Hybrid power management in real time embedded systems: an interplay of DVFS and DPM techniques

Energy-aware scheduling of real time applications over multiprocessor systems is considered in this paper. Early research reports that while various energy-saving policies, for instance Dynamic Power Management (DPM) and Dynamic Voltage & Frequency scaling (DVFS) policies, perform well individually for a specific set of operating conditions, they often outperform each other under different workload and/or architecture configuration. Thus, no single policy fits perfectly all operating conditions. Instead of designing new policies for specific operating conditions, this paper proposes a generic power/energy management scheme that takes a set of well-known existing (DPM and DVFS) policies, each of which performs well for a set of conditions, and adapts at runtime to the best-performing policy for any given workload. Experiments are performed using state-of the-art DPM and DVFS policies and the results show that our proposed scheme adapts well to the changing workload and always achieves overall energy savings comparable to that of best-performing policy at any point in time.     

Conserving disk energy in virtual machine based environments by amplifying bursts

Computer systems are now powerful enough to run multiple virtual machines (VMs), each one running a separate operating system (OS) instance. In such an environment, direct and centralized energy management employed by a single OS is unfeasible. Accurately predicting the idle intervals is one of the major approaches to save energy of disk drives. However, for the intensive workloads, it is difficult to find long idle intervals. Even if long idle intervals exist, it is very difficult for a predictor to catch the idle spikes in the workloads. This paper proposes to divide the workloads into buckets which are equal in time length, and predict the number of the forthcoming requests in each bucket instead of the length of the idle periods. By doing so, the bucket method makes the converted workload more predictable. The method also squeezes the executing time of each request to the end of its respective bucket, thus extending the idle length. By deliberately reshaping the workloads such that the crests and troughs of each workload become aligned, we can aggregate the peaks and the idle periods of the workloads. Due to the extended idle length caused by this aggregation, energy can be conserved. Furthermore, as a result of aligning the peaks, resource utilization is improved when the system is active. A trace driven simulator is designed to evaluate the idea. Three traces are employed to represent the workloads issued by three web servers residing in three VMs. The experimental results show that our method can save significant amounts of energy by sacrificing a small amount of quality of service.     

Energy efficient utilization of resources in cloud computing systems

The energy consumption of under-utilized resources, particularly in a cloud environment, accounts for a substantial amount of the actual energy use. Inherently, a resource allocation strategy that takes into account resource utilization would lead to a better energy efficiency; this, in clouds, extends further with virtualization technologies in that tasks can be easily consolidated. Task consolidation is an effective method to increase resource utilization and in turn reduces energy consumption. Recent studies identified that server energy consumption scales linearly with (processor) resource utilization. This encouraging fact further highlights the significant contribution of task consolidation to the reduction in energy consumption. However, task consolidation can also lead to the freeing up of resources that can sit idling yet still drawing power. There have been some notable efforts to reduce idle power draw, typically by putting computer resources into some form of sleep/power-saving mode. In this paper, we present two energy-conscious task consolidation heuristics, which aim to maximize resource utilization and explicitly take into account both active and idle energy consumption. Our heuristics assign each task to the resource on which the energy consumption for executing the task is explicitly or implicitly minimized without the performance degradation of that task. Based on our experimental results, our heuristics demonstrate their promising energy-saving capability.     

An improved approach for load balancing among heterogeneous resources in computational grids

In recent days, due to the rapid technological advancements, the grid computing has become an important area of research in distributed systems. The load balancing is a very important and complex problem in grid computing. In this paper, we propose a dynamic-distributed load-balancing technique called the improved load balancing on enhanced GridSim with deadline control (IEGDC) for computational grids. Here, we provide a new mechanism of scheduling to enhance the utilization of the resources and to prevent the resource overloading. A selection method for scheduling by considering the state of resource bandwidth and capacity of various resources is presented. We simulate the proposed load-balancing strategy on the GridSim platform. The proposed mechanism on comparison is found to outperform the existing schemes in terms of response time, resubmitted time, finished and unfinished Gridlets. The simulation results are presented.     

A three-phase energy-saving strategy for cloud storage systems

In the running process of cloud data center, the idle data nodes will generate a large amount of unnecessary energy consumption. Furthermore, the resource misallocation will also cause a great waste of energy. This paper proposes a three-phase energy-saving strategy named TPES in order to save energy and operational costs for cloud suppliers. The three phases are replica management based on variable replication factor, cluster reconfiguration according to the optimal total costs and state transition based on observed and predicted workloads. These three phases save energy for the system at different levels which enhance the adaptability of our strategy. We evaluate our strategy using the expanded CloudSim toolkit and the results show that the proposed strategy achieves better energy reduction under different conditions in comparison with the existing schemes.     

Multiperiod robust optimization for proactive resource provisioning in virtualized data centers

Energy management has become a significant concern in data centers for reducing operational costs. Using virtualization allows server consolidation, which increases server utilization and reduces energy consumption by turning off idle servers. This needs to consider the power state change overhead. In this paper, we investigate proactive resource provisioning in short-term planning for performance and energy management. To implement short-term planning based on workload prediction, this requires dealing with high fluctuations that are inaccurately predictable by using single value prediction. Unlike long-term planning, short-term planning can not depend on periodical patterns. Thus, we propose an adaptive range-based prediction algorithm instead of a single value. We implement and extensively evaluate the proposed range-based prediction algorithm with different days of real workload. Then, we exploit the range prediction for implementing proactive provisioning using robust optimization taking into consideration uncertainty of the demand. We formulate proactive VM provisioning as a multiperiod robust optimization problem. To evaluate the proposed approach, we use several experimental setups and different days of real workload. We use two metrics: energy savings and robustness for ranking the efficiency of different scenarios. Our approach mitigates undesirable changes in the power state of servers. This enhances servers’ availability for accommodating new VMs, its robustness against uncertainty in workload change, and its reliability against a system failure due to frequent power state changes.     

Experiences in managing energy with ECOSystem

Mobile devices are becoming increasingly popular, from laptops, PDAs, and cell phones to emerging platforms such as wireless sensor networks. Available battery energy has become a critical mobile-system resource. A mobile device's usefulness is often limited not by its hardware's raw speed but by its battery's energy. Energy consumption is a major systems-design challenge. We designed our ECOSystem (Energy-Centric Operating System) prototype to manage energy consumption at the OS level, complementing existing power-management techniques, such as DVS and application adaptation. It's based on the ideas that energy management should be a system-wide effort, that we should explicitly recognize energy as a resource, and that we should unify energy management across the system. Even managing one hardware device might require coordination with other system components. Without unified management, application-level energy-saving efforts might not result in reduced energy consumption. ECOSystem incorporates the "currentcy model", which lets the operating system manage energy as a first-class resource. It can also express complex energy-related goals and behaviors, leading to more effective, unified management policies.     

云计算资源调度研究综述

资源调度是云计算的一个主要研究方向.首先对云计算资源调度的相关研究现状进行深入调查和分析;然后重点讨论以降低云计算数据中心能耗为目标的资源调度方法、以提高系统资源利用率为目标的资源管理方法、基于经济学的云资源管理模型,给出最小能耗的云计算资源调度模型和最小服务器数量的云计算资源调度模型,并深入分析和比较现有的云资源调度方法;最后指出云计算资源管理的未来重要研究方向:基于预测的资源调度、能耗与性能折衷的调度、面向不同应用负载的资源管理策略与机制、面向计算能力(CPU、内存)和网络带宽的综合资源分配、多目标优化的资源调度,以便为云计算研究提供有益的参考.     

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load.     

云数据中心基于负载权重的负载均衡调度算法

针对现有云数据中心的多维资源利用不均衡问题,提出基于资源负载权重的动态多资源负载均衡调度算法。算法结合服务器各维度资源动态负载情况,构造层次分析法（AHP）判断矩阵来处理多维资源对于负载均衡影响权重大小,在此基础上综合考虑任务资源需求,将任务放置到合适服务器来改善资源利用,实现资源间负载均衡。平台仿真显示新算法可有效提高利用率低的资源的利用效率,在提高整体资源利用率、降低资源间负载不均衡率方面有优势。     

Exploiting fine-grained idle periods in networks of workstations

Studies have shown that for a significant fraction of the time, workstations are idle. In this paper, we present a new scheduling policy called Linger-Longer that exploits the fine-grained availability of workstations to run sequential and parallel jobs. We present a two-level workload characterization study and use it to simulate a cluster of workstations running our new policy. We compare two variations of our policy to two previous policies: Immediate-Eviction and Pause-and-Migrate. Our study shows that the Linger-Longer policy can improve the throughput of foreign jobs on a cluster by 60 percent with only a 0.5 percent slowdown of local jobs. For parallel computing, we show that the Linger-Longer policy outperforms reconfiguration strategies when the processor utilization by the local process is 20 percent or less in both synthetic bulk synchronous and real data-parallel applications     

Enhanced GridSim architecture with load balancing

Grid is a network of computational resources that may potentially span many continents. Maximization of the resource utilization hinges on the implementation of an efficient load balancing scheme, which provides (i) minimization of idle time, (ii) minimization of overloading, and (iii) minimization of control overhead. In this paper, we propose a dynamic and distributed load balancing scheme for grid networks. The distributed nature of the proposed scheme not only reduces the communication overhead of grid resources but also cuts down the idle time of the resources during the process of load balancing. We apply the proposed load balancing approach on Enhanced GridSim in order to gauge the effectiveness in terms of communication overhead and response time reduction. We show that significant savings are delivered by the proposed technique compared to other approaches such as centralized load balancing and no load balancing.     

NoMan-Grid计算原型系统

设计并实现一个无集中资源管理的网格计算原型系统——NoMan-Grid,描述该原型系统的体系结构、信息管理、任务调度机制,对原型系统进行验证与评价。在该原型系统中,不存在任何集中管理节点,所有节点功能相同,有效避免了中央控制节点存在的情况下网格规模受限的情况。对系统性能进行初步测试和分析的结果表明,该原型系统能充分利用Internet上空闲的计算资源,网络通信流量比较少且分布均衡,适合解决大规模分布式应用问题。     

Understanding the relationship between energy conservation and reliability in parallel disk arrays

Energy conservation schemes based on power management or workload skew for disk arrays adversely affect disk reliability due to either workload concentration or frequent disk speed transitions. A thorough understanding of the relationship between energy-saving techniques and disk reliability is still an open problem, which prevents effective design of new energy-saving techniques and application of existing approaches in reliability-critical environments. This paper presents an empirical reliability model, called PRESS (Predictor of Reliability for Energy-Saving Schemes). Fed by operating temperature, disk utilization, and disk speed transition frequency, PRESS estimates the reliability of an entire disk array. Further, a new energy-saving strategy with reliability awareness named READ (Reliability and Energy Aware Distribution) is developed in the light of the insights provided by PRESS. Experimental results demonstrate that READ consistently performs better than existing approaches in performance and reliability while achieving a comparable level of energy consumption.     

An efficient resource allocation for maximizing benefit of users and resource providers in ad hoc grid environment

Ad hoc grids allow a group of individuals to accomplish a mission that involves computation and communication among the grid components, often without fixed structure. In an ad hoc grid, every node in the network can spontaneously arise as a resource consumer or a resource producer at any time when it needs a resource or it possesses an idle resource. At the same time, the node in ad hoc grid is often energy constrained. The paper proposes an efficient resource allocation scheme for grid computing marketplace where ad hoc grid users can buy usage of memory and CPU from grid resource providers. The ad hoc grid user agents purpose to obtain the optimized quality of service to accomplish their tasks on time with a given budget, and the goal of grid resource providers as profit-maximization. Combining perspectives of both ad hoc grid users and resource providers, the paper present ad hoc grid resource allocation algorithm to maximize the global utility of the ad hoc grid system which are beneficial for both grid users and grid resource providers. Simulations are conducted to compare the performance of the algorithms with related work.     

Efficient Energy-Aware Resource Management Model (EEARMM) Based Dynamic VM Migration

In cloud environment, an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands. The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure. However, it is very difficult to persuade the objectives of the Cloud Service Providers (CSPs) and end users in an impulsive cloud domain with random changes of workloads, huge resource availability and complicated service policies to handle them, With that note, this paper attempts to present an Efficient Energy-Aware Resource Management Model (EEARMM) that works in a decentralized manner. Moreover, the model involves in reducing the number of migrations by definite workload management for efficient resource utilization. That is, it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine. The Estimation Model Algorithm (EMA) is given for determining the virtual machine migration. Further, VM-Selection Algorithm (SA) is also provided for choosing the appropriate VM to migrate for resource management. By the incorporation of these algorithms, overloading of VM instances can be avoided and energy efficiency can be improved considerably. The performance evaluation and comparative analysis, based on the dynamic workloads in different factors provides evidence to the efficiency, feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.     

一种优化MapReduce系统能耗的数据布局算法

在云计算技术和大数据技术的推动下,IT资源的规模不断扩大,其能耗问题日益显著.研究表明:节点资源利用率不高、资源空闲导致的能源浪费,是目前大规模分布式系统的主要问题之一.研究了MapReduce系统的能耗优化.传统的基于软件技术的能耗优化方法多采用负载集中和节点开关算法,但由于MapReduce任务的特点,集群节点不仅要完成运算,还需要存储数据,因此,传统方法难以应用到MapReduce集群.提出了良好的数据布局可以优化集群能耗.基于此,首先定义了数据布局的能耗优化目标,并提出相应的数据布局算法;接着,从理论上证明该算法能够实现数据布局的能耗优化目标;最后,在异构集群中部署3种数据布局不同的MapReduce系统,通过对比三者在执行CPU密集型、I/O密集型和交互型这3种典型运算时的集群能耗,验证了所提出的数据布局算法的能耗优化效果.理论和实验结果均表明,所提出的布局算法能够有效地降低MapReduce集群的能耗.上述工作都将促进高能耗计算和大数据分析的应用.     

An improved load-balancing mechanism based on deadline failure recovery on GridSim

Grid computing has emerged a new field, distinguished from conventional distributed computing. It focuses on large-scale resource sharing, innovative applications and in some cases, high performance orientation. The Grid serves as a comprehensive and complete system for organizations by which the maximum utilization of resources is achieved. The load balancing is a process which involves the resource management and an effective load distribution among the resources. Therefore, it is considered to be very important in Grid systems. For a Grid, a dynamic, distributed load balancing scheme provides deadline control for tasks. Due to the condition of deadline failure, developing, deploying, and executing long running applications over the grid remains a challenge. So, deadline failure recovery is an essential factor for Grid computing. In this paper, we propose a dynamic distributed load-balancing technique called “Enhanced GridSim with Load balancing based on Deadline Failure Recovery” (EGDFR) for computational Grids with heterogeneous resources. The proposed algorithm EGDFR is an improved version of the existing EGDC in which we perform load balancing by providing a scheduling system which includes the mechanism of recovery from deadline failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed load-balancing technique gives 7 % better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets, it gives 11 % better performance than EGDC.     

云计算数据中心虚拟机节能管理机制研究

云计算数据中心的耗电量巨大,但绝大多数的云计算数据中心并没有取得较高的资源利用率,通常只有15%-20%,有相当数量的服务器处于闲置工作状态,导致大量的能耗白白浪费。为了能够有效降低云计算数据中心的能耗,提出了一种适用于异构集群系统的云计算数据中心虚拟机节能调度算法(PVMAP算法),仿真实验结果表明：与经典算法PABFD相比,PVMAP算法的能耗明显更低,可扩展性与稳定性都更好。与此同时,随着〈Hosts,VMs〉数目的不断增加,PVMAP 算法虚拟机迁移总数和关闭主机总数的增长幅度都要低于PABFD算法。     

A workload-driven approach to database query processing in the cloud

This paper is concerned with data provisioning services (information search, retrieval, storage, etc.) dealing with a large and heterogeneous information repository. Increasingly, this class of services is being hosted and delivered through Cloud infrastructures. Although such systems are becoming popular, existing resource management methods (e.g. load-balancing techniques) do not consider workload patterns nor do they perform well when subjected to non-uniformly distributed datasets. If these problems can be solved, this class of services can be made to operate in more a scalable, efficient, and reliable manner. The main contribution of this paper is a approach that combines proprietary cloud-based load balancing techniques and density-based partitioning for efficient range query processing across relational database-as-a-service in cloud computing environments. The study is conducted over a real-world data provisioning service that manages a large historical news database from Thomson Reuters. The proposed approach has been implemented and tested as a multi-tier web application suite consisting of load-balancing, application, and database layers. We have validated our approach by conducting a set of rigorous performance evaluation experiments using the Amazon EC2 infrastructure. The results prove that augmenting a cloud-based load-balancing service (e.g. Amazon Elastic Load Balancer) with workload characterization intelligence (density and distribution of data; composition of queries) offers significant benefits with regards to the overall system’s performance (i.e. query latency and database service throughput).