SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE International Conference on Cloud and Service Computing (CSC 2011, IEEE Press, USA), Hong Kong, China, December 12-14, 201

A peer-to-peer infrastructure for resilient web services

This work is funded by GR/M78403 “Supporting Internet Computation in Arbitrary Geographical Locations” and GR/R51872 “Reflective Application Framework for Distributed Architectures”, and by Nuffield Grant URB/01597/G “Peer-to-Peer Infrastructure for Autonomic Storage Architectures”This paper describes an infrastructure for the deployment and use of Web Services that are resilient to the failure of the nodes that host those services. The infrastructure presents a single interface that provides mechanisms for users to publish services and to find hosted services. The infrastructure supports the autonomic deployment of services and the brokerage of hosts on which services may be deployed. Once deployed, services are autonomically managed in a number of aspects including load balancing, availability, failure detection and recovery, and lifetime management. Services are published and deployed with associated metadata describing the service type. This same metadata may be used subsequently by interested parties to discover services. The infrastructure uses peer-to-peer (P2P) overlay technologies to abstract over the underlying network to deploy and locate instances of those services. It takes advantage of the P2P network to replicate directory services used to locate service instances (for using a service), Service Hosts (for deployment of services) and Autonomic Managers which manage the deployed services. The P2P overlay network is itself constructed using novel Web Services-based middleware and a variation of the Chord P2P protocol, which is self-managing.Postprin

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Hardware as a service - enabling dynamic, user-level bare metal provisioning of pools of data center resources.

We describe a “Hardware as a Service (HaaS)” tool for isolating pools of compute, storage and networking resources. The goal of HaaS is to enable dynamic and flexible, user-level provisioning of pools of resources at the so-called “bare-metal” layer. It allows experimental or untrusted services to co-exist alongside trusted services. By functioning only as a resource isolation system, users are free to choose between different system scheduling and provisioning systems and to manage isolated resources as they see fit. We describe key HaaS use cases and features. We show how HaaS can provide a valuable, and somehwat overlooked, layer in the software architecture of modern data center management. Documentation and source code for HaaS software are available at: https://github.com/CCI-MOC/haasPartial support for this work was provided by the MassTech Collaborative Research Matching Grant Program, National Science Foundation award #1347525 and several commercial partners of the Mass Open Cloud who may be found at http://www.massopencloud.org.http://www.ieee-hpec.org/2014/CD/index_htm_files/FinalPapers/116.pd

Measuring and Managing Answer Quality for Online Data-Intensive Services

Online data-intensive services parallelize query execution across distributed software components. Interactive response time is a priority, so online query executions return answers without waiting for slow running components to finish. However, data from these slow components could lead to better answers. We propose Ubora, an approach to measure the effect of slow running components on the quality of answers. Ubora randomly samples online queries and executes them twice. The first execution elides data from slow components and provides fast online answers; the second execution waits for all components to complete. Ubora uses memoization to speed up mature executions by replaying network messages exchanged between components. Our systems-level implementation works for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the EasyRec Recommendation Engine, and the OpenEphyra question answering system. Ubora computes answer quality much faster than competing approaches that do not use memoization. With Ubora, we show that answer quality can and should be used to guide online admission control. Our adaptive controller processed 37% more queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor