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Consistency-Based Service Level Agreements for Cloud Storage
"... Choosing a cloud storage system and specific operations for reading and writing data requires developers to make decisions that trade off consistency for availability and performance. Applications may be locked into a choice that is not ideal for all clients and changing conditions. Pileus is a repl ..."
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Choosing a cloud storage system and specific operations for reading and writing data requires developers to make decisions that trade off consistency for availability and performance. Applications may be locked into a choice that is not ideal for all clients and changing conditions. Pileus is a replicated key-value store that allows applications to declare their consistency and latency priorities via consistencybased service level agreements (SLAs). It dynamically selects which servers to access in order to deliver the best service given the current configuration and system conditions. In application-specific SLAs, developers can request both strong and eventual consistency as well as intermediate guarantees such as read-mywrites. Evaluations running on a worldwide test bed with geo-replicated data show that the system adapts to varying client-server latencies to provide service that matches or exceeds the best static consistency choice and server selection scheme.
Autonomic SLA-driven Provisioning for Cloud Applications
"... Abstract—Significant achievements have been made for automated allocation of cloud resources. However, the performance of applications may be poor in peak load periods, unless their cloud resources are dynamically adjusted. Moreover, although cloud resources dedicated to different applications are v ..."
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Abstract—Significant achievements have been made for automated allocation of cloud resources. However, the performance of applications may be poor in peak load periods, unless their cloud resources are dynamically adjusted. Moreover, although cloud resources dedicated to different applications are virtually isolated, performance fluctuations do occur because of resource sharing, and software or hardware failures (e.g. unstable virtual machines, power outages, etc.). In this paper, we propose a decentralized economic approach for dynamically adapting the cloud resources of various applications, so as to statistically meet their SLA performance and availability goals in the presence of varying loads or failures. According to our approach, the dynamic economic fitness of a Web service determines whether it is replicated or migrated to another server, or deleted. The economic fitness of a Web service depends on its individual performance constraints, its load, and the utilization of the resources where it resides. Cascading performance objectives are dynamically calculated for individual tasks in the application workflow according to the user requirements. By fully implementing our framework, we experimentally proved that our adaptive approach statistically meets the performance objectives under peak load periods or failures, as opposed to static resource settings. Index Terms—cost-efficiency, replication, migration, net benefit, performance elasticity, web services I.
Improving data center resource management, deployment, and availability with virtualization
, 2009
"... The increasing demand for storage and computation has driven the growth of large data centers–the massive server farms that run many of today’s Internet and business applications. A data center can comprise many thousands of servers and can use as much energy as a small city. The massive amounts of ..."
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Cited by 6 (0 self)
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The increasing demand for storage and computation has driven the growth of large data centers–the massive server farms that run many of today’s Internet and business applications. A data center can comprise many thousands of servers and can use as much energy as a small city. The massive amounts of computation power required to drive these systems results in many challenging and interesting distributed systems and resource management problems. In this thesis I investigate challenges related to data centers, with a particular emphasis on how new virtualization technologies can be used to simplify deployment, improve resource efficiency, and reduce the cost of reliability. I first study problems that relate the initial capacity planning required when deploying applications into a virtualized data center. I demonstrate how models iv of virtualization overheads can be utilized to accurately predict the resource needs of virtualized applications, allowing them to be smoothly transitioned into a data center. I next study how memory similarity can be used to guide placement when
PipeCloud: Using Causality to Overcome Speed-of-Light Delays in Cloud-Based Disaster Recovery
"... Disaster Recovery (DR) is a desirable feature for all enterprises, and a crucial one for many. However, adoption of DR remains limited due to the stark tradeoffs it imposes. To recover an application to the point of crash, one is limited by financial considerations, substantial application overhead, ..."
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Disaster Recovery (DR) is a desirable feature for all enterprises, and a crucial one for many. However, adoption of DR remains limited due to the stark tradeoffs it imposes. To recover an application to the point of crash, one is limited by financial considerations, substantial application overhead, or minimal geographical separation between the primary and recovery sites. In this paper, we argue for cloud-based DR and pipelined synchronous replication as an antidote to these problems. Cloud hosting promises economies of scale and on-demand provisioning that are a perfect fit for the infrequent yet urgent needs of DR. Pipelined synchrony addresses the impact of WAN replication latency on performance, by efficiently overlapping replication with application processing for multi-tier servers. By tracking the consequences of the disk modifications that are persisted to a recovery site all the way to client-directed messages, applications realize forward progress while retaining full consistency guarantees for client-visible state in the event of a disaster. PipeCloud, our prototype, is able to sustain these guarantees for multi-node servers composed of black-box VMs, with no need of application modification, resulting in a perfect fit for the arbitrary nature of VM-based cloud hosting. We demonstrate disaster failover to the Amazon EC2 platform, and show that PipeCloud can increase throughput by an order of magnitude and reduce response times by more than half compared to synchronous replication, all while providing the same zero data loss consistency guarantees.
An untold story of redundant clouds: Making your service deployment truly reliable
- In ACM Workshop on Hot Topics in Dependable Systems
, 2013
"... To enhance the reliability of cloud services, many application providers leverage multiple cloud providers for redundancy. Unfortunately, such techniques fail to recognize that seem-ingly independent redundant clouds may share third-party infrastructure components, e.g., power sources and Internet r ..."
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To enhance the reliability of cloud services, many application providers leverage multiple cloud providers for redundancy. Unfortunately, such techniques fail to recognize that seem-ingly independent redundant clouds may share third-party infrastructure components, e.g., power sources and Internet routers, which could potentially undermine this redundancy. This paper presents iRec, a cloud independence recom-mender system. iRec recommends at best-effort indepen-dent redundancy services to application providers based on their requirements, minimizing costly and ineffective redun-dancy deployments. At iRec’s heart lies a novel protocol that calculates the weighted number of overlapping infras-tructure components among different cloud providers, while preserving the secrecy of each cloud provider’s proprietary information. We sketch the iRec design, and discuss chal-lenges and practical issues. 1.
Brokering Algorithms for Optimizing the Availability and Cost of Cloud Storage Services
"... Abstract—In recent years, cloud storage providers have gained popularity for personal and organizational data, and provided highly reliable, scalable and flexible resources to cloud users. Although cloud providers bring advantages to their users, most cloud providers suffer outages from time-to-time ..."
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Abstract—In recent years, cloud storage providers have gained popularity for personal and organizational data, and provided highly reliable, scalable and flexible resources to cloud users. Although cloud providers bring advantages to their users, most cloud providers suffer outages from time-to-time. Therefore, relying on a single cloud storage services threatens service availability of cloud users. We believe that using multi-cloud broker is a plausible solution to remove single point of failure and to achieve very high availability. Since highly reliable cloud storage services impose enormous cost to the user, and also as the size of data objects in the cloud storage reaches magnitude of exabyte, optimal selection among a set of cloud storage providers is a crucial decision for users. To solve this problem, we propose an algorithm that determines the minimum replication cost of objects such that the expected availability for users is guaranteed. We also propose an algorithm to optimally select data centers for striped objects such that the expected availability under a given budget is maximized. Simulation experiments are conducted to evaluate our algorithms, using failure probability and storage cost taken from real cloud storage providers.
Taming the Cloud Object Storage with MOS
"... Abstract Cloud object stores today are deployed using a single set of configuration parameters for all different types of applications. This homogeneous setup results in all applications experiencing the same service level (e.g., data transfer throughput, etc.). However, the vast variety of applica ..."
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Abstract Cloud object stores today are deployed using a single set of configuration parameters for all different types of applications. This homogeneous setup results in all applications experiencing the same service level (e.g., data transfer throughput, etc.). However, the vast variety of applications expose extremely different latency and throughput requirements. To this end, we propose MOS, a Micro Object Storage architecture with independently configured microstores each tuned dynamically for a particular type of workload. We then expose these microstores to the tenant who can then choose to place their data in the appropriate microstore according the latency and throughput requirements of their workloads. Our evaluation shows that compared with default setup, MOS can improve the performance up to 200% for small objects and 28% for large objects while providing opportunity of tradeoff between two.
Utilizing Linear Algebra Subspaces to Improve Cloud Security
"... Abstract—Cloud computing is quickly becoming the infrastructure of choice for hosting data and software solutions for many individuals, businesses, and governmental organizations. While such systems may provide increased flexibility and utility, efficient and easily-managed cloud storage solutions t ..."
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Abstract—Cloud computing is quickly becoming the infrastructure of choice for hosting data and software solutions for many individuals, businesses, and governmental organizations. While such systems may provide increased flexibility and utility, efficient and easily-managed cloud storage solutions that ensure data confidentiality are needed to maintain this trend. In this work, we propose an algebraic-based encoding solution to provide data confidentiality. Additionally, through the use of the various algebraic subspaces present in the coding process, we are able to verify basic Service Level Agreement (SLA) guarantees. We demonstrate the feasibility of our solution through implementations and deployments on test systems. I.
Distributed and Parallel Databases manuscript No. (will be inserted by the editor) DYFRAM: Dynamic Fragmentation and Replica Management in Distributed Database Systems
"... the date of receipt and acceptance should be inserted later Abstract In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sit ..."
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the date of receipt and acceptance should be inserted later Abstract In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.
Client-aware Cloud Storage
"... Abstract—Cloud storage is receiving high interest in both academia and industry. As a new storage model, it provides many attractive features, such as high availability, resilience, and cost efficiency. Yet, cloud storage also brings many new challenges. In particular, it widens the already-signific ..."
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Abstract—Cloud storage is receiving high interest in both academia and industry. As a new storage model, it provides many attractive features, such as high availability, resilience, and cost efficiency. Yet, cloud storage also brings many new challenges. In particular, it widens the already-significant semantic gap between applications, which generate data, and storage systems, which manage data. This widening semantic gap makes end-to-end dif-ferentiated services extremely difficult. In this paper, we present a client-aware cloud storage framework, which allows semantic information to flow from clients, across multiple intermediate layers, to the cloud storage system. In turn, the storage system can differentiate various data classes and enforce predefined policies. We showcase the effectiveness of enabling such client awareness by using Intel’s Differentiated Storage Services (DSS) to enhance persistent disk caching and to control I/O traffic to different storage devices. We find that we can significantly outperform LRU-style caching, improving upload bandwidth by 5x and download bandwidth by 1.6x. Further, we can achieve 85 % of the performance of a full-SSD solution at only a fraction (14%) of the cost. Index Terms—Storage, Operating systems, Cloud computing, Cloud storage
