Io stack software defined object storage

Oct 7, 20160 likes44 views

IOStack is a software-defined storage architecture for object stores like OpenStack Swift that simplifies cloud storage management. At the control plane, tenants are provisioned storage services according to policies defined in a domain-specific language that can target storage automation or service-level agreements. At the data plane, policies enforce software-defined storage services like filters on tenant requests. IOStack also provides a framework for building various types of filters to manage tenant data and storage.

IOStack: Software-Defined Object Storage
Abstract:
As the complexity and scale of cloud storage systems grow, software-defined
storage (SDS) has become a prime candidate to simplify cloud storage
management. Here, the authors present IOStack, the first SDS architecture for
object stores (such as OpenStack Swift). At the control plane, the provisioning of
SDS services to tenants is made according to a set of policies managed via a high-
level domain-specific language (DSL). Policies can target storage automation or
specific service-level agreement (SLA) objectives. At the data plane, policies
define the enforcement of SDS services, namely filters, on a tenant's requests.
Moreover, IOStack is a framework to build a variety of filters, ranging from
general-purpose computations close to the data to specialized data management
mechanisms. Experiments illustrate that IOStack enables easy and effective
policy-based provisioning, which can significantly improve the operation of a
multitenant object store.

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Power optimization with bler constraint for wireless fronthauls in c ranieeepondy

This document proposes two power optimization techniques for wireless fronthauls in cloud radio access networks (C-RAN) that minimize energy consumption at remote radio heads while satisfying quality of service constraints. It derives a closed-form upper bound on system block error rate via union bound analysis. Then, it proposes adaptive transmission schemes and practical power optimizations based on both block error rate and pair-wise error probability to reduce energy use at remote radio heads while meeting a predefined non-zero block error rate quality of service requirement.

Performance aware cloud resource allocation via fitness-enabled auctionieeepondy

This document discusses a new algorithm for cloud resource allocation using a fitness-enabled auction approach, aimed at optimizing the performance traits of cloud resources and services. It emphasizes the importance of matching cloud resources to specific service requirements to enhance efficiency and overall system performance. Experimental results indicate that this method outperforms traditional allocation methods, like continuous double auctions, by introducing the concept of fitness.

Performance limitations of a text search application running in cloud instancesieeepondy

This article examines the performance limitations of MySQL in cloud environments using commodity hardware, focusing on how it affects text search applications. It compares a physical machine to a cloud instance, revealing that the extensive use of relational databases leads to significant performance degradation due to constraints in the cloud's primary storage system. The findings highlight the bottlenecks encountered when executing SQL scripts on a large dataset.

Performance analysis and optimal cooperative cluster size for randomly distri...ieeepondy

The document analyzes the performance and optimal cooperative cluster size of randomly distributed small cells in a cloud radio access network. It highlights the trade-off between increased spectrum efficiency through cooperative cells and potential delays caused by necessary feedback and processing, proposing a throughput optimization problem based on cluster size. Simulation results confirm the effectiveness of the analytical framework in evaluating the performance of cloud-based small cell networks with clustered cooperation.

Predictive control for energy aware consolidation in cloud datacentersieeepondy

This paper presents an energy-aware consolidation strategy using predictive control for cloud datacenters to optimize power consumption and reduce operational costs. It introduces a discrete-time dynamic model and migration strategies based on solving optimal control problems with integer variables. The effectiveness of the approach is validated through simulations against heuristics from existing literature, demonstrating a balance between energy savings and service level agreement compliance.