8. CONCLUSION AND FUTURE WORK. To address the formulated research issues, this thesis has achieved each of the objectives delineated in Chapter 1.

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1 CONCLUSION AND FUTURE WORK 8.1 CONCLUSION Virtualization and internet availability has increased virtualized server cluster or cloud computing environment deployments. With technological advances, faster network access with reduced latencies over internet is driving proliferation compute resource accesses or services using cloud computing model. This will further increase number of cloud or virtualized datacenters and corresponding increase in energy consumption. Thus, energy efficient management of cloud or virtualized datacenter resources is crucial problem to reduce operating costs. This thesis has proposed and investigated approaches to reduce energy consumption in a virtualized datacenter accounting for server heterogeneity aspects especially with servers have different performance and power consumption characteristics; usage of sleep power states; joint compute and cooling aware approach. To address the formulated research issues, this thesis has achieved each of the objectives delineated in Chapter 1. Chapter 2 presented an in-depth review, analysis, and taxonomy study on energy-efficient resource management in computing systems at various datacenters levels. The research literature analysis has helped to identify gaps, open challenges, and clearly determine the research direction undertaken in the thesis. Chapter 3 presented an approach for achieving better energy efficiency metric value in virtualized datacenter with infrastructure as a service (IaaS) model by considering heterogeneity aware PPM metric. This approach drives server selection, VM selection, VM allocation, server overload, server under-load and over-load detection, server consolidation. The proposed approach results are compared against recent power management approaches.

2 135 Chapter 4 presented an approach to use sleep state transitions, server state startup and shutdown transitions to improve energy efficiency of the datacenter. Chapter 5 presented an approach to use dynamic computer room air conditioner controls to improve energy efficiency of the datacenter. Chapter 6 presented an approach for virtualized server cluster environment, to use with heterogeneity PPM metric. This approach drives server selection, VM selection, server startup and shutdowns. Detailed analysis of the results from DPM and DVFS control runs with respect to datacenter energy consumption and request response times is presented. Overall, this work distinguishes itself from existing research literature with the following unique contributions: Energy efficiency metric considering servers peak power consumption as opposed to idle or utilization based power consumption gives better energy savings in a VM migration enabled virtualized cloud datacenter. Use of sleep states transitions gives improvement in energy savings in workload processing in virtualized cloud datacenter. Joint compute and cooling power aware workload processing approach gives better energy savings than independent approach in a virtualized datacenter. This research contributes towards the design, development and deployment of server heterogeneity and thermal aware energy efficiency improvement heuristic approach in virtualized datacenter. DVFS controls do not give savings with respect to energy consumption. 8.2 LIMITATIONS The prototype implementation using CloudSim toolkit [81] allows comprehensive evaluations, which are readily not possible to evaluate on current real cloud environments. Although performance evaluation of the algorithms with simulation setup is possible, it would also be worthwhile to evaluate its performance with larger real system composition. As part of future

3 136 work, plan is to implement the proposed algorithms into real-world platforms such as OpenStack cloud [85]. Although the conducted simulation experiment on test-bed stubs developed using CloudSim development framework gives realistic scenarios as much as possible, but several reasonable extensions and shortcomings are recognized: Firstly, in the simulation setup, network bandwidth, traffic and hop distance factor have not been considered, though a heuristic driven overhead especially with VM migration scenarios was accounted. Need to understand or quantify network cost expend with VM migrations is required and these cost should not out-weigh the benefits. This area need to be strengthened as cloud operations are more network dependent. Unlike assumption considered in the simulation setup that each modeled VM type has limited number of hardware configuration with a specific virtualization technology and physical servers have one processor low power sleep state, real Clouds provide VMs based on different hardware with very different and virtualization technologies, very different setup times, and future physical servers with different sleep states. Thus, the impact of these different sleep mode operations and specific virtualization technologies on the system and VM performance has to be taken into account. The preliminary work with dynamic provisioning using limited sleep states and state transitions in virtualized datacenter server is interesting and results shows marginal gains. This is an encouraging result, and warrants further research. Also, impact of power consumption due to server fans to the overall datacenter power consumption has not been considered and will need to accounted in the future work. 8.3 FUTURE RESEARCH DIRECTIONS Despite contributions of the current thesis in improving energy efficiency of a virtualized datacenter, there are a number of open research challenges that need to be addressed in order to further advance the area.

4 137 Exploring VM to Server and VM to VM affinities An area of further research would be analyze and study the impact of policies around between VM and VM to server compatibilities like VM to physical server affinities, VMs that can co-exist in a physical server, VMs that cannot co-exist in a physical servers, government legalities etc. With Cloud systems breaking geo boundaries, policies like these are likely to gain prominence. Optimality solution should also consider these important factors. Exploring Resource Usages Infrastructure as a Service (IaaS) cloud service model allows users to provision VMs on-demand and deploy any kind of applications in them. This leads to the fact that different applications (e.g., HPC applications, web services) may share a physical server. More importantly, cloud service provider would need to understand as to how these applications influence each other in terms of performance, energy consumption, as they exert data or network or compute intensive load on the resources. The problem is to determine what kinds of applications can be co-allocated to provide the most efficient overall resources usage and thereby save energy. There is a need to understand cloud application workloads in order to identify common behaviors, patterns, shared resource usages, and explore load forecasting approaches in a more holistic way, which can potentially lead to reducing resource usage overlaps, more efficient resource provisioning and consequently higher energy efficiency. Exploring VM Network Data Transfer cost In virtualized datacenters, application architecture and workload usage etc. influences the need for VMs to communicate. Understanding the application, workload and traffic volume etc., could help quantify data transfer cost. With regards to VMs which communicate heavily, it is better to account for the network cost (data transfer). Would it be better to place such VMs in same physical server or place the VMs in closely located servers to reduce on

5 138 communication overhead and still achieve cost and performance optimality? This could have an impact on VM migrations with respect to server or workload consolidation. Hence, it is necessary to account for network transfer cost overheads into the VM migration cost, so that both performance and energy optimality is met.