A cyber-physical approach to combined HW-SW monitoring for improving energy efficiency in data centers

High-Performance Computing, Cloud computing and next-generation applications such e-Health or Smart Cities have dramatically increased the computational demand of Data Centers. The huge energy consumption, increasing levels of CO2 and the economic costs of these facilities represent a challenge for industry and researchers alike. Recent research trends propose the usage of holistic optimization techniques to jointly minimize Data Center computational and cooling costs from a multilevel perspective. This paper presents an analysis on the parameters needed to integrate the Data Center in a holistic optimization framework and leverages the usage of Cyber-Physical systems to gather workload, server and environmental data via software techniques and by deploying a non-intrusive Wireless Sensor Net- work (WSN). This solution tackles data sampling, retrieval and storage from a reconfigurable perspective, reducing the amount of data generated for optimization by a 68% without information loss, doubling the lifetime of the WSN nodes and allowing runtime energy minimization techniques in a real scenario

Leveraging heterogeneity for energy minimization in data centers

Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research

Energy optimization methods for Virtual Machine Placement in Cloud Data Center

The Information Technology industry has been upheaved by the influx of cloud computing. The extension of Cloud computing has resulted in the creation of huge data centers globally containing numbers of computers that consume large amounts of energy resulting in high operating costs. To reduce energy consumption providers must optimize resource usage by performing dynamic consolidation of virtual machines (VMs) in an efficient way. The problems of VM consolidation are host overload detection, host under-load detection, VM selection and VM placement. Each of the aforestated sub-problems must operate in an optimized manner to maintain the energy usage and performance. The process of VM placement has been focused in this work, and energy efficient, optimal virtual machine placement (E2OVMP) algorithm has been proposed. This minimizes the expenses for hosting virtual machines in a cloud provider environment in two different plans such as i) reservation and ii) on-demand plans, under future demand and price uncertainty. It also reduces energy consumption. E2OVMP algorithm makes a decision based on the gilt-edged solution of stochastic integer programming to lease resources from cloud IaaS providers. The performance of E2OVMP is evaluated by using CloudSim with inputs of planet lab workload. It minimized the user’s budget, number of VM migration resulting efficient energy consumption. It ensures a high level of constancy to the Service Level Agreements (SLA).Keywords: Cloud resource management; virtualization; dynamic consolidation; stochastic integer programming (SIP)*Cite as: Esha Barlaskar, N. Ajith Singh, Y. Jayanta Singh, “Energy optimization methods for Virtual Machine Placementin Cloud Data Center†ADBU J.Engg.Tech., 1(2014) 0011401(7pp

Evolutionary computing based QoS oriented energy efficient VM consolidation scheme for large scale cloud data centers using random work load bench

In order to assess the performance of an approach, it is unavoidable to inspect the performance with distinct datasets with diverse characteristics. In this paper we had assessed the system performance with random workbench datasets. A-GA (Adaptive Genetic Algorithm) based consolidation technique has been compared with other consolidation techniques including dynamic CPU utilization techniques, VM (Virtual Machine) selection and placement policies. The proposed consolidation system had exhibited better results in terms of energy conservation, minimal Service Level Agreement (SLA) violation and Quality of Service (QoS) assurance

Vers une gestion coopérative des infrastructures virtualisées à large échelle (le cas de l'ordonnancement)

Les besoins croissants en puissance de calcul sont généralement satisfaits en fédérant de plus en plus d ordinateurs (ou noeuds) pour former des infrastructures distribuées. La tendance actuelle est d utiliser la virtualisation système dans ces infrastructures, afin de découpler les logiciels des noeuds sous-jacents en les encapsulant dans des machines virtuelles. Pour gérer efficacement ces infrastructures virtualisées, de nouveaux gestionnaires logiciels ont été mis en place. Ces gestionnaires sont pour la plupart hautement centralisés (les tâches de gestion sont effectuées par un nombre restreint de nœuds dédiés). Cela limite leur capacité à passer à l échelle, autrement dit à gérer de manière réactive des infrastructures de grande taille, qui sont de plus en plus courantes. Au cours de cette thèse, nous nous sommes intéressés aux façons d améliorer cet aspect ; l une d entre elles consiste à décentraliser le traitement des tâches de gestion, lorsque cela s avère judicieux. Notre réflexion s est concentrée plus particulièrement sur l ordonnancement dynamique des machines virtuelles, pour donner naissance à la proposition DVMS (Distributed Virtual Machine Scheduler). Nous avons mis en œuvre un prototype, que nous avons validé au travers de simulations (notamment via l outil SimGrid), et d expériences sur le banc de test Grid 5000. Nous avons pu constater que DVMS se montrait particulièrement réactif pour gérer des infrastructures virtualisées constituées de dizaines de milliers de machines virtuelles réparties sur des milliers de nœuds. Nous nous sommes ensuite penchés sur les perspectives d extension et d amélioration de DVMS. L objectif est de disposer à terme d un gestionnaire décentralisé complet, objectif qui devrait être atteint au travers de l initiative Discovery qui fait suite à ces travaux.The increasing need in computing power has been satisfied by federating more and more computers (called nodes) to build the so-called distributed infrastructures. Over the past few years, system virtualization has been introduced in these infrastructures (the software is decoupled from the hardware by packaging it in virtual machines), which has lead to the development of software managers in charge of operating these virtualized infrastructures. Most of these managers are highly centralized (management tasks are performed by a restricted set of dedicated nodes). As established, this restricts the scalability of managers, in other words their ability to be reactive to manage large-scale infrastructures, that are more and more common. During this Ph.D., we studied how to mitigate these concerns ; one solution is to decentralize the processing of management tasks, when appropriate. Our work focused in particular on the dynamic scheduling of virtual machines, resulting in the DVMS (Distributed Virtual Machine Scheduler) proposal. We implemented a prototype, that was validated by means of simulations (especially with the SimGrid tool) and with experiments on the Grid 5000 test bed. We observed that DVMS was very reactive to schedule tens of thousands of virtual machines distributed over thousands of nodes. We then took an interest in the perspectives to improve and extend DVMS. The final goal is to build a full decentralized manager. This goal should be reached by the Discovery initiative,that will leverage this work.NANTES-ENS Mines (441092314) / SudocSudocFranceF

Energy-Efficient Virtual Machine Placement using Enhanced Firefly Algorithm

The consolidation of the virtual machines (VMs) helps to optimise the usage of resources and hence reduces the energy consumption in a cloud data centre. VM placement plays an important part in the consolidation of the VMs. The researchers have developed various algorithms for VM placement considering the optimised energy consumption. However, these algorithms lack the use of exploitation mechanism efficiently. This paper addresses VM placement issues by proposing two meta-heuristic algorithms namely, the enhanced modified firefly algorithm (MFF) and the hierarchical cluster based modified firefly algorithm (HCMFF), presenting the comparative analysis relating to energy optimisation. The comparisons are made against the existing honeybee (HB) algorithm, honeybee cluster based technique (HCT) and the energy consumption results of all the participating algorithms confirm that the proposed HCMFF is more efficient than the other algorithms. The simulation study shows that HCMFF consumes 12% less energy than honeybee algorithm, 6% less than HCT algorithm and 2% less than original firefly. The usage of the appropriate algorithm can help in efficient usage of energy in cloud computing

A bio-inspired algorithm for energy optimization in a self-organizing data center

none4noneD. Barbagallo; E. Di Nitto; D. J. Dubois; R. MirandolaBarbagallo, Donato; DI NITTO, Elisabetta; Dubois, DANIELE JOSEPH; Mirandola, Raffael