In the service landscape, the issues of service selection, negotiation of Service Level Agreements (SLA), and
SLA-compliance monitoring have typically been used in separate and disparate ways, which affect the
quality of the services that consumers obtain from their providers. In this work, we propose a broker-based
framework to deal with these concerns in an integrated mannerfor Software as a Service (SaaS)
provisioning. The SaaS Broker selects a suitable SaaS provider on behalf of the service consumer by using
a utility-driven selection algorithm that ranks the QoS offerings of potential SaaS providers. Then, it
negotiates the SLA terms with that provider based on the quality requirements of the service consumer. The
monitoring infrastructure observes SLA-compliance during service delivery by using measurements
obtained from third-party monitoring services. We also define a utility-based bargaining decision model
that allows the service consumer to express her sensitivity for each of the negotiated quality attributes and
to evaluate the SaaS provider offer in each round of negotiation. A use-case with few quality attributes and
their respective utility functions illustrates the approach.
ADAPTIVE MULTI-TENANCY POLICY FOR ENHANCING SERVICE LEVEL AGREEMENT THROUGH R...IJCNCJournal
The appearance of infinite computing resources that available on demand and fast enough to adapt with
load surges makes Cloud computing favourable service infrastructure in IT market. Core feature in Cloud
service infrastructures is Service Level Agreement (SLA) that led seamless service at high quality of service
to client. One of the challenges in Cloud is providing heterogeneous computing services for the clients.
With the increasing number of clients/tenants in the Cloud, unsatisfied agreement is becoming a critical
factor. In this paper, we present an adaptive resource allocation policy which attempts to improve
accountable in Cloud SLA while aiming for enhancing system performance. Specifically, our allocation
incorporates dynamic matching SLA rules to deal with diverse processing requirements from
tenants.Explicitly, it reduces processing overheadswhile achieving better service agreement. Simulation
experiments proved the efficacy of our allocation policy in order to satisfy the tenants; and helps improve
reliable computing.
Evaluating Contract Compatibility for Service Composition in The SeCO2 FrameworkHong-Linh Truong
We describe a
novel approach for modeling and mapping different service contract specifications,
and a set of techniques for evaluating service contract compatibility.
Our techniques consider contract terms associated with data and
control flows, as well as composition patterns. Illustrating
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ADAPTIVE MULTI-TENANCY POLICY FOR ENHANCING SERVICE LEVEL AGREEMENT THROUGH R...IJCNCJournal
The appearance of infinite computing resources that available on demand and fast enough to adapt with
load surges makes Cloud computing favourable service infrastructure in IT market. Core feature in Cloud
service infrastructures is Service Level Agreement (SLA) that led seamless service at high quality of service
to client. One of the challenges in Cloud is providing heterogeneous computing services for the clients.
With the increasing number of clients/tenants in the Cloud, unsatisfied agreement is becoming a critical
factor. In this paper, we present an adaptive resource allocation policy which attempts to improve
accountable in Cloud SLA while aiming for enhancing system performance. Specifically, our allocation
incorporates dynamic matching SLA rules to deal with diverse processing requirements from
tenants.Explicitly, it reduces processing overheadswhile achieving better service agreement. Simulation
experiments proved the efficacy of our allocation policy in order to satisfy the tenants; and helps improve
reliable computing.
Evaluating Contract Compatibility for Service Composition in The SeCO2 FrameworkHong-Linh Truong
We describe a
novel approach for modeling and mapping different service contract specifications,
and a set of techniques for evaluating service contract compatibility.
Our techniques consider contract terms associated with data and
control flows, as well as composition patterns. Illustrating
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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WGroup perspective paper on how to develop a successful outsourcing contratct--A few key aspects of an outsourcing contract typically drive its projected savings and return on investment (ROI). You must carefully consider all of these areas to avoid mixed financial results on your outsourcing project. Strategizing the following five areas can help you develop a successful outsourcing contract. The 5 key ways are contract components, unit pricing, resource volume, dead bands, and renegotiation bands.
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This research paper introduces a framework to identify the quality requirements of cloud computing services. It considered two dominant sub-layers; functional layer and runtime layer against cloud characteristics. SERVQUAL model attributes and the opinions of the industry experts were used to derive the quality constructs in cloud computing environment. The framework gives proper identification of cloud computing service quality expectations of users. The validity of the framework was evaluated by using
questionnaire based survey. Partial least squares-structural equation modelling (PLS-SEM) technique was
used to evaluate the outcome. The research findings shows that the significance of functional layer is
higher than runtime layer and prioritized quality factors of two layers are Service time, Information and
data security, Recoverability, Service Transparency, and Accessibility.
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Processes, tools and techniques used to manage the negotiations of an outsourcing contract. Presented to the healthcare specific interest group of PMI in July 2009
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As cloud computing is increasingly transforming the information technology landscape, organizations and
businesses are exhibiting strong interest in Software-as-a-Service (SaaS) offerings that can help them
increase business agility and reduce their operational costs. They increasingly demand services that can
meet their functional and non-functional requirements. Given the plethora and the variety of SaaS
offerings, we propose, in this paper, a framework for SaaS provisioning, which relies on brokered Service
Level agreements (SLAs), between service consumers and SaaS providers. The Cloud Service Broker (CSB)
helps service consumers find the right SaaS providers that can fulfil their functional and non-functional
requirements. The proposed selection algorithm ranks potential SaaS providers by matching their offerings
against the requirements of the service consumer using an aggregate utility function. Furthermore, the CSB
is in charge of conducting SLA negotiation with selected SaaS providers, on behalf of service consumers,
and performing SLA compliance monitoring
Topic The top 5 details that should be included in your cloud SLA..docxjuliennehar
Topic The top 5 details that should be included in your cloud SLA.
Read and respond to below two student’s discussions. (5-6 lines would be more sufficient) reflecting on your own experience, challenging assumptions, pointing out something new you learned, offering suggestions
#1.Posted by Krishnaveni
A service-level agreement (SLA) defines the level of service expected by a customer from a supplier, laying out the metrics by which that service is measured, and the remedies or penalties, if any, should the agreed-on service levels not be achieved. Usually, SLAs are between companies and external suppliers, but they may also be between two departments within a company. In my point of view below are five key things we need to consider.
Disaster recovery and backup
In the event of a disaster, our cloud provider should have a plan in place to prevent total loss of our data. Cloud providers should have a section of the SLA that describes their disaster recovery and backup solutions in detail. Depending on the provider, they may provide automatic backups and snapshots of our data. If the user is required to set up backup and recovery systems, the SLA should outline that. It may not specifically state how to activate them, but we should be aware if we need to activate them or not.
Data source.
“Where is the information coming from that will be used to measure the provider’s compliance?” In many situations, a customer may have to rely on the cloud provider to provide the information; however, increasingly, there are third parties that can help the customer with this. In a perfect world, the data should be auditable by the customer to verify the data’s accuracy, reliability and validity.
Acceptable performance.
This item specifies the minimum level of service that must be provided, and it answers the question, “What do we want from the provider for this particular service?” It flows from and is directly related to the customer’s objectives under the contract. In the cloud, many SLAs are expressed in terms of availability. For example, a SaaS provider may commit to an application availability of 99.5%.
Scalability
Many SLAs are designed to meet the needs of the customer at the time of signing, but we all know organizations can change dramatically in size over time. Make sure the SLA details intervals for reviewing a contract so that if our organization grows larger, our cloud capacity can grow with it (and if our organization happens to grow smaller, we’ll want the option to reduce capacity; no sense it paying for unused capacity).
Customer responsibilities
The SLA is a contract that outlines responsibilities that both the provider and customer agree to. Our cloud provider needs to inform us of what we’re liable for when we enter the agreement. It could be its own section or sprinkled throughout the agreement, but it must tell us what’s expected of us. Make sure we mull over the entirety of the SLA to know what our provider will manage and what we nee ...
Pricing Models for Cloud Computing Services, a SurveyEditor IJCATR
Recently, citizens and companies can access utility computing services by using Cloud Computing. These services such as
infrastructures, platforms and applications could be accessed on-demand whenever it is needed. In Cloud Computing, different types of
resources would be required to provide services, but the demands such as requests rates and user's requirements of these services and
the cost of the required resources are continuously varying. Therefore, Service Level Agreements would be needed to guarantee the
service's prices and the offered Quality of Services which are always dependable and interrelated to guarantee revenues maximization
for cloud providers as well as improve customers' satisfaction level. Cloud consumers are always searching for a cloud provider who
provides good service with the least price, so Cloud provider should use advanced technologies and frameworks to increase QoS, and
decrease cost. This paper provides a survey on cloud pricing models and analyzes the recent and relevant research in this field.
Topic: Web Services Agreement Specification. The presentation contains all the basic details required for Web Services Agreement. The research paper has been quoted in the references. Cheers!
An InterCloud is an interconnected global “Cloud of Clouds” that enables each Cloud to tap into resources of other Clouds. This is the earliest work to devise an agent-based InterCloud economic model for analyzing consumer-to-Cloud and Cloud-to-Cloud interactions. While economic encounters between consumers and Cloud providers are modeled as a many-to-many negotiation, economic encounters among Clouds are modeled as a coalition game. To bolster many-to-many consumer-to-Cloud negotiations, this work devises a novel interaction protocol and a novel negotiation strategy that is characterized by both 1) adaptive concession rate (ACR) and 2) minimally sufficient concession (MSC). Mathematical proofs show that agents adopting the ACR-MSC strategy negotiate optimally because they make minimum amounts of concession. By automatically controlling concession rates, empirical results show that the ACR-MSC strategy is efficient because it achieves significantly higher utilities than the fixed-concession-rate time-dependent strategy. To facilitate the formation of InterCloud coalitions, this work devises a novel four-stage Cloud-to-Cloud interaction protocol and a set of novel strategies for InterCloud agents. Mathematical proofs show that these InterCloud coalition formation strategies 1) converge to a subgame perfect equilibrium and 2) result in every Cloud agent in an InterCloud coalition receiving a payoff that is equal to its Shapley value.
In the present atmosphere of tighter budgets and pressure on resources, many public sector organiza-tions, including local authorities, are outsourcing services to outer organizations under service level agreements in cloud computing. Cloud computing is an approach to convey facilitated benefits over the web. Services are available to the users relying upon cloud arrangement and the Service Level Agreement (SLA) between the service providers and the cli-ents. Service level agreements are being utilized inside associations, directing connection between various sections of the association. It requires a commitment from both parties to support and adhere to the agreement in order for the SLA to work effectively. In spite of the fact that it gives a straightforward view about the cloud condition, such as cloud services, cloud distribution, security issues, responsibilities, agreements and warranties of the services. However, there are several issues occur from incorrect SLA which can cause misunderstanding among service providers and clients. SLA checking device confirm the SLA effectively whether it deals with all administrations as per SLA. In this paper, we represent a SLA confirmation and checking process that can distinguish SLA verification in gathering the information. We consider IaaS (Infrastructure as a Service) parameters for SLA verification in Cloud.
Five ways to develop a successful outsourcing contractWGroup
WGroup perspective paper on how to develop a successful outsourcing contratct--A few key aspects of an outsourcing contract typically drive its projected savings and return on investment (ROI). You must carefully consider all of these areas to avoid mixed financial results on your outsourcing project. Strategizing the following five areas can help you develop a successful outsourcing contract. The 5 key ways are contract components, unit pricing, resource volume, dead bands, and renegotiation bands.
Retail Distribution Review: Preparing Insurance IT for Compliance and Strateg...Cognizant
The Retail Distribution Review offers a significant compliance challenge for UK insurers and advisors. CIOs must prepare by ensuring they have the right enabling technologies to support changing distribution strategies.
SERVICE ORIENTED QUALITY REQUIREMENT FRAMEWORK FOR CLOUD COMPUTINGijcsit
This research paper introduces a framework to identify the quality requirements of cloud computing services. It considered two dominant sub-layers; functional layer and runtime layer against cloud characteristics. SERVQUAL model attributes and the opinions of the industry experts were used to derive the quality constructs in cloud computing environment. The framework gives proper identification of cloud computing service quality expectations of users. The validity of the framework was evaluated by using
questionnaire based survey. Partial least squares-structural equation modelling (PLS-SEM) technique was
used to evaluate the outcome. The research findings shows that the significance of functional layer is
higher than runtime layer and prioritized quality factors of two layers are Service time, Information and
data security, Recoverability, Service Transparency, and Accessibility.
Outsourcing Contract Negotiations - Structure, Process & Toolsknowlan
Processes, tools and techniques used to manage the negotiations of an outsourcing contract. Presented to the healthcare specific interest group of PMI in July 2009
A FRAMEWORK FOR SOFTWARE-AS-A-SERVICE SELECTION AND PROVISIONINGIJCNCJournal
As cloud computing is increasingly transforming the information technology landscape, organizations and
businesses are exhibiting strong interest in Software-as-a-Service (SaaS) offerings that can help them
increase business agility and reduce their operational costs. They increasingly demand services that can
meet their functional and non-functional requirements. Given the plethora and the variety of SaaS
offerings, we propose, in this paper, a framework for SaaS provisioning, which relies on brokered Service
Level agreements (SLAs), between service consumers and SaaS providers. The Cloud Service Broker (CSB)
helps service consumers find the right SaaS providers that can fulfil their functional and non-functional
requirements. The proposed selection algorithm ranks potential SaaS providers by matching their offerings
against the requirements of the service consumer using an aggregate utility function. Furthermore, the CSB
is in charge of conducting SLA negotiation with selected SaaS providers, on behalf of service consumers,
and performing SLA compliance monitoring
Topic The top 5 details that should be included in your cloud SLA..docxjuliennehar
Topic The top 5 details that should be included in your cloud SLA.
Read and respond to below two student’s discussions. (5-6 lines would be more sufficient) reflecting on your own experience, challenging assumptions, pointing out something new you learned, offering suggestions
#1.Posted by Krishnaveni
A service-level agreement (SLA) defines the level of service expected by a customer from a supplier, laying out the metrics by which that service is measured, and the remedies or penalties, if any, should the agreed-on service levels not be achieved. Usually, SLAs are between companies and external suppliers, but they may also be between two departments within a company. In my point of view below are five key things we need to consider.
Disaster recovery and backup
In the event of a disaster, our cloud provider should have a plan in place to prevent total loss of our data. Cloud providers should have a section of the SLA that describes their disaster recovery and backup solutions in detail. Depending on the provider, they may provide automatic backups and snapshots of our data. If the user is required to set up backup and recovery systems, the SLA should outline that. It may not specifically state how to activate them, but we should be aware if we need to activate them or not.
Data source.
“Where is the information coming from that will be used to measure the provider’s compliance?” In many situations, a customer may have to rely on the cloud provider to provide the information; however, increasingly, there are third parties that can help the customer with this. In a perfect world, the data should be auditable by the customer to verify the data’s accuracy, reliability and validity.
Acceptable performance.
This item specifies the minimum level of service that must be provided, and it answers the question, “What do we want from the provider for this particular service?” It flows from and is directly related to the customer’s objectives under the contract. In the cloud, many SLAs are expressed in terms of availability. For example, a SaaS provider may commit to an application availability of 99.5%.
Scalability
Many SLAs are designed to meet the needs of the customer at the time of signing, but we all know organizations can change dramatically in size over time. Make sure the SLA details intervals for reviewing a contract so that if our organization grows larger, our cloud capacity can grow with it (and if our organization happens to grow smaller, we’ll want the option to reduce capacity; no sense it paying for unused capacity).
Customer responsibilities
The SLA is a contract that outlines responsibilities that both the provider and customer agree to. Our cloud provider needs to inform us of what we’re liable for when we enter the agreement. It could be its own section or sprinkled throughout the agreement, but it must tell us what’s expected of us. Make sure we mull over the entirety of the SLA to know what our provider will manage and what we nee ...
Pricing Models for Cloud Computing Services, a SurveyEditor IJCATR
Recently, citizens and companies can access utility computing services by using Cloud Computing. These services such as
infrastructures, platforms and applications could be accessed on-demand whenever it is needed. In Cloud Computing, different types of
resources would be required to provide services, but the demands such as requests rates and user's requirements of these services and
the cost of the required resources are continuously varying. Therefore, Service Level Agreements would be needed to guarantee the
service's prices and the offered Quality of Services which are always dependable and interrelated to guarantee revenues maximization
for cloud providers as well as improve customers' satisfaction level. Cloud consumers are always searching for a cloud provider who
provides good service with the least price, so Cloud provider should use advanced technologies and frameworks to increase QoS, and
decrease cost. This paper provides a survey on cloud pricing models and analyzes the recent and relevant research in this field.
Topic: Web Services Agreement Specification. The presentation contains all the basic details required for Web Services Agreement. The research paper has been quoted in the references. Cheers!
An InterCloud is an interconnected global “Cloud of Clouds” that enables each Cloud to tap into resources of other Clouds. This is the earliest work to devise an agent-based InterCloud economic model for analyzing consumer-to-Cloud and Cloud-to-Cloud interactions. While economic encounters between consumers and Cloud providers are modeled as a many-to-many negotiation, economic encounters among Clouds are modeled as a coalition game. To bolster many-to-many consumer-to-Cloud negotiations, this work devises a novel interaction protocol and a novel negotiation strategy that is characterized by both 1) adaptive concession rate (ACR) and 2) minimally sufficient concession (MSC). Mathematical proofs show that agents adopting the ACR-MSC strategy negotiate optimally because they make minimum amounts of concession. By automatically controlling concession rates, empirical results show that the ACR-MSC strategy is efficient because it achieves significantly higher utilities than the fixed-concession-rate time-dependent strategy. To facilitate the formation of InterCloud coalitions, this work devises a novel four-stage Cloud-to-Cloud interaction protocol and a set of novel strategies for InterCloud agents. Mathematical proofs show that these InterCloud coalition formation strategies 1) converge to a subgame perfect equilibrium and 2) result in every Cloud agent in an InterCloud coalition receiving a payoff that is equal to its Shapley value.
In the present atmosphere of tighter budgets and pressure on resources, many public sector organiza-tions, including local authorities, are outsourcing services to outer organizations under service level agreements in cloud computing. Cloud computing is an approach to convey facilitated benefits over the web. Services are available to the users relying upon cloud arrangement and the Service Level Agreement (SLA) between the service providers and the cli-ents. Service level agreements are being utilized inside associations, directing connection between various sections of the association. It requires a commitment from both parties to support and adhere to the agreement in order for the SLA to work effectively. In spite of the fact that it gives a straightforward view about the cloud condition, such as cloud services, cloud distribution, security issues, responsibilities, agreements and warranties of the services. However, there are several issues occur from incorrect SLA which can cause misunderstanding among service providers and clients. SLA checking device confirm the SLA effectively whether it deals with all administrations as per SLA. In this paper, we represent a SLA confirmation and checking process that can distinguish SLA verification in gathering the information. We consider IaaS (Infrastructure as a Service) parameters for SLA verification in Cloud.
Cloud service brokerage is an emerging hot topic for cloud computing. These intermediary services are positioned between the cloud subscriber and one or more cloud providers, to enable fast and on-demand provisioning. This session will explore the new Cloud Services Brokering usage model including key considerations and usage scenarios.
JAVA 2013 IEEE CLOUDCOMPUTING PROJECT Mining contracts for business events an...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst
Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall
data processing time in cloud.
Cloud Computing is an attractive research area for the last few years; and there have been a tremendous
grows in the number of educational institutions all over the world who have either adopted or are
considering migrating to cloud computing. However, there are many concerns and reservations about
adopting conventional or public cloud based solutions. A new paradigm of cloud based solution has been
proposed, namely, the private cloud based solutions, which becomes an attractive choice to educational
Institutions. This paper presents the adjustment and implementation of private-based cloud solution for
multi-campus educational institution, namely, Al-Balqa Applied University (BAU) in Jordan.
Implementation of the Open Source Virtualization Technologies in Cloud Computingneirew J
The “Virtualization and Cloud Computing” is a recent buzzword in the digital world. Behind this fancy
poetic phrase there lies a true picture of future computing for both in technical and social perspective.
Though the “Virtualization and Cloud Computing are recent but the idea of centralizing computation and
storage in distributed data centres maintained by any third party companies is not new but it came in way
back in 1990s along with distributed computing approaches like grid computing, Clustering and Network
load Balancing. Cloud computing provide IT as a service to the users on-demand basis. This service has
greater flexibility, availability, reliability and scalability with utility computing model. This new concept of
computing has an immense potential in it to be used in the field of e-governance and in the overall IT
development perspective in developing countries like Bangladesh.
Comparative Study of Various Platform as a Service Frameworks neirew J
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(SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). This paper focuses on
different kinds of PaaS frameworks. PaaS model provides choice of cloud, developer framework and
application service. In this paper, detailed study of four open PaaS frameworks like AppScale, Cloud
Foundry, Cloudify, and OpenShift are explained with the architectural components. We also explained
more PaaS packages like Stratos, mOSAIC, BlueMix, Heroku, Amazon Elastic Beanstalk, Microsoft Azure,
Google App Engine and Stakato briefly. In this paper we present the comparative study of PaaS
frameworks.
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computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
A Proposed Model for Improving Performance and Reducing Costs of IT Through C...neirew J
Information technologies are affecting the big business enterprises of todays from data processing and
transactions to achieve the goals efficiently and effectively, affecting creates new business opportunities
and towards new competitive advantage, service must be enough to match the recent trends of IT such as
cloud computing. Cloud computing technology has provided all IT services. Therefore, cloud computing
offers an alternative to adaptable with technology model current , creating reducing cost (Fixed costs and
ongoing), the proliferation of high speed Internet connections through Rent, not acquisitions, cheaper
powerful computing technology and effective performance. The public and private clouds are characterized
by flexibility, operational efficiency that reduces costs improve performance. Also cloud computing
generates business creativity and innovation resulted from collaborative ideas of users; presents cloud
infrastructure and services; paving new markets; offering security in public and private clouds; and
providing environmental impact regarding utilizing green energy technology. In this paper, the main
concentrate the cloud computing.
Secure cloud transmission protocol (SCTP) was proposed to achieve strong authentication and secure
channel in cloud computing paradigm at preceding work. SCTP proposed with its own techniques to attain
a cloud security. SCTP was proposed to design multilevel authentication technique with multidimensional
password generations System to achieve strong authentication. SCTP was projected to develop multilevel
cryptography technique to attain secure channel. SCTP was proposed to blueprint usage profile based
intruder detection and prevention system to resist against intruder attacks. SCTP designed, developed and
analyzed using protocol engineering phases. Proposed SCTP and its techniques complete design has
presented using Petrinet production model. We present the designed SCTP petrinet models and its
analysis. We discussed the SCTP design and its performance to achieve strong authentication, secure
channel and intruder prevention. SCTP designed to use in any cloud applications. It can authorize,
authenticates, secure channel and prevent intruder during the cloud transaction. SCTP designed to protect
against different attack mentioned in literature. This paper depicts the SCTP performance analysis report
which compares with existing techniques that are proposed to achieve authentication, authorization,
security and intruder prevention.
Attribute Based Access Control (ABAC) for EHR in Fog Computing Environmentneirew J
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and real time applications. It is a connection of billions of devices nearest to the network edge. This
computing will be appropriate for Electronic Medical Record (EMR) systems that are latency-sensitive in
nature. In this paper, we aim to achieve two goals: (1) Managing and sharing Electronic Health Records
(EHRs) between multiple fog nodes and cloud, (2) Focusing on security of EHR, which contains highly
confidential information. So, we will secure access into EHR on Fog computing without effecting the
performance of fog nodes. We will cater different users based on their attributes and thus providing
Attribute Based Access Control ABAC into the EHR in fog to prevent unauthorized access. We focus on
reducing the storing and processes in fog nodes to support low capabilities of storage and computing of fog
nodes and improve its performance.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
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Topics covered:
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Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
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Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
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Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
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See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Knowledge engineering: from people to machines and back
A Broker-based Framework for Integrated SLA-Aware SaaS Provisioning
1. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
DOI: 10.5121/ijccsa.2016.6201 1
A BROKER-BASED FRAMEWORK FOR INTEGRATED
SLA-AWARE SAAS PROVISIONING
Elarbi Badidi
College of Information Technology, United Arab Emirates University, Al-Ain, United
Arab Emirates
ABSTRACT
In the service landscape, the issues of service selection, negotiation of Service Level Agreements (SLA), and
SLA-compliance monitoring have typically been used in separate and disparate ways, which affect the
quality of the services that consumers obtain from their providers. In this work, we propose a broker-based
framework to deal with these concerns in an integrated mannerfor Software as a Service (SaaS)
provisioning. The SaaS Broker selects a suitable SaaS provider on behalf of the service consumer by using
a utility-driven selection algorithm that ranks the QoS offerings of potential SaaS providers. Then, it
negotiates the SLA terms with that provider based on the quality requirements of the service consumer. The
monitoring infrastructure observes SLA-compliance during service delivery by using measurements
obtained from third-party monitoring services. We also define a utility-based bargaining decision model
that allows the service consumer to express her sensitivity for each of the negotiated quality attributes and
to evaluate the SaaS provider offer in each round of negotiation. A use-case with few quality attributes and
their respective utility functions illustrates the approach.
KEYWORDS
Cloud computing; Quality-of-Service; Key Performance Indicators; Utility function; SLA monitoring;
Brokerage service
1. INTRODUCTION
Over the last few years, Software as a Service (SaaS) has emerged as one of the most promising
service delivery models in cloud computing. The number of SaaS services in various business
domains grows continually, and new SaaS service offerings emerge on a steady basis. SaaS is
becoming an accepted delivery model for many enterprise applications, including accounting,
collaboration, customer relationship management, enterprise resource planning, human resources
management, etc. As SaaS services proliferate, users and organizations are becoming more
demanding when consuming such services. They increasingly demand services that meet their
functional and non-functional requirements. Therefore, SaaS providers need to negotiate Service
Level Agreements (SLAs) with their service consumers and adhere to their service level
commitments if they want to remain competitive in a changing and demanding business
environment.
Given the variety of consumers’ requirements, SaaS providers need to manage an increasing
number of SLAs, all with potentially changing quality requirements. The SLA lifecycle, as
described in the SLA Management Handbook [24], includes six main phases: SLA Establishment,
Negotiation and Sales, Implementation, Execution, Assessment, and Decommissioning.
Initiatives in standardizing the use of SLAs are (1) WSLA [11] by IBM, (2) the WS-Agreement
specification [1] by the Open Grid Forum, and (3) the SLAng Specification Language [14].
Several efforts-- that we discuss in the Related Work section-- investigated the issue of SLA
Negotiation [8] [10] [11] [22] [25]. This work shares with these efforts the common goal of
2. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
2
providing support for automated SLA negotiation and management. Finding the right SaaS
provider is a daunting task for consumers given the abundance and the variety of SaaS offerings.
In the service landscape, the critical issues of service-based systems include service discovery,
service selection, SLA negotiation, and SLA-compliance monitoring. These concerns were
investigated separately using different approaches. We propose in this work an integrated
framework to deal with these issues in the context of a SaaS environment. Its principal
components are the SaaS Broker and the Monitoring Infrastructure. The SaaS Broker 1) mediates
between service consumers and SaaS providers, 2) selects suitable SaaS providers capable of
delivering required functionality and quality-of-service, 3) negotiates SLAs on behalf of service
consumers, and 4) assesses compliance of service delivery with agreed upon SLA. In each round
of negotiation, the SaaS Broker and the selected SaaS provider bargain on multiple SLA
parameters by trying to maximize their global utility functions. The Monitoring Infrastructure is
in charge of observing service delivery, during SLA implementation, using measurements
obtained from independent third party monitoring services.
The remainder of the paper is organized as follows. Next section presents background
information on the issues of negotiation and Service Level Agreements in cloud computing.
Section 3 highlights related work on SLA negotiation in the context of the Service Oriented
Architecture (SOA) and cloud computing environments. Section 4 presents the proposed
framework, describes its components and describes our proposed algorithm for QoS-aware SaaS
selection and our proposed SLA negotiation model. Section 5 presents a SaaS project
management scenario that illustrates the SaaS selection and SLA negotiation. Section 6 concludes
the paper and highlights future work.
2. BACKGROUND
2.1. NEGOTIATION
Negotiation is an important process commonly used in business and personal life to solve
conflicts. It aims at reaching a mutual agreement. For instance, negotiations between unions and
employers aim at reaching a deal regarding salaries and benefits. It is defined in Wikipedia [30]
as: “Negotiation is a dialogue between two or more people or parties intended to reach a
mutually beneficial outcome”.
Negotiation can take different forms and adopt different strategies depending on the multiplicity
of the participating parties:
• One-to-one: This is the most prevalenttype of negotiation, in which a consumer bargains with
a provider for the acquisition of an item or the delivery of a service.
• One-to-many: This is a less familiar form of negotiation, in which a consumer bargains with
several businesses or providers. These providers can compete or cooperate to share the
service delivery to the consumer. An example is when the government negotiates with several
telecom operators for mobile service provisioning.
• Many-to-one: This is also a less common form of negotiation. In this case, several consumers
bargain with a single provider or business. They may compete to reach a deal with the
provider or cooperate to share the item or service offered by the provider.
• Many-to-many: This form of negotiation involves several consumers and several providers or
businesses. A typical example is a negotiation between unions and employers.
Companiescooperate to reach an integrated agreement that saves their interests and unions
bargain to get the best benefits from employers.
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The simplest form of negotiation is a one-to-one negotiation where the two parties bargain on a
single issue [15]. However, in practice, the two sides often need to negotiate several issues. For
example, a car buyer needs to negotiate with the auto dealer the price, the color, the warranty, the
safety options, and the infotainment options. Multi-issue negotiation is notably more complex
than single-issue negotiation as a human decision relies on her preference on all issues [13]. A
win-win negotiation, in this case, aims to reach a deal that is acceptable to both parties and lets
them feel that they have won. An established way to make the negotiation manageable is first to
characterize the preferences of each participant with a utility function, and then both parties make
decisions based on the evaluation of their respective utility functions. With the proliferation of e-
commerce and electronic transactions, automated negotiation is being increasingly used in
different multi-agent domains including network bandwidth allocation, robotics, space
applications, etc. In these domains, a software agent acts on behalf of a user and negotiates an
SLA with a service provider [4] [3].The negotiation process involves three main components:
• Negotiation objects: The set of issues the negotiating parties negotiate to reach a mutual
agreement. These can include, for example in the case of service provisioning negotiation, the
QoS attributes such as availability, response time, reliability, price, and so on.
• Negotiation protocol: It determines the cardinality of the participating parties, states their
roles, and includes the rules that govern the interaction between the parties. It specifies the
negotiation states, the events that cause the change in those states, and the legal actions of the
participants in each state. Negotiation protocols fall in general into two classes: bilateral
negotiations and auctions. Bilateral negotiations involve two parties, a service provider and a
consumer, and protocol for submitting offers and counter-offers until the two sides reach a
mutually acceptable agreement on the terms and conditions of business, or one of them
withdraws from the negotiation.
• Decision model: the decision-making tool that the negotiating parties use to compute their
negotiation moves and act in line with the negotiation protocol to achieve their objectives.
2.2. SAAS SERVICE LEVEL AGREEMENTS
A Service Level Agreement is anagreement between a service provider and a service consumer
consisting of sections concerning the guarantees and commitments to service levels that the
service provider will deliver. It describes common understandings and expectations of service
between the two parties. The typical components of an SLA are parties, activation-time, scope,
service-level objectives (SLOs), penalties, exclusions, and methods to assess the SLOs [8]. SLOs
represent the goals of the service provider, such as the percentage of service requests the service
provider want handled in a specified number of seconds (e.g., 95% within 10 seconds). They
represent a commitment of the service provider to maintain a particular level of the service in a
predefined period. A typical SLA may have the following SLOs: service availability, system
response time, service outage resolution time, and the reason for an outage.
Every cloud provider uses its key performance indicators (KPIs). There is no standard at this level
yet even though there are some efforts with this regards by the Cloud Services Measurement
Initiative Consortium (CSMIC) [21]. Meegan et al. [18] consider that service consumers should
expect to have general SaaS indicators in their SLA: cumulative monthly downtime, response
time, the persistence of consumer information, and automatic scalability. Burkon L. [2]
summarizes the SLA dimensions and their associated quality indicators for the SaaS delivery
model. These dimensions areAvailability, Response time, Throughput, Timeliness, Reliability,
Scalability, Security, Uptime History, Granularity, Elasticity, Interoperability, Usability,
andTestability.
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3. RELATED WORK
SLA negotiation and specification of machine-readable SLAs have been the subjects of several
efforts in the context of SOA-based environments, computational grid settings, and recently
cloud-based environments.
Dan et al. [8] described a framework for providing differentiated service levels to service
consumers in an SOA environment using SLAs and automated management. The framework
encompasses WSLA – for the creation and negotiation of SLAs --, a system for dynamic
allocation of resources based on SLOs, a workload management system that orders requests
consistent with the corresponding SLAs, and a system to monitor compliance with the SLA.
Chieng et al. [5] described an SLA-driven service provisioning architecture that allows flexible
SLA negotiation of services. The emphasis is on bandwidth reservation, which is the most
important factor to affect connection’s QoS. The negotiation high-level service parameters are
price, starting time, session length, and guaranteed bandwidth. Di Modica et al. [9] focused on the
use of WS-Agreement for the specification of SLAs and proposed to improve their approach by
adding new functionality to the protocol to allow the parties to an agreement to renegotiate and
adjust its terms during the service provision.
The ambitious goal of several efforts is to support the automated negotiation of the conditions
specified in the SLA. Silaghi et al. [22] introduced a framework for building SLA automatic
negotiation strategies under time constraints in computational grids. The framework relies on the
recent results from negotiation based on learning opponent strategies that agent-based systems
use. They extended the Bayesian learning agent to deal with the limited duration of a bargaining
session and showed that opponent learning strategies lead to satisfaction of participants and an
optimal allocation of resources. Resinas et al. [20] tackled the problem of building automated
service agreement negotiation systems that rely on a bargaining protocol and work in open
environments. They proposed a bargaining architecture that provides support for many
requirements, which are missing from other proposals, such as multi-term negotiation,
heterogeneity of the parties, management of partial information about the parties, and
simultaneous negotiations with different parties. Hasselmeyer et al. [10] described a brokered-
based approach to SLA negotiation. Their solution relies on the idea of outsourcing SLA
negotiation to third parties (agent brokers) acting on behalf of their clients.
An essential component of the European project mOSAIC is the Cloud Agency. The project aims
at enabling service consumers to delegate all management tasks, such as SLA management and
monitoring of the resources to the agency [28]. The Cloud Agency framework includes several
agents that are in charge of managing cloud resources and services provided by various cloud
providers.
The SLA@SOI project aims at defining a comprehensive view of SLAs’ management and
developing a framework for SLA management. A Service Oriented Infrastructure (SOI) can
integrate the framework to manage SLA activities [23]. Theilmann et al. [25] developed, in the
context of this project, a reference architecture for multi-Level SLA management. This
architecture objective is to provide a generic solution for SLA management that can i) cover the
complete SLA and service life cycle; ii) support multiple layers SLA management in a service-
oriented infrastructure, and iii) be used in various use cases and industrial domains.
The CONTRAIL Project proposed and implemented a cloud federation framework, which aims at
relieving the user from managing access to individual service providers [7]. Its federation model,
which relies on SLAs, aims at coordinating management and deployment of applications on
multiple clouds. Each application could then exploit multiple cloud providers. The user negotiates
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an SLA with the federation, and the federation negotiates SLAs with one or several providers on
behalf of the user. The federation acts as a broker of providers. Selection of providers is SLA-
based. Furthermore, SLA specification in Contrail relies on the SLA@SOI syntax.
Macias et al. [16][17] consider a cloud services market in which a client starts negotiating service
provisioning with the providers that fulfill its requirements. The client creates an SLA proposal
for each provider by filling an SLA template with its requirements. Upon receiving the SLA
proposal from the client, each provider evaluates the proposal and returns its SLA offer with
pricing information and terms of violations. Afterwards, the client selects the provider whose
SLA offer suits best its requirements.
Wu et al. [32] proposed a SaaS broker-based framework for automated SLA negotiation with
multiple SaaS providers. SLA bargaining aims to meet customer needs and relies on some
heuristics and strategies for generating counter proposals to the SaaS provider’s offers. These
heuristics take into account some constraints such as market constraints, time and the trade-off
between QoS parameters. This work and ours share the same goals and objectives.
Our work shares with these efforts the common goal of providing support for automated SLA
negotiation and management. The SaaS Broker with its know-how and value-added services can
assist service consumers to (a) express their service requirements (functional and nonfunctional)
(b) select appropriate SaaS offerings, (c) negotiate SLA terms, and (d) monitor the
implementation of SLAs. However, our approach differs from many of the above approaches,
which select a suitable provider after having conducted SLA negotiation with several providers.
We consider that the selection of potential SaaS providers should be led first, based on the most
recent knowledge on the providers’ offerings, and then SLA negotiation has to be carried out only
with selected SaaS providers in several rounds of proposals and counter-proposals. We also
believe that SLA compliance monitoring should be carried out in collaboration with third-party
monitoring services to guarantee the independence of metrics’ measurement.
4. METHODS
4.1.FRAMEWORK OVERVIEW
Figure 1 depicts our proposed integrated framework for SaaS provisioning. The components of
the framework are service consumers, the SaaS Broker, the monitoring infrastructure, and SaaS
providers.
4.1.1.SAAS BROKER
The SaaS Broker is a mediator service that decouples service consumers from SaaS providers. It
allows users to subscribe to some services and enables SaaS providers to market their service
offerings. Given that service providers and consumers want to focus more on their core
businesses rather than negotiating, managing, and monitoring QoS, they delegate management
tasks, such as service selection and SLA negotiation, to the SaaS Broker.
The SaaS Broker offers the following management operations: Identity and Access Management,
QoS-based SaaSSelection, SLA Negotiation, and Policies Management. Several components
implement these operations and cooperate to deliver personalized services to service consumers
and SaaS providers. Figure 2 depicts the architecture of the SaaS Broker, which includes the
following components: theCoordinator, the Profile Manager, the Selection Manager, the SLA
Manager, the Policy Manager, and the SLA Monitoring Manager. The back-end databases
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6
maintain information about profiles and preferences of service consumers, SaaS providers’
policies, SLAs, and dynamic QoS information. The Profile Manager manages the profiles of
service consumers, including their preferences for personalized services and QoS. The Selection
Manager is in charge of implementing policies and strategies for the selection of suitable SaaS
providers, based on the service consumer’s functional and non-functional requirements and the
SaaS providers’ QoS offerings. The SLA Manager is in charge of carrying out the negotiation
process between a service consumer and a SaaS provider to reach agreement as to the service
terms and conditions. The Policy Manager manages different kinds of policies such as
authorization policies, and policies for monitoring services and their QoS. Specific requirements
or capabilities of a service provider are declared using XML policy assertion elements. Each
assertion describes an atomic aspect of service requirements (e.g. authentication scheme,
authorization scheme, QoS characteristics).
Figure 1. Framework for SLA-driven service provisioning
4.1.2.THE MONITORING INFRASTRUCTURE
SaaS services work well most of the time. But, it does not mean that outages and performance
issues never happen. Most SaaS providers guarantee availability levels of 99.9% uptime, which
translates into about 43 minutes of downtime per month that one can consider as not too much
downtime.However, the more a service consumer uses the SaaS service extensively, the more any
disruption in service delivery is amplified. The consequences of an interruption in the middle of
an important business transaction could be costly.
Many SaaS providers use a “Trust” website, which provides information on service status and
disruptions to facilitate accountability for service provision. However, the cause of any disruption
in service delivery often comes later after an in-depth assessment of the situation has been made
and posted publicly.
Repeated outages or slowdowns might lead service consumers to complain or go away trying to
find other alternative providers. With an agreement on a proactive approach to monitoring in real
time of the SaaS delivery, the provider can quickly make corrections to mitigate disruptions.
Independent performance monitoring from a third-party helps the provider put the required
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resources to keep the service running at its full potential. It also helps the service consumer
understand the level of service and performance obtained from the provider, and, therefore
optimize its business processes the SaaS application is supporting. An example of a third-party
monitoring service is Keynote [12] with its solution for “Web monitoring for SaaS
applications”.Other monitoring services includeMonitis[19] and Uptrends [27].
As depicted in Figure 2, the Monitoring Infrastructure includes Monitoring Plug-insthat allow
interacting withthird-party Monitoring Services. Each Monitoring Plug-in is in charge of mapping
resource metrics, measured by the Monitoring Service, into SLA parameters and monitoring
current service levels and their compliance with SLA. Monitoring Plug-ins may be added to the
framework if service consumers and SaaS providers agree on using the services of particular
monitoring companies. Plug-ins typically use APIs provided by Monitoring Services to get
service measurement data. For example, Monitis is providing an open API that allows accessing
most of the commands that are available from Monitis dashboard. Similarly, Keynote is offering
an API to access Keynote measurement data.
Figure 2. Architecture of the SaaS Broker and the monitoring infrastructure
4.2.SLA-BASED SERVICE PROVISIONING
Service provisioning is typically carried out in four phases: Expression of the consumer’s
requirements and service level expectations, selection of a potential SaaS provider, SLA
negotiation and finalization of the contract, service delivery,and SLA compliance monitoring.
4.2.1. EXPRESSION OF THE CONSUMER REQUIREMENTS
In this phase, the service consumer submits an SLA Request to the SaaS Broker seeking an
appropriate SaaS provider that meets its service functional and non-functional requirements. The
request describes the level of service that the service consumer is willing to accept and includes
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some QoS indicators such as response time, availability, and throughput. After processing the
service consumer’s authentication, the Coordinator requests its profile from the Profile Manager.
If the service consumer’s profile is not available in the profile repository, then the Coordinator
asks the service consumer to provide additional information, such as service preferences and
desired levels of service, to create a new profile for the service consumer. Figure 3 illustrates an
example of quality requirements for a project management SaaS service.
Figure 3. An example of quality requirements specification
4.2.2. QoS-aware Service Selection
Before starting the SLA negotiation, the Coordinator requests from the Selection Manager to
select appropriate SaaS providers based on the user’s QoS requirements and the QoS offerings of
SaaS providers advertised with the SaaS Broker or a public registry.
To enter the market, SaaS providers often advertise their service offerings using templates that
express the functional and non-functional characteristics of their services. Not all SaaS providers
are created equal. They vary by the vertical they are serving, by their maturity, and by the type of
application they are offering to their customers. Therefore, service offerings can vary widely
regarding service levels and QoS they can deliver. Service descriptions may also vary as they
may use different description languages such as WSDL (Web services Description Language) [6],
WSOL (Web Services Offering Language) [26], or proprietary languages. The description of their
QoS offerings may also be heterogeneous. Various approaches have been used to add QoS
support to Web services’ description. The first approach advocates incorporating QoS parameters,
such as response time, throughput and cost, into the WSDL service description. Other works
propose extensions to the Web services Policy Framework to represent QoS policies of Web
services. With this heterogeneity in service descriptions, it would be difficult to match services
offerings to the user’s needs without using a common standard description model (such as WSDL
model with QoS support) to map all service offerings to that common model.
A SaaS provider may exceed the service consumer's expectations for some quality attributes
while its offering might be below the user’s expectations for other quality metrics. Thus, it is
imperative to evaluate the overall offering of every potential SaaS provider by taking into account
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all quality attributes. Such evaluation will allow ranking SaaS providers and selecting the most
promising one that can satisfy the consumer request. We describe in the following our proposed
algorithm that takes into consideration the QoS requirements of the service consumer and the
knowledge of the SaaS Broker on the various service offerings.
Let = { , , … , } be the list of QoS attributes, such as availability, throughput, response
time, reputation, and cost of service, required by the service consumer. We classify these QoS
attributes into two categories. The first category includes utility-driven QoS attributessuch as
availability and throughput that service consumers would like to maximize. The second category
includes cost-driven QoS attributes such as response time and cost of the service that service
consumers would like to minimize.
Let = { , , … , } be the list of potential SaaS providers, which are capable of
providing the type of service requested by the consumer. Let = { , , … , }, be the vector of
quality requirements of the service consumer for each QoS attribute in Q.The following vector
expresses the QoS offering of the SaaS providerSP .
= { , , … , }
The above QoS attributes are expressed in different units and cover a range of values. To be able
to sort and rank the SaaS providers’ offerings regarding their QoS offerings, we need to
normalize values of the QoS offers and define utility functions that map the vector of QoS values
into a single real value. We define:
= max
= min
as the maximum and minimum values of the QoS attribute Qi considering all the offers of the
potential SaaS providers in SP. The requirement of the service consumer for may fall in the
range [ , ] or outside of this range. To take account of the requirements of the service
consumer during the normalization, we define the two following values:
′ = "#$ ( , )
′ = "'(( ), )
We define * , the normalized value for the offer of the SaaS provider regarding the QoS
attribute Q in equation (1) as:
* = ,
-./
012
3-/
4
-./
012
3-./
0/5 , for 9:;< − >?'@A( QoS attributes
GH
I
3G.H
JHK
G.H
JLM
3G.H
JHK , for N<'O'<P − >?'@A( QoS attributes
Q (1)
Let R = {9 , 9 , … , 9 }, with 0 ≤ 9 ≤ 1, be the vector of normalized values of the quality
requirements that the service consumer. When * ≥ 9 , it means that the offer of SaaS
provider exceeds the expectation of the service consumer regarding the quality attribute .
Otherwise, it means that the offer does not meet the consumer’s expectation for .The SaaS
Broker may assign (on behalf of the service consumer) an importance weight to each quality
attribute.Let W = {X , X , … , X } be the weight vector associated with the quality attributes
10. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
10
respectively.We define the Utility function (weighted combined level of satisfaction) associated
with the offer of SaaS provider as:
Y = ∑ X *[ (2)
Equation 2 allows ranking the QoS offers of the potential SaaS providers capable of offering the
functionality required by the service consumer. The best service offer corresponds to the offer
that maximizes the utility function Y .
Best service offer "#$ [ Y (3)
Figure 4 summarizes the different steps of the algorithm.Once the Selection Manager has
selectedthe most promisingSaaS provider, the SaaS Brokerstarts negotiating SLOs with that SaaS
providerthrough its SLA Manager.
Figure 1. QoS-aware selection algorithm
4.2.3. NEGOTIATION MODEL
The SLA negotiation process of the proposed framework is a bargaining protocol where the SLA
Managers of the SaaS Broker and SaaS provider negotiate multiple attributes (QoS attributes).
The steps of the SLA negotiation protocol are as follows:
• Step1: The SLA Manager of the SaaS Broker forwards the SLA Request to the SLA Manager
of the selected SaaS provider requesting an SLA proposal. The SLA request describes only
the user’s required level of service regarding quality indicators. The SaaS provider parses the
SLA Request and validates it against its SLA templates.
11. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
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• Step 2: If the SLA Request is acceptable to the SaaS provider, then its SLA Manager
responds to the SLA Request by sending back an SLA proposal to the SaaS Broker. The SaaS
Brokeranalyses it to find out if it meets or not to all the service consumer’s functional and
non-functional requirements. A decision model is used at this point to evaluate the proposal
of the SaaS provider.
• Step 3: If the SaaS provider can meet the service consumer’s expectations, then the SaaS
Broker accepts the offer of the SaaS provider and sends an SLA Confirmation to the SLA
Manager of the SaaS provider. Otherwise, it rejects the offer and makes a counter-proposal
with different conditions, terms, and cost.
• Step 4: In the case of the SLA Confirmation, the two parties, service consumer and SaaS
provider, approve the agreement, and the service consumer can start using the service
according to the terms of the agreement. The agreement specifies the service that the provider
should offer to the service consumer, the activation time, the level of QoS to guarantee, the
cost of service, the validity period, and the actions to take in the case of a violation of the
agreement.
In this process, a multi-issues negotiation takes place between the SLA Managers of both parties,
as they have to negotiate multiple QoS attributes concurrently. To reach agreement, The SLA
Managers of both parties have to go through several rounds of negotiation of offers and counter-
offers until they reach agreement or reach a predefined maximum number of rounds. Figure 5
shows the state charts of the SLA Managers of the negotiating parties. The SaaS Broker, upon
reception of an SLA request, maps the service consumer's expectations into normalized QoS
values in the range]0,1^.
In each round of negotiation, as illustrated in Figure 5, the SLA Manager of the SaaS Broker
evaluates the utility function of each QoS attribute and the global utility function to determine
whether the offer of the SaaS provider is acceptable or not.Various functions may be used to
express the service consumer’s utility for eachquality attribute . If is a utility-driven attribute,
such as the availability, its utility function is at its maximal when the SaaS provider can ensure
100% for that quality. If is a cost-driven attribute, such asresponse time, the corresponding
utility function is at its maximal when the SaaS provider can guarantee a lower value, which is
very close to zero, for that quality. Let V be the set of utility-driven quality attribute from Q; and
Let R be the set of cost-driven quality attribute from Q. = _ ∪ a.
We adopt in Equation (4)the following function b to express the utility function of a utility-driven
quality attribute X:
b($) =
cd ( efd)
efd
cd
(4)
$ is the normalized value of the offer made by the SaaS providerfor X. gh is the SLA value for X.
ihrepresents the sensitivity of the service consumerwith respect to the quality attribute X. When
ih is equal to zero, the service consumer is indifferent to the quality attribute X. When ihis
equal to one, the service consumer is moderately sensitive to the quality attribute X (the
relationship is linear). When ihis greater than one, the service consumer is increasingly sensitive
to the quality attribute X. Whenih increases, the service consumer is expressing increasing
concern about it. For values of ih smaller than one, the service consumer is expressing increasing
indifference for the quality attribute X when ihdecreases to reachzero.
Similarly, we consider in Equation (5)the function jthat expresses the utility function of a cost-
driven quality attribute Y:
12. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
12
j(P) =
3kcl
eflkcl
(5)
y is the normalized value of the offer made by the SaaS provider for the quality attribute Y. gm is
the SLA value for Y. imrepresents the sensitivity of the service consumer toward the quality
attribute Y.jreaches its maximum, which is 1, when y is 0 and decreases to 0 when y reaches 1.
(a) SLA Manager of the SaaS Broker
(b) SLA Manager of the SaaS Provider
Figure 5. State chart of the SLA Managers of the negotiating parties.
We define in Equation (6) U as the global utility function, which serves to evaluate the SaaS
provider offer during the negotiation phase. We assume that the QoS attributes are independent.
Therefore, U can be expressed by the additive linear function of the individual utility functions (F
and G) of the quality attributes . 1 ≤ ' ≤ (
Y =
∑ X b
'
∈ _
+
∑ X j
p
∈ a
(6)
b represents the utility function associated with the utility-driven QoS attribute . j represents
the utility function associated with the cost-driven QoS attribute .X is the weight that the
13. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
13
service consumer assigns to that attribute . Each weight is a number in the range [0,1]
and∑ X = 1.
5. RESULTS
In this section, we consider a scenario from an application domain in which many SaaS offerings
are available to businesses. The scenario is used to illustrate the selection process and the
negotiation model.
A business company that we call COMPANY would like to enhance its productivity by using
project management solutions. On-premise project management tools have limitations when it
comes to the mobility of the workforce and the variety of devices that employees may use ranging
from desktops to tablets and smartphones. Therefore, a cloud-based solution for project
management is increasingly becoming attracting given the way teams may communicate and
collaborate, the benefits, and the flexibility it offers to businesses.
Indeed, today businesses have many SaaS solutions for project management support. Although
the solutions have many similarities, each solution has its unique features, strengths, and
weaknesses. SaaS services for project management are changing the way businesses manage
projects. Managers no longer have to print or email on a weekly basis Gantt charts to team
members to update them on project status. Besides, the management of multiple projects does not
require anymore tracking and exchanging multiple Microsoft Project documents. Examples of
SaaS solutions for project management are basecamp, GroupCamp, Planzone, Clarizen, Zoho
project, and Beesy. Table 1 presents the main features of these SaaS offerings.
Table 1- SaaS Project Management Solutions
Operation SaaS PM
solution
basecamp GroupCamp Planzone Clarizen Zoho
project
Beesy
Defining tasks ✓ ✓ ✓ ✓ ✓ ✓
Assigning resources ✓ ✓ ✓ ✓ ✓ ✓
Specifying milestones ✓ ✓ ✓ ✓ ✓ ✓
Generating detailed
reports
✓ ✓ ✓ ✓ ✓ ✓
Parallel tasks support ✓
Sending email to
create new task
✓ ✓ ✓ ✓ ✓
Templates to specify
project data
structures (team
members, tasks, wiki
page, etc..)
✓ ✓ ✓
Advanced reporting ✓ ✓ ✓
Pricing scheme Monthly
subscription
& yearly
subscription
Six pricing
plans: (Free,
Start, Plus,
Best, Max)
Four
monthly
plans
(Basic,
Team,
Business,
Enterprise)
&
customized
plan.
Three
Monthly
plans (
Professional/
Enterprise/
Unlimited)
Four
monthly
plans (Free,
Express,
Premium,
Enterprise)
Four
monthly
plans
(Free,
Sync, Pro,
Team)
SLA support Yes for
yearly
subscription
SLA
management
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Selecting a SaaS solution by COMPANY is not an easy task given the plethora of features,
pricing schemes, and SLAs. COMPANY has first to understand and specify its project
management needs. Then, it can delegate the selection of an appropriate SaaS solution to the SaaS
Broker, who has better knowledge on the features of each SaaS solution. The selection process
starts by considering the solutions that might satisfy the functional requirements of COMPANY.
Then, potential SaaS offerings will be ranked based on its non-functional requirements. In this
first phase, high-level requirements, such as the scope of COMPANY’s project management
needs, might be used to identify candidate services. The scope may be project specific,
department level, or enterprise level. The selection process might then be refined with other
detailed or low-level functional requirements. Assuming that the SaaS Broker has knowledge on
the non-functional offerings of the potential SaaS providers, our proposed algorithm might be
used to rank them.
We assume that the QoS requirement of the service consumer are: Availability=99.97%,
Reliability=99.96%, Cost=25$, and Response time=6ms. Table 2 shows the QoS values offered
by 24 potential SaaS providers. Table 3 lists the weights that the SaaS Broker assigns to these
QoS parameters according to the consumer requirements.By using our selection algorithm,
normalized values are calculated for each QoS parameter while differentiating between utility-
driven QoS attributes and cost-driven QoS attributes. Then, using the weight table, the aggregate
utility function is calculated for each SaaS offering. Figure 6 depicts the normalized values of
each of these QoS attributes.
Table 2- Qos Offerings of SaaS Providers
SaaS_ID Availability Reliability Cost ($) Response time (ms)
1 0.99988 0.9995 16.1 6
2 0.99968 0.99953 38.1 2
3 0.99935 0.99962 8.4 3
4 0.99988 0.99964 40.2 3
5 0.99959 0.99954 12.6 4
6 0.99963 0.99958 22.2 6
7 0.99939 0.99971 33.2 7
8 0.99918 0.99975 25.3 2
9 0.99995 0.9999 30.8 7
10 0.99958 0.99956 24.2 3
11 0.99945 0.99971 22.8 7
12 0.99981 0.99976 6.7 7
13 0.99911 0.99987 15 3
14 0.99924 0.99983 11.7 5
15 0.99912 0.9998 24.8 5
16 0.99948 0.99973 22.7 3
17 0.99952 0.99967 31.9 5
18 0.99999 0.99962 23.9 2
19 0.99944 0.99975 33.7 3
20 0.99943 0.99972 20.7 5
21 0.99987 0.99957 19.6 5
22 0.99959 0.99977 27.2 2
23 0.9997 0.99952 20.4 4
24 0.99999 0.99992 25.4 5
Table 4 depicts the rank table generated by the SaaS Broker using our proposed algorithm. The
offer of the SaaS provider with ID 24 is the best offer as it maximizes the aggregated utility. In
parallel with that, we also use the TOPSIS method [31] for ranking the SaaS offerings. We
identified first the ideal solution (IS) and the negative ideal solution (NIS). Then, we calculated
15. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
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for each SaaS offer the separation from IS from the NIS. Afterwards, we calculate the closeness
to the ideal solution (CIS). Table 5 depicts the ranking using the TOPSIS method.
Figure 6. Normalized values of the QoS attributes
Table 3- Weighted table of the QoS attributes
Availability (%) Reliability (%) Cost ($) Response time (ms)
Weight 0.305 0.267 0.197 0.231
The results show that both algorithms have obtained the same best SaaS provider (SaaS ID: 24).
Subsequent SaaS offerings in the two rankings are very close, especially for the first ten best
offerings. This process results in identifying a suitable SaaS provider for project management.
Reaching an agreement with that provider would be the next phase before COMPANY would be
able to use the SaaS solution for its project management activities.
To illustrate our negotiation model, we consider that negotiation would be mainly about two
quality attributes: availability and response time. In this scenario, both availability and response
time are important qualities for COMPANY. The desired level of the availability is 99.97%
uptime and 20% (6ms) for the response time (qr= 0.99 and qs= 0.20). In addition, COMPANY
gives 70% importance weight to the availability and 30% weight to response time (tr= 0.70 and
ts= 0.30).
Figure 7 shows the evolution of the utility function associated with Availability for different
values of βv, the sensitivity of COMPANY to the availability of the service (iw= 1, 2, and 4
respectively). At low offers of availability by the SaaS provider, the utility is also low. As the
SaaS offer increases, the utility rapidly increases when COMPANY is not sensitive to the
availability (iw= 1), moderately when iw= 2, and slowly as availability becomes a sensitive issue
for the business (iw= 4).
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Table 4- Ranking of SaaS Offerings using our
proposed algorithm
SaaS_ID Utility (%) Rank
24 65.90 1
12 60.49 2
9 60.07 3
18 47.71 4
21 42.90 5
14 42.24 6
22 41.45 7
1 40.86 8
13 38.34 9
16 37.74 10
20 36.54 11
4 35.59 12
5 35.41 13
11 35.37 14
3 34.65 15
6 33.69 16
23 33.36 17
19 31.15 18
17 29.90 19
10 29.51 20
15 28.47 21
7 27.17 22
8 27.08 23
2 22.90 24
Table 5- Ranking of SaaS Offerings using the
TOPSIS method [31]
SaaS_ID CIS (%) Rank
24 70.60 1
18 65.05 2
22 62.23 3
9 58.97 4
12 57.62 5
4 55.29 6
16 54.66 7
21 52.70 8
13 51.54 9
19 49.76 10
3 49.12 11
14 48.81 12
2 48.22 13
1 47.97 14
8 47.94 15
5 47.91 16
23 47.71 17
10 47.71 18
20 45.03 19
6 40.47 20
17 40.42 21
15 39.25 22
11 37.70 23
7 31.35 24
Figure 7. Utility function of the availability (bw) with different values of the sensitivity factoriw
Similarly, Figure 8 shows the evolution of the utility function associated with response time for
different values of ix, the sensitivity of COMPANY to the response time quality attribute (ix= 1,
2, and 4 respectively). At low response time offers by the SaaS provider, the utility is high. As the
SaaS offer for response time increases, the utility rapidly decreases when COMPANY is not
sensitive to the response time (ix= 1), moderately when ix= 2, and slowly when response time is
a sensitive issue for the business (ix= 4).
17. International Journal on Cloud Comput
Figure 8. Utility of the response time (
Assuming that COMPANY is more sensitive to availability than to
2).Figure 9 plots the additive utility function for this scenario. It shows that the
function reaches its maximal value when the
is high (close to 1).The SaaS Broker, acting on behalf of COMPANY, may set a threshold for the
overall utility function. When the offer of the SaaS provider is below that threshold, the SaaS
Broker rejects the offer and sends a counter
Figure 9.Overall u
6. CONCLUSION
With the rising adoption of Software
providers need to implement and
highly demanding business environment. On the other hand, service consumers need to find
appropriate SaaS providers that meet their functional and non
negotiate with them the terms of service delivery.
International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
. Utility of the response time (jx) with different values of the sensitivity factor
COMPANY is more sensitive to availability than to response time (yr
plots the additive utility function for this scenario. It shows that the overall
function reaches its maximal value when the response time is low (close to 0), and the
The SaaS Broker, acting on behalf of COMPANY, may set a threshold for the
overall utility function. When the offer of the SaaS provider is below that threshold, the SaaS
offer and sends a counter-proposal to the SaaS provider.
Overall utility function of the service consumer
With the rising adoption of Software-as-a-Service as a service provisioning model, SaaS
providers need to implement and manage a growing number of SLAs to remain competitive in a
highly demanding business environment. On the other hand, service consumers need to find
appropriate SaaS providers that meet their functional and non-functional requirements and
m the terms of service delivery.
ing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
17
) with different values of the sensitivity factor ix
r= 4 and ys=
overall utility
and the availability
The SaaS Broker, acting on behalf of COMPANY, may set a threshold for the
overall utility function. When the offer of the SaaS provider is below that threshold, the SaaS
Service as a service provisioning model, SaaS
manage a growing number of SLAs to remain competitive in a
highly demanding business environment. On the other hand, service consumers need to find
functional requirements and
18. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 2, April 2016
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In this paper, we have presented a framework for SLA-based service provisioning, which relies
on a SaaS Broker for a) mediating between service consumers and SaaS providers, b) selecting
suitable SaaS providers that can meet service consumers’ expectations, and c) negotiating the
terms of the SLA on behalf of service consumers. SLA negotiation involves the negotiation of
multiple QoS attributes with both parties trying to maximize their utility function in several
rounds of proposals and counter-proposals. We proposed a SaaS selection algorithm and a
decision negotiation model that the SaaS Broker may implement to evaluate the offers of the
selected SaaS provider in each round of negotiation. We have considered a scenario with two
quality attributes, availability and response time, to illustrate the approach. The proposed decision
model allows the consumer to specify her sensitivity for each negotiated quality attribute. Our
approach differs from other approaches that carry out SLA negotiation with several cloud service
providers, and then select the one with the best offer. It first selects the best suitable SaaS
provider based on prior knowledge of the SaaS Broker on the providers’ offerings. Then, it
negotiates with the selected SaaS provider the SLA terms in several rounds of proposals and
counter-proposals until an agreement or a timeout is reached.
As future work, we intend to investigate how the SLA negotiation evolves when the importance
weights, associated with the QoS attributes, vary in time and, then, study the effect of the
interdependence between QoS attributes on the SLA negotiation. Also, we intend to build a
prototype of our proposed framework.
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