1,411 research outputs found
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
PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers
Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%
Building adaptable, QoS-aware dependable embedded systems
Most of today’s embedded systems are required to work
in dynamic environments, where the characteristics of the
computational load cannot always be predicted in advance.
Furthermore, resource needs are usually data dependent
and vary over time. Resource constrained devices may need
to cooperate with neighbour nodes in order to fulfil those requirements
and handle stringent non-functional constraints.
This paper describes a framework that facilitates the distribution
of resource intensive services across a community of
nodes, forming temporary coalitions for a cooperative QoSaware
execution. The increasing need to tailor provided
service to each application’s specific needs determines the
dynamic selection of peers to form such a coalition. The
system is able to react to load variations, degrading its performance
in a controlled fashion if needed. Isolation between
different services is achieved by guaranteeing a minimal
service quality to accepted services and by an efficient
overload control that considers the challenges and opportunities
of dynamic distributed embedded systems
Composing Systemic Aspects into Component-Oriented DOC Middleware
The advent and maturation of component-based middleware frameworks have sim-plified the development of large-scale distributed applications by separating system devel-opment and configuration concerns into different aspects that can be specified and com-posed at various stages of the application development lifecycle. Conventional component middleware technologies, such as J2EE [73] and .NET [34], were designed to meet the quality of service (QoS) requirements of enterprise applications, which focus largely on scalability and reliability. Therefore, conventional component middleware specifications and implementations are not well suited for distributed real-time and embedded (DRE) ap-plications with more stringent QoS requirements, such as low latency/jitter, timeliness, and online fault recovery. In the DRE system development community, a new generation of enhanced commercial off-the-shelf (COTS) middleware, such as Real-time CORBA 1.0 (RT-CORBA)[39], is increasingly gaining acceptance as (1) the cost and time required to develop and verify DRE applications precludes developers from implementing complex DRE applications from scratch and (2) implementations of standard COTS middleware specifications mature and encompass key QoS properties needed by DRE systems. However, although COTS middleware standardizes mechanisms to configure and control underlying OS support for an application’s QoS requirements, it does not yet provide sufficient abstractions to separate QoS policy configurations such as real-time performance requirements, from application functionality. Developers are therefore forced to configure QoS policies in an ad hoc way, and the code to configure these policies is often scattered throughout and tangled with other parts of a DRE system. As a result, it is hard for developers to configure, validate, modify, and evolve complex DRE systems consistently. It is therefore necessary to create a new generation of QoS-enabled component middleware that provides more comprehensive support for addressing QoS-related concerns modularly, so that they can be introduced and configured as separate systemic aspects. By analyzing and identifying the limitations of applying conventional middleware technologies for DRE applications, this dissertation presents a new design and its associated techniques for enhancing conventional component-oriented middleware to provide programmability of DRE relevant real-time QoS concerns. This design is realized in an implementation of the standard CORBA Component Model (CCM) [38], called the Component-Integrated ACE ORB (CIAO). This dissertation also presents both architectural analysis and empirical results that demonstrate the effectiveness of this approach. This dissertation provides three contributions to the state of the art in composing systemic behaviors into component middleware frameworks. First, it illustrates how component middleware can simplify development and evolution of DRE applications while ensuring stringent QoS requirements by composing systemic QoS aspects. Second, it contributes to the design and implementation of QoS-enabled CCM by analyzing and documenting how systemic behaviors can be composed into component middleware. Finally, it presents empirical and analytical results to demonstrate the effectiveness and the advantage of composing systemic behaviors in component middleware. The work in this dissertation has a broader impact beyond the CCM in which it was developed, as it can be applied to other component-base middleware technologies which wish to support DRE applications
Intelligent Resource Scheduling at Scale: a Machine Learning Perspective
Resource scheduling in a computing system addresses the problem of packing tasks with multi-dimensional resource requirements and non-functional constraints. The exhibited heterogeneity of workload and server characteristics in Cloud-scale or Internet-scale systems is adding further complexity and new challenges to the problem. Compared with,,,, existing solutions based on ad-hoc heuristics, Machine Learning (ML) has the potential to improve further the efficiency of resource management in large-scale systems. In this paper we,,,, will describe and discuss how ML could be used to understand automatically both workloads and environments, and to help to cope with scheduling-related challenges such as consolidating co-located workloads, handling resource requests, guaranteeing application's QoSs, and mitigating tailed stragglers. We will introduce a generalized ML-based solution to large-scale resource scheduling and demonstrate its effectiveness through a case study that deals with performance-centric node classification and straggler mitigation. We believe that an MLbased method will help to achieve architectural optimization and efficiency improvement
Cloudbus Toolkit for Market-Oriented Cloud Computing
This keynote paper: (1) presents the 21st century vision of computing and
identifies various IT paradigms promising to deliver computing as a utility;
(2) defines the architecture for creating market-oriented Clouds and computing
atmosphere by leveraging technologies such as virtual machines; (3) provides
thoughts on market-based resource management strategies that encompass both
customer-driven service management and computational risk management to sustain
SLA-oriented resource allocation; (4) presents the work carried out as part of
our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a
Service software system containing SDK (Software Development Kit) for
construction of Cloud applications and deployment on private or public Clouds,
in addition to supporting market-oriented resource management; (ii)
internetworking of Clouds for dynamic creation of federated computing
environments for scaling of elastic applications; (iii) creation of 3rd party
Cloud brokering services for building content delivery networks and e-Science
applications and their deployment on capabilities of IaaS providers such as
Amazon along with Grid mashups; (iv) CloudSim supporting modelling and
simulation of Clouds for performance studies; (v) Energy Efficient Resource
Allocation Mechanisms and Techniques for creation and management of Green
Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape
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QoS-aware mechanisms for improving cost-efficiency of datacenters
Warehouse Scale Computers (WSCs) promise high cost-efficiency by amortizing power, cooling, and management overheads. WSCs today host a large variety of jobs with two broad performance requirements categories: latency-critical (LC) and best-effort (BE). Ideally, to fully utilize all hardware resources, WSC operators can simply fill all the nodes with computing jobs. Unfortunately, because colocated jobs contend for shared resources, systems with high loads often experience performance degradation, which negatively impacts the Quality of Service (QoS) for LC jobs. In fact, service providers usually over-provision resources to avoid any interference with LC jobs, leading to significant resource inefficiencies. In this dissertation, I explore opportunities across different system-abstraction layers to improve the cost-efficiency of dataceters by increasing resource utilization of WSCs with little or no impact on the performance of LC jobs. The dissertation has three main components. First, I explore opportunities to improve the throughput of multicore systems by reducing the performance variation of LC jobs. The main insight is that by reshaping the latency distribution curve, performance headroom of LC jobs can be effectively converted to improved BE throughput. I develop, implement, and evaluate a runtime system that achieves this goal with existing hardware. I leverage the cache partitioning, per-core frequency scaling, and thread masking of server processors. Evaluation results show the proposed solution enables 30% higher system throughput compared to solutions proposed in prior works while maintaining at least as good QoS for LC jobs. Second, I study resource contention in near-future heterogeneous memory architectures (HMA). This study is motivated by recent developments in non-volatile memory (NVM) technologies, which enable higher storage density at the cost of same performance. To understand the performance and QoS impact of HMAs, I design and implement a performance emulator in the Linux kernel that runs unmodified workloads with high accuracy, low overhead, and complete transparency. I further propose and evaluate multiple data and resource management QoS mechanisms, such as locality-aware page admission, occupancy management, and write buffer jailing. Third, I focus on accelerated machine learning (ML) systems. By profiling the performance of production workloads and accelerators, I show that accelerated ML tasks are highly sensitive to main memory interference due to fine-grained interaction between CPU and accelerator tasks. As a result, memory resource contention can significantly decreases the performance and efficiency gains of accelerators. I propose a runtime system that leverages existing hardware capabilities and show 17% higher system efficiency compared to previous approaches. This study further exposes opportunities for future processor architecturesElectrical and Computer Engineerin
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