Geo-distributed Infrastructure Research Articles

Efficient Replica Migration Scheme for Distributed Cloud Storage Systems

With the wide adoption of large-scale internet services and big data, the cloud has become the ideal environment to satisfy the ever-growing storage demand. In this context, data replication has been touted as the ultimate solution to improve data availability and reduce access time. However, replica management systems usually need to migrate and create a large number of data replicas over time between and within data centers, incurring a large overhead in terms of network load and availability. In this paper, we propose CRANE, an effiCient Replica migrAtion scheme for distributed cloud Storage systEms. CRANE complements any replica placement algorithm by efficiently managing replica creation in geo-distributed infrastructures in order to (1) minimize the time needed to copy the data to the new replica location, (2) avoid network congestion, and (3) ensure the minimum desired availability for the data. Through simulation and experimental results, we show that CRANE provides a sub-optimal solution for the replica migration problem with lower computational complexity than its integer linear program formulation. We also show that, compared to OpenStack Swift, CRANE is able to reduce by up to 60 percent the replica creation and migration time and by up to 50 percent the inter-data center network traffic while ensuring the minimum required data availability.

Server placement with shared backups for disaster-resilient clouds

A key strategy to build disaster-resilient clouds is to employ backups of virtual machines in a geo-distributed infrastructure. Today, the continuous and acknowledged replication of virtual machines in different servers is a service provided by different hypervisors. This strategy guarantees that the virtual machines will have no loss of disk and memory content if a disaster occurs, at a cost of strict bandwidth and latency requirements. Considering this kind of service, in this work, we propose an optimization problem to place servers in a wide area network. The goal is to guarantee that backup machines do not fail at the same time as their primary counterparts. In addition, by using virtualization, we also aim to reduce the amount of backup servers required. The optimal results, achieved in real topologies, reduce the number of backup servers by at least 40%. Moreover, this work highlights several characteristics of the backup service according to the employed network, such as the fulfillment of latency requirements.

Efficient Notification Ordering for Geo-Distributed Pub/Sub Systems

A distributed event notification service (ENS) is at the core of modern messaging infrastructures providing applications with scalable and robust publish/subscribe communication primitives. Such ENSs can route events toward subscribers using multiple paths with different lengths and latencies. As a consequence, subscribers can receive events out of order. In this paper, we propose a novel solution for ordered notifications on top of an existing distributed topic-based ENS. Our solutions guarantees that each pair of events published in the system will be notified in the same order to all their target subscribers independently from the topics they are published in. It endows a distributed timestamping mechanism based on a multistage sequencer that produces timestamps whose size is dynamically adjusted to accommodate changing subscriptions in the system. An extensive experimental evaluation based on a prototype implementation shows that the timestamping mechanism is able to scale from several points of view (i.e., number of publisher and subscribers, event rate). Furthermore, it shows how the deployment flexibility of our solution makes it perform better in terms of timestamp size and timestamp generation latency when the system load exhibits geographic topic popularity, that is, matching subscriptions and publications are geographically clustered. This makes our solution particularly well suited to be deployed in geo-distributed infrastructures.

Greenslater: On Satisfying Green SLAs in Distributed Clouds

With the massive adoption of cloud-based services, high energy consumption and carbon footprint of cloud infrastructures have become a major concern in the IT industry. Consequently, many governments and IT advisory organizations have urged IT stakeholders (i.e., cloud provider and cloud customers) to embrace green IT and regularly monitor and report their carbon emissions and put in place efficient strategies and techniques to control the environmental impact of their infrastructures and/or applications. Motivated by this growing trend, we investigate, in this paper, how cloud providers can meet Service Level Agreements (SLAs) with green requirements. In such SLAs, a cloud customer requires from cloud providers that carbon emissions generated by the leased resources should not exceed a fixed bound. We hence propose a resource management framework allowing cloud providers to provision resources in the form of Virtual Data Centers (VDCs) (i.e., a set of virtual machines and virtual links with guaranteed bandwidth) across a geo-distributed infrastructure with the aim of reducing operational costs and green SLA violation penalties. Extensive simulations show that the proposed solution maximizes the cloud provider's profit and minimizes the violation of green SLAs.

Multi-stage optimization for periodic inspection planning of geo-distributed infrastructure systems

This paper develops a multi-stage optimization framework for determining periodic inspection intervals for geo-distributed infrastructure systems subject to hidden failures. We assume that the unit’s failure can only be rectified at periodic inspection when a perfect repair is carried out to restore the unit to as-good-as-new condition. For large-scale systems such as fire hydrants in a city, water supply and power transformer distribution networks in urban areas, firstly, we partition the units of a system into groups based on their pre-specified features by a clustering algorithm; then compute an optimal periodic inspection interval for each group based on the expected cost per cycle; and finally, we find the inspection schedule for the whole system that not only minimizes the total inspection and maintenance cost but also satisfies the workforce and budget resource constraints. An integer nonlinear programming formulation and a heuristics are introduced to determine the inspection schedule. The proposed approach is applied to scheduling periodic inspection of large amounts of fire hydrants in a major U.S. city.