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In the task context server, task context is managed under each task.
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A slimmed-down mobile JVM, for instance, lacks the ability to perform complex server tasks, which are a drain on processing power and would unnecessarily slow down a smartphone.
The main thrust behind Smooth-Stone is that ARM chips have grown up enough to handle basic server tasks like cranking out Web pages and can do so at a lower cost while consuming less power than server chips from Intel.
A request for an application not deployed at the moment is briefly sent to a server tasked with entertaining the user and showing that the request is being processed until the application has been successfully deployed, after which it is delivered to the correct server.
Due to this variety of jobs and servers, task scheduling mechanisms for single system may not be effective in a distributed environment [13 15].
The cloud service time is correlated to CPU frequency and memories of the virtual server and task type; it is assumed to be a constant t s for any determined task in this paper.
Upon receiving a work request from a reducer (7), the server follows task scheduling procedure mentioned earlier in this section and looks through the database to find a task that can be assigned.
The random-based task-scheduling scheme assigns tasks to servers on a random basis and without constraints, except for available queue at each server, for task allocation or server selection.
The servers receive task assignments and their energy profile is updated.
The Linux-based apache web-server and task management system supports BLAST analysis and MCL clustering.
As a practical approach, we propose the most-efficient-server-first task-scheduling scheme to minimize energy consumption of servers in a data center.
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