2 research outputs found
Recommended from our members
VM Allocation in Cloud Datacenters Based on the Multi-Agent System. An Investigation into the Design and Response Time Analysis of a Multi-Agent-based Virtual Machine (VM) Allocation/Placement Policy in Cloud Datacenters
Recent years have witnessed a surge in demand for infrastructure and services to cover high demands on processing big chunks of data and applications resulting in a mega Cloud Datacenter. A datacenter is of high complexity with increasing difficulties to identify, allocate efficiently and fast an appropriate host for the requested virtual machine (VM). Establishing a good awareness of all datacenter’s resources enables the allocation “placement” policies to make the best decision in reducing the time that is needed to allocate and create the VM(s) at the appropriate host(s). However, current algorithms and policies of placement “allocation” do not focus efficiently on awareness of the resources of the datacenter, and moreover, they are based on conventional static techniques. Which are adversely impacting on the allocation progress of the policies. This thesis proposes a new Agent-based allocation/placement policy that employs some of the Multi-Agent system features to get a good awareness of Cloud Datacenter resources and also provide an efficient allocation decision for the requested VMs. Specifically, (a) The Multi-Agent concept is used as a part of the placement policy (b) A Contract Net Protocol is devised to establish good awareness and (c) A verification process is developed to fully dimensional VM specifications during allocation. These new results show a reduction in response time of VM allocation and the usage improvement of occupied resources. The proposed Agent-based policy was implemented using the CloudSim toolkit and consequently was compared, based on a series of typical numerical experiments, with the toolkit’s default policy. The comparative study was carried out in terms of the time duration of VM allocation and other aspects such as the number of available VM types and the amount of occupied resources. Moreover, a two-stage comparative study was introduced through this thesis. Firstly, the proposed policy is compared with four state of the art algorithms, namely the Random algorithm and three one-dimensional Bin-Packing algorithms. Secondly, the three Bin-Packing algorithms were enhanced to have a two-dimensional verification structure and were compared against the proposed new algorithm of the Agent-based policy. Following a rigorous comparative study, it was shown that, through the typical numerical experiments of all stages, the proposed new Agent-based policy had superior performance in terms of the allocation times. Finally, avenues arising from this thesis are included.Al al-Bayt University in Jordan
Intelligent Management of Virtualised Computer Based Workloads and Systems
Managing the complexity within virtualised IT infrastructure platforms is a common problem for many organisations today. Computer systems are often highly consolidated into a relatively small physical footprint compared with previous decades prior to late 2000s, so much thought, planning and control is necessary to effectively operate such systems within the enterprise computing space. With the development of private, hybrid and public cloud utility computing this has become even more relevant; this work examines how such cloud systems are using virtualisation technology and embedded software to leverage advantages, and it uses a fresh approach of developing and creating an Intelligent decision engine (expert system). Its aim is to help reduce the complexity of managing virtualised computer-based platforms, through tight integration, high-levels of automation to minimise human inputs, errors, and enforce standards and consistency, in order to achieve better management and control. The thesis investigates whether an expert system known as the Intelligent Decision Engine (IDE) could aid the management of virtualised computer-based platforms. Through conducting a series of mixed quantitative and qualitative experiments in the areas of research, the initial findings and evaluation are presented in detail, using repeatable and observable processes and provide detailed analysis on the recorded outputs. The results of the investigation establish the advantages of using the IDE (expert system) to achieve the goal of reducing the complexity of managing virtualised computer-based platforms. In each detailed area examined, it is demonstrated how using a global management approach in combination with VM provisioning, migration, failover, and system resource controls can create a powerful autonomous system