Mehiar Dabbagh

Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers

Energy efficiency has recently become a major issue in large data centers due to financial and environmental concerns. This paper proposes an integrated energy-aware resource provisioning framework for cloud data centers. The proposed framework: i) predicts the number of virtual machine (VM) requests, to be arriving at cloud data centers in the near future, along with the amount of CPU and memory resources associated with each of these requests, ii) provides accurate estimations of the number of physical machines (PMs) that cloud data centers need in order to serve their clients, and iii) reduces energy consumption of cloud data centers by putting to sleep unneeded PMs. Our framework is evaluated using real Google traces collected over a 29-day period from a Google cluster containing over 12,500 PMs. These evaluations show that our proposed energy-aware resource provisioning framework makes substantial energy savings.

Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment

Energy consumption has become a significant concern for cloud service providers due to financial as well as environmental factors. As a result, cloud service providers are seeking innovative ways that allow them to reduce the amount of energy that their data centers consume. They are calling for the development of new energy-efficient techniques that are suitable for their data centers. The services offered by the cloud computing paradigm have unique characteristics that distinguish them from traditional services, giving rise to new design challenges as well as opportunities when it comes to developing energy-aware resource allocation techniques for cloud computing data centers. In this article we highlight key resource allocation challenges, and present some potential solutions to reduce cloud data center energy consumption. Special focus is given to power management techniques that exploit the virtualization technology to save energy. Several experiments, based on real traces from a Google cluster, are also presented to support some of the claims we make in this article.

Software-defined networking security: pros and cons

Software-defined networking (SDN) is a new networking paradigm that decouples the forwarding and control planes, traditionally coupled with one another, while adopting a logically centralized architecture aiming to increase network agility and programability. While many efforts are currently being made to standardize this emerging paradigm, careful attention needs to be paid to security at this early design stage too, rather than waiting until the technology becomes mature, thereby potentially avoiding previous pitfalls made when designing the Internet in the 1980s. This article focuses on the security aspects of SDN networks. We begin by discussing the new security advantages that SDN brings and by showing how some of the long-lasting issues in network security can be addressed by exploiting SDN capabilities. Then we describe the new security threats that SDN is faced with and discuss possible techniques that can be used to prevent and mitigate such threats.

Efficient datacenter resource utilization through cloud resource overcommitment

We propose an efficient resource allocation framework for overcommitted clouds that makes great energy savings by 1) minimizing PM overloads via resource usage prediction, and 2) reducing the number of active PMs via efficient VM placement and migration. Using real Google traces collected from a cluster containing more than 12K PMs, we show that our proposed techniques outperform existing ones by minimizing migration overhead, increasing resource utilization, and reducing energy consumption.

Energy-efficient cloud resource management

We propose a resource management framework that reduces energy consumption in cloud data centers. The proposed framework predicts the number of virtual machine requests along with their amounts of CPU and memory resources, provides accurate estimations of the number of needed physical machines, and reduces energy consumption by putting to sleep unneeded physical machines. Our framework is based on real Google traces collected over a 29-day period from a Google cluster containing over 12,500 physical machines. Using this Google data, we show that our proposed framework makes substantial energy savings.

An Energy-Efficient VM Prediction and Migration Framework for Overcommitted Clouds

We propose an integrated, energy-efficient, resource allocation framework for overcommitted clouds. The framework makes great energy savings by 1) minimizing Physical Machine (PM) overload occurrences via VM resource usage monitoring and prediction, and 2) reducing the number of active PMs via efficient VM migration and placement. Using real Google data consisting of a 29-day traces collected from a cluster containing more than 12K PMs, we show that our proposed framework outperforms existing overload avoidance techniques and prior VM migration strategies by reducing the number of unpredicted overloads, minimizing migration overhead, increasing resource utilization, and reducing cloud energy consumption.

Exploiting Task Elasticity and Price Heterogeneity for Maximizing Cloud Computing Profits

This paper exploits cloud task elasticity and price heterogeneity to propose an online resource management framework that maximizes cloud profits while minimizing energy expenses. This is done by reducing the duration during which servers need to be left on and maximizing the monetary revenues when the charging cost for some of the elastic tasks depends on how fast these tasks complete, while meeting all the resource requirements. Comparative studies conducted using Google data traces show the effectiveness of our proposed framework in terms of improving resource utilization, reducing energy expenses, and increasing cloud profits.

Internet of Things Security and Privacy

This chapter outlines the main security and privacy issues in IoT and surveys the techniques that were proposed to address them. Some of the discussed techniques prevent security breaches from taking place while others try to detect malicious behavior and trigger an appropriate mitigating countermeasure.

Peak shaving through optimal energy storage control for data centers

We propose efficient control strategies for deciding the amount of energy that a battery needs to charge/discharge over time with the objective of minimizing the Peak Charge and the Energy Charge components of the Data Center (DC) electricity bill. We consider first the case where the DCs power demands throughout the whole billing cycle are known and we present an optimal peak shaving control strategy for a battery that has certain leakage and conversion losses. We then relax this assumption and propose an efficient battery control strategy when we only know predictions of the DCs power demands in a short duration in the future. Several comparative studies are conducted based on real traces from a Google DC in order to validate the proposed techniques.

Online Assignment and Placement of Cloud Task Requests with Heterogeneous Requirements

Managing cloud resources in a way that reduces the consumed energy while also meeting clients demands is a challenging task. In this paper, we propose an energy-aware resource allocation framework that: i) places the submitted tasks (elastic/inelastic) in an energy-efficient way, ii) decides initially how much resources should be assigned to the elastic tasks, and iii) tunes periodically the allocated resources for the currently hosted elastic tasks. This is all done with the aim of reducing the number of ON servers and the time for which servers need to be kept ON allowing them to be turned to sleep early to save energy while meeting all clients demands. Comparative studies conducted on Google traces show the effectiveness of our framework in terms of energy savings and utilization gains.