Energy-aware service allocation

IETE Technical Review, 2019

The demand for cloud computing infrastructure is increasing day by day to meet the requirement of small and medium enterprises. The data center-centric cloud technology has a high share of energy consumption from the IT-industry. The amount of energy consumption in a data center depends on the allocation of user service requests to virtual machines running on the different host. Minimization of energy consumption in the data center is a significant issue and addressed by optimal allocation of cloud resources. In this paper, we have discussed how service allocation strategies have been used to optimize the energy consumption in a cloud system. A generalized system architecture is presented based on which we define the service allocation problem and energy model. Further, we present the taxonomy of various energy-efficient resource allocation techniques found in the literature. In the end, various research challenges related to the energy-efficient service allocation in cloud are discussed.

Journal of Cloud Computing: Advances, Systems and Applications, 2015

In this paper, we introduce a model of task scheduling for a cloud-computing data center to analyze energy-efficient task scheduling. We formulate the assignments of tasks to servers as an integer-programming problem with the objective of minimizing the energy consumed by the servers of the data center. We prove that the use of a greedy task scheduler bounds the constraint service time whilst minimizing the number of active servers. As a practical approach, we propose the most-efficient-server-first task-scheduling scheme to minimize energy consumption of servers in a data center. Most-efficient-server-first schedules tasks to a minimum number of servers while keeping the data-center response time within a maximum constraint. We also prove the stability of most-efficient-server-first scheme for tasks with exponentially distributed, independent, and identically distributed arrivals. Simulation results show that the server energy consumption of the proposed most-efficient-server-first scheduling scheme is 70 times lower than that of a random-based task-scheduling scheme.

Journal of Systems and Software, 2012

Complex distributed architectures, like Grid, supply effective platforms to solve computations on huge datasets, often at the cost of increased power consumption. This energy issue affects the sustainability of the infrastructures and increases their environmental impact. On the other hand, due to Grid heterogeneity and scalability, possible power savings could be achieved if effective energy-aware allocation policies were adopted. These policies are meant to implement a better coupling between application requirements and the Grid resources, also taking energy parameters into account. In this paper, we discuss different allocation strategies which address jobs submitted to Grid resources, subject to efficiency and energy constraints. Our aim is to analyse the potential benefits that can be obtained from the adoption of a metric able to capture both performance and energy-savings. Based on an experimental study, we simulated two alternative scenarios aimed at comparing the behaviour of different strategies for allocating jobs to resources. Moreover we introduced the Performance/Energy Trade-off function as a useful means to evaluate the tendency of an allocation strategy towards efficiency or power consumption. Our conclusion seems to suggest that performance and energy-savings are not always enemies, and these objectives may be combined if suitable energy metrics are adopted.

2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops, 2013

In this paper, we present an optimization model for task scheduling for minimizing energy consumption in cloudcomputing data centers. The proposed approach was formulated as an integer programming problem to minimize the cloudcomputing data center energy consumption by scheduling tasks to a minimum numbers of servers while keeping the task response time constraints. We prove that the average task response time and the number of active servers needed to meet such time constraints are bounded through the use of a greedy taskscheduling scheme. In addition, we propose the most-efficientserver-first task-scheduling scheme to minimize energy expenditure as a practical scheduling scheme. We model and simulate the proposed scheduling scheme for a data center with heterogeneous tasks. The simulation results show that the proposed taskscheduling scheme reduces server energy consumption on average over 70 times when compared to the energy consumed under a (not-optimized) random-based task-scheduling scheme. We show that energy savings are achieved by minimizing the allocated number of servers.

A cloud system uses virtualization technology to provide cloud resources (e.g. CPU, memory) to users in form of virtual machines. Job requests are assigned on these VMs for execution. Efficient job assignment on VMs will reduce the number of hosts used. Hence, it is essential to achieve energy optimization in cloud computing environments. Therefore, in this paper, a job scheduling mechanism is proposed to assign job to a VM of the existing active hosts itself by considering job classification and preemption. So that minimizing the number of host used in allocation intern reduces the energy consumption in the Cloud datacenter. In our proposed job scheduling algorithm, categorizing the job in to three different types and assigned based on preemption policy with the earliest available time of the resource (VM) which is attached to a host. Thereby, we reduce the energy consumption by making less number of hosts in the active state and increase the utilization of active host. Finally, we conduct simulations using CloudSim and compare our algorithm with other existing methods. Significant energy savings can be obtained depending on system loads. Energy saving is about 2% to 46% with respect to the non-energy aware algorithm, 1% to 7% than the energy aware algorithms.

SOFSEM 2009: Theory and Practice of Computer …, 2009

Green Computing is a recent trend towards designing, building, and operating computer systems to be energy efficient. While programs such as Energy Star have been around since the early 1990s, recent concerns regarding global climate change and the energy crisis have led to renewed interest in Green Computing. Data centers are a significant consumers of energy -both to power the computers as well as to provide the necessary cooling. This paper proposes a new approach to reduce energy utilization in data centers. In particular, our approach relies on consolidating services dynamically onto a subset of the available servers and temporarily shutting down servers in order to conserve energy. We present initial work on a probabilistic service dispatch algorithm that aims at minimizing the number of running servers such that they suffice for meeting the quality of service required by service-level agreements. Given the estimated energy consumption and projected growth in data centers, the proposed effort has the potential to positively impact energy consumption.

NC&SC’2015 - Network Computing & Supercomputing workshop. In conjunction with RuSCDays'15 - The Russian Supercomputing Days, September 28-29, 2015, Moscow

In this paper, we present an energy optimization model of Cloud computing, and formulate novel energy-aware resource allocation problem that provides energy-efficiency by heterogeneous job consolidation taking into account types of applications. Data centers process heterogeneous workloads that include CPU intensive, disk I/O intensive, memory intensive, network I/O intensive and other types of applications. When one type of applications creates a bottleneck and resource contention either in CPU, disk or network, it may result in degradation of the system performance and increasing energy consumption. We discuss energy characteristics of applications, and how an awareness of their types can help in intelligent allocation strategy to improve energy consumption.

Journal of Electrical Engineering and Information Technologies

Cloud computing is an emerging technology designed as replacement of the traditional physical hardware solutions. Consisted of virtual resources provided on user demand, it allows scalable pay-as-you-go model of data sharing. Cloud computing is a model that allows the division of a shared network with commonly configured computing resources where, before being assigned to customers, the available resources must be reviewed and scheduled using an efficient resource scheduler. Most existing standard schedulers do not meet the corresponding standards and user requirements. Therefore, additional research and analysis are needed in order to create a scheduler that would perform an energy efficient and optimal allocation of resources in any cloud computing network. In this research work, a new task allocation and scheduling algorithm is proposed, based on the use of newly created metric, enabling the selection of the most suitable server, frame and module in a standard cloud computing architecture.

International Symposium on Quality Electronic Design (ISQED), 2017

—Cloud computing has become an attractive computing paradigm in recent years to offer on demand computing resources for users worldwide. Through Virtual Machine (VM) technologies, the cloud service providers (CSPs) can provide users the infrastructure, platform, and software with a quite low cost. With the drastically growing number of data centers, the energy efficiency has drawn a global attention as CSPs are faced with the high energy cost of data centers. Many previous works have contributed to improving the energy efficiency in data centers. However, the computational complexity may lead to unacceptable run time. In this paper, we propose a fast and energy-aware resource provisioning and task scheduling algorithm to achieve low energy cost with reduced computational complexity for CSPs. In our iterative algorithm, we divide the provisioning and scheduling to multiple steps which can effectively reduce the complexity and minimize the run time while achieving a reasonable energy cost. Experimental results demonstrate that compared to the baseline algorithm, the proposed algorithm can achieve up to 79.94% runtime improvement with an acceptable energy cost increase.

2010 IEEE 3rd International Conference on Cloud Computing, 2010

Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, associated with running a data center, not only reflects on its carbon footprint, but also increases the costs of running the data center itself. This paper addresses the problem of maximizing the revenues of Cloud providers by trimming down their electricity costs. As a solution allocation policies which are based on the dynamic powering servers on and off are introduced and evaluated. The policies aim at satisfying the conflicting goals of maximizing the users' experience while minimizing the amount of consumed electricity. The results of numerical experiments and simulations are described, showing that the proposed scheme performs well under different traffic conditions.