OUTLINE:
Whitepaper:
Service Level Agreements
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Self-Assessment with Hyperledger Fabric
Purpose of This Paper
The purpose of this paper is to describe and build define a framework prototype for Service Level Agreement (SLA) prototype Agreements (SLAs) Self-Assessment on Hyperledger Fabric. The proposed approach incorporates SLA Self-Assessment on Hyperledger Fabric using Composer while using existing software components as much as possible.
Our participants will leverage knowledge Trusted Execution Environments (TEEs).
Knowledge gathered during earlier collaborative projects to be able to deliver effectively and to not reinvent the wheelis leveraged, such as Fabric Private Chaincode[1].
Intended Audience
Our The intended audience is CIO's, CDO's, CTO's and Developers working in organizations whose performance is dependent on meeting Service Level Agreements both internally and externally. Our proposed solution will adhere to the Service Provider - Vendor model but can also be aligned to other technical service management models like those described in the ITIL methodologycomprises of various organization representatives, their developers, and their clientele, contracting with different SLA types. The proposed approach aims to cover dissimilar types of SLAs through a generic approach model where the requested actors' roles (e.g. service provider, service vendor, service seller, application provider, application client, customer, end-user, etc.) and contract activity are defined and addressed completely inside the dedicated framework.
1. Introduction
Service Level Agreement (SLA)
A Service Level Agreement A service level agreement (SLA) quantifies the details defines the promised service(s) of a business contract interrelationship between two business entities (in our case a service provider and a service vendor). These details include the minimum quality of the service provided, service uptime commitments (reliability) and technical support availability (responsiveness). Depending on the nature of them (internal, external etc.), the breach of SLAs could have legal consequences for the breaching party.(2) actor entities. A promised service is offered by the actor that provides the service to their customer that receives it. Particular parameters and metrics of such services include service availability, minimum Quality of Service (QoS), service up-time commitments (i.e. reliability), technical support responsiveness, etc. In case that the promised service metrics of the provider actor do not match to the actual offered service metrics of the customer actor (i.e. the receiver of the service), SLA violation(s) occur with possible legal consequences for the entity that provides them.
SLA Monitoring and Computation
The process of SLA Monitoring requires specific questions to be answered:
Which parameters are contained in the SLA?
How are SLA parameters computed?
Is the computation of the parameters in line with the definition of the provider actor?
In order to validly monitor an SLA, crucial points are found on the definition of the SLA parameters' values which acknowledge an SLA as violated, and on the ways through which the parameters' values are computed. The respective metrics computation are fully dependent on factors such as the sampling rate, the period of evaluation and the formula that calculates the parameters.
Envisioning the concept of SLA self-assessment, this whitepaper aims to present a securely computational privacy environment where the deployed agreements are monitored and their metrics computed inside the immutable ecosystem.
SLA Self-Assessment
The provider actor is able to propose different parameters that state an SLA definition and its parametric values, while the client actor engages confidently in a secure ecosystem where their metric data related to services consumption and utilization are characterized by integrity and precision. Computational transparency and privacy of metric data is assured through the dedicated computational workflow that adopts TEE properties. The entire SLA intelligence unfolds within TEE-deployed smart contracts ensuring the isolated and protected monitoring and computation of the SLA metric data. This framework prototype allows for the independent assessment of SLAs with agreed metrics rules prior to the contract commitment.
2. Existing Solutions
In relevant scientific research, the following approaches aim to solve important problems in neighboring scientific areas without addressing all the SLA assessment questions through a complete ecosystem that relies on transparency and privacy.
- Nguyen et al. [2] present an evaluating and enforcing architecture for SLA agreements based on distributed ledgers. Their approach relies on keeping the SLA assessment procedure safe and unmodified due to blockchain immutability while utilizing an automated SLA monitoring and computation process that takes place within the blockchain infrastructure and guarantees the successful completion of the SLA evaluation with status acknowledgment for the end-user. However, their solution does not perceive the system in a whole decentralization model, neglecting the possibilities for fairer agreements in terms of transparency and privacy.
Similar approaches are bounded by the same kind of models where the SLA intelligence lacks a holistic view, while privacy and transparency issues should be tackled. Ranchal and Choudhury [3] present an autonomous and trusted framework for unceasing SLA monitoring in multi-cloud ecosystems. In order to fairly detect SLA violations in a hierarchical system structure, their solution is aiming to address the SLA assessment procedure in a multilevel cloud environment with diverse regulations and laws. Furthermore, Alowayed et al. [4] propose a provider evaluation solution according to the providers’ commitments to their interconnection SLA agreements. Through a metric measurement mechanism the SLA scores are verified for each provider towards their on chain evaluation. By endorsing a privacy-preserving protocol for SLA agreements, it is pursued to objectively define the provider's SLA score and privately store it on chain in order for the interested end-user to access. - Other approaches suggest more focused solutions as far as the privacy of the blockchain participants is concerned, however, they still do not tackle entirely the on chain privacy of the user data from third blockchain parties. Uriarte et al. [5] present an SLA management framework that resolves the specification and enforcement of dynamic SLAs that track and define the service parameter which render SLAs changes over time. The proposed architecture manages to convert an SLA to its smart contract equivalent that dynamically unfolds service provisioning and sequentially generates objective measurements for the SLA assessment through a federation of monitoring entities. In similar research, Alzubaidi et al. [6] proposed on chain assessing SLA compliance and consequences enforcement through dependability validation. By employing a diagnostic accuracy method, trust is assumed in service providers in order to acknowledge SLA breach incidents and execute the corresponding compensations.
- Finally, other approaches try to address the related research area, however, they lack the kind of simplicity and utility in the system's workflow as far as the actor users participation is concerned. D’Angelo et al. [7] inspect the challenges for enforcing accountability in Cloud infrastructures where SLA violations form an important and usual circumstance while arguing that blockchains seem to establish a key contributor towards accountable Clouds. Also, Tan et al. [8] propose a novel performing and safe SLA model where the trust among the different actors are addressed through blockchain. There is a clear argument on lack of effective supervision for the third-parties that manage the monitoring and lack of efficient compensation mechanism on SLA violations. The presented model supervises the provider actors on the blockchain with dedicated smart contracts.
| Author | Topic | Neighboring Area | SLA Self-Assessment on Hyperledger Fabric |
|---|---|---|---|
| Nguyen et al. [2] | SLA Assessment | SLA Evaluation | Private from Third-Parties |
| Ranchal et al. [3] | Multicloud SLA | SLA Monitoring | Different Rules in Single Blockchain |
| Alowayed et al. [4] | Cloud IaaS Evaluation | Privacy-Preserving Protocol | Distributed and Enclaved Operations |
| Uriarte et al. [5] | Dynamic SLA | SLA Provisioning | Dynamic SLA Self-Assessment |
| Alzubaidi et al. [6] | SLA Compliance | SLA Dependability Validation | Trustless of Cloud IaaS |
| D’Angelo et al. [7] | Cloud Accountability | Blockchain SLA Monitoring | SLA Self-Assessment |
| Tan et al. [8] | Performant SLA | Cloud IaaS Supervision | Without On-chain Intermediaries |
3. Architectural Approach
To be completed.
4. Framework Prototype Solution
To be completed.
5. Conclusions
To be completed.
References
[1] Hyperledger Fabric Private Chaincode: https://github.com/hyperledger/fabric-private-chaincode
[2] Nguyen, T.-V.; Lê, T.-V.; Dao, B.; Nguyen-An, K. Leveraging Blockchain in Monitoring SLA-Oriented Tourism Service Provisioning. In Proceedings of the International Conference on Advanced Computing and Applications (ACOMP), Nha Trang, Vietnam, 26–28 November 2019; pp. 42–50.
[3] Ranchal, R.; Choudhury, O. SLAM: A Framework for SLA Management in Multicloud ecosystem using Blockchain. In Proceedings of the IEEE Cloud Summit, Harrisburg, PA, USA, 21–22 October 2020; pp. 33–38.
[4] Alowayed, Y.; Canini, M.; Marcos, P.; Chiesa, M.; Barcellos, M. Picking a Partner: A Fair Blockchain Based Scoring Protocol for Autonomous Systems. Proc. Appl. Netw. Res. Workshop 2018, 33–39.
[5] Uriarte, R.B.; Zhou, H.; Kritikos, K.; Shi, Z.; Zhao, Z.; De Nicola, R. Distributed service-level agreement management with smart contracts and blockchain. Concurr. Comput. Pract. Exper 2021, 33, e5800.
[6] Alzubaidi, A.; Mitra, K.; Patel, P.; Solaiman, E. A Blockchain-based Approach for Assessing Compliance with SLA-guaranteed IoT Services. In Proceedings of the IEEE International Conference on Smart Internet of Things (SmartIoT), Beijing, China, 14–16 August 2020; pp. 213–220.
[7] D’Angelo, G.; Ferretti, S.; Marzolla, M. A Blockchain-based Flight Data Recorder for Cloud Accountability. In Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems, Munich, Germany, 15 June 2018; pp. 93–98.
[8] Tan, W.; Zhu, H.; Tan, J.; Zhao, Y.; Xu, L.D.; Guo, K. A novel service level agreement model using blockchain and smart contract for cloud manufacturing in industry 4.0. Enterp. Inf. Syst. 2021, 1–26.
License
This work is licensed under a Creative Commons Attribution 4.0 International License, creativecommons.org/licenses/by/4.0.
Acknowledgements
Kapsoulis, Nikolaos
Psychas, Alexandros
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OLDER Content
The steps of an SLA can be introduced as follow:
1- Negotiation and agreement: An essential step to agree on an SLA is quantifying the service levels using performance metrics. For instance in ISP/Customer SLAs, the service uptime is typically provided by a percentage e.g., 99.999%.
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