Energy-aware Service Allocation for Cloud Computing

Energy efficiency has become an important managerial variable of IT management. Whereas cloud computing promises significantly higher levels of energy efficiency, it is still not known, if and to what extent outsourcing of software applications to cloud service providers affects the overall energy efficiency. This research is concerned with the allocation of cloud services from providers to customers and addresses the problem of energy-aware service allocation. The distributed nature of the problem, i.e., the multiple loci of control, entails the failure of centralised solutions. Hence, we approach this problem from a multiagent system perspective, which preserves the distributed setting of multiple service providers and customers. The contribution of our research is a game-theoretic framework for analysing service provider and customer interactions and a novel distributed allocation mechanism based on this framework to approximate energy-efficient, optimal allocations. We demonstrate the usefulness and efficacy of the proposed artifact in several simulation experiments

Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA, July 12-15, 201

Thermal-aware cloud middleware to reduce cooling needs

International audienceAs we are living in a data-driven world and directed toward internet, the need for data enter is growing. The main limitation for building cloud infrastructures is their energy consumption. Moreover, their conception is not perfect because servers are not the ones consuming all the power, cooling systems are responsible for half of the consumption. Cooling costs can be reduced by intelligent scheduling, and in our case through virtual machines migrations. In this paper, we propose a dynamic reconfiguration based on evolution of temperatures and load of the servers. The idea is to share heat production to reduce cooling costs and consolidate the workload when possible to reduce servers costs. The challenge resides in satisfying these opposite objectives. We tested our algorithm on an experimental test bed, and achieve to cap the temperature of the data enter room while not forgetting to optimize the server use, and without impacting on applications performance

A Fine-grained Approach for Power Consumption Analysis and Prediction

Power consumption has became a critical concern in modern computing systems for various reasons including financial savings and environmental protection. With battery powered devices, we need to care about the available amount of energy since it is limited. For the case of supercomputers, as they imply a large aggregation of heavy CPU activities, we are exposed to a risk of overheating. As the design of current and future hardware is becoming more and more complex, energy prediction or estimation is as elusive as that of time performance. However, having a good prediction of power consumption is still an important request to the computer science community. Indeed, power consumption might become a common performance and cost metric in the near future. A good methodology for energy prediction could have a great impact on power-aware programming, compilation, or runtime monitoring. In this paper, we try to understand from measurements where and how power is consumed at the level of a computing node. We focus on a set of basic programming instructions, more precisely those related to CPU and memory. We propose an analytical prediction model based on the hypothesis that each basic instruction has an average energy cost that can be estimated on a given architecture through a series of micro-benchmarks. The considered energy cost per operation includes all of the overhead due to context of the loop where it is executed. Using these precalculated values, we derive an linear extrapolation model to predict the energy of a given algorithm expressed by means of atomic instructions. We then use three selected applications to check the accuracy of our prediction method by comparing our estimations with the corresponding measurements obtained using a multimeter. We show a 9.48\% energy prediction on sorting

Coordination of Just-in-Time Deliveries with Multi-attribute Auctions

Just-in-time deliveries are crucial for many industries. They are particularly essential when the properties of the delivered resource or the demanding processes are sensitive in time. Rigid, centralized planning tends to fail, especially in dynamic environments with distributed decisions and control. Under the constraints of distributed decisions and control, auctions promise an efficient allocation of resources. However, a dedicated design of auctions for just-in-time deliveries, which can be incorporated into the design of an IT artifact, is still lacking. We contribute a linear and a quadratic multi-attribute scoring rule for an automated execution by software. We evaluate the artifact in a simulation experiment and reveal the effects of the scoring rules for just-in-time deliveries. Our results provide evidence that the artifact effectively coordinates just-in-time deliveries, which also holds when considering one additional side constraint

Enhanced Graph Rewriting Systems for Complex Software Domain

International audienceMethodologies for correct by construction reconfigurations can efficiently solve consistency issues in dynamic software architecture. Graph-based models are appropriate for designing such architectures and methods. At the same time, they may be unfit to characterize a system from a non functional perspective. This stems from efficiency and applicability limitations in handling time-varying characteristics and their related dependencies. In order to lift these restrictions, an extension to graph rewriting systems is proposed herein. The suitability of this approach, as well as the restraints of currently available ones, are illustrated, analysed and experimentally evaluated with reference to a concrete example. This investigation demonstrates that the conceived solution can: (i) express any kind of algebraic dependencies between evolving requirements and properties; (ii) significantly ameliorate the efficiency and scalability of system modifications with respect to classic methodologies; (iii) provide an efficient access to attribute values; (iv) be fruitfully exploited in software management systems; (v) guarantee theoretical properties of a grammar, like its termination

Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center

Presently, massive energy consumption in cloud data center tends to be an escalating threat to the environment. To reduce energy consumption in cloud data center, an energy efficient virtual machine allocation algorithm is proposed in this paper based on a proposed energy efficient multiresource allocation model and the particle swarm optimization (PSO) method. In this algorithm, the fitness function of PSO is defined as the total Euclidean distance to determine the optimal point between resource utilization and energy consumption. This algorithm can avoid falling into local optima which is common in traditional heuristic algorithms. Compared to traditional heuristic algorithms MBFD and MBFH, our algorithm shows significantly energy savings in cloud data center and also makes the utilization of system resources reasonable at the same time

Using Ant Colony Optimization on the Quadratic Assignment Problem to Achieve Low Energy Cost in Geo-distributed Data Centers

There are many problems associated with operating a data center. Some of these problems include data security, system performance, increasing infrastructure complexity, increasing storage utilization, keeping up with data growth, and increasing energy costs. Energy cost differs by location, and at most locations fluctuates over time. The rising cost of energy makes it harder for data centers to function properly and provide a good quality of service. With reduced energy cost, data centers will have longer lasting servers/equipment, higher availability of resources, better quality of service, a greener environment, and reduced service and software costs for consumers. Some of the ways that data centers have tried to using to reduce energy costs include dynamically switching on and off servers based on the number of users and some predefined conditions, the use of environmental monitoring sensors, and the use of dynamic voltage and frequency scaling (DVFS), which enables processors to run at different combinations of frequencies with voltages to reduce energy cost. This thesis presents another method by which energy cost at data centers could be reduced. This method involves the use of Ant Colony Optimization (ACO) on a Quadratic Assignment Problem (QAP) in assigning user request to servers in geo-distributed data centers.In this paper, an effort to reduce data center energy cost involves the use of front portals, which handle users� requests, were used as ants to find cost effective ways to assign users requests to a server in heterogeneous geo-distributed data centers. The simulation results indicate that the ACO for Optimal Server Activation and Task Placement algorithm reduces energy cost on a small and large number of users� requests in a geo-distributed data center and its performance increases as the input data grows. In a simulation with 3 geo-distributed data centers, and user�s resource request ranging from 25,000 to 25,000,000, the ACO algorithm was able to reduce energy cost on an average of $.70 per second. The ACO for Optimal Server Activation and Task Placement algorithm has proven to work as an alternative or improvement in reducing energy cost in geo-distributed data centers.Computer Scienc