer susceptibility in vivo. Thus, the HNF4α isoforms play distinct roles in colon cancer, which could be due to differential interactions with the Wnt/β-catenin/TCF4 and AP-1 pathways