An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads

THE ADVANTAGE OF GENETIC ALGORITHM IN ENERGY-EFFICIENT SCHEDULING FOR HETEROGENEOUS CLOUD COMPUTING

Nowadays Energy Consumption has been a heavy burden on the enterprise cloud computing infrastructure. This paper focuses on the hardware factors in energy consumption. Inspired by DVFS, it proposes a new energy-efficient (EE) model. This paper formulates the scheduling problem and genetic algorithm is applied to obtain higher efficiency value. Simulations are implemented to verify the advantage of genetic algorithm. In addition, the robustness of our strategy is validated by modifying the relevant parameters of the experimen

A Power Efficient Genetic Algorithm for Resource Allocation in Cloud Computing Data Centers

One of the main challenges in cloud computing is to increase the availability of computational resources, while minimizing system power consumption and operational expenses. This article introduces a power efficient resource allocation algorithm for tasks in cloud computing data centers. The developed approach is based on genetic algorithms which ensure performance and scalability to millions of tasks. Resource allocation is performed taking into account computational and networking requirements of tasks and optimizes task completion time and data center power consumption. The evaluation results, obtained using a dedicated open source genetic multi-objective framework called jMetal show that the developed approach is able to perform the static allocation of a large number of independent tasks on homogeneous single-core servers within the same data center with a quadratic time complexity

Reclaiming the energy of a schedule: models and algorithms

We consider a task graph to be executed on a set of processors. We assume that the mapping is given, say by an ordered list of tasks to execute on each processor, and we aim at optimizing the energy consumption while enforcing a prescribed bound on the execution time. While it is not possible to change the allocation of a task, it is possible to change its speed. Rather than using a local approach such as backfilling, we consider the problem as a whole and study the impact of several speed variation models on its complexity. For continuous speeds, we give a closed-form formula for trees and series-parallel graphs, and we cast the problem into a geometric programming problem for general directed acyclic graphs. We show that the classical dynamic voltage and frequency scaling (DVFS) model with discrete modes leads to a NP-complete problem, even if the modes are regularly distributed (an important particular case in practice, which we analyze as the incremental model). On the contrary, the VDD-hopping model leads to a polynomial solution. Finally, we provide an approximation algorithm for the incremental model, which we extend for the general DVFS model.Comment: A two-page extended abstract of this work appeared as a short presentation in SPAA'2011, while the long version has been accepted for publication in "Concurrency and Computation: Practice and Experience

A Trusted Real-Time Scheduling Model for Wireless Sensor Networks

Heterogeneous multicore and multiprocessor systems have been widely used for wireless sensor information processing, but system energy consumption has become an increasingly important issue. To ensure the reliable and safe operation of sensor systems, the task scheduling success rate of heterogeneous platforms should be improved, and energy consumption should be reduced. This work establishes a trusted task scheduling model for wireless sensor networks, proposes an energy consumption model, and adopts the ant colony algorithm and bee colony algorithm for the task scheduling of a real-time sensor node. Experimental result shows that the genetic algorithm and ant colony algorithm can efficiently solve the energy consumption problem in the trusted task scheduling of a wireless sensor and that the performance of the bee colony algorithm is slightly inferior to that of the first two methods

Energy-aware simulation with DVFS

International audienceIn recent years, research has been conducted in the area of large systems models, especially distributed systems, to analyze and understand their behavior. Simulators are now commonly used in this area and are becoming more complex. Most of them provide frameworks for simulating application scheduling in various Grid infrastructures, others are specifically developed for modeling networks, but only a few of them simulate energy-efficient algorithms. This article describes which tools need to be implemented in a simulator in order to support energy-aware experimentation. The emphasis is on DVFS simulation, from its implementation in the simulator CloudSim to the whole methodology adopted to validate its functioning. In addition, a scientific application is used as a use case in both experiments and simulations, where the close relationship between DVFS efficiency and hardware architecture is highlighted. A second use case using Cloud applications represented by DAGs, which is also a new functionality of CloudSim, demonstrates that the DVFS efficiency also depends on the intrinsic middleware behavior

Balancing the use of batteries and opportunistic scheduling policies for maximizing renewable energy consumption in a Cloud data center

International audienceThe fast growth of cloud computing considerably increases the energy consumption of cloud infrastructures, especially , data centers. To reduce brown energy consumption and carbon footprint, renewable energy such as solar/wind energy is considered recently to supply new green data centers. As renewable energy is intermittent and fluctuates from time to time, this paper considers two fundamental approaches for improving the usage of renewable energy in a small/medium-sized data center. One approach is based on opportunistic scheduling: more jobs are performed when renewable energy is available. The other approach relies on Energy Storage Devices (ESDs), which store renewable energy surplus at first and then, provide energy to the data center when renewable energy becomes unavailable. In this paper, we explore these two means to maximize the utilization of on-site renewable energy for small data centers. By using real-world job workload and solar energy traces, our experimental results show the energy consumption with varying battery size and solar panel dimensions for opportunistic scheduling or ESD-only solution. The results also demonstrate that opportunistic scheduling can reduce the demand for ESD capacity. Finally, we find an intermediate solution mixing both approaches in order to achieve a balance in all aspects, implying minimizing the renewable energy losses. It also saves brown energy consumption by up to 33% compared to ESD-only solution

An Efficient Biobjective Heuristic for Scheduling Workflows on Heterogeneous DVS-Enabled Processors

Energy consumption has recently become a major concern to multiprocessor computing systems, of which the primary performance goal has traditionally been reducing execution time of applications. In the context of scheduling, there have been increasing research interests on algorithms using dynamic voltage scaling (DVS), which allows processors to operate at lower voltage supply levels at the expense of sacrificing processing speed, to acquire a satisfactory trade-off between quality of schedule and energy consumption. The problem considered in this paper is to find a schedule for a workflow, which is normally a precedence constrained application, on a bounded number of heterogeneous DVS-enabled processors, so as to minimize both makespan (overall execution time of the application) and energy consumption. A fast and efficient heuristic is proposed and evaluated using simulation with two real-world applications as well as randomly generated ones