Energy-aware scheduling in virtualized datacenters

The reduction of energy consumption in large-scale datacenters is being accomplished through an extensive use of virtualization, which enables the consolidation of multiple workloads in a smaller number of machines. Nevertheless, virtualization also incurs some additional overheads (e.g. virtual machine creation and migration) that can influence what is the best consolidated configuration, and thus, they must be taken into account. In this paper, we present a dynamic job scheduling policy for power-aware resource allocation in a virtualized datacenter. Our policy tries to consolidate workloads from separate machines into a smaller number of nodes, while fulfilling the amount of hardware resources needed to preserve the quality of service of each job. This allows turning off the spare servers, thus reducing the overall datacenter power consumption. As a novelty, this policy incorporates all the virtualization overheads in the decision process. In addition, our policy is prepared to consider other important parameters for a datacenter, such as reliability or dynamic SLA enforcement, in a synergistic way with power consumption. The introduced policy is evaluated comparing it against common policies in a simulated environment that accurately models HPC jobs execution in a virtualized datacenter including power consumption modeling and obtains a power consumption reduction of 15% with respect to typical policies.Peer ReviewedPostprint (published version

Transiency-driven Resource Management for Cloud Computing Platforms

Modern distributed server applications are hosted on enterprise or cloud data centers that provide computing, storage, and networking capabilities to these applications. These applications are built using the implicit assumption that the underlying servers will be stable and normally available, barring for occasional faults. In many emerging scenarios, however, data centers and clouds only provide transient, rather than continuous, availability of their servers. Transiency in modern distributed systems arises in many contexts, such as green data centers powered using renewable intermittent sources, and cloud platforms that provide lower-cost transient servers which can be unilaterally revoked by the cloud operator. Transient computing resources are increasingly important, and existing fault-tolerance and resource management techniques are inadequate for transient servers because applications typically assume continuous resource availability. This thesis presents research in distributed systems design that treats transiency as a first-class design principle. I show that combining transiency-specific fault-tolerance mechanisms with resource management policies to suit application characteristics and requirements, can yield significant cost and performance benefits. These mechanisms and policies have been implemented and prototyped as part of software systems, which allow a wide range of applications, such as interactive services and distributed data processing, to be deployed on transient servers, and can reduce cloud computing costs by up to 90\%. This thesis makes contributions to four areas of computer systems research: transiency-specific fault-tolerance, resource allocation, abstractions, and resource reclamation. For reducing the impact of transient server revocations, I develop two fault-tolerance techniques that are tailored to transient server characteristics and application requirements. For interactive applications, I build a derivative cloud platform that masks revocations by transparently moving application-state between servers of different types. Similarly, for distributed data processing applications, I investigate the use of application level periodic checkpointing to reduce the performance impact of server revocations. For managing and reducing the risk of server revocations, I investigate the use of server portfolios that allow transient resource allocation to be tailored to application requirements. Finally, I investigate how resource providers (such as cloud platforms) can provide transient resource availability without revocation, by looking into alternative resource reclamation techniques. I develop resource deflation, wherein a server\u27s resources are fractionally reclaimed, allowing the application to continue execution albeit with fewer resources. Resource deflation generalizes revocation, and the deflation mechanisms and cluster-wide policies can yield both high cluster utilization and low application performance degradation

Enabling Distributed Applications Optimization in Cloud Environment

The past few years have seen dramatic growth in the popularity of public clouds, such as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Container-as-a-Service (CaaS). In both commercial and scientific fields, quick environment setup and application deployment become a mandatory requirement. As a result, more and more organizations choose cloud environments instead of setting up the environment by themselves from scratch. The cloud computing resources such as server engines, orchestration, and the underlying server resources are served to the users as a service from a cloud provider. Most of the applications that run in public clouds are the distributed applications, also called multi-tier applications, which require a set of servers, a service ensemble, that cooperate and communicate to jointly provide a certain service or accomplish a task. Moreover, a few research efforts are conducting in providing an overall solution for distributed applications optimization in the public cloud. In this dissertation, we present three systems that enable distributed applications optimization: (1) the first part introduces DocMan, a toolset for detecting containerized application’s dependencies in CaaS clouds, (2) the second part introduces a system to deal with hot/cold blocks in distributed applications, (3) the third part introduces a system named FP4S, a novel fragment-based parallel state recovery mechanism that can handle many simultaneous failures for a large number of concurrently running stream applications

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

Using Workload Prediction and Federation to Increase Cloud Utilization

The wide-spread adoption of cloud computing has changed how large-scale computing infrastructure is built and managed. Infrastructure-as-a-Service (IaaS) clouds consolidate different separate workloads onto a shared platform and provide a consistent quality of service by overprovisioning capacity. This additional capacity, however, remains idle for extended periods of time and represents a drag on system efficiency.The smaller scale of private IaaS clouds compared to public clouds exacerbates overprovisioning inefficiencies as opportunities for workload consolidation in private clouds are limited. Federation and cycle harvesting capabilities from computational grids help to improve efficiency, but to date have seen only limited adoption in the cloud due to a fundamental mismatch between the usage models of grids and clouds. Computational grids provide high throughput of queued batch jobs on a best-effort basis and enforce user priorities through dynamic job preemption, while IaaS clouds provide immediate feedback to user requests and make ahead-of-time guarantees about resource availability.We present a novel method to enable workload federation across IaaS clouds that overcomes this mismatch between grid and cloud usage models and improves system efficiency while also offering availability guarantees. We develop a new method for faster-than-realtime simulation of IaaS clouds to make predictions about system utilization and leverage this method to estimate the future availability of preemptible resources in the cloud. We then use these estimates to perform careful admission control and provide ahead-of-time bounds on the preemption probability of federated jobs executing on preemptible resources. Finally, we build an end-to-end prototype that addresses practical issues of workload federation and evaluate the prototype's efficacy using real-world traces from big data and compute-intensive production workloads

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS