The document discusses service selection and quality of service (QoS) considerations. It proposes extending the soft constraint satisfaction problem (SCSP) approach to handle penalties. Specifically, it defines a soft service level agreement (SSLA) model that includes user preferences and penalties defined in terms of QoS variables. If a selected service fails, the approach aims to automatically switch to another service that fits the agreed upon QoS levels while applying any defined penalties. The key points are mapping the SSLA definitions to the SCSP framework and extending the SCSP constraints and operations to incorporate the defined penalties.
User-Rating Based QoS Aware Approach for Selection of Updated Web Services to...IDES Editor
The concept of dynamic composition of Web services
has been given major importance in the recent years. While
there are many systems available to select the services with
the highest QoS score, very little thought has been given to
the fact of constructing a model which takes in the user’s
rating of a service as a major input for selection. A robust
system for fetching the most updated version of the selected
services and selection of the services based on users’ ratings
of the QoS values rather than on the actual QoS values has
been presented in our paper.
Eptica provides a multi-channel customer service suite that allows companies to improve customer service across multiple channels. It aims to provide high quality customer service at the lowest cost while increasing sales conversions. Key features include migrating customer contacts to online channels, reducing call volumes, and improving agent resource utilization to drive sales. The suite offers web self-service, communities, email management and a self-learning knowledge base to deliver a consistent customer experience across all touchpoints.
A dynamic routing action assigns a route for a message based on routing information available in an XQuery resource. A stage node is a container of actions. A routing action identifies a target service for the message and configures how the message is routed to that service.
The document provides information on various interactive user tasks, activities, and flow elements in business process modeling and notation (BPMN). It describes common task types like complex user tasks, initiator user tasks, call activities, and group user tasks. It also outlines common flow markers like sequence flows, conditional flows, and default flows. Finally, it lists different gateway types, events, and activity markers used in BPMN modeling.
The service solution architect defines the target state of operations and related third party involvement. Their deliverable is documentation of a suitable service solution that addresses the customer's needs and pain points independently of technology or vendors. It typically takes several weeks to gather information from the customer, document the target solution description including costs and transition plans, and finalize all associated documentation, assumptions, and risks. Additional internal activities include meetings, knowledge sharing, documentation control, and getting business case approval.
Client/server computing is an architecture where thin client machines make requests to centralized servers for applications and data. A basic definition is that a client makes a request for data from a server, which then returns the results. The major focus in client/server systems is on software, with most application processing done on the client side and services like databases accessed from the server side. Common types of servers include file servers, data servers, compute servers, database servers, and communication servers.
You are viewing presentations from conferences that I have attended. Please enjoy & if we can help you with any logistics projects in the Americas please contact me at 678.364.3475
Bill was also on the Board of Directors for the St.Vincent DePaul Foodbank in Roseville California helping with the fund raising and meals to the poor program. While based in Northern California he was successful in fund raising programs for the Crusade of Mercy and helped Father Dan Madigan at the Sacramento Food Bank also. For 2008, Bill is a member of the Board for WORKTEC on also an Advisory Board Member for Boys and Girls Club for Metro Atlanta-Clayton County Chapter. See www.worktec.biz or www.bgcma.org . Bill is also on the Board of Directors for the Southeastern Warehouse Association & represents Georgia for 2010-2012.
Regards,
Bill Stankiewicz
Vice President and General Manager
Shippers Warehouse
Email: williams@shipperswarehouse.com
www.shipperswarehousega.com
http://www.linkedin.com/in/billstankiewicz2006
http://twitter.com/BillStankiewicz
http://www.topexecutivesnet.com/index.aspx
S-CUBE LP: Indentify User Tasks from Past Usage Logsvirtual-campus
This document discusses extracting task information from past process execution logs. It proposes clustering queries from search engine logs to identify user tasks. The goal is to reconstruct tasks and processes users perform on the web based on their search queries. The methodology involves clustering queries based on features to discover task-related sessions. Experiments on the 2006 AOL query log show the technique outperforms state-of-the-art approaches. The conclusions discuss implications for applying this technique to service-based systems by clustering service invocation logs to identify processes.
User-Rating Based QoS Aware Approach for Selection of Updated Web Services to...IDES Editor
The concept of dynamic composition of Web services
has been given major importance in the recent years. While
there are many systems available to select the services with
the highest QoS score, very little thought has been given to
the fact of constructing a model which takes in the user’s
rating of a service as a major input for selection. A robust
system for fetching the most updated version of the selected
services and selection of the services based on users’ ratings
of the QoS values rather than on the actual QoS values has
been presented in our paper.
Eptica provides a multi-channel customer service suite that allows companies to improve customer service across multiple channels. It aims to provide high quality customer service at the lowest cost while increasing sales conversions. Key features include migrating customer contacts to online channels, reducing call volumes, and improving agent resource utilization to drive sales. The suite offers web self-service, communities, email management and a self-learning knowledge base to deliver a consistent customer experience across all touchpoints.
A dynamic routing action assigns a route for a message based on routing information available in an XQuery resource. A stage node is a container of actions. A routing action identifies a target service for the message and configures how the message is routed to that service.
The document provides information on various interactive user tasks, activities, and flow elements in business process modeling and notation (BPMN). It describes common task types like complex user tasks, initiator user tasks, call activities, and group user tasks. It also outlines common flow markers like sequence flows, conditional flows, and default flows. Finally, it lists different gateway types, events, and activity markers used in BPMN modeling.
The service solution architect defines the target state of operations and related third party involvement. Their deliverable is documentation of a suitable service solution that addresses the customer's needs and pain points independently of technology or vendors. It typically takes several weeks to gather information from the customer, document the target solution description including costs and transition plans, and finalize all associated documentation, assumptions, and risks. Additional internal activities include meetings, knowledge sharing, documentation control, and getting business case approval.
Client/server computing is an architecture where thin client machines make requests to centralized servers for applications and data. A basic definition is that a client makes a request for data from a server, which then returns the results. The major focus in client/server systems is on software, with most application processing done on the client side and services like databases accessed from the server side. Common types of servers include file servers, data servers, compute servers, database servers, and communication servers.
You are viewing presentations from conferences that I have attended. Please enjoy & if we can help you with any logistics projects in the Americas please contact me at 678.364.3475
Bill was also on the Board of Directors for the St.Vincent DePaul Foodbank in Roseville California helping with the fund raising and meals to the poor program. While based in Northern California he was successful in fund raising programs for the Crusade of Mercy and helped Father Dan Madigan at the Sacramento Food Bank also. For 2008, Bill is a member of the Board for WORKTEC on also an Advisory Board Member for Boys and Girls Club for Metro Atlanta-Clayton County Chapter. See www.worktec.biz or www.bgcma.org . Bill is also on the Board of Directors for the Southeastern Warehouse Association & represents Georgia for 2010-2012.
Regards,
Bill Stankiewicz
Vice President and General Manager
Shippers Warehouse
Email: williams@shipperswarehouse.com
www.shipperswarehousega.com
http://www.linkedin.com/in/billstankiewicz2006
http://twitter.com/BillStankiewicz
http://www.topexecutivesnet.com/index.aspx
S-CUBE LP: Indentify User Tasks from Past Usage Logsvirtual-campus
This document discusses extracting task information from past process execution logs. It proposes clustering queries from search engine logs to identify user tasks. The goal is to reconstruct tasks and processes users perform on the web based on their search queries. The methodology involves clustering queries based on features to discover task-related sessions. Experiments on the 2006 AOL query log show the technique outperforms state-of-the-art approaches. The conclusions discuss implications for applying this technique to service-based systems by clustering service invocation logs to identify processes.
S-CUBE LP: Quality of Service Models for Service Oriented Architecturesvirtual-campus
The document provides an overview of an S-Cube learning package on quality of service models for service-oriented architectures. It discusses quality of service and the service lifecycle. It also describes different quality models and meta-models that can be used to define quality, including service quality models, service quality meta-models, and service level agreement meta-models. The learning package aims to help understand approaches for describing quality of service.
The document discusses Service Level Agreements (SLAs) and SLA negotiation. It defines an SLA as a formal contract between a service provider and client specifying service quality guarantees and penalties. SLA negotiation is the process where providers and clients agree on desired service levels. There are two types: reactive after decisions are made or violations occur, and proactive prior to service binding or violations. The document outlines triggers for proactive negotiation, approaches to handling violations, a two-phase negotiation process, and an architecture and rules for proactive and reactive negotiation. It also describes a case study to evaluate proactive negotiation.
This document discusses a policy-based architecture for quality of service (QoS) management. It describes different types of policies needed to specify QoS requirements and actions when requirements are not met. Expectation policies are used to state QoS requirements and monitoring actions. The architecture includes sensors to monitor application attributes, coordinators to retrieve policies and handle events, and policy decision points that make decisions based on events. When requirements are violated, actions may include notifying event managers or adjusting resources. The goal is to support dynamic and soft QoS requirements through policy-driven management.
This document discusses proactive service level agreement (SLA) negotiation. It defines SLA and SLA negotiation, and describes two types of negotiation: reactive and proactive. It outlines scenarios that could trigger proactive SLA negotiation, and describes a two-phase proactive negotiation process involving identification of potential providers and pre-agreement/final agreement. The document also presents an architecture and process for proactive SLA negotiation and evaluates the approach through a case study.
Personalized qos aware web service recommendation and visualizationJPINFOTECH JAYAPRAKASH
This document proposes a personalized QoS-aware web service recommendation system that uses a novel collaborative filtering algorithm and visualization technique. Existing recommendation systems do not account for variation in QoS based on user location and have poor time complexity. The proposed system combines model-based and memory-based collaborative filtering to improve accuracy and time complexity. It also provides a visualization of recommended services to improve user understanding of recommendations. The system was evaluated using a real-world dataset of over 1.5 million QoS records from more than 20 countries.
Hierarchical SLA-based Service Selection for Multi-Cloud EnvironmentsSoodeh Farokhi
Cloud computing popularity is growing rapidly and consequently the number of companies offering their services in the form of Software-as-a-Service (SaaS) or Infrastructure-as-a-Service (IaaS) is increasing. The diversity and usage benefits of IaaS offers are encouraging SaaS providers to lease resources from the Cloud instead of operating their own data centers. However, the question remains for them how to, on the one hand, exploit Cloud benefits to gain less maintenance overheads and on the other hand, maximize the satisfactions of customers with a wide range of requirements. The complexity of addressing these issues prevent many SaaS providers to benefit from the Cloud infrastructures. In this paper, we propose HS4MC approach for automatic service selection by considering SLA claims of SaaS providers. The novelty of our approach lies
in the utilization of prospect theory for the service ranking that represents a natural choice for scoring of comparable services due to the users preferences. The HS4MC approach first constructs a set of SLAs based on the given accumulated SaaS provider requirements. Then, it selects a set of services that best fulfills the SLAs. We evaluate our approach in a simulated environment by comparing it with a state-of-the-art utility based algorithm. The evaluation results show that our approach selects services that more effectively satisfy the SLAs.
QOS WITH RELIABILITY AND SCALABILITY IN ADAPTIVE SERVICE-BASED SYSTEMScscpconf
Service-based systems that are dynamically composed at runtime to provide complex, adaptive
functionality are currently one of the main development paradigms in software engineering.
However, the Quality of Service (QoS) delivered by these systems remains an important
concern, and needs to be managed in an equally adaptive and predictable way. To address this
need, we introduce a novel, tool-supported framework for the development of adaptive servicebased
systems called QoSMOS (QoS Management and Optimization of Service-based systems). QoSMOS can be used to develop service-based systems that achieve their QoS requirements through dynamically adapting to changes in the system state, environment, and workload. QoSMOS service-based systems translate high-level QoS requirements specified by their administrators into probabilistic temporal logic formulae, which are then formally and automatically analyzed to identify and enforce optimal system configurations. The QoSMOS self-adaptation mechanism can handle reliability and performance-related QoS requirements, and can be integrated into newly developed solutions or legacy systems. The effectiveness and scalability of the approach are validated using simulations and a set of experiments based on an implementation of an adaptive service-based system for remote medical assistance.
This document proposes a cloud service selection model called CloudEval that evaluates non-functional properties and selects an optimal service based on both user-specified quality of service levels and goals. CloudEval uses grey relational analysis, a multi-attribute decision making technique, in the selection process. It considers attributes like availability, response time, price, reputation, performance, and financial credit from service level agreements. The model aims to address limitations of prior works that either focus on specific service types or require user involvement in evaluation.
A Cloud Service Selection Model Based on User-Specified Quality of Service Levelcsandit
Recently, it emerges lots of cloud services in the
cloud service market. After many candidate
services are initially chosen by satisfying both th
e behavior and functional criteria of a target
cloud service. Service consumers need a selection m
odel to further evaluate nonfunctional QOS
properties of the candidate services. Some prior wo
rks have focused on objective and
quantitative benchmark-testing of QOS by some tools
or trusted third-party brokers, as well as
reputation from customers. Service levels have been
offered and designated by cloud service
providers in their Service Level Agreement (SLA). C
onversely, in order to meet user
requirement, it is important for users to discover
their own optimal parameter portfolio for
service level. However, some prior works focus only
on specific kinds of cloud services, or
require users to involve in some evaluating process
. In general, the prior works cannot evaluate
the nonfunctional properties and select the optimal
service which satisfies both user-specified
service level and goals most either. Therefore, the
aim of this study is to propose a cloud service
selection model, CloudEval, to evaluate the nonfunc
tional properties and select the optimal
service which satisfies both user-specified service
level and goals most. CloudEval applies a
well-known multi-attribute decision making techniqu
e, Grey Relational Analysis, to the
selection process. Finally, we conduct some experim
ents. The experimental results show that
CloudEval is effective, especially while the quanti
ty of the candidate cloud services is much
larger than human raters can handle.
This document discusses definitions and concepts related to cloud computing. It begins by looking at definitions from NIST and WhatIs.com, which describe cloud computing as enabling on-demand access to configurable computing resources via a network. The document then covers central ideas like utility computing, service-oriented architecture (SOA), and service level agreements (SLAs). It discusses properties and characteristics of clouds like scalability, availability, reliability, manageability, interoperability, performance, and accessibility. Finally, it delves into concepts that enable these properties, such as virtualization, parallel computing, load balancing, fault tolerance, and system monitoring.
This document discusses QoS deployment experiences at a telecommunications company. It outlines the key steps as: 1) identifying application requirements, 2) defining policies, 3) testing policies in a lab, 4) implementing policies incrementally starting at the edge of the network, and 5) monitoring performance and adjusting policies as needed. The presentation provides details on each step, such as mapping customer service classes to the provider's model and testing QoS functionality and behavior under different conditions.
Towards Realizing Dynamic QoS-aware Web Service CompositionGeorge Baryannis
In this presentation, we identify two major issues related to Web service composition: the lack of formal specifications for services and service compositions and the inability of current service composition approaches to support dynamicity and QoS-awareness in an effective and scalable way. We analyze the underlying research challenges for each of these issues and propose a tentative research plan to address them.
Qo s ranking prediction for cloud services abstractravi778787
This paper proposes a QoS ranking prediction framework for cloud services that does not require additional real-world service invocations. It takes advantage of past service usage experiences from other consumers to predict QoS rankings for cloud services. Two personalized approaches are developed to directly predict QoS rankings. Experiments using real-world data from 300 distributed users and 500 cloud services worldwide show the approaches outperform other methods.
This document discusses four design patterns for service abstraction in SOA:
1) Capability Composition hides logic outside a service's boundary and invokes other services' capabilities.
2) Capability Recomposition uses a single capability to solve multiple problems.
3) Decomposed Capability optimizes how a service can be divided into new functional contexts over time.
4) Validation Abstraction separates a service's validation logic from its contract to more easily adapt to changes in business rules.
This document proposes a QoS (quality of service) ranking prediction framework for cloud services that does not require additional real-world service invocations. It aims to address the problem of predicting personalized QoS rankings for different cloud applications without the time and costs of testing multiple service candidates. The framework takes advantage of past usage experiences of other users to predict rankings. Experiments on real-world data involving 300 distributed users and 500 cloud services showed that the proposed approaches outperform other methods.
This document discusses quality of service (QoS) requirements and defines QoS as the overall performance of a network, especially as seen by users. It notes that QoS is important for transporting certain types of traffic like streaming media, IPTV, VoIP, and video conferencing. The document outlines steps for QoS management, including baselining the network, deploying QoS techniques for targeted applications, and evaluating results. It also describes three levels of end-to-end QoS: best-effort service with no guarantees, differentiated service that treats some traffic better statistically, and guaranteed service with an absolute reservation of resources.
SASSY is a framework that allows software systems to self-architect and adapt to changes in their computing environment or requirements. It uses models called Service Activity Schemas (SAS) to specify system requirements and generate an initial architecture. SASSY then monitors quality of service metrics and dynamically changes the architecture by applying patterns if the overall utility falls, in order to maintain the best QoS. It transforms SAS models into executable architectures and facilitates simulation and testing through model transformations and existing tools.
This document presents a QoS-enabled architecture for efficient web services. It introduces a QoS broker module between service clients and providers to minimize resource wastage and analyze QoS evaluation. The key components are web servers, web services, and QoS attributes like availability, accuracy, reliability, security, latency, and jitter. The QCWS architecture includes servers that provide functionality and QoS information, a QoS broker that handles negotiation and analysis, and clients that request services. The broker aims to optimize system performance while reducing instability through homogeneous and non-homogeneous resource allocation algorithms.
The document discusses capacity planning, which is determining the production capacity needed by an organization to meet changing demands while meeting service level agreements. It outlines the overall capacity planning process, including identifying SLAs, analyzing current baseline capacity, forecasting future capacity needs, and adding capacity through lead, lag, or match strategies. The key steps discussed are identifying SLAs, analyzing current capacity, workload forecasting, performance modeling, and adding capacity as needed through a match strategy. Various capacity planning models and considerations are also mentioned.
S-CUBE LP: Analysis Operations on SLAs: Detecting and Explaining Conflicting ...virtual-campus
Here are the key types of conflicts that can occur within temporal-aware WS-Agreement documents:
- Inconsistencies between terms, parts of terms, or creation constraints that are defined in overlapping time periods, making it impossible to satisfy all constraints simultaneously.
- Dead terms, where a guarantee term's qualifying condition can never be satisfied within the specified time periods due to contradictions with other terms or constraints.
- Ludicrous terms, where a guarantee term's service level objective cannot be fulfilled even when its qualifying condition is met, again due to contradictions arising from overlapping time periods.
The approach is to detect these three types of conflicts if and only if the involved terms or constraints are defined within overlapping time
S-CUBE LP: Chemical Modeling: Workflow Enactment based on the Chemical Metaphorvirtual-campus
This document provides an overview of a chemical metaphor for workflow enactment in large-scale heterogeneous environments. It discusses problems with current workflow enactment approaches and requirements for improvement. Specifically, it proposes modeling workflow enactment like chemical reactions, which are autonomous, distributed, concurrent and adaptive to local conditions. Resources are represented as "resource quantums" and a coordination model is formalized using the pi-calculus. This approach aims to provide more autonomy, adaptation and distribution for workflow enactment in complex environments.
More Related Content
Similar to S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection
S-CUBE LP: Quality of Service Models for Service Oriented Architecturesvirtual-campus
The document provides an overview of an S-Cube learning package on quality of service models for service-oriented architectures. It discusses quality of service and the service lifecycle. It also describes different quality models and meta-models that can be used to define quality, including service quality models, service quality meta-models, and service level agreement meta-models. The learning package aims to help understand approaches for describing quality of service.
The document discusses Service Level Agreements (SLAs) and SLA negotiation. It defines an SLA as a formal contract between a service provider and client specifying service quality guarantees and penalties. SLA negotiation is the process where providers and clients agree on desired service levels. There are two types: reactive after decisions are made or violations occur, and proactive prior to service binding or violations. The document outlines triggers for proactive negotiation, approaches to handling violations, a two-phase negotiation process, and an architecture and rules for proactive and reactive negotiation. It also describes a case study to evaluate proactive negotiation.
This document discusses a policy-based architecture for quality of service (QoS) management. It describes different types of policies needed to specify QoS requirements and actions when requirements are not met. Expectation policies are used to state QoS requirements and monitoring actions. The architecture includes sensors to monitor application attributes, coordinators to retrieve policies and handle events, and policy decision points that make decisions based on events. When requirements are violated, actions may include notifying event managers or adjusting resources. The goal is to support dynamic and soft QoS requirements through policy-driven management.
This document discusses proactive service level agreement (SLA) negotiation. It defines SLA and SLA negotiation, and describes two types of negotiation: reactive and proactive. It outlines scenarios that could trigger proactive SLA negotiation, and describes a two-phase proactive negotiation process involving identification of potential providers and pre-agreement/final agreement. The document also presents an architecture and process for proactive SLA negotiation and evaluates the approach through a case study.
Personalized qos aware web service recommendation and visualizationJPINFOTECH JAYAPRAKASH
This document proposes a personalized QoS-aware web service recommendation system that uses a novel collaborative filtering algorithm and visualization technique. Existing recommendation systems do not account for variation in QoS based on user location and have poor time complexity. The proposed system combines model-based and memory-based collaborative filtering to improve accuracy and time complexity. It also provides a visualization of recommended services to improve user understanding of recommendations. The system was evaluated using a real-world dataset of over 1.5 million QoS records from more than 20 countries.
Hierarchical SLA-based Service Selection for Multi-Cloud EnvironmentsSoodeh Farokhi
Cloud computing popularity is growing rapidly and consequently the number of companies offering their services in the form of Software-as-a-Service (SaaS) or Infrastructure-as-a-Service (IaaS) is increasing. The diversity and usage benefits of IaaS offers are encouraging SaaS providers to lease resources from the Cloud instead of operating their own data centers. However, the question remains for them how to, on the one hand, exploit Cloud benefits to gain less maintenance overheads and on the other hand, maximize the satisfactions of customers with a wide range of requirements. The complexity of addressing these issues prevent many SaaS providers to benefit from the Cloud infrastructures. In this paper, we propose HS4MC approach for automatic service selection by considering SLA claims of SaaS providers. The novelty of our approach lies
in the utilization of prospect theory for the service ranking that represents a natural choice for scoring of comparable services due to the users preferences. The HS4MC approach first constructs a set of SLAs based on the given accumulated SaaS provider requirements. Then, it selects a set of services that best fulfills the SLAs. We evaluate our approach in a simulated environment by comparing it with a state-of-the-art utility based algorithm. The evaluation results show that our approach selects services that more effectively satisfy the SLAs.
QOS WITH RELIABILITY AND SCALABILITY IN ADAPTIVE SERVICE-BASED SYSTEMScscpconf
Service-based systems that are dynamically composed at runtime to provide complex, adaptive
functionality are currently one of the main development paradigms in software engineering.
However, the Quality of Service (QoS) delivered by these systems remains an important
concern, and needs to be managed in an equally adaptive and predictable way. To address this
need, we introduce a novel, tool-supported framework for the development of adaptive servicebased
systems called QoSMOS (QoS Management and Optimization of Service-based systems). QoSMOS can be used to develop service-based systems that achieve their QoS requirements through dynamically adapting to changes in the system state, environment, and workload. QoSMOS service-based systems translate high-level QoS requirements specified by their administrators into probabilistic temporal logic formulae, which are then formally and automatically analyzed to identify and enforce optimal system configurations. The QoSMOS self-adaptation mechanism can handle reliability and performance-related QoS requirements, and can be integrated into newly developed solutions or legacy systems. The effectiveness and scalability of the approach are validated using simulations and a set of experiments based on an implementation of an adaptive service-based system for remote medical assistance.
This document proposes a cloud service selection model called CloudEval that evaluates non-functional properties and selects an optimal service based on both user-specified quality of service levels and goals. CloudEval uses grey relational analysis, a multi-attribute decision making technique, in the selection process. It considers attributes like availability, response time, price, reputation, performance, and financial credit from service level agreements. The model aims to address limitations of prior works that either focus on specific service types or require user involvement in evaluation.
A Cloud Service Selection Model Based on User-Specified Quality of Service Levelcsandit
Recently, it emerges lots of cloud services in the
cloud service market. After many candidate
services are initially chosen by satisfying both th
e behavior and functional criteria of a target
cloud service. Service consumers need a selection m
odel to further evaluate nonfunctional QOS
properties of the candidate services. Some prior wo
rks have focused on objective and
quantitative benchmark-testing of QOS by some tools
or trusted third-party brokers, as well as
reputation from customers. Service levels have been
offered and designated by cloud service
providers in their Service Level Agreement (SLA). C
onversely, in order to meet user
requirement, it is important for users to discover
their own optimal parameter portfolio for
service level. However, some prior works focus only
on specific kinds of cloud services, or
require users to involve in some evaluating process
. In general, the prior works cannot evaluate
the nonfunctional properties and select the optimal
service which satisfies both user-specified
service level and goals most either. Therefore, the
aim of this study is to propose a cloud service
selection model, CloudEval, to evaluate the nonfunc
tional properties and select the optimal
service which satisfies both user-specified service
level and goals most. CloudEval applies a
well-known multi-attribute decision making techniqu
e, Grey Relational Analysis, to the
selection process. Finally, we conduct some experim
ents. The experimental results show that
CloudEval is effective, especially while the quanti
ty of the candidate cloud services is much
larger than human raters can handle.
This document discusses definitions and concepts related to cloud computing. It begins by looking at definitions from NIST and WhatIs.com, which describe cloud computing as enabling on-demand access to configurable computing resources via a network. The document then covers central ideas like utility computing, service-oriented architecture (SOA), and service level agreements (SLAs). It discusses properties and characteristics of clouds like scalability, availability, reliability, manageability, interoperability, performance, and accessibility. Finally, it delves into concepts that enable these properties, such as virtualization, parallel computing, load balancing, fault tolerance, and system monitoring.
This document discusses QoS deployment experiences at a telecommunications company. It outlines the key steps as: 1) identifying application requirements, 2) defining policies, 3) testing policies in a lab, 4) implementing policies incrementally starting at the edge of the network, and 5) monitoring performance and adjusting policies as needed. The presentation provides details on each step, such as mapping customer service classes to the provider's model and testing QoS functionality and behavior under different conditions.
Towards Realizing Dynamic QoS-aware Web Service CompositionGeorge Baryannis
In this presentation, we identify two major issues related to Web service composition: the lack of formal specifications for services and service compositions and the inability of current service composition approaches to support dynamicity and QoS-awareness in an effective and scalable way. We analyze the underlying research challenges for each of these issues and propose a tentative research plan to address them.
Qo s ranking prediction for cloud services abstractravi778787
This paper proposes a QoS ranking prediction framework for cloud services that does not require additional real-world service invocations. It takes advantage of past service usage experiences from other consumers to predict QoS rankings for cloud services. Two personalized approaches are developed to directly predict QoS rankings. Experiments using real-world data from 300 distributed users and 500 cloud services worldwide show the approaches outperform other methods.
This document discusses four design patterns for service abstraction in SOA:
1) Capability Composition hides logic outside a service's boundary and invokes other services' capabilities.
2) Capability Recomposition uses a single capability to solve multiple problems.
3) Decomposed Capability optimizes how a service can be divided into new functional contexts over time.
4) Validation Abstraction separates a service's validation logic from its contract to more easily adapt to changes in business rules.
This document proposes a QoS (quality of service) ranking prediction framework for cloud services that does not require additional real-world service invocations. It aims to address the problem of predicting personalized QoS rankings for different cloud applications without the time and costs of testing multiple service candidates. The framework takes advantage of past usage experiences of other users to predict rankings. Experiments on real-world data involving 300 distributed users and 500 cloud services showed that the proposed approaches outperform other methods.
This document discusses quality of service (QoS) requirements and defines QoS as the overall performance of a network, especially as seen by users. It notes that QoS is important for transporting certain types of traffic like streaming media, IPTV, VoIP, and video conferencing. The document outlines steps for QoS management, including baselining the network, deploying QoS techniques for targeted applications, and evaluating results. It also describes three levels of end-to-end QoS: best-effort service with no guarantees, differentiated service that treats some traffic better statistically, and guaranteed service with an absolute reservation of resources.
SASSY is a framework that allows software systems to self-architect and adapt to changes in their computing environment or requirements. It uses models called Service Activity Schemas (SAS) to specify system requirements and generate an initial architecture. SASSY then monitors quality of service metrics and dynamically changes the architecture by applying patterns if the overall utility falls, in order to maintain the best QoS. It transforms SAS models into executable architectures and facilitates simulation and testing through model transformations and existing tools.
This document presents a QoS-enabled architecture for efficient web services. It introduces a QoS broker module between service clients and providers to minimize resource wastage and analyze QoS evaluation. The key components are web servers, web services, and QoS attributes like availability, accuracy, reliability, security, latency, and jitter. The QCWS architecture includes servers that provide functionality and QoS information, a QoS broker that handles negotiation and analysis, and clients that request services. The broker aims to optimize system performance while reducing instability through homogeneous and non-homogeneous resource allocation algorithms.
The document discusses capacity planning, which is determining the production capacity needed by an organization to meet changing demands while meeting service level agreements. It outlines the overall capacity planning process, including identifying SLAs, analyzing current baseline capacity, forecasting future capacity needs, and adding capacity through lead, lag, or match strategies. The key steps discussed are identifying SLAs, analyzing current capacity, workload forecasting, performance modeling, and adding capacity as needed through a match strategy. Various capacity planning models and considerations are also mentioned.
Similar to S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection (20)
S-CUBE LP: Analysis Operations on SLAs: Detecting and Explaining Conflicting ...virtual-campus
Here are the key types of conflicts that can occur within temporal-aware WS-Agreement documents:
- Inconsistencies between terms, parts of terms, or creation constraints that are defined in overlapping time periods, making it impossible to satisfy all constraints simultaneously.
- Dead terms, where a guarantee term's qualifying condition can never be satisfied within the specified time periods due to contradictions with other terms or constraints.
- Ludicrous terms, where a guarantee term's service level objective cannot be fulfilled even when its qualifying condition is met, again due to contradictions arising from overlapping time periods.
The approach is to detect these three types of conflicts if and only if the involved terms or constraints are defined within overlapping time
S-CUBE LP: Chemical Modeling: Workflow Enactment based on the Chemical Metaphorvirtual-campus
This document provides an overview of a chemical metaphor for workflow enactment in large-scale heterogeneous environments. It discusses problems with current workflow enactment approaches and requirements for improvement. Specifically, it proposes modeling workflow enactment like chemical reactions, which are autonomous, distributed, concurrent and adaptive to local conditions. Resources are represented as "resource quantums" and a coordination model is formalized using the pi-calculus. This approach aims to provide more autonomy, adaptation and distribution for workflow enactment in complex environments.
S-CUBE LP: Quality of Service-Aware Service Composition: QoS optimization in ...virtual-campus
This document discusses quality of service (QoS) optimization in service-based processes. It describes how to select and optimize composed web services to satisfy QoS constraints. The key aspects covered are QoS definition for web services, optimization at both the local service selection level and global process level, and rebinding services to maintain QoS as processes execute.
S-CUBE LP: The Chemical Computing model and HOCL Programmingvirtual-campus
This document provides an overview of the Chemical Computing model and the Higher Order Chemical Language (HOCL). It describes the vision of chemical computing using multiset rewriting to express inherently parallel problems. The Gamma language is presented as the first to capture chemical programming. The γ-calculus improved on Gamma by making it higher order and modeling reaction rules as active molecules. HOCL is then presented as a language based on γ-calculus, allowing active molecules to capture and produce other active molecules. Examples are given to demonstrate the chemical approach.
S-CUBE LP: Executing the HOCL: Concept of a Chemical Interpretervirtual-campus
The document describes an interpreter for a chemical language called Higher Order Chemical Language (HOCL) based on the chemical computing model. The interpreter uses a production system approach with RETE pattern matching to enable efficient execution of the chemical language. Key constructs of the language include passive molecules to represent facts, active molecules to represent rules, and solutions to represent independent computational threads. The interpreter was implemented using Jess rule engine and experiences showed the importance of random conflict resolution and intelligent compilation for chemical modeling applications.
S-CUBE LP: SLA-based Service Virtualization in distributed, heterogenious env...virtual-campus
The document describes SLA-based service virtualization (SSV) in distributed, heterogeneous environments. SSV uses a meta-negotiation component for SLA management, a meta-broker for diverse broker management, and automatic service deployment for virtualizing resources on clouds. It presents the SSV architecture and how it can be extended to Federated Cloud Management using a two-level brokering approach for cloud selection and optimal VM placement. The SSV and FCM architectures aim to provide a unified system for managing different service infrastructures through SLA-based user interaction and an autonomic system for inner interactions.
S-CUBE LP: Service Discovery and Task Modelsvirtual-campus
The document describes a learning package on service discovery and task models. It discusses using task models to help select services that fit with a user's goals and constraints. A two-stage approach to task-based service discovery is presented: 1) specifying a user task model with a description, ConcurTaskTree diagram, and associated services; and 2) discovering services using the task model. The task model captures the task hierarchy, types, and temporal relationships. Services are matched based on analyzing subtasks and associated service classes.
S-CUBE LP: Impact of SBA design on Global Software Developmentvirtual-campus
This document provides an overview of a learning package about designing and migrating service-based applications and the impact of service-based application design on global software development. It discusses how service-oriented architecture (SOA), cloud computing, and agile service networks can help address challenges with global software development by facilitating collaboration across geographic boundaries. Specifically, it outlines how SOA can support increased modularity, clear work division, and standards adoption to help distribute development tasks.
S-CUBE LP: Techniques for design for adaptationvirtual-campus
This document describes a learning package on designing and migrating service-based applications. It discusses techniques for designing applications to enable self-adaptation. It presents three motivating scenarios involving supply chains, wine production, and mobile users that require different types of adaptation. The key aspects of adaptable service-based applications are life cycles, adaptation strategies, triggers, and the association between strategies and triggers. Guidelines are provided for modeling triggers, realizing strategies, and relating them through various design approaches like built-in, abstraction-based, and dynamic adaptation.
S-CUBE LP: Self-healing in Mixed Service-oriented Systemsvirtual-campus
This document provides an overview of self-healing in mixed service-oriented systems. It describes self-healing research from IBM on autonomic computing and self-adaptive systems. The key aspects of self-healing covered include the self-healing loop, requirements, states (normal, broken, degraded), failure classification, and policies for detection and recovery. The goal of self-healing is to maintain system health by detecting disruptions, diagnosing causes, and applying recovery strategies in a closed feedback loop.
S-CUBE LP: Analyzing and Adapting Business Processes based on Ecologically-aw...virtual-campus
The document describes a learning package on analyzing and adapting business processes based on ecologically-aware indicators. It discusses using green business process reengineering to optimize an auto finishing process to reduce its environmental impact by considering additional dimensions like water consumption and carbon emissions. A key part of green BPR is extending the traditional BPR architecture to include defining key ecological indicators, monitoring environmental impacts during process execution, and analyzing the data to identify opportunities for process adaptation and improvement.
S-CUBE LP: Preventing SLA Violations in Service Compositions Using Aspect-Bas...virtual-campus
This document discusses an approach to preventing violations of service level agreements (SLAs) in composite services using aspect-based fragment substitution. The approach defines checkpoints in the service composition and uses machine learning to generate predictions of SLA violations at checkpoints. If a violation is predicted, the service composition is adapted by substituting an alternative process fragment that is expected to prevent the predicted SLA violation. Background information is provided on related work in S-Cube on runtime prediction of SLA violations using machine learning on event logs, and on aspect-oriented programming concepts used in the fragment substitution approach.
S-CUBE LP: Analyzing Business Process Performance Using KPI Dependency Analysisvirtual-campus
This document describes a method for analyzing dependencies between Key Performance Indicators (KPIs) and lower-level metrics in business processes. It involves defining KPIs and metrics, monitoring process instances, and using classification algorithms like decision trees to learn relationships between metrics and KPI classes from historical data. The approach automates dependency analysis, is efficient compared to manual methods, and produces understandable decision tree models. Potential limitations include needing historical event logs to train models and ensuring all relevant data can be monitored.
S-CUBE LP: Process Performance Monitoring in Service Compositionsvirtual-campus
This document describes process performance monitoring in service compositions. It discusses monitoring a single BPEL process using a resource event model and complex event definitions to calculate performance metrics. It also covers monitoring across partner processes by specifying a monitoring agreement based on a BPEL4Chor choreography model. Key events are correlated using identifiers. A prototype implements monitoring using an Apache ODE BPEL engine and ESPER CEP engine.
S-CUBE LP: Service Level Agreement based Service infrastructures in the conte...virtual-campus
This document describes a learning package on SLA-aware service infrastructures that aim to 1) hide differences between service infrastructures, 2) support higher layers of service-based applications through SLA-constrained autonomous decisions, and 3) allow for SLA-oriented self-adaptation and violation propagation across layers through monitoring and adaptation mechanisms. The research focuses on autonomous behavior in service infrastructures while considering constraints from SLAs agreed to at higher composition and business process layers.
S-CUBE LP: Runtime Prediction of SLA Violations Based on Service Event Logsvirtual-campus
This document describes an approach for predicting violations of service level agreements (SLAs) based on analyzing event logs from a service composition runtime. It discusses defining checkpoints during service execution to collect monitoring data on factors that influence performance. Missing or future data can be estimated. Machine learning techniques are then used to generate predictions at checkpoints based on historical monitoring data. The accuracy of predictions is evaluated by comparing predictions to actual outcomes. Prediction error is found to decrease as execution progresses, showing the potential for early warning of possible SLA violations to allow corrective actions.
S-CUBE LP: Variability Modeling and QoS Analysis of Web Services Orchestrationsvirtual-campus
This document summarizes research on using pairwise testing to model variability and analyze quality of service (QoS) for web service orchestrations. Feature diagrams are used to explicitly represent variability in composite services, and pairwise testing is applied to select configurations covering all pairwise feature interactions. QoS distributions are computed for these configurations to predict overall orchestration QoS in a way that accounts for variability. The approach provides more realistic service level agreements than considering only worst-case scenarios.
S-CUBE LP: Run-time Verification for Preventive Adaptationvirtual-campus
The document describes an approach called SPADE for preventive adaptation of service-based applications using runtime verification. SPADE uses monitoring data from service executions, assumptions about service response times, and formalized requirements to predict if the application will violate requirements. If a violation is predicted, SPADE identifies the need for adaptation to prevent an actual failure. SPADE was designed as part of the S-Cube project to enable service-based applications to adapt preventively based on runtime monitoring and verification.
S-CUBE LP: Online Testing for Proactive Adaptationvirtual-campus
This document discusses online testing for proactive adaptation of service-based applications. It describes how online testing can be used to predict failures through monitoring services and applications during operation. This allows issues to be detected early and adaptations to be made proactively before failures occur externally. Two approaches are discussed: PROSA predicts violations of quality of service by testing stateless services, while JITO predicts violations of interaction protocols for conversational services. Online testing extends traditional testing into the operational phase to improve failure prediction accuracy and allow more proactive adaptation for service-based applications.
S-CUBE LP: Using Data Properties in Quality Predictionvirtual-campus
The document discusses using data properties in quality prediction for service compositions. It notes that the quality of service (QoS) of a composition depends on factors like the QoS of component services, composition structure, and data. An automotive scenario example is provided where a parts provider composition selects among multiple part makers. The computation cost of the provider composition depends on the number of parts and characteristics of the chosen maker. Data properties like the number of parts can thus impact QoS predictions for service compositions.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection
1. S-Cube Learning Package
A Soft-Constraint Based Approach to QoS-Aware Service
Selection
Université Paris-DESCARTES
Mohamed-Anis ZEMNI, Salima BENBERNOU, Manuel CARRO
www.s-cube-network.eu
2. Learning Package Categorization
S-Cube
Quality Definition,
Negotiation and Assurance
Quality Management and Prediction
Analysis Operations on SLAs:
Detecting and Explaining Conflicting SLAs
3. Service Selection and QoS
Service selection is the first step to improve service
composition within Service-Oriented-Architecture (SOA):
• Searches for services fitting users’ requirements
• Explores services’ properties
• Aims at putting together several elementary services
• Generates new value-added service
Quality of Service (QoS) for selection often critically important:
• Software services expose not only functional characteristics, but also
non-functional attributes describing their QoS
• Defines the service level (Key Performance Indicator)
• A service fulfilling all the functionality but with low QoS is not
interesting
4. Learning Package Overview
Problem Description
Extending SCSP with Penalties & new SLA Model
Conclusions
5. Problem Description:
Service Selection Scenario
Select only one service
among the available
services that have the
same functionalities but
with different QoS
Functionalities
+
QoS
User request (criteria)
1
2
Used Approach at Design-time
6. Problem Description:
Service Selection Techniques in the Literature 1
Constraint Satisfaction Problem (CSP):
• Classical formulation of constraints
• Quite expressive to represent several real life problems
• Defines a set of variables, each of them ranging on a finite domain,
and a set of constraints restricting the values that these variables can
take simultaneously
• All the constraints must be satisfied simultaneously
!
Lack of built-in capabilities to express preferences among constraints
and the lack of possibility of giving approximate solutions for problems
which are overconstrained
7. Problem Description:
Service Selection Techniques in the Literature 1
Soft Constraint Satisfaction Problem (SCSP)
• Include the concept of preferences into every constraint in order to
obtain a suitable solution which can be optimal or, in general, a
reasonable estimation, maybe at the expense of not fulfilling all
constraints
• Relies on composing the constraints in order to obtain the optimal
solution
• Applied to the requirements (in terms of preferences) of the users
! Only one solution returned that is optimal
* Stefano Bistarelli, Ugo Montanari, and Francesca Rossi. Semiring-
based constraint satisfaction and optimization. J. ACM, 44(2):201–
236, 1997
8. Problem Description:
Service Selection Techniques in the Literature 1
C-semi-ring : Algebraic structure
Only one domain for
all variables
Example : Searching for services Available at y% of the time and with reputation = z
9. Problem Description:
Problem at Design-time
2. I have to fix
new criteria
1. Required criteria
cannot match any service!!!
User request (criteria)
10. Problem Description:
Problem at Runtime
!
Some problems, encountered by the service may
lead to service malfunctions
activity interrupted,
must apply penalty!!!
Out of
service Out of
service
contract violation
11. Problem Description:
SLA
SLA - Definition:
“An XML document and a contract for…
• Advertising the quality level of the services
• Taking note about the user preferences
• …”
I want an SLA
ensuring the
performances I
am searching for
Propertie
s
Pro perties
QoS
?
13. Learning Package Overview
Problem Description
Extending SCSP with Penalties & new SLA Model
Conclusions
14. Main Objective
Automatically switch from a faulty
service to a new one
User request (preferences,
… Out of
service Out of
penalties)
service
Design-time
Runtime
15. Approach Main Points
Definition of Soft Service Level Agreement (SSLA) an SLA
model extended with preferences and penalties
Extension of Soft Constraint Solving Problem handling
penalties: Define in SSLA the penalty artifacts, such that, if a
selected service failed, another one should replace it that
fitting with the agreed QoS in the contract with penalties if
some of them are not fulfilled
SSLA to SCSP mapping
16. Kinds of penalties
Arithmetical Penalties
• In relation with measurable qualities of service
• Direct relation to service variables
• E.g. availability, the response time, the reputation, etc.
• The application of arithmetical penalties is a consequence of a
contract breach and therefore the transition to a different selection
using the choices expressed by the customer in the form of
preferences
Behavioural Penalties
• Related to the behavior of either the customer or the service provider
• The application of behavioral penalties is not always a consequence of
a contract breach and so, switching to another choice is not obligatory
and even less replacing the service
18. Soft SLA Definition:
Preferences & Penalties
I prefer to get a payment
service and delivery service
having response time < 5ms. I
also accept services with
response time between 5ms
and 20ms with preference =0,5
Etc.
Response time
Preferences
If the first Most preferred
preference is not <5ms
fulfilled during the
execution I would
apply penalty P7
[5ms,20ms[
>20ms
Less preferred
19. Soft SLA Definition
Guarantee terms are expressed in terms of preferences and
penalties
• Preferences are ranked (most preferred to less preferred)
• Penalties are applied if a preference is not fulfilled
The service broker search for service fulfilling the QoS from
the most preferred to the less preferred (at design-time)
Penalties are applied only at runtime and never at design-
time, on the faulty service
SSLA document
QoS Variable Preference Preferences Penalties Preferences/Penalties
variables doamins degree association
20. Extending SCSP Using Penalties
SCSP
Constraint
System
Constraints
Operations
Solution
21. Extending Constraint System
SCSP
Constraint
CS = <S; D{}; V>
System
S = algebraic structure
including preference
Constraints values
V = QoS variables
D{} = Variable domains
Operations
Penalties into S
Solution
22. Extending Constraints Using Penalties
SCSP
Constraint
Def = Definition of the
System
constraint in terms of
preference value
Constraints Type = in terms of
variable intervening in
the constraint
Operations
Penalties into Def
Solution
23. Rewrite operations Logic
SCSP
Constraint
System Combination =
combination of the
constraints (pref)
Constraints Projection = generates
the optimal solution
Operations Rank generated
solutions and
keep them all
Combination of penalties
Solution
24. Extending SCSP Using Penalties
SCSP
Constraint
System
Global Preferences
Constraints
Most preferred
+
Operations
Less preferred -
Solution
25. Penalty based SCSP
Case Study
Penalty based SCSP
Constraint
System
Constraints = Penalty values
= Preference values
Operations
Solutions
26. Penalty based SCSP
Case Study
Penalty based SCSP
Constraint
System
Constraints
Operations
Solutions
27. Penalty based SCSP
Case Study
Penalty based SCSP
Constraint
System
Constraints
Operations
Solutions
28. Penalty based SCSP
Case Study
Penalty based SCSP
Constraint
System
Constraints
Operations
Solutions
29. Proposed Approach Logic
Input: Constraints, penalties, table of constraint definitions
Output: Choices with their possible alternatives ordered
Begin
For each selection alternative do
Combine all the constraints together (apply the min operator);
End for;
Order the results according to preference values into groups;
For each preference value group do
Order the elements corresponding to the penalty value;
End for;
End;
31. Learning Package Overview
Problem Description
Extending SCSP with Penalties & new SLA Model
Conclusions
32. Conclusions
1. Soft constraint-based framework
2. Express QoS properties reflecting both customer
preferences and penalties applied to unfitting situations
3. Solution for overconstrained problems
– The application of soft constraints makes it possible to work around
overconstrained problems and offer a feasible solution
4. Provide ranked choice to offer more flexibility at design-time
to find required services, and at runtime to ensure users’
rights
5. Concept of penalties in SCSP
We plan to extend this framework to also deal with
behavioral penalties
34. Further S-Cube Reading
[ZBC10] Mohamed Anis Zemni, Salima Benbernou, and
Manuel Carro
A Soft Constraint-Based Approach to QoS-Aware
Service Selection
In proceeding of the Service-Oriented Computing - 8th
International Conference (ICSOC 2010), volume 6470
of Lecture Notes in Computer Science, pages 596-602
San Francisco, CA, USA, December 7-10, 2010
35. Acknowledgements
The research leading to these results has received
funding from:
The European Community’s Seventh Framework
Programme [FP7/2007-2013] under grant agreement
215483 (S-Cube).