Service Level Agreement Negotiation Research Articles

Model of an Intelligent and Automated Negotiation Agent for the Service Level Agreement Negotiation Process in Cloud Computing

With the help of an intelligent agent, a cloud service provider (CSP) offers various services and resources to their customers over the internet. Consumers and CSPs in the cloud market are in a constant state of negotiation over their individual needs. Competition among CSPs is fierce because of the enormous number of CSPs in the market. Intelligent negotiation agents are highly demanding as a timesaving and success rate-enhancing tool. While negotiating, multiple attributes such as price, resources, reliability, CSP performance, utility value, and many more are considered. There’s increased false negotiations, faulty servers, and exorbitant fees are all issues arising because of the forever changing CSP market. An intelligent and automated negotiation agent (IANA) understands the conduct of the opponent and the needs of both parties. This need of users and CSP information is utilized in negotiations to find the best possible delivery of services between the customer and the CSP. As a result of the proposed design, negotiations are expedited, and there is a higher success rate and better oversight of service delivery.

Hybrid Clouds Arising from Software as a Service Adoption: Challenges, Solutions, and Future Research Directions

Information technology (IT) departments are increasingly challenged to replace legacy applications with novel public cloud software as a service (SaaS) to innovate the organization's business processes. The resulting hybrid cloud is formed by integrating the added SaaS with existing IT services. The decision to adopt SaaS in such a hybrid cloud service composition requires appropriate consideration of the respective service level agreements (SLA) to ensure the proper execution of the supported business process. Previous studies focused strongly on cloud service selection or SLA negotiation rather than functionally desired SaaS adoption from the perspective of the hybrid cloud provider as integrator. This research paper identifies economic and technical issues in hybrid cloud arising from SaaS adoption and discusses challenges and existing solutions. A structured literature survey revealed that 52 issues in 36 identified publications are transferable to our context. These issues were classified in six different challenges. We were able to explore the relations between solution artifacts of various challenges and provide guidance for future research concerning hybrid clouds. The relations between the challenges are also found to vary in their research maturity and offer promising fields for further research.

BEAT: Blockchain-Enabled Accountable and Transparent Network Sharing in 6G

Infrastructure sharing is a widely discussed and implemented approach and is successfully adopted in telecommunications networks today. In practice, it is implemented through prior negotiated service level agreements (SLAs) between the parties involved. However, it is recognized that these agreements are difficult to negotiate, monitor, and enforce. For future 6G networks, resource and infrastructure sharing is expected to play an even greater role. It will be a crucial technique for reducing over-all infrastructure costs and increasing operational efficiencies for operators. More efficient SLA mechanisms are thus crucial to the success of future networks. In this work, we present BEAT, an automated, transparent, and accountable end-to-end architecture for network sharing based on blockchain and smart contracts. This work focuses on a particular type of blockchain, permissioned distributed ledger, due to its permissioned nature allowing for industry-compliant SLAs with stringent governance. Our architecture can be implemented with minimal hardware changes and with minimal overheads.

Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing

The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also taken into account. Currently, there is no such policy of cloud SLA monitoring to minimize SLA violations. In this work, we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts, for critical and non-critical parameters. The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation. This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach (RPAA) which will monitor SLA at runtime, analyze the SLA parameters and try to find the possibility of SLA violations. We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection. We have defined two main components of SLA-PRAA i.e., (a) Handler and (b) Accounting and Billing Manager. We have also described the function of both components through algorithms. The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies.

Broker-based optimization of SLA negotiations in cloud computing

Cloud computing provisions and allocates resources, in advance or real-time, to dynamic applications planned for execution. This is a challenging task as the Cloud-Service-Providers (CSPs) may not have sufficient resources at all times to satisfy the resource requests of the Cloud-Service-Users (CSUs). Further, the CSPs and CSUs have conflicting interests and may have different utilities. Service-Level-Agreement (SLA) negotiations among CSPs and CSUs can address these limitations. User Agents (UAs) negotiate for resources on behalf of the CSUs and help reduce the overall costs for the CSUs and enhance the resource utilization for the CSPs. This research proposes a broker-based mediation framework to optimize the SLA negotiation strategies between UAs and CSPs in Cloud environment. The impact of the proposed framework on utility, negotiation time, and request satisfaction are evaluated. The empirical results show that these strategies favor cooperative negotiation and achieve significantly higher utilities, higher satisfaction, and faster negotiation speed for all the entities involved in the negotiation.

Conceptual Service Level Agreement Mechanism to Minimize the SLA Violation with SLA Negotiation Process in Cloud Computing Environment

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective

Service-Level Agreement Negotiation in Cloud Computing Buying Organizations

The cloud archetype is fundamentally dynamic for both service customer and service provider. From the provider's perspective, resources are removed and/or added irregularly and urgently together with updating of service level agreements (SLAs). However, this dynamical behavior makes contracting sophisticated. Yet, there is a lack of knowledge on how to handle these dynamics contractually. To address this knowledge gap, the authors have investigated “how to improve SLA management with dynamic SLA iterations.” For this reason, a model for cloud computing SLA negotiation is proposed to facilitate the buying organization in determining uncertainty by analyzing attributes of asset specificity and risks associated with them. Survey research has been used for testing the model. The respondents rationalized the SLA negotiation with a better cognitive understanding in all phases within the model. The study's findings could support cloud service buying organizations to minimize the risks with ever-changing requirements.

A Trust Model for SLA Negotiation Candidates Selection in a Dynamic IoT Environment

The Internet of Things envisions billions of physical devices connecting over the Internet to provide a near real-time view of the state of the world. These devices' capabilities can be abstracted as IoT services and provided on demand. To enable quality-aware service provision, Service Level Agreements (SLA) are widely used as legally binding contracts to obligate service providers to comply with a pre-negotiated Quality of Service (QoS). With a possible ever-increasing number of service providers in an IoT environment, multi-bilateral SLA negotiation is likely to be prohibitively time-consuming without an a-priori process to select trusted candidate providers with whom to negotiate. In this article, a trust model is proposed to identify trusted service providers in a dynamic IoT environment before attempting to negotiate an SLA. A trust credit that indicates both the SLA’s fulfillment and the possible negotiation success rate is derived based on historical information relating to a service’s previous negotiations and its monitored run-time performance. Indiscernibility analysis in Rough Set theory is used to predict the negotiation success rate, while Bayesian inference is applied to deduce the possibility of SLA violation according to the monitored data. The simulation results demonstrate the feasibility and efficiency of the proposed trust model.

Cloud service negotiation framework for real-time E-commerce application using game theory decision system

A major demanding issue is developing a Service Level Agreement (SLA) based negotiation framework in the cloud. To provide personalized service access to consumers, a novel Automated Dynamic SLA Negotiation Framework (ADSLANF) is proposed using a dynamic SLA concept to negotiate on service terms and conditions. The existing frameworks exploit a direct negotiation mechanism where the provider and consumer can directly talk to each other, which may not be applicable in the future due to increasing demand on broker-based models. The proposed ADSLANF will take very less total negotiation time due to complicated negotiation mechanisms using a third-party broker agent. Also, a novel game theory decision system will suggest an optimal solution to the negotiating agent at the time of generating a proposal or counter proposal. This optimal suggestion will make the negotiating party aware of the optimal acceptance range of the proposal and avoid the negotiation break off by quickly reaching an agreement.

An automated resource management framework for minimizing SLA violations and negotiation in collaborative cloud

Collaborative Cloud computing is an emergent and encouraging computational prototype for sharing on-demand cloud resources in a multi-cloud environment. In a collaborative cloud environment, we make use of unused computational resources within an interconnected network to provide seamless service to the customers. SLA (Service Level Agreement) means a legal agreement signed among the consumer and the cloud service provider to guarantee a better quality of service. Nowadays due to more user request, suppliers of cloud service are not able to deliver guaranteed Quality of Service (QoS) to the customers leading to SLA violation. Existing SLA- based resource allocation models in today's cloud market consider an agreement for each QoS parameters independently and follow fixed pricing models, which results in lacking an end-to-end approach for the client task that requires composing multiple services and inefficient utilization of computing resources, which has direct negative effect on performance and cost. In this paper, we have designed a framework model for the effective dynamic provision of resources considering various categories of SLAs laterally with their penalty. We used Hybrid Cuckoo Search algorithm for scheduling of user requests in collaborative cloud computing environment. The proposed framework services the customer requests from its interconnected cloud servers and has an SLA Negotiation policy to minimize the penalty cost and maximize customer satisfaction by reducing SLA violations.

Statistical Model Checking-Based Evaluation and Optimization for Cloud Workflow Resource Allocation

Due to the existence of resource variations, it is very challenging for Cloud workflow resource allocation strategies to guarantee a reliable Quality of Service (QoS). Although dozens of resource allocation heuristics have been developed to improve the QoS of Cloud workflow, it is hard to predict their performance under variations because of the lack of accurate modeling and evaluation methods. So far, there is no comprehensive approach that can quantitatively reason the capability of resource allocation strategies or enable the tuning of parameters to optimize resource allocation solutions under variations. To address the above problems, this paper proposes a novel framework that can evaluate and optimize resource allocation strategies effectively and quantitatively. By using the statistical model checker UPPAAL-SMC and supervised learning approaches, our framework can: i) conduct complex QoS queries on resource allocation instances considering resource variations; ii) make quantitative and qualitative comparisons among resource allocation strategies; iii) enable the tuning of parameters to improve the overall QoS; and iv) support the quick optimization of overall workflow QoS under customer requirements and resource variations. The experimental results demonstrate that our automated framework can support both the Service Level Agreement (SLA) negotiation and workflow resource allocation optimization efficiently.

Adaptive SLA mechanism based on fuzzy system for dynamic cloud environment

ABSTRACT The volatile nature of Cloud user’s needs is considered a worthy challenge in formulating the Service Level Agreement (SLA) negotiation. The sterility of the standard SLA templates in handling the diversity in the users’ requirements motives the concept of the dynamic SLA. Establishing the SLA contract is mainly based on converting the user’s QoS requirements into a meaningful Service Level Objectives (SLO) during the negotiation phase, but in case of modification in these QoS requirements especially during the run-time, the current negotiation may be violated and the provider may resort to terminate the current negotiation and re-establish it. This paper presents a novel methodology based on arithmetic and fuzzy logic model to establish a dynamic SLA’s Negotiation system commensurate with the dynamic aspects of the Cloud environment. The proposed negotiation system mainly based on defining pre-conditions and decisions to modify only the required SLO at the run-time without completely hang-up the SLA.

Tiered Cloud Storage via Two-Stage, Latency-Aware Bidding

In cloud storage, the digital data is stored in logical storage pools, backed by heterogeneous physical storage media and computing infrastructure that are managed by a cloud service provider (CSP). One of the key advantages of cloud storage is its elastic pricing mechanism, in which the users need only pay for the resources/services they actually use, e.g., depending on the storage capacity consumed, the number of file accesses per month, and the negotiated service level agreement. To balance the tradeoff between service performance and cost, CSPs often employ different storage tiers, for instance, cold storage and hot storage. Storing data in hot storage incurs high storage cost yet delivers low access latency, whereas cold storage is able to inexpensively store massive amounts of data and thus provides lower cost with higher latency. In this paper, we address a major challenge confronting the CSPs utilizing such tiered storage architecture—how to maximize their overall profit over a variety of storage tiers that offer distinct characteristics, as well as file placement and access request scheduling policies. To this end, we propose a scheme where the CSP offers a two-stage auction process for: 1) requesting storage capacity and 2) requesting accesses with latency requirements. Our two-stage bidding scheme provides a hybrid storage and access optimization framework with the objective of maximizing the CSP’s total net profit over four dimensions: file acceptance decision, placement of accepted files, file access decision and access request scheduling policy. The proposed optimization is a mixed-integer nonlinear program that is hard to solve. We propose an efficient heuristic to relax the integer optimization and to solve the resulting nonlinear stochastic programs. The algorithm is evaluated under different scenarios and with different storage system parameters, and insightful numerical results are reported by comparing the proposed approach with other profit-maximization models. We see a profit increase of over 60% of our proposed method compared to other baseline algorithms in certain simulation scenarios.

Energy-efficient fuzzy-based approach for dynamic virtual machine consolidation

In cloud environments, the overload leads to performance degradation and Service Level Agreement (SLA) violation while underload results in inefficient utilisation of resources and needless energy consumption. Dynamic Virtual Machine (VM) consolidation is considered as an effective solution to deal with both overload and underload problems. However, dynamic VM consolidation is not a trivial solution as it can also lead to violation of negotiated SLA due to runtime overheads in VM migration. Further, dynamic VM consolidation approaches need to answer many questions such as (i) when to migrate a VM? (ii) which VM is to be migrated? and (iii) where to migrate the selected VM? In this work, efforts are made to develop a comprehensive approach to achieve better solution to above discussed problems. In the proposed approach, future forecasting methods for host overload detection are explored; a fuzzy logic based VM selection approach that enhances the performance of VM selection strategy is developed; and a VM placement algorithm based on destination CPU utilisation is also developed. The performance evaluation of the proposed approaches is carried out on CloudSim toolkit using PlanetLab data set. The simulation results have exhibited significant improvement in the number of VM migrations, energy consumption, and SLA violations.

SLA Definition for Multi-tenant DBMS and its Impact on Query Optimization

In the cloud context, users are often called tenants. A cloud DBMS shared by many tenants is called a multi-tenant DBMS. The resource consolidation in such a DBMS allows the tenants to only pay for the resources that they consume, while providing the opportunity for the provider to increase its economic gain. For this, a Service Level Agreement (SLA) is usually established between the provider and a tenant. However, in the current systems, the SLA is often defined by the provider, while the tenant should agree with it before using the service. In addition, only the availability objective is described in the SLA, but not the performance objective. In this paper, an SLA negotiation framework is proposed, in which the provider and the tenant define the performance objective together in a fair way. To demonstrate the feasibility and the advantage of this framework, we evaluate its impact on query optimization. We formally define the problem by including the cost-efficiency aspect, we design a cost model and study the plan search space for this problem, we revise two search methods to adapt to the new context, and we propose a heuristic to solve the resource contention problem caused by concurrent queries of multiple tenants. We also conduct a performance evaluation to show that, our optimization approach (i.e., driven by the SLA) can be much more cost-effective than the traditional approach which always minimizes the query completion time.

SLA-Aware Fine-Grained QoS Provisioning for Multi-Tenant Software-Defined Networks

Multi-tenant software-defined networks have emerged as a promising service platform to manage and share virtual network services between tenants and end-users. However, when using a service level agreement (SLA) in a multi-tenant circumstance, the resource competition among tenants incurred in many challenges on the quality of services (QoS) provisioning. First, as the existing QoS provisioning was to translate the high-level SLA contact into a group of corresponding low-level QoS rules, this traditional top-down translation mechanism was too ossified to support the dynamic SLA negotiation. Second, since an end-user usually requested multiple services and different services had different QoS requirements, the QoS provisioning required tenants to monitor and control the network status though OpenFlow protocol in a fine-grained way. To address these issues, we propose an SLA-aware fine-grained QoS provisioning (SFQP) scheme for MTSDN. The SFQP scheme extracts the eigen characteristics of each packet by application-aware technology automatically. Considering tenants are more willing to serve for users which have popular SLA contact to earn more revenues, we predict the SLA popularity by K-nearest neighbor. Moreover, we present an SLA-aware dynamic bandwidth allocation algorithm to implement the proposed SFQP scheme. The feasibility of the proposed scheme is verified by simulations of the SLA taxonomy, bandwidth utilization, and bandwidth fluctuation.

Decentralized Energy Resource Management Problems of Multi Agent Negotiation Framework

This paper proposes the idea of Distributed Energy Resource (DER) management, in light of dynamic bunching of energy assets for better coordination of free market activity. An audit on load profiling and estimating components is given which to be used in building information, required for nearby and worldwide coordination. To stay away from substantial scale complexities, a decentralized control approach with Multi Agent Negotiation Framework (MANF) is conveyed. An audit on MANF innovations and their application in power framework is displayed. With MANF we give a system to planning maker, shopper and group specialists. These specialists additionally negotiate Service Level Agreements for overseeing free market activity and ideally using the energy assets.

Agent-based fuzzy constraint-directed negotiation for service level agreements in cloud computing

Establishing a service level agreement (SLA) between a cloud provider and a cloud consumer is becoming increasingly critical: consumers expect a specified quality of service (QoS) for their cloud applications, and providers must be able to guarantee that the agreed-upon QoS will be maintained. These concepts require the SLA negotiation to be performed in a manner whereby a provider and a consumer can effectively bargain on various QoS preferences, such as price, response time and service level. This paper presents a novel agent-based fuzzy constraint-directed negotiation (AFCN) model for SLA negotiation. It provides a framework for integrating time, resource (market) and behavioral factors into the decision making process for service level agreements and cloud load balancing. The proposed AFCN model supports an iterative many-to-many bargaining negotiation infrastructure that is a fully distributed and autonomous approach and that does not require a broker to coordinate the negotiation process. The novelty of the proposed model is that it uses the concept of a fuzzy membership function to represent imprecise QoS preferences. This added information sharing is critical for the effectiveness of distributed coordination. It can not only speed up the convergence but also enforce global consistency through iterative exchanges of offers and counter-offers with limited information sharing and without privacy breaches. To consider the behavior of different agents, the AFCN model can also flexibly adopt different negotiation strategies such as the competitive, win-win, and collaborative strategies in different cloud computing environments. The experimental results demonstrate that the proposed model consistently outperforms other agent-based SLA negotiation models in terms of the degree of satisfaction, the ratio of successful negotiation, the buying price of the consumer agent (CA), the revenue of the provider agent (PA), and the convergence speed. Consequently, the proposed AFCN is both flexible and useful for fully distributed SLA negotiations.

Management of Service Level Agreements for Cloud Services in IoT: A Systematic Mapping Study

Cloud computing and Internet of Things (IoT) are computing technologies that provide services to consumers and businesses, allowing organizations to become more agile and flexible. Therefore, ensuring quality of service (QoS) through service-level agreements (SLAs) for such cloud-based services is crucial for both the service providers and service consumers. As SLAs are critical for cloud deployments and wider adoption of cloud services, the management of SLAs in cloud and IoT has thus become an important and essential aspect. This paper investigates the existing research on the management of SLAs in IoT applications that are based on cloud services. For this purpose, a systematic mapping study (a well-defined method) is conducted to identify the published research results that are relevant to SLAs. This paper identifies 328 primary studies and categorizes them into seven main technical classifications: SLA management, SLA definition, SLA modeling, SLA negotiation, SLA monitoring, SLA violation and trustworthiness, and SLA evolution. This paper also summarizes the research types, research contributions, and demographic information in these studies. The evaluation of the results shows that most of the approaches for managing SLAs are applied in academic or controlled experiments with limited industrial settings rather than in real industrial environments. Many studies focus on proposal models and methods to manage SLAs, and there is a lack of focus on the evolution perspective and a lack of adequate tool support to facilitate practitioners in their SLA management activities. Moreover, the scarce number of studies focusing on concrete metrics for qualitative or quantitative assessment of QoS in SLAs urges the need for in-depth research on metrics definition and measurements for SLAs.

Cloud Customer's Historical Record Based Resource Pricing

Media content in its digital form has been rapidly scaling up, resulting in popularity gain of cloud computing. Cloud computing makes it easy to manage the vastly increasing digital content. Moreover, additional features like, omnipresent access, further service creation, discovery of services, and resource management also play an important role in this regard. The forthcoming era is interoperability of multiple clouds, known as cloud federation or inter-cloud computing. With cloud federation, services would be provided through two or more clouds. Once matured and standardized, inter-cloud computing is supposed to provide services which would be more scalable, better managed, and efficient. Such tasks are provided through a middleware entity called cloud broker. A broker is responsible for reserving resources, managing them, discovering services according to customer's demands, Service Level Agreement (SLA) negotiation, and match-making between the involved service provider and the customer. So far existing studies discuss brokerage in a narrow focused way. In the research outcome presented in this paper, we provide a holistic brokerage model to manage on-demand and advance service reservation, pricing, and reimbursement. A unique feature of this study is that we have considered dynamic management of customer's characteristics and historical record in evaluating the economics related factors. Additionally, a mechanism of incentive and penalties is provided, which helps in trust build-up for the customers and service providers, prevention of resource underutilization, and profit gain for the involved entities. For practical implications, the framework is modeled on Amazon Elastic Compute Cloud (EC2) On-Demand and Reserved Instances service pricing. For certain features required in the model, data was gathered from Google Cluster trace.