الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
Cloud computing is an Internet-based computing with dynamically scalable
resources provided to users as services. These services are delivered to users based on
Service-Level Agreements (SLA) between the cloud providers and the users. Cloud
computing has several data centers at different geographical locations. Each data center
has a number of physical hosts configured to users as Virtual Machines (VMs). On the
other hand, cloud users have large scale applications with different requirements. They
need high-performance computing environment to process their applications. The
problem now is how to map the applications’ tasks onto the available heterogeneous
VMs to achieve some Quality of Service (QoS) parameters and meet the SLA. Such
problem is known as multi-objective scheduling problem.
This thesis tackles the multi-objective scheduling problem and presents metaheuristic algorithms to schedule applications’ tasks onto the available VMs in the cloud
environment to achieve some objectives in a reasonable amount of time. Further, the
meta-heuristic algorithms are hybridized with some other strategies to improve the
system performance in terms of QoS parameters including minimizing makespan, total
response time and processing cost, as well as maximizing resource utilization.
In this thesis, two new hybrid approaches are developed for solving the multiobjective scheduling problem. The first approach is called “Enhanced Particle Swarm
Optimization based Chaotic Strategies (EPSOCHO)” while the second approach is called
“Enhanced Binary Artificial Bee Colony based Pareto Front (EBABC-PF)”. Further, other
two algorithms are developed for achieving load balancing while scheduling tasks in the
cloud environment. The new algorithms called Improved Active Monitoring Load
Balancer with Hill Climbing Algorithm (IAMLBHC) and Enhanced Load Balancing based on
Hybrid Artificial Bee Colony with Enhanced β-Hill Climbing in Cloud (ELBABCEβHC).
The implementation and verification of the proposed algorithms are done using
CloudSim simulator, WorkflowSim simulator, or CloudAnalyst simulator. The
experimental results clearly demonstrate that the proposed approaches achieve better
performance in terms of makespan, response time, processing cost and resources
utilization compared to the most recent similar algorithms.