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A many-objective ensemble optimization algorithm for the edge cloud resource scheduling problem
An edge cloud architecture plays a key role in improving the user task computing service system by combining the powerful data processing capability of cloud centres with the low latency of edge computing. Existing methods for maximizing the efficiency of an edge cloud architecture take into account time and task parameters but ignore other factors such as load balancing, cost, and user satisfaction when scheduling resources. In this work, we propose a many-objective resource scheduling model for optimizing the performance of an edge cloud architecture, which takes into account the time spent on task, cost, load balance, user satisfaction, and trust measurement. The resource scheduling model converges to the optimal solution using a novel many-objective ensemble optimization algorithm based on a dynamic selection mechanism. The study also explores the support set convergence of eight evolutionary operators using the ensemble algorithm. The model solutions are dynamically updated with the help of the dynamic integration probability, and then a selection criteria is used to pick the best solutions from the pool of generated solutions. Two simulations on a benchmark dataset are used to verify the usefulness and performance of the designed algorithm. Our approach was able to locate more than half of the best solutions on the benchmark functions, and it also showed to be a better model solution than the some of the popular many-objective algorithms for dealing with the edge cloud resource scheduling problem, according to the results obtained from the simulations
Cloud-based desktop services for thin clients
Cloud computing and ubiquitous network availability have renewed people's interest in the thin client concept. By executing applications in virtual desktops on cloud servers, users can access any application from any location with any device. For this to be a successful alternative to traditional offline applications, however, researchers must overcome important challenges. The thin client protocol must display audiovisual output fluidly, and the server executing the virtual desktop should have sufficient resources and ideally be close to the user's current location to limit network delay. From a service provider viewpoint, cost reduction is also an important issue
Design Architecture-Based on Web Server and Application Cluster in Cloud Environment
Cloud has been a computational and storage solution for many data centric
organizations. The problem today those organizations are facing from the cloud
is in data searching in an efficient manner. A framework is required to
distribute the work of searching and fetching from thousands of computers. The
data in HDFS is scattered and needs lots of time to retrieve. The major idea is
to design a web server in the map phase using the jetty web server which will
give a fast and efficient way of searching data in MapReduce paradigm. For real
time processing on Hadoop, a searchable mechanism is implemented in HDFS by
creating a multilevel index in web server with multi-level index keys. The web
server uses to handle traffic throughput. By web clustering technology we can
improve the application performance. To keep the work down, the load balancer
should automatically be able to distribute load to the newly added nodes in the
server
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