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Scalable Data Management Workshop, IEEE BigData Conference, Santa Clara, US, 6-9 October 2013

Scalable Data Management Workshop, IEEE BigData Conference, Santa Clara, US, 6-9 October 2013

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  • 1. SLA data management criteria Katerina Stamou, Verena Kantere, Jean-Henry Morin Institute of Services Science, University of Geneva, Switzerland 10/6/20131Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US
  • 2. In a nutshell… 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 2 The systematic management of SLA data isrequired,as it increases SLA and service manipulation opportunities in the cloud computing setting. Thus, it contributes to additional business value in a service-oriented economy. The term SLA data managementencloses data operations that may take place before, during or after SLA/service execution. We propose that the systematic management of SLAs can be efficiently achieved using a digraph data model that perceives SLA elements and their data relations as an operational pipeline.
  • 3. Agenda 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 3 Systematic SLA data management Current SLA role in virtual economies SLA data complexity SLA data analysis SLA digraph data model Ongoing work
  • 4. Definitions and assumptions 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 4 Service Level Agreements (SLAs) express mutually agreed service levels between providers and customers [1]. SLAs define quality of service (QoS) criteria, along with functional service properties. The definition and structure of SLAs for cloud computing services are not yet standardized. The term “systematic SLA data management” describes the process of SLA formulation, storage and processing by a backend supporting data-store or DBMS. SLAs are automated and cloud providers use automated processing systems for the management of their offered services. SLA templates can be used aswhat-you-see-is-what-you-get (WYSIWYG) artifacts that customers use to negotiate and finalize their service selection.
  • 5. Systematic SLA data management 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 5 Automated formulation: using a modular and adaptive data structure that addresses SLA data intricacies. Storage: finding the optimal storage mode for, typically, short-term data. Processing: SLA information contains inner-dependencies and internal functions that take place during service execution. SLA information is not BigData; it is about managing and processingcomplex information that may result to or involve operations on massive data-sets. SLAs represent semi-structured or even unstructured data, where no rigid schema applies. Thus, an efficient data model is required to allow for dynamic data processing.
  • 6. SLA anatomy - Web Service Level Agreement (WSLA), IBM [2] 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 6 Signatories, third parties: customer- provider pair and their connections to third party support for the service execution. Service description: decomposition and hierarchical classification of service objects, whose accumulation or combination constitutes the service definition. Guarantees: obligations, typically from the provider part, to fulfill agreed and promised levels or service provisioning. IBM distinguishes between measureable targets (objectives) and predefined actions that occur during the service up-time.
  • 7. Challenges for SLA manipulation in a cloud service economy 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 7 The SLA definition provides an explicit view on how the service provisioning is planned. It indicates precise bounds on service levels that a provider can afford. 1. SLAs as automated processes versus static documents that currently appear in cloud marketplaces. 2. Diversified service offerings, various vocabularies of service descriptions => SLA semantic and structural heterogeneity. 3. SLA formulation depends from resource availability and is typically subject to customer-provider variations. Given heterogeneity and unbounded length, SLAs represent a fine example of semi-structured information that needs concurrent processing over distributed computing settings.
  • 8. SLA data complexity stems from: 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 8 Heterogeneity of data format and structure Service dependencies between internal SLA components. Service dependencies within an SLA lifecycle can be thought as actions that have to occur, when a predefined condition is triggered. Real time measurement/updates: internal SLA components may be used for the definition and computation of other SLA components that typically reside within the same SLA instance. Data relationships may deal with monitoring and measurement of values that are described by data end-point sources. Data connections may also deal with updates of SLA component values that are dependent from the values of neighbor SLA components. A persisted SLA instance needs to be accessed by both external sourcesas well as DBMS internal processes.
  • 9. SLA data analysis I 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 9 The term SLA data management encloses all data operations that may take place before, during or after SLA/service execution. Such operations can be classified according to pre-instantiated, active and terminated SLAs. They typically include fine-grained SLA elements that need dynamic processing. Compared to other types of service contracts (e.g. terms-and-conditions, software licences) the values of SLA terms need to be monitored and measured during service execution to verify that SLOs are met and that no service violations have occured. The requirement for real-time data updates particularly applies in the cloud computing setting, where services are exchanged on demand and business relationships may enclose financial responsibilities. Nested SLA information may include dependencies between diverse components or component sets (e.g. a change in an SLA parameter value may affect respective SLO values).
  • 10. SLA data analysis II 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 10 Data criteria SLA parameter Metrics Measurable objectives Action guarantees complete SLA doc accessibility, integrity ✔ ✔ ✔ ✔ ✔ velocity rate high high high low ~ replication, staging ✔ ✔ ✔ dependencies ✔ ✔ ✔ ✔ cleanness ✔ ✔ ✔ ✔ ✔ accuracy ✔ ✔ ✔ ✔ ✔ ownership, authenticity ✔ ✔ ✔ ✔ ✔ heterogeneity ✔ ✔ ✔ ✔ ✔
  • 11. SLA digraph formalization 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 11
  • 12. SLA into property graph 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 12 According to [5], a property graph G=(V, E, ) represents a directed, attributed, edge-labeled graph that contains multi-relations, which are expressed as key/value pairs on the graph elements. The computing structure is the graph and the computing process consists of the graph traversals. The SLA digraph representation includes only uni-directed edges to denote the flow of dependencies withinanySLA “pipeline”. Three immediate advantages: Modular: decomposable, flexible structure, extensible Adaptive: with respect to diversified service environments, inclusion/exclusion of additional elements Dynamic: concurrent execution of operations and transactions within the same or multiple graphs.
  • 13. SLA dependencies 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 13 According to [3], service dependencies represent customer/provider relationships that are reflected to the various cooperating components within a distributed service management system. A dependency denotes the directed relationshipbetween a dependent service or application component that requires an operation performed by an antecedent component in order for the former to execute its function. SL A data elements are connected according to structural or operational dependencies, where satisfactory dependency conditions are defined as edge-property triggers. SLA dependency examples: <ActionGuarantee, SLO>uses, <CompositeMetric, ResourceMetric>uses, <SupportParty, ActionGuarantee>obliged, where for every pair of SLA nodes the following relationship holds: <Dependent, Antecedent>rel, Dependentvalue -> function(Antecedentvalue) ,while a predefined set of conditions is valid and 'rel’ represents an outgoing edge from the dependentto the antecedent component.
  • 14. Query expressiveness, clear information flow, SLA questions: 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 14 Provider aspect: 1. reach resourcex and get value of metricy;return value and update all relations, where value is used. 2. update SLAxy; add new branch ServiceDefinition and in Obligations add SLO branches and ActionGuarantees;updatethe dependencies/relations between the newly added components. 3. update SLAqw23; delete SupportPartyoldwith name ’someCompany’ and update all obligations of SupportPartyold to SupportPartynew Customer aspect: 1. reach SupportPartynew; ask to return monitored values from a givenlist of metrics 2. how can I add a new SLA to my currently running one(s)? 3. which service is best for me? what are my service criteria?
  • 15. Conclusions 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 15 The SLA digraph has been initially implemented using an in-memory graph database, NetworkX [4]. Next, the data model has been re-implemented using the Titan distributed graph database [6], where Gremlin [7] is used as the primary DSL. Query comparison between Graph DSL, XQuery and MySQL. Currently, we are testing the digraph efficiency using Cassandra as the persistence backend behind Titan. We exercise the scenario, where massive http requests reach the SLA information concurrently and request information retrieval and filtered operations. Actual SLA data represents a requirement…to avoid the use of fictitious information. TPC benchmark to be used to further testing.
  • 16. Thank you :) Questions? -> http://www.cui.unige.ch/~stamou/ slides, full paper: http://www.slideshare.net/kat_slides/scdm 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 16
  • 17. References 10/6/2013Scalable Cloud Data Management workshop, IEEE BigData Conference, Santa Clara, US 17 1. A. Dan, H. Ludwig, and G. Pacifici, “Web service differentiation with service level agreements,” White Paper IBM Corporation, 2003. 2. H. Ludwig, A. Keller, A. Dan, R. King, and R. Franck, “Web Service Level Agreement (WSLA) Language Specification,” IBM Corporation, 2003. 3. A. Keller, U. Blumenthal, and G. Kar, “Classification and Computation of Dependencies for Distributed Management,” in Proc. of the Fifth IEEE Symposium on Computers and Communications (ISCC 2000), ser. ISCC ’00. IEEE Computer Society, 2000. 4. A. Hagberg, D. Schult, and P. Swart, “NetworkX,” http://networkx. github.io/, accessed: March, 2013. 5. M. Rodriguez, “Property Graph Algorithms,” http://markorodriguez. com/2011/02/08/property-graph-algorithms/, accessed: July, 2013. 6. ThinkAurelius, “Titan Distributed Graph Database,” http://thinkaurelius.github.io/titan/, accessed: July, 2013. 7. ThinkAurelius team, “Gremlin graph query language,” https://github.com/tinkerpop/gremlin/wiki, accessed: July, 2013.