Abstract

As the network technology continues to grow at a high rate of speed, the traditional network topology is improved with novel distributed topologies such as the Cloud computing network. A cloud computing environment consists of a huge number of processors and memories, high-speed networks, and various application services to provide a lot of services over the Internet for users. However, many services need to search for suitable service nodes, and the workload of each node can be unbalanced. Based on the reason above, the Reference Queue based Cloud Service Architecture (RQCSA) and Fitness Service Queue Selection Mechanism (FSQSM) are proposed to handle more tasks, lower the makespan and queue waiting time, and improve efficiency. Moreover, the tasks can be distributed more evenly to avoid overloading cluster managers and lower the efficiency of the system.

Highlights

  • In the early days, the distributed system was composed of a certain number of servers

  • The makespan is defined as the total time that each task needs when traveling from the Cloud Interface (CI), through the Cluster Manager (CM) queue, to the Cloud Service Cluster (CSC)

  • How to provide better services to users and use lots of resources more efficiently is an issue that service providers cannot ignore

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Summary

Introduction

The distributed system was composed of a certain number of servers. The cloud computing provides Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) by charging the user or rental. The most famous example is the AWS EC2 [1,3,4] Such platform services enable users to use supplier-provided API Unlike the two examples above, SaaS is a developed cloud computing software used to provide services to users through the Internet [9]. The users can rent those resources from the providers The advantage of this service is that the users do not need to worry about the expensive cost of building servers. Instead, they only need to rent the resources. The fifth section gives the conclusion and lists future works

Literature Review
The Cloud Computing
The Hierarchical Virtualization Topology
The Related Study of a Scheduling Algorithm
The Study Method
Experimental Simulation
The Experimental Environment
Performance Analysis
Conclusions and Future Works

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