Ferran Julià

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.

Resource-level QoS metric for CPU-based guarantees in cloud providers

Success of Cloud computing requires that both customers and providers can be confident that signed Service Level Agreements (SLA) are supporting their respective business activities to their best extent. Currently used SLAs fail in providing such confidence, especially when providers outsource resources to other providers. These resource providers typically support very simple metrics, or metrics that hinder an efficient exploitation of their resources. In this paper, we propose a resource-level metric for specifying finegrain guarantees on CPU performance. This metric allows resource providers to allocate dynamically their resources among the running services depending on their demand. This is accomplished by incorporating the customers CPU usage in the metric definition, but avoiding fake SLA violations when the customers task does not use all its allocated resources. As demonstrated in our evaluation, which has been conducted in a virtualized provider where we have implemented the needed infrastructure for using our metric, our solution presents fewer SLA violations than other CPU-related metrics.

Checkpoint-based fault-tolerant infrastructure for virtualized service providers

Crash and omission failures are common in service providers: a disk can break down or a link can fail anytime. In addition, the probability of a node failure increases with the number of nodes. Apart from reducing the providers computation power and jeopardizing the fulfillment of his contracts, this can also lead to computation time wasting when the crash occurs before finishing the task execution. In order to avoid this problem, efficient checkpoint infrastructures are required, especially in virtualized environments where these infrastructures must deal with huge virtual machine images. This paper proposes a smart checkpoint infrastructure for virtualized service providers. It uses Another Union File System to differentiate read-only from read-write parts in the virtual machine image. In this way, read-only parts can be checkpointed only once, while the rest of checkpoints must only save the modifications in read-write parts, thus reducing the time needed to make a checkpoint. The checkpoints are stored in a Hadoop Distributed File System. This allows resuming a task execution faster after a node crash and increasing the fault tolerance of the system, since checkpoints are distributed and replicated in all the nodes of the provider. This paper presents a running implementation of this infrastructure and its evaluation, demonstrating that it is an effective way to make faster checkpoints with low interference on task execution and efficient task recovery after a node failure.

Multifaceted resource management for dealing with heterogeneous workloads in virtualized data centers

As long as virtualization has been introduced in data centers, it has been opening new chances for resource management. Now, it is not just used as a tool for consolidating underused nodes and save power, it also allows new solutions to well-known challenges, such as fault tolerance or heterogeneity management. Virtualization helps to encapsulate Web-based applications or HPC jobs in virtual machines and see them as a single entity which can be managed in an easier way.

Introducing Virtual Execution Environments for Application Lifecycle Management and SLA-Driven Resource Distribution within Service Providers

Resource management is a key challenge that service providers must adequately face in order to ensure their profitability. This paper describes a proof-of-concept framework for facilitating resource management in service providers, which allows reducing costs and at the same time fulfilling the quality of service agreed with the customers. This is accomplished by means of virtualization. Our approach provides application-specific virtual environments and consolidates them in order to achieve a better utilization of the providers resources. In addition, it implements self-adaptive capabilities for dynamically distributing the providers resources among these virtual environments based on Service Level Agreements. The proposed solution has been implemented as a part of the Semantically-Enhanced Resource Allocator prototype developed within the BREIN European project. The evaluation shows that our prototype is able to react in very short time under changing conditions and avoid SLA violations by rescheduling efficiently the resources.

SLA-Driven Semantically-Enhanced Dynamic Resource Allocator for Virtualized Service Providers

In order to be profitable, service providers must be able to undertake complex management tasks such as provisioning, deployment, execution and adaptation in an autonomic way. This paper introduces a framework, the semantically-enhanced resource allocator (SERA), aimed to facilitate service provider management, reducing costs and at the same time fulfilling the QoS agreed with the customers. The SERA assigns resources depending on the information given by service providers according to its business goals and on the resource requirements of the tasks. Tasks and resources are semantically described and these descriptions are used to infer the resource assignments. Virtualization is used to provide a full-customized and isolated virtual environment for each task. In addition, the system supports fine-grain dynamic resource distribution among these virtual environments based on SLAs. The required adaptation is implemented using agents, guarantying to each task enough resources to meet the agreed performance goals.

Should the grid middleware look to self-managing capabilities?

Grid technologies have enabled the clustering of a wide variety of geographically distributed resources and services. While providing this, the grid middleware layers that make up the supporting platform for grid applications have taken an increased level of importance in the overall performance of such distributed applications. In this paper we discuss the necessity of introducing self-managing capabilities to the core functionalities of the grid middleware. Our opinions are based on a simple example that shows how Globus Toolkit 4 (GT4) can be driven to a state of unavailability under certain overloading conditions, and how a simple but effective self-managing policy applied to its resource management mechanisms could overcome such an unfavourable scenario. We present an approach showing the benefits that a resource management introduced in the middleware can provide to the user. This approach is the base of a self-managing layer that is being developed for use under more generic conditions

Efficient Data Management Support for Virtualized Service Providers

Virtualization has been lately introduced for supporting and simplifying service providers management with promising results. Nevertheless, using virtualization introduces also new challenges that must be considered. One of them relates with the data management in the provider. This paper proposes an innovative approach for performing efficiently all the data-related processes in a virtualized service provider, namely VM creation, VM migration and data stage-in/out. Our solution provides a global repository where clients can upload the task input files and retrieve the output files. In addition, the provider implements a distributed file system (using NFS) in which each node can access its own local disk and the disk of the other nodes. As demonstrated in the evaluation, this allows efficient VM creation and task execution, but also task migration with minimum overhead, while keeping it accessible during the whole process.

Monitoring and analysing a Grid Middleware Node

Distributed grid applications are becoming more and more popular as the use of complex grid middlewares becomes extensive, and more facilities are offered by these complex pieces of software. But as grid middlewares grow and offer more advanced features, they become more complex and weighty, as well as hard to tune. As the performance of a distributed grid applications can be strongly influenced by the operation of the underlying grid middleware, it becomes important to study and analyze their behaviour and performance. In this paper we present the eDragon monitoring framework (eDMF), a set of tools for instrumentation and analysis of grid middleware, that provides an unique environment to study the performance of grid applications. The eDMF is composed of a set of specialised monitoring tools as well as a flexible and powerful performance analysis platform. Additionally we also provide a practical application of the eDMF to the Globus toolkit 4 (GT4), one of the most extended and popular grid middlewares, showing how it helped us in the detection and resolution of several job management problems observed in the GT4 middleware

Business-driven IT Management for Cloud computing providers

Nowadays, enterprises have high expectations on Cloud systems to achieve their Business-Level Objectives (BLOs). However, those systems are becoming very complex, thus leading to critical management issues. For these reasons, new self-management strategies of virtualized entities driven by business interests have to be explored. In this paper, we present a business-driven self-management optimization loop, which can firmly contribute to fulfill business strategies of Cloud providers. In this sense, a Business-Driven IT Management (BDIM) model is aimed to assess the impact of IT-related events on business-level metrics. Thereupon those high-level impacts are consumed by a policy management frame-work able to autonomously determine the most suitable IT-level management actions in terms of providers BLOs compliance. Furthermore, we show the benefits achieved by a Platform as a Service (PaaS) provider when adopting such business-driven management. The results obtained through experimen-tation demonstrate that it is able to maximize, individually and simultaneously, two high-level objectives, in this case economical profit and ecological efficiency.