PaaS-IaaS Inter-Layer Adaptation in an Energy-Aware Cloud Environment

Cloud computing providers resort to a variety of techniques to improve energy consumption at each level of the cloud computing stack. Most of these techniques consider resource-level energy optimization at IaaS layer. This paper argues energy gains can be obtained by creating a cooperation between the PaaS layer (in charge of hosting the application/service) and the IaaS layer (in charge of handling the computing resources). It presents a novel method based on steering information and decision taking to trigger the PaaS and IaaS layers to adapt their energy mode in service operation, therefore enabling the Cloud stack to actively adapt to changing situations. Experimental results demonstrate such adaptation achieves dynamic energy management in each of the PaaS and IaaS cloud layers

Energy-based Cost Model of Virtual Machines in a Cloud Environment

The cost mechanisms employed by different service providers significantly influence the role of cloud computing within the IT industry. With the increasing cost of electricity, Cloud providers consider power consumption as one of the major cost factors to be maintained within their infrastructures. Consequently, modelling a new cost mechanism for Cloud services that can be adjusted to the actual energy costs has attracted the attention of many researchers. This paper introduces an Energy-based Cost Model that considers energy consumption as a key parameter with respect to the actual resource usage and the total cost of the Virtual Machines (VMs). A series of experiments conducted on a Cloud testbed show that this model is capable of estimating the actual cost for heterogeneous VMs based on their resource usage with consideration of their energy consumption

Performance and Energy-Based Cost Prediction of Virtual Machines Auto-Scaling in Clouds

Virtual Machines (VMs) auto-scaling is an important technique to provision additional resource capacity in a Cloud environment. It allows the VMs to dynamically increase or decrease the amount of resources as needed in order to meet Quality of Service (QoS) requirements. However, the auto-scaling mechanism can be time-consuming to initiate (e.g. in the order of a minute), which is unacceptable for VMs that need to scale up/out during the computation, besides additional costs due to the increase of the energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs auto-scaling by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the auto-scaling workload, power consumption and total cost for heterogeneous VMs, with a cost-saving of up to 25% for the predicted total cost of VM self-configuration as compared to the current approaches in literature

Cloud Forensic: Issues, Challenges and Solution Models

Cloud computing is a web-based utility model that is becoming popular every day with the emergence of 4th Industrial Revolution, therefore, cybercrimes that affect web-based systems are also relevant to cloud computing. In order to conduct a forensic investigation into a cyber-attack, it is necessary to identify and locate the source of the attack as soon as possible. Although significant study has been done in this domain on obstacles and its solutions, research on approaches and strategies is still in its development stage. There are barriers at every stage of cloud forensics, therefore, before we can come up with a comprehensive way to deal with these problems, we must first comprehend the cloud technology and its forensics environment. Although there are articles that are linked to cloud forensics, there is not yet a paper that accumulated the contemporary concerns and solutions related to cloud forensic. Throughout this chapter, we have looked at the cloud environment, as well as the threats and attacks that it may be subjected to. We have also looked at the approaches that cloud forensics may take, as well as the various frameworks and the practical challenges and limitations they may face when dealing with cloud forensic investigations.Comment: 23 pages; 6 figures; 4 tables. Book chapter of the book titled "A Practical Guide on Security and Privacy in Cyber Physical Systems Foundations, Applications and Limitations", World Scientific Series in Digital Forensics and Cybersecurit

Rapid and accurate energy models through calibration with IPMI and RAPL

Energy consumption in Cloud and High Performance Computing platforms is a significant issue and affects aspects such as the cost of energy and the cooling of the data center. Host level monitoring and prediction provides the groundwork for improving energy efficiency through the placement of workloads. Monitoring must be fast and efficient without unnecessary overhead, to enable scalability. This precludes the use of Watt meters attached per host, requiring alternative approaches such as integrated measurements and models. IPMI and RAPL are subject to error and partial measurement, which may be mitigated. Models allow for prediction and more responsive measures of power consumption, but require calibrating. The causes of calibration error are discussed, along with mitigation strategies, without overly complicating the underlying model. An outcome is a Watt meter emulator that provides hosts level power measurement along with estimated power consumption for a given workload, with an average error of 0.20W