Conferences related to Storage Consolidation

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2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

Promote the exchange of ideas between academia and industry in the field of computer and networks dependability


2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)

The conference is the primary forum for cross-industry and multidisciplinary research in automation. Its goal is to provide a broad coverage and dissemination of foundational research in automation among researchers, academics, and practitioners.


2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

Industrial information technologies


2019 IEEE International Symposium on High Performance Computer Architecture (HPCA)

HPCA offers a high-quality forum for scientists and engineers to present their latest research findings in the rapidly-changing field of computer architecture.


2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)

Management of information and communication technology focusing on research, development, integration, standards, service provisioning, and user communities.


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Periodicals related to Storage Consolidation

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Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


Computer Architecture Letters

Rigorously peer-reviewed forum for publishing early, high-impact results in the areas of uni- and multiprocessors computer systems, computer architecture workload characterization, performance evaluation and simulation techniques, and power-aware computing


Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


Dependable and Secure Computing, IEEE Transactions on

The purpose of TDSC is to publish papers in dependability and security, including the joint consideration of these issues and their interplay with system performance. These areas include but are not limited to: System Design: architecture for secure and fault-tolerant systems; trusted/survivable computing; intrusion and error tolerance, detection and recovery; fault- and intrusion-tolerant middleware; firewall and network technologies; system management ...


Distributed Systems Online, IEEE

After nine years of publication, DS Online will be moving into a new phase as part of Computing Now (http://computingnow.computer.org), a new website providing the front end to all of the Computer Society's magazines. As such, DS Online will no longer be publishing standalone peer-reviewed articles.


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Most published Xplore authors for Storage Consolidation

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Xplore Articles related to Storage Consolidation

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Storage Consolidation on SSDs: Not Always a Panacea, but Can We Ease the Pain?

2015 International Conference on Parallel Architecture and Compilation (PACT), 2015

Storage Consolidation is increasing being adopted to reduce system costs, simplify the storage infrastructure, and enhance availability and resource management. However, consolidation leads to interference in shared resources. This poster shows the effect of consolidation on performance of co-located applications and proposes a static approach to reduce interference.


IO Tetris: Deep Storage Consolidation for the Cloud via Fine-Grained Workload Analysis

2011 IEEE 4th International Conference on Cloud Computing, 2011

Intelligent workload consolidation in storage systems leads to better Return On Investment (ROI), in terms of more efficient use of data center resources, better Quality of Service (QoS), and lower power consumption. This is particularly significant yet challenging in a cloud environment, in which a large set of different workloads multiplex on a shared, heterogeneous infrastructure. However, the increasing availability ...


Storage consolidation: Not always a panacea, but can we ease the pain?

2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2016

The system administrator, faces an arduous task of figuring out whether to consolidate storage workloads, which workloads to isolate, which workloads to co-locate, and how to reduce the interference on co-located workloads? This paper presents an approach to ease this arduous task. We consider different mixes of enterprise storage applications on a high-end SSD to study their performance, system throughput ...


A power efficient persistent storage consolidation algorithm for cloud computing

2012 International Green Computing Conference (IGCC), 2012

Persistent data storage is a necessity in local cloud computing architectures. This research investigates how these architectures can reduce their energy consumption by dynamically scaling the number of storage nodes needed to accommodate user's needs. While a user is actively using cloud computational resources, their accommodating persistent data is stored on hot storage nodes that are powered on. Inactive users ...


A Novel Server Consolidation Method Based on Local Storage Integrated with Resource Demand Prediction

2018 15th International Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN), 2018

Server consolidation plays a significant part in energy-saving technology in data centers. Traditionally, cloud service instances commonly use shared storage architecture. Nowadays, data and I/O intensive applications are preferred in this big data era and are used in the majority of Internet companies, much more attention has been paid to the local storage that offer perform better in I/O at ...


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Educational Resources on Storage Consolidation

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IEEE-USA E-Books

  • Storage Consolidation on SSDs: Not Always a Panacea, but Can We Ease the Pain?

    Storage Consolidation is increasing being adopted to reduce system costs, simplify the storage infrastructure, and enhance availability and resource management. However, consolidation leads to interference in shared resources. This poster shows the effect of consolidation on performance of co-located applications and proposes a static approach to reduce interference.

  • IO Tetris: Deep Storage Consolidation for the Cloud via Fine-Grained Workload Analysis

    Intelligent workload consolidation in storage systems leads to better Return On Investment (ROI), in terms of more efficient use of data center resources, better Quality of Service (QoS), and lower power consumption. This is particularly significant yet challenging in a cloud environment, in which a large set of different workloads multiplex on a shared, heterogeneous infrastructure. However, the increasing availability of fine grained workload logging facilities allows better insights to be gained from workload profiles. As a consequence, consolidation can be done more deeply, according to a detailed understanding of how well given workloads mix. We describe IO Tetris, which takes a first look at fine-grained consolidation in large-scale storage systems by leveraging temporal patterns found in real-world I/O traces gathered from enterprise storage environments. The core functionality of IO Tetris consists of two stages. A grouping stage performs hierarchical grouping of storage workloads to find complementary groupings that consolidate well together over time and conflicting ones that do not. After that, a migration stage examines the discovered groupings to determine how to maximize resource utilization efficiency while minimizing migration costs. Experiments based on customer I/O traces from a high-end enterprise class IBM storage controller show that a non-trivial number of IO Tetris groupings exist in real-world storage workloads, and that these groupings can be leveraged to achieve better storage consolidation in a cloud setting.

  • Storage consolidation: Not always a panacea, but can we ease the pain?

    The system administrator, faces an arduous task of figuring out whether to consolidate storage workloads, which workloads to isolate, which workloads to co-locate, and how to reduce the interference on co-located workloads? This paper presents an approach to ease this arduous task. We consider different mixes of enterprise storage applications on a high-end SSD to study their performance, system throughput and fairness in their consolidated execution. The paper also considers a static approach using an integrated combination of resource partitioning and data placement to reduce the interference between the workloads.

  • A power efficient persistent storage consolidation algorithm for cloud computing

    Persistent data storage is a necessity in local cloud computing architectures. This research investigates how these architectures can reduce their energy consumption by dynamically scaling the number of storage nodes needed to accommodate user's needs. While a user is actively using cloud computational resources, their accommodating persistent data is stored on hot storage nodes that are powered on. Inactive users have their persistent data stored on powered down, cold storage nodes. Design of the proposed cloud architecture considers network throughput and storage capacity of storage nodes a priority. This paper introduces a persistent data consolidation algorithm with decreased power consumption. As the cloud architecture increases in size, the power savings are compounded. Also, since consideration is given to decreasing power consumption, this paper addresses the data latency tradeoff of the proposed consolidation scheme.

  • A Novel Server Consolidation Method Based on Local Storage Integrated with Resource Demand Prediction

    Server consolidation plays a significant part in energy-saving technology in data centers. Traditionally, cloud service instances commonly use shared storage architecture. Nowadays, data and I/O intensive applications are preferred in this big data era and are used in the majority of Internet companies, much more attention has been paid to the local storage that offer perform better in I/O at a lower price compared with shared storage clouds. But these cloud instances usually contain much more data than shared storage cloud instances. Thus, in such local storage based clouds, the migration cost can be really high. Unfortunately, most existing work about did not consider integrating the demand prediction algorithm that plays a significant part in server consolidation, especially for local storage based cloud, where the migration cost is very high and is in badly need of an efficient resource pre- allocation mechanism. To address this issue, we proposes Aricon, a consolidation method based on local storage. Our approach uses a time series model to forecast the CPU or memory utilization of instances within servers. We investigate the effectiveness of instance and server resource utilization prediction in server consolidation performance in workload traces from real world. To validate the performance of the proposed Aricon, we test the prediction accuracy and compare it several existing consolidation method, and the results show that Aricon not only has low prediction error rate in 10.7% but also schedules computing resources efficiently.

  • Local Storage-Based Consolidation With Resource Demand Prediction and Live Migration in Clouds

    Server consolidation is a useful solution aiming at cost-efficiency and high resource utilization of data centers and clusters. Nowadays, as the data- intensive and I/O intensive applications are widely used, more attention is paid to the local storage-based clouds which can offer much better I/O performance at relatively low price compared with the shared storage. However, it will obviously increase the migration cost (e.g., energy and time). Meanwhile, there is few suitable resource demand estimation method for local storage-based clouds at present, which plays an important role in system's migration efficiency. And we find out that in this specific storage architecture, almost all the existing server consolidation algorithms do not have a suitable resource demand estimation method and a live migration scheme. To solve this problems, this paper designs and implements Combining Three (C3), a cloud architecture for local storage, C3 significant modules: prediction, consolidation, and migration. It was proved in statistical analysis that ARIMA may be the most suitable prediction model for the server workload, which motivates us to propose the resource estimation predictor. Also, we improve the existing consolidation method by adjusting the sorting index and fit degree during migration. Then, we propose a live migration scheme for local storage environment as the third module of C3. We conduct extensive experiments using real-world traces from Google to validate the effectiveness and superiority of our proposed algorithm.

  • Modeling and performance evaluation of iSCSI storage area networks over TCP/IP-based MAN and WAN networks

    This paper provides a concise modeling and performance evaluation of the iSCSI storage area network (SAN) architecture and protocol. SANs play a key role in business continuity, enterprise-wide storage consolidation and disaster recovery strategies in which storage resources are most often distributed over many distant data center locations. In the future, SAN traffic will be transported over IP-based networks, e.g., enterprise virtual private networks, to benefit from converged networks and save cost. In these scenarios, the impact of end-to-end delay and QoS of broadband networks on SAN performance is critical and has to be well understood by IT departments when deploying IP- storage solutions and network operators when designing transport network services for SAN applications. In this context, we propose models for iSCSI write requests over TCP/IP networks, e.g., as used in asynchronous mirroring applications. In addition to the analysis for individual requests we present - to the best of our knowledge for the first time - the evaluation of an iSCSI session under a realistic request traffic model with and without interleaving. We analyze the throughput and total request write times for different network dimensions, i.e., round-trip times, and QoS levels, processing delays in the iSCSI layer as well as request characteristics

  • Virtualization-aware access control for multitenant filesystems

    In a virtualization environment that serves multiple tenants, storage consolidation at the filesystem level is desirable because it enables data sharing, administration efficiency, and performance optimizations. The scalable deployment of filesystems in such environments is challenging due to intermediate translation layers required for networked file access or identity management. First we present several security requirements in multitenant filesystems. Then we introduce the design of the Dike authorization architecture. It combines native access control with tenant namespace isolation and compatibility to object-based filesystems. We use a public cloud to experimentally evaluate a prototype implementation of Dike that we developed. At several thousand tenants, our prototype incurs limited performance overhead up to 16%, unlike an existing solution whose multitenancy overhead approaches 84% in some cases.

  • Point to multipoint ABR flow control in ATM networks

    The key issue at a branch-point switch for point-to-multipoint (multicast) available bit rate (ABR) flow control is how to consolidate backward resource management (BRM) cells returning from each branch to avoid feedback implosion. However, consolidating BRM cells at a branch point can cause undesirable effects such as consolidation noise, consolidation delay, and consolidation loss. This paper first investigates the consolidation problems and proposes their solutions. Furthermore, it introduces various implementation alternatives for the proposed solutions. A combination of the proposed solutions, in which each branch point stores feedback information on a per- branch basis for each multicast virtual connection and only passes BRM cells returning from the farthest destination, provides a good fairness, a higher efficiency and an excellent scalability. In addition, if a fast overload indication is additionally employed, the feedback delay can be significantly reduced in an overload situation.

  • eMuse: QoS Guarantees for Shared Storage Servers

    Storage consolidation as a perspective paradigm inevitably leads to the extensive installations of shared storage servers in product environments. However, owing to the dynamics of both workloads and storage systems, it is pragmatic only if each workload accessing common storage servers can surely possess a specified minimum share of system resources even when competing with other workloads and consequently obtain predictable quality of service (QoS). This paper presents an I/O scheduling framework for shared storage servers. The eMuse algorithm in the framework employs a dynamic assignment mechanism that not only accommodates a weighted bandwidth share for every active workload, but also fulfills their latency requirements through a fair queuing policy. Experimental results demonstrate that our scheduling framework can accomplish performance isolation among multiple competing workloads as well as the effective utilization of system resources.



Standards related to Storage Consolidation

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Jobs related to Storage Consolidation

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