TARGO: Transition and Reallocation Based Green Optimization for Cloud VMs

Abstract

-Much research has been conducted focusing on improving resource utilization efficiency in data centers in the context of Green Cloud Computing (GCC). While virtualization enables better resource provision and utilization for various computational resources, different approaches are proposed based on virtual machine (VM) optimizations using either server or workload consolidation techniques. Nonetheless, these solutions can only be applied inside the Cloud. In fact, Infrastructure-as-a-Service (IaaS) users can hardly proactively achieve better VM resource utilization efficiency, as they typically have no control over any hypervisor or hardware in any Clouds. The issue gets more critical when workloads on VMs alter dramatically from time to time. This paper presents a novel approach namely Transition and Reallocation Based Green Optimization (TARGO) for such users. Through fully automated and intelligent VM optimization according to customizable optimization rules, TARGO guarantees that VMs or their successors being optimized will always run at their customizable green optimal states regardless how workloads vary. Experiments conducted on Amazon EC2 instances in the EU region show that, even under extreme random workloads, TARGO is still capable of selecting and retaining VM successors which run at an average CPU utilization of 50%-60%.

BEFTIGRE: Behaviour‐driven full‐tier green evaluation of mobile cloud applications

“…Resource usage (specifically CPU and memory) is a commonly investigated metrics when monitoring workload impact on the cloud. 12,13 In the current Beftigre approach, the scope of the energy measurement is at the application level, ie, the overall energy consumption of the mobile execution. The overall mobile energy is measured using power tutor model and applies to all network types (including WLAN and 3G).…”

“…Mobile energy is increasingly gaining research interest because of the resource‐constrained nature of mobile devices and the increasing demand for rich mobile applications. Resource usage (specifically CPU and memory) is a commonly investigated metrics when monitoring workload impact on the cloud . In the current Beftigre approach, the scope of the energy measurement is at the application level, ie, the overall energy consumption of the mobile execution.…”

“…The above parameters are used to maintain the same level of rigour for both Beftigre and non-BDD approaches. The simulation parameters 12 (also used in Tables 4 and 5) are as follows:…”

OCSO: Off-the-cloud service optimization for green efficient service resource utilization

“…In our previous work [3], we have demonstrated the VM transition and workload reallocation approach that can actively transit VMs to appropriate VM sizes for varied workloads, called TARGO. In contrast, OCSO is a more advanced approach as: 1) it works for a wider range of cloud services, i.e., both IaaS and PaaS services; 2) it runs a more advanced and sophisticated threshold algorithm that rests on dynamic threshold and scaling parameters.…”

“…In the era of cloud computing (CC), however, green optimization should involve two sets of major objectives: green service (resource) provision [2] as well as green service (resource) consumption [3]. While the former is largely focused with a diversity of approaches proposed, the latter is seldom adequately addressed.…”