Fabien Hermenier

Bio: Fabien Hermenier is an academic researcher from University of Nice Sophia Antipolis. The author has contributed to research in topics: Constraint programming & Energy consumption. The author has an hindex of 11, co-authored 32 publications receiving 1016 citations. Previous affiliations of Fabien Hermenier include École des mines de Nantes & French Institute for Research in Computer Science and Automation.

Entropy: a consolidation manager for clusters

Abstract: Clusters provide powerful computing environments, but in practice much of this power goes to waste, due to the static allocation of tasks to nodes, regardless of their changing computational requirements. Dynamic consolidation is an approach that migrates tasks within a cluster as their computational requirements change, both to reduce the number of nodes that need to be active and to eliminate temporary overload situations. Previous dynamic consolidation strategies have relied on task placement heuristics that use only local optimization and typically do not take migration overhead into account. However, heuristics based on only local optimization may miss the globally optimal solution, resulting in unnecessary resource usage, and the overhead for migration may nullify the benefits of consolidation.In this paper, we propose the Entropy resource manager for homogeneous clusters, which performs dynamic consolidation based on constraint programming and takes migration overhead into account. The use of constraint programming allows Entropy to find mappings of tasks to nodes that are better than those found by heuristics based on local optimizations, and that are frequently globally optimal in the number of nodes. Because migration overhead is taken into account, Entropy chooses migrations that can be implemented efficiently, incurring a low performance overhead.

An energy aware framework for virtual machine placement in cloud federated data centres

Abstract: Data centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals.

BtrPlace: A Flexible Consolidation Manager for Highly Available Applications

Abstract: The massive amount of resources found in data centers makes it possible to provide high availability to multitier applications. Virtualizing these applications makes it possible to consolidate them on servers, reducing runtime costs. Nevertheless, replicated VMs have to be carefully placed within the data center to provide high availability and good performance. This requires resolving potentially conflicting application and data center requirements, while scaling up to the size of modern data centers. We present BtrPlace, a flexible consolidation manager that is customized through configuration scripts written by the application and data center administrators. BtrPlace relies on constraint programming and an extensible library of placement constraints. The present library of 14 constraints subsumes and extends the capabilities of existing commercial consolidation managers. Scalability is achieved by splitting the data center into partitions and computing placements in parallel. Overall, BtrPlace repairs a nonviable placement after a major load increase or a maintenance operation for a 5,000 server data center hosting 30,000 VMs and involving thousands of constraints in 3 minutes. Using partitions of 2,500 servers, placement computing is reduced to under 30 seconds.

Bin repacking scheduling in virtualized datacenters

Abstract: A datacenter can be viewed as a dynamic bin packing system where servers host applications with varying resource requirements and varying relative placement constraints When those needs are no longer satisfied, the system has to be reconfigured Virtualization allows to distribute applications into Virtual Machines (VMs) to ease their manipulation In particular, a VM can be freely migrated without disrupting its service, temporarily consuming resources both on its origin and destination We introduce the Bin Repacking Scheduling Problem in this context This problem is to find a final packing and to schedule the transitions from a given initial packing, accordingly to new resource and placement requirements, while minimizing the average transition completion time Our CP-based approach is implemented into Entropy, an autonomous VM manager which detects reconfiguration needs, generates and solves the CP model, then applies the computed decision CP provides the awaited flexibility to handle heterogeneous placement constraints and the ability to manage large datacenters with up to 2,000 servers and 10,000 VMs

Power management in grid computing with xen

Abstract: While chip vendors still stick to Moore's law, and the performance per dollar keeps going up, the performance per watt has been stagnant for last few years. Moreover energy prices continue to rise worldwide. This poses a major challenge to organisations running grids, indeed such architectures require large cooling systems. Indeed the one-year cost of a cooling system and of the power consumption may outfit the grid initial investment. We observe, however, that a grid does not constantly run at peak performance. In this paper, we propose a workload concentration strategy to reduce grid power consumption. Using the Xen virtual machine migration technology, our power management policy can dispatch transparently and dynamically any applications of the grid. Our policy concentrates the workload to shutdown nodes that are unused with a negligible impact on performance. We show through evaluations that this policy decreases the overall power consumption of the grid significantly.

An overview of AspectJ

Abstract: Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport For processing large amounts of data, management and switching of communications may contribute significantly to energy consumption and cloud computing seems to be an alternative to office-based computing.

Abstract: Network-based cloud computing is rapidly expanding as an alternative to conventional office-based computing. As cloud computing becomes more widespread, the energy consumption of the network and computing resources that underpin the cloud will grow. This is happening at a time when there is increasing attention being paid to the need to manage energy consumption across the entire information and communications technology (ICT) sector. While data center energy use has received much attention recently, there has been less attention paid to the energy consumption of the transmission and switching networks that are key to connecting users to the cloud. In this paper, we present an analysis of energy consumption in cloud computing. The analysis considers both public and private clouds, and includes energy consumption in switching and transmission as well as data processing and data storage. We show that energy consumption in transport and switching can be a significant percentage of total energy consumption in cloud computing. Cloud computing can enable more energy-efficient use of computing power, especially when the computing tasks are of low intensity or infrequent. However, under some circum- stances cloud computing can consume more energy than conventional computing where each user performs all com- puting on their own personal computer (PC).

Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport

Abstract: Network-based cloud computing is rapidly expanding as an alternative to conventional office-based computing. As cloud computing becomes more widespread, the energy consumption of the network and computing resources that underpin the cloud will grow. This is happening at a time when there is increasing attention being paid to the need to manage energy consumption across the entire information and communications technology (ICT) sector. While data center energy use has received much attention recently, there has been less attention paid to the energy consumption of the transmission and switching networks that are key to connecting users to the cloud. In this paper, we present an analysis of energy consumption in cloud computing. The analysis considers both public and private clouds, and includes energy consumption in switching and transmission as well as data processing and data storage. We show that energy consumption in transport and switching can be a significant percentage of total energy consumption in cloud computing. Cloud computing can enable more energy-efficient use of computing power, especially when the computing tasks are of low intensity or infrequent. However, under some circumstances cloud computing can consume more energy than conventional computing where each user performs all computing on their own personal computer (PC).

CloudScale: elastic resource scaling for multi-tenant cloud systems

Abstract: Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale employs online resource demand prediction and prediction error handling to achieve adaptive resource allocation without assuming any prior knowledge about the applications running inside the cloud. CloudScale can resolve scaling conflicts between applications using migration, and integrates dynamic CPU voltage/frequency scaling to achieve energy savings with minimal effect on application SLOs. We have implemented CloudScale on top of Xen and conducted extensive experiments using a set of CPU and memory intensive applications (RUBiS, Hadoop, IBM System S). The results show that CloudScale can achieve significantly higher SLO conformance than other alternatives with low resource and energy cost. CloudScale is non-intrusive and light-weight, and imposes negligible overhead (

Optimization of Resource Provisioning Cost in Cloud Computing

Abstract: In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments.