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Data & Computation Interoperability in Cloud Services - Seamless ComputationsSerguei Boldyrev

Services & Software technologies Technology evaluation

ruSMART 2011

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Agenda• “Skies are rocketing”• Data• Computation• Managed Cloud Performance• Privacy in the Cloud• Closing Thoughts

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“Skies are rocketing”

The ICT industry is undergoing major shifts, one is towards cloud computing

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Why is this happening? (agree with Google )

As digital transformation of products and services are demanded by consumers (with increasing complexity driven by new customer

expectations)

Can you think of the changes and issues they pose on transforming the portfolio of products and services ICT companies have?

Multiple & continuous Interaction

Consumption based

Always on

Personalized and

custom

Transparent and

trusted

Everything as a service

Consumerexpectatio

n

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Technological standpoint• Integrating multiple partners and services, and

providing users a seamless experience across multiple platforms and devices,

• incorporating both “in-house” and outsourced storage and computing infrastructures

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Business side• there is an anticipation of improved flexibility and

elasticity in terms of multisided business models

• introducing new, multisided business models where value can be created through interactions with multiple (different types of) players

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Main questions • Who might find a certain information valuable?

• What would happen if we provided our service for free of charge?

• What if my competitor(s) did so?

The answers to such questions will highlight• opportunities for disruption and • identification of vulnerabilities

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Opportunity of New Digital ExperienceWorld of fully deployed and available cloud infrastructures

The emergence of Distributed Systems that can seamlessly span the “information spaces” of multiple • hardware, • software, • information and • infrastructure providers

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Cloud Solution, technological disruptionsUtilizing the best parts of the Web development paradigm

– …Web-based applications, not Web sites…– and leveraging the special contextual capabilities of mobile devices (ex. WURFL databases on sensors)

Moving from a device-cloud and Web-centric models to a Hybrid model (Cloud & Edge computing)– Reuse (partition) information and computation in the Cloud, Infrastructure and expand it to devices– Elimination of special-purpose software to download, install applications, Service oriented infrastructure constructed as

a functional flows requested on demand– Enabling balancing of computation and relevant data between heterogeneous Cloud Back-Ends, Infrastructures (ex.

Spanner, HDFS, Web Intents etc) and devicesFostering faster, easier, richer application innovation and deployment through Cloud Back-End computation recycling

– Diverse Cloud Back-Ends can all leverage the infrastructure capabilities– Atomic computations are now deployed once to the Back-Ends and composed down to Infrastructure and clients, where

a set of functional flows or description of those form the actual service– Allowing Network Infrastructure to leverage data and computation workload, by taking care of services or provider of

services capabilities, distributing computation between Back-Ends and Infrastructure– Allowing more efficient contextual composition of services than purely device or Web-centric models

PlatformData

App

Device-centric model Web-centric model

ABC Cloud

DataPlatformApp

“Hybrid” model

ABC Cloud

Computation & DataPlatform

App

XYZ Cloud

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New Digital Experience"My digital experience follows me regardless the computing environments around me“

• take advantage of finely granular and accountable processing services,

• integrate heterogeneous information sources, and

• ultimately free users of mundane challenges and details of technology usage

Greater reuse of information and computational tasks is just few steps away

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Opportunities and ChallengesClear framework for interoperability should be defined

Main aspects of • data and computation semantics, • performance and scalability of computation and

networking, • threats to security and privacy

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Data

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Semantics, the “meaning” of dataContemporary software system (or any part of it) defines the semantics of any particular data item

The semantics is defined implicitly or explicitly

The “meaning” of a data item arises from one of two sources:1. The relationships this item has with other data items and/or definitions (including

the semantics of the relationships themselves). – object-oriented polymorphism, where the runtime system can pick the right software to

process an object because the class of the object happens to be a subclass of a “known” class;

– more elaborate cases include ontology-based reasoning that infers new things about an object

2. Some software (executing in the system runtime) that “knows” how to process this data item directly– including the system runtime itself, as this typically “grounds” the semantics of primitive

datatypes, etc. – the semantics is “hard-wired” in the software.

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Constituent Technologies of “Data Infrastructure”All applications need to query, transmit, store and manipulate data All these functions have dependencies to technologies - Data Infrastructure

The constituent technologies have various dependencies

Data semantics standpoint is on the technologies inside the “Data Modeling Framework”

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Common Data Model (CDM) enables portability of data across application domains

Policy Engine

Shared Data

IntegrationSemantics

Service1Logic

Service1 API

ClientA

ClientB

Service2Logic

Service2 API

Data API

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Computation

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Computation, the componentsClientA

ClientB

Backend EnvironmentClient API

Computation run-time

Environment

Agent 1

Agent 2

Agent 3

Client API

Computation run-time

EnvironmentRecycling

& Marshaling

Computations Store

Agent 4

Agent 5

Convenience APIPHP API Java API

Dist

ribut

ion Back end API

Dist

ribut

ion

Control/Ontologies

Computation

Dist

ribut

ion

To enable and create:• consistent (from

the semantics standpoint), and

• accountable components

Components that can be leveraged and reused in a larger, distributed information system

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Computation perspectiveObjectives (but not limited to listed below)

• Design and implementation of scenarios where computation can be described and represented in a system-wide understandable way

• enabling computation to be “migrated” (transmitted) from one computational environment to another

• such transmission would involve reconstruction of the computational task at the receiving end to match the defined semantics of task and data involved

• Construction of a system where larger computational tasks can be decomposed into smaller tasks or units of computation

• independent of what the eventual execution environment(s) of these tasks (that is, independent of hardware platforms or operating systems)

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Example and quiz

Computations Store

Object <compute_1, compute_2, compute_3, …>

compute_3=filter(object)

context{object<compute_3>}

object=project(compute_3)

1. User Computations 6. Reconstruct computation

Computation set 3

Computation set 1

Computation set 2

Applications, User Context

Applications and Back End logic

3. Computation distributionput(compute_3)

5. Project computation to the Functional chain with User device context

get(compute_3)

4. Computation extraction

Users’ Computations [ ]

( ) { }

Granular and Reflective Process Representation

User Device

2. Selection of the context (functional chain)

Agent 3

Agent 1

lift’n’compose

Developer specifies ECMAScript bounded to the ComputationsBack End provides the

basic Computation sets

Agent 2

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Managed Cloud Performance

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RequirementsThe new emerging paradigm of Distributed Systems requires an explicit orchestration of computation across

• the Edge, (thin elements of the Cloud “skin”)• Cloud and • the Core

Orchestration depends on components’ states and resource demand, through the proper exploitation of granular and therefore accountable mechanisms

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Scalable Computing

URIs

Computation environment

Computation environment

Legacy routines

Legacy routines

0xFFFF

0x0 0x0

0xFFFF … FFFF

Legacy routines

Distributed Computations based platform

Legacy routines

URIs

Front-End Side

Back-End SideDistribution/

sync

Com

puta

tions

SU

PERS

ET

Computations

subset

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A number of options• Granular latency control for diverse data and computational

load in hybrid cloud architectures• Resolution of short term capacity decisions, including

• the determination of optimal configuration, • the number of (live) servers or • the migration of computation

• Resolution of long term capacity decisions • decisions concerning the development and • extension of data- and service architecture, choice of technology,

etc• Control of SLAs at different levels,

• e.g. consumer services, platform components, etc• Quality assessment of distributed software architecture

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Business impactBusiness is impacted when performance metrics of a solution are inadequate: • The latency of OVI Store of over 3 second during 18 days in

Jan 2011 decreased the number of active users (AU30) by 25%

• Amazon reports that 100ms of (additional) latency cost them 1% in sales

• Goldman Sachs makes profit of a 500ms trading advantage

Software behavioral analysis has crucial role in order to achieve proper level of accountability of the whole system

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Privacy in the Cloud

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Privacy, the scopeTo provide mechanisms by which users can finely tune the • ownership, • granularity, • content, • semantics and • visibility of their data towards other parties

Security is very closely linked with privacy, however, tends to be about access control, whether an agent has access to a particular piece of data, whereas privacy is about constraints on the usage of data

• Security controls whether a particular party has access to a particular item of data

• Privacy controls whether that data can or will be used for a particular purpose

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Security, identity, privacy the placement and scope

(note that in this diagram, by “ontology” the semantics is meant)

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Distributed Data Infrastructure requirementsGiven any Distributed Data Infrastructure we require at minimum: • a security model to support the integrity of the data, • an identity model to denote the owner or owners of data

(and ideally also provenance of the data) and • an ontology to provide semantics or meaning to the data

Given these we can construct a privacy model where • the flow of data through the system can be monitored and • to establish boundaries where the control points for the data

can be placed

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Privacy Boundary• Inside the boundary• Outside the boundary• On the edge

Implications for 3rd Parties– Compliance with standards– Ensuring compliance– Termination of contract

Application orService

Application orService

Hardware orO/S

UserInterface

otherApplication orService by IPC Application or

Service

otherApplication orService by IPC

Client

”Internet”

Back-End

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Closing Thoughts …

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Conclusions• Trends of

• anything as a service, e.g. giving hardware for “free”, just use the services

• multisided business models and • innovation from the bottom of the industry pyramid

– are highlighting far-reaching changes in the business environment and require radical shifts in strategy

• Standards

• The fine-grained (and thus easily accountable) software frameworks developments

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Driving interoperability agenda• Creation of tools and effective standards for the next

developer and consumer applications, • Computing platform for the Continues Service Delivery• Backend infrastructure, backed up by application

technologies to enable qualitative leap in ICT industry offerings, from the perspectives of

– scalable technology and – business models with high elasticity

Aiming and supporting future strategic growth areas and maintenance requirements

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Q&A[ ] ( ) { }sergey.boldyrev@nokia.com

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References• ULS project• SlipStream project• Boldyrev, S., Kolesnikov, D., Lassila, O., & Oliver, I..

(2012). Data and Computation Interoperability in Internet Services. CLOSER.

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Thank you