On the feasibility of collaborative green data center ecosystems

The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, service workloads and to reduce data centers’ carbon footprints by adopting demand-response mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers can have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance and the interests of data centers and customers.Peer ReviewedPostprint (author's final draft

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System

Autonomous management of cost, performance, and resource uncertainty for migration of applications to infrastructure-as-a-service (IaaS) clouds

2014 Fall.Includes bibliographical references.Infrastructure-as-a-Service (IaaS) clouds abstract physical hardware to provide computing resources on demand as a software service. This abstraction leads to the simplistic view that computing resources are homogeneous and infinite scaling potential exists to easily resolve all performance challenges. Adoption of cloud computing, in practice however, presents many resource management challenges forcing practitioners to balance cost and performance tradeoffs to successfully migrate applications. These challenges can be broken down into three primary concerns that involve determining what, where, and when infrastructure should be provisioned. In this dissertation we address these challenges including: (1) performance variance from resource heterogeneity, virtualization overhead, and the plethora of vaguely defined resource types; (2) virtual machine (VM) placement, component composition, service isolation, provisioning variation, and resource contention for multitenancy; and (3) dynamic scaling and resource elasticity to alleviate performance bottlenecks. These resource management challenges are addressed through the development and evaluation of autonomous algorithms and methodologies that result in demonstrably better performance and lower monetary costs for application deployments to both public and private IaaS clouds. This dissertation makes three primary contributions to advance cloud infrastructure management for application hosting. First, it includes design of resource utilization models based on step-wise multiple linear regression and artificial neural networks that support prediction of better performing component compositions. The total number of possible compositions is governed by Bell's Number that results in a combinatorially explosive search space. Second, it includes algorithms to improve VM placements to mitigate resource heterogeneity and contention using a load-aware VM placement scheduler, and autonomous detection of under-performing VMs to spur replacement. Third, it describes a workload cost prediction methodology that harnesses regression models and heuristics to support determination of infrastructure alternatives that reduce hosting costs. Our methodology achieves infrastructure predictions with an average mean absolute error of only 0.3125 VMs for multiple workloads

Capture and analysis of the NFS workload of an ISP email service

Tese de mestrado Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Os objectivos desta tese são capturar a carga de comandos NFS de um serviço de email de um provedor de Internet, converter a captura para um formato mais flexível, e analisar as características do mesmo. Até ao momento, nenhum outro trabalho publicado, analisou a carga de comandos de um serviço de email de um provedor de Internet. Um novo estudo, irá ajudar a compreender qual o impacto das diferenças na carga de comandos de um sistema de ficheiros de rede, e o que caracteriza a carga de comandos de um sistema de email real. A captura será analisada, de forma a encontrar novas propriedades que futuros sistemas de ficheiros poderão suportar ou explorar. Nesta tese, fazemos uma análise exaustiva de como capturar altos débitos de tráfego, que envolve vários desafios. Identificamos os problemas encontrados e explicamos como contornar esses problemas. Devido ao elevado tamanho da captura e devido ao espaço limitado de armazenamento disponível, precisámos de converter a captura para um formato mais compacto e flexível, de forma a podermos fazer uma análise de forma eficiente. Descrevemos os desafios para analisar grandes volumes de dados e quais as técnicas utilizadas. Visto que a captura contém dados sensíveis das caixas de correio dos utilizadores, tivemos que anonimizar a captura. Descrevemos que dados têm de ser anonimizados de forma a disponibilizarmos a captura gratuitamente. Também analisamos a captura e demonstramos as características únicas da captura estudada, tais como a natureza periódica da actividade do sistema de ficheiros, a distribuição de tamanhos de todos os ficheiros acedidos, a sequencialidade dos dados acedidos e os tipos de anexos mais comuns numa típica caixa de correio.The aims of this thesis are to capture a real-world NFS workload of an ISP email service, convert the traces to a more useful and flexible format and analyze the characteristics of the workload. No published work has ever analyzed a large-scale, real-world ISP email workload. A new study will help to understand how these changes impact network file system workloads and what characterizes a real-world email workload. Storage traces are analyzed to find properties that future systems should support or exploit. In this thesis, we provide an in-depth explanation of how we were able to capture high data rates, which involves several challenges. We identify the bottlenecks faced and explain how we circumvented them. Due to the large size of the captured workload and limited available storage, we needed to convert the traces to a more compact and flexible format so we could further analyze the workload in an efficient manner. We describe the challenges of analyzing large datasets and the techniques that were used. Since the workload contains sensitive information about the mailboxes, we had to anonymize the workload. We will describe what needed to be anonymized and how it was done. This was an important step to get permission from the ISP to publish the anonymized traces, which will be available for free download. We also performed several analyses that demonstrate unique characteristics of the studied workload, such as the periodic nature of file system activity, the file size distribution for all accessed files, the sequentiality of accessed data, and the most common type of attachments found in a typical mailbox

Architecting Efficient Data Centers.

Data center power consumption has become a key constraint in continuing to scale Internet services. As our society’s reliance on “the Cloud” continues to grow, companies require an ever-increasing amount of computational capacity to support their customers. Massive warehouse-scale data centers have emerged, requiring 30MW or more of total power capacity. Over the lifetime of a typical high-scale data center, power-related costs make up 50% of the total cost of ownership (TCO). Furthermore, the aggregate effect of data center power consumption across the country cannot be ignored. In total, data center energy usage has reached approximately 2% of aggregate consumption in the United States and continues to grow. This thesis addresses the need to increase computational efficiency to address this grow- ing problem. It proposes a new classes of power management techniques: coordinated full-system idle low-power modes to increase the energy proportionality of modern servers. First, we introduce the PowerNap server architecture, a coordinated full-system idle low- power mode which transitions in and out of an ultra-low power nap state to save power during brief idle periods. While effective for uniprocessor systems, PowerNap relies on full-system idleness and we show that such idleness disappears as the number of cores per processor continues to increase. We expose this problem in a case study of Google Web search in which we demonstrate that coordinated full-system active power modes are necessary to reach energy proportionality and that PowerNap is ineffective because of a lack of idleness. To recover full-system idleness, we introduce DreamWeaver, architectural support for deep sleep. DreamWeaver allows a server to exchange latency for full-system idleness, allowing PowerNap-enabled servers to be effective and provides a better latency- power savings tradeoff than existing approaches. Finally, this thesis investigates workloads which achieve efficiency through methodical cluster provisioning techniques. Using the popular memcached workload, this thesis provides examples of provisioning clusters for cost-efficiency given latency, throughput, and data set size targets.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91499/1/meisner_1.pd

Energy Efficient Servers

Computer scienc