A Unified Management Model for Data IntensiveStorage Clouds

Athanasios Voulodimos1, Spyridon V. Gogouvitis1, Nikoletta Mavrogeorgi1, Roman

Talyansky2, Dimosthenis Kyriazis1, Stefanos Koutsoutos1, Vasileios Alexandrou1,

Elliot Kolodner3, Per Brand4, Theodora Varvarigou1

1School of Electrical and Computer Engineering, National Technical University of Athens

Athens, Greece2SAP Research Israel

Ra’anana, Israel3IBM Haifa Research Lab

Haifa, Israel4Swedish Institute of Computer Science

Kista, Sweden

E-mail addresses: {thanosv, spyrosg, nikimav}@mail.ntua.gr, roman.talyansky@sap.com,

{dimos, stefk, alexv}@mail.ntua.gr, kolodner@il.ibm.com, perbrand@sics.se,

dora@telecom.ntua.gr

Abstract—Cloud storage has recently appeared as apromising solution to handle the immense volume ofdata produced in nowadays’ rich and ubiquitous digitalenvironment. Existing approaches, however, do not fullyaddress certain significant issues, such as Service LevelAgreements, which tend to be rudimentary, and manage-ment of cloud systems, which often lacks sophistication.In this paper, we propose the definition and developmentof management models that systematically describe theresources, services, usage, and requirements in the contextof a cloud storage environment. Furthermore, we intro-duce content oriented SLA Schemas, which are adaptedaccording to the content term involved in each SLA. Com-bining all this often heterogeneous information in a smart,structured, and uniform manner can provide significantadded value, allowing correlations, links and extraction ofuseful inferences. We thus propose to incorporate thesemodels in a rich Unified Management Model, which willcontribute to optimum decision making, data movement, aswell as better resource allocation and energy management,also leading to enhanced Quality of Service. The researchpresented in this paper is work in progress carried out inthe context of the VISION Cloud project.

I. INTRODUCTION

The growth of digital data during the past yearshas increased in a such rate that is challenging

our ability of storing it. Storage Cloud solutions,by which storage is virtualized and offered ondemand, promise to address the proliferation of data.Aspects of Cloud computing, such as the pay-as-you-go model, essentially unlimited scalability andcapacity, lower costs are also prominent in StorageClouds. Especially important to the uptake of suchsolutions is their ease of use and management.In this context VISION Cloud [1] has presentedthe architecture of a scalable and flexible cloudenvironment addressing the challenge of providingdata-intensive storage cloud services through rais-ing the abstraction level of storage, enabling datamobility across providers, allowing computationaland content-centric access to storage and deployingnew data-oriented mechanisms for QoS and securityguarantees [2].

The QoS mechanisms and SLAs offered by cur-rent cloud storage providers are rather rudimentary.They guarantee only service availability and giveservice credit or refunds for lack thereof [3], [4], [5],but do not address data availability and protection.In research, an architecture and protocol for anSLA-based trust model for cloud computing havebeen presented in [6] and approaches for managing

2011 First International Symposium on Network Cloud Computing and Applications

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DOI 10.1109/NCCA.2011.18

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the mappings of low-level resource metrics to high-level SLAs in [7], [8], [9] have been proposed.

In our approach, models, requirements and SLASchemas are expressed not only on storage re-sources and services, but also on the content de-scriptions for the underlying storage objects, in sup-port of content centric storage. Moreover, models inVISION Cloud will combine resource and data con-tent information providing a cross-discipline combi-nation both for storage resources and data contentcharacteristics in order to be used as an integratedapproach.

Therefore the desired functionality with the guar-anteed QoS levels can be implemented, while assur-ing that service utilization is improved. We focus onmodeling the resources by aggregating informationrelated to them (e.g. expressing capabilities such asread / write performance) with information relatedto the content (e.g. content type, request frequency,etc) as well as capturing information regardingusage and requirements parameters with the finalgoal of producing a Unified Model, able to captureall information related to management. The afore-mentioned unified model allows for optimum man-agement of resources and services given their richrepresentation and characterization. For example, inthe cases of frequent access / request to a specificservice the infrastructure will be adopted to meet thedemand by increasing the bandwidth to the resource.The models produced also express the importance ofa service and a resource in comparison to other onesand can consist as input for management decisions,such as geographic placement of data or securitymechanisms that shall be put in place to protect thespecific “high importance” resources and servicesetc.

II. RESOURCE MODELING

Modeling the resources in VISION Cloud con-tributes to the efficient management of nodes withina cluster, of clusters within a data center, and ofdata centers within a cloud. It offers a standardized,organized way to have an overview of all resourceswithin the cloud.

A set of parameters for each level of the physicalmodel (e.g. node, cluster, cloud) is defined. Themodeling of nodes within the platform is consistedof parameters related to the identity of the node,its storage, compute and network capabilities. The

same format of distinguishing between identity, stor-age, compute and network parameters is also usedin modeling a cluster. A data center comprises ofone or, usually, more clusters. Since the emphasisin VISION Cloud is more on clusters than on datacenters, the data center model is an aggregationinformation regarding the clusters included with theaddition of energy consumption information.

III. SERVICE MODELING

Modeling of services deployed and providedwithin VISION Cloud also constitute part of themanagement models. Presently within the scopeof our platform we can differentiate between twomain categories of services: data object upload-ing/ingestion, access and update, and storlets. Astorlet is a construct which specifies a computationto be executed on a set of data objects. The twomost important characteristics of storlets are thatthey execute close to their data and that the style ofcomputation is not limited to batch job executions.

For the upload/access/update services, signifi-cant parameters include the resource requirementsneeded, the content type the service refers to, thepermission of the data object, the device used toaccess a data object and key performance incicatorsused to evaluate the success of service provisioning.

Regarding storlets, the elements that are of signif-icance are determined in the computational storagemodel and include information such as a descriptionof the computation to be executed, the idenity ofthe associated data objects, constraints on the stor-let (such as maximum working memory), triggersdefining when and if a strolet is activated andcredentials that the storlet should use to access dataobjects.

IV. USAGE MODELING

Usage characteristics (e.g. geographical accessdistribution or read / write access frequency) en-code the knowledge that is automatically harvestedand captures application access patterns with re-gard to storage. These characteristics are of majorimportance since they may be used afterwards foroptimising mapping of cloud storage resources toapplication requirements. In addition to automaticharvesting of usage characteristics, applications mayinject into the cloud storage infrastructure a pri-ori knowledge on the application behaviour. Self

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knowledge may be used to further optimise map-ping of application requirements to cloud storageresources. Therefore, usage models include repre-sentations and descriptions to inject application self-knowledge. More specifically, the model is used tobuild predictive models for the Planner part of theMAPE loop [10].

To build a usage model a time axis is split intotime intervals of the same duration, e.g. hours, daysor weeks. For each interval a vector of attributesis calculated from the aggregated monitoring infor-mation of this interval. The attributes describe theapplication’s characteristics such as total the numberof reads and writes, the size and standard deviationof accessed objects, etc. Thus a vector of attributevalues is generated for each time interval. A usagemodel consists of all the attribute vectors that weregenerated in the past.

V. REQUIREMENTS MODELING

An application running over the VISION Cloudinfrastructure will be able to specify requirements,which will be later used by the infrastructure todrive the data access operations of the application.To this direction, a Requirements Model capturingboth the requirements emerging from applicationattributes modeling and the ones included in theSLAs is used.

Applications specify attributes that are automati-cally translated to storage requirements. For exam-ple, attributes such as “Master Data” or “Transac-tional Data” are translated into requirements “lowupdate rates, replicated with eventual consistency”and “high update rates, strong consistency” respec-tively. Once the set of requirements is identified, themethods to specify their values need to be provided.Such methods comprise generic models for storagerequirements. For example, high availability maybe specified by the number of replicas required,as well as the coarse geographical location foreach replica. It should be noted that while storagerequirements are technical in nature and they reflectthe applications’ requirements, SLA Schemas have amore business-oriented flavour (e.g., as they specifypenalties in case of SLA breaches). However, giventhat the SLA Schemas will include content-relatedterms, these will be considered as additional storagerequirements.

VI. SLA SCHEMAS

An SLA is a service level agreement between thetenant and the service provider, where a contract iscreated that defines the terms and conditions underwhich the services will be provided. In VISIONCloud, the SLAs are enriched versions of conven-tional ones. They contain not only requirementsrelated to cost, availability, performance, etc., butalso content related terms, in the context of VISIONCloud’s content centric approach. For example, thepricing models and the performance estimation fordata transfer is different depending on the SLAcontent term.

An SLA Schema is a schema that represents anSLA. It defines what data can be contained in anSLA (e.g. cost, contract dates, user requirements,fines, etc) and in which format. The language thatis used for the SLA Schema is XSD (XML SchemaDocument).

The basic SLA Schema’s elements include a termattribute that represents the content term related tothe SLA (e.g. media, healthcare, etc.), attributessuch as availability, jitter, ingest rate, etc., cost andpenalties.

VII. UNIFIED MODEL

The aforementioned models and schemata containand organize various pieces of information whichare of importance to the Cloud in different ways.Combining all this often heterogeneous informationin a smart, structured, and uniform manner canprovide significant added value, allowing associa-tions, links and extraction of useful inferences. Wethus propose the design of a Unified Model thatunifies the earlier models presented. In particular,in VISION Cloud we aim at combining all typesof metadata and information characterizing differentparts / “elements” of the system (data objects,resources, services, requirements) into a UnifiedModel, which will allow for optimum decision mak-ing, data movement, as well as resource and servicemanagement, also leading to enhanced Quality ofService.

Through the platform’s Management Interface,the users (both customers - in the case of require-ments / SLAs, and developers - in the case ofcontent descriptions for the storage objects) areable to specify the different parameters needed to

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produce each individual model in a secure environ-ment. These, along with parameters generated bythe platform itself (such as usage parameters) areautomatically aggregated to form the Unified Model.

The Unified Model’s usefulness lies in the factthat various kinds of information pertaining to dif-ferent aspects of interest to VISION Cloud areorganized, structured, and presented in a standard-ized, formalized, consistent, and unified manner. Itenables the ability to extract correlations and makeinferences, linking information stored in differentmodels. For example, the significance of a resourcecan be determined not only from its “obvious”hardware characteristics (Resource Model), but alsobased on the content stored in that resource, theusage patterns linked to it (Usage Model), the trig-gers or constraints of the storlets that could possiblybe associated with this content (Service Model), oreven the strictness of the SLA terms connected tothe data.

Moreover, one of the goals of VISION Cloud isto substantially extend the limited data migrationcapabilities available in contemporary infrastruc-tures so as to migrate and federate data acrossgeographically distributed administrative domains,thus ensuring comprehensive and transparent datainteroperability and overcoming the problem of datalock-in. A Unified Management Model helps infederation scenarios by providing a common way inwhich all aspects of a platform can be defined. Inthis way, knowledge that is accumulated over longperiods of time regarding particular data objectshosted on one provider can be easily migrated toa new provider.

VIII. CONCLUSION

We have presented a series of management mod-els aimed at capturing the most crucial informationin the context of a cloud environment for dataintensive storage services. Through the modelingof resources, services, usage and requirements, to-gether with the definition of content oriented SLAs,we aim at providing a standardized, structured anduniform way to organize the important informationinvolved in the cloud. Through a less conventional,more “modern” notion of metadata model, which isinclusive and emphasizes content, our goal is to pro-duce rich, useful models, and smartly combine themin a Unified Management Model, which will be able

to express the importance of data and/or resourcesin comparison to other data and/or resources andto efficiently provide input to decision making andplacement mechanisms. This contributes to betterresource allocation and energy management withinthe platform. Currently the VISION Cloud projectis in its early stages, thus the implementation andevaluation of the concepts outlined in this paperare underway. Discussing outcomes and results isdeferred for future work.

ACKNOWLEDGMENT

The research leading to these results is par-tially supported by the European Community’s Sev-enth Framework Programme (FP7/2007-2013) un-der grant agreement n° 257019, in the context ofthe VISION Cloud Project.

REFERENCES

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[9] S. Gogouvitis, K. Konstanteli, S. Waldschmidt, G. Kousiouris,G. Katsaros, A. Menychtas, D. Kyriazis, and T. Varvarigou,“Workflow management for soft real-time interactive applica-tions in virtualized environments,” Future Generation ComputerSystems, vol. 28, no. 1, pp. 193 – 209, 2012.

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