Cloud Service Level Agreement Research Articles

Load balancing in cloud computing via intelligent PSO-based feedback controller

Load balancing effectively distributes network load and balances the load during the scheduling and allocation process. Hence various load balancing techniques in task scheduling and resource allocation along with VM migration has been presented previously but they have a heavy load on some VM and violate cloud service level agreement with a single point of failure. Therefore, a novel Intelligent PSO-based Feedback Controller has been proposed with regulated Scheduling, Allocation, and VM migration to perform optimal load balancing. In this proposed technique, a novel Intelligent Weighted filtering based PSO Approach is used to reduce computation time during task scheduling and resource allocation. This approach uses a multi-objective PSO algorithm with Pareto dominance to achieve high quality of service, throughput, scalability, low response time, and optimal bilateral transposed conv filtering. Moreover, during VM migration existing techniques result in service level agreement violations owing to inefficient VM placement among PMs. To overcome these issues, a Double Deep Q proximal model with a feedback controller has been proposed. The double weight set in the offline and online updating process in the decision model maintains a smooth service level agreement with the cloud. Also, centralized and decentralized controller algorithm fails with a single point of failure and coordination issue in complicated situations with instruction mixing of processes. Finally, the conditional GAN feedback controller has been used to eliminate a single point of failure with high fault tolerance, low energy consumption and migration time.

Towards Automated and Optimized Security Orchestration in Cloud SLA

In cloud computing, providers pool their resources and make them available to customers. Next-generation computer scientists are flocking to the cutting-edge field of cloud computing for their research and exploration of uncharted territory. There are still several barriers that cloud service providers must overcome in order to provide cloud services in accordance with service level agreements. Each cloud service provider aspires to achieve maximum performance as per Service Level Agreements (SLAs), and this is especially true when it comes to the delivery of services. A cloud service level agreement (SLA) guarantees that cloud service providers will satisfy the needs of large businesses and offer their clients with a specified list of services. The authors offer a web service level agreement–inspired approach for cloud service agreements. We adopt patterns and antipatterns to symbolize the best and worst practices of OCCI (Open Cloud Computing Interface Standard), REST (Representational State Transfer), and TOSCA (Topology and Orchestration Specification for Cloud Applications) with DevOps solutions, all of which API developers should bear in mind when designing APIs. When using this method, everything pertaining to the cloud service, from creation to deployment to measurement to evaluation to management to termination, may be handled mechanically. When distributing resources to cloud apps, our system takes into account the likelihood of SLA breaches and responds by providing more resources if necessary. We say that for optimal performance, our suggested solution should be used in a private cloud computing setting. As more and more people rely on cloud computing for their day-to-day workloads, there has been a corresponding rise in the need for efficient orchestration and management strategies that foster interoperability.

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.

Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System

Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

AWESOME: an auction and witness enhanced SLA model for decentralized cloud marketplaces

In recent decades, the world has witnessed cloud computing as an essential technology that changes the traditional application Development and Operation (DevOps) lifecycle. However, current cloud software DevOps and Service Level Agreement (SLA) management often face challenges of 1) selecting the best fitting service providers, customizing services and planning capacities for large-scale distributed applications; 2) guaranteeing high-quality and trustworthy SLAs among multiple service providers; 3) enhancing the interoperability of cloud services across different providers; and 4) designing effective incentive models among stakeholders. This paper proposes a novel framework called Auction and Witness Enhanced trustworthy SLA for Open, decentralized service MarkEtplaces (AWESOME) to build a trustworthy cloud marketplace and address the above challenges. The proposed framework contains four subsystems: a customizable graphical user interface, an auction-based service selection model, a witness committee management mechanism, and a smart contract factory orchestration. We developed a prototype AWESOME decentralized application (DApp) based on the Ethereum blockchain. Extensive experiments are designed to evaluate the latency and cost of our model. The experimental results demonstrate that our model is economical and feasible.

An improved discrete harris hawk optimization algorithm for efficient workflow scheduling in multi-fog computing

Workflow scheduling in fog computing is an NP-hard problem that tries to allocate the best possible set of resources for the workflows considering various objectives such as deadlines, costs, energy, and Quality of Service (QoS). However, fog providers may be under heavy loads from the IoT networks, leading to Service Level Agreement (SLA) violations. This article considers an architecture consisting of multiple fog computing providers and provides a Hidden Markov Model (HMM) model for predicting the availability of each fog computing provider regarding factors such as the number of incoming requests to each fog, deadline-missed workflows, and the offloaded tasks from the fogs to the cloud computing. This HMM model is trained using the unsupervised Baum-Welch algorithm, and the availability probability of each fog is computed using the Viterbi algorithm. Then, the fog provider availability probability is used to select a fog computing provider and schedule IoT workflows on it. Additionally, we have improved Harris Hawks Optimization (HHO) algorithm and presented a Discrete Opposition-based of this algorithm, denoted as DO-HHO, for scientific workflow scheduling. The results of extensive experiments conducted using iFogSim demonstrate that our proposed scheme can significantly reduce offloaded tasks on cloud computing, deadline-missed workflows, and SLA violations, outperforming state-of-the-art works.

A Semantically Rich Framework to Automate Cloud Service Level Agreements

Consumers evaluate and choose cloud-based services based on the Service Level Agreements (SLA). These agreements list the service terms and metrics to be agreed upon by the service providers and the customers. Current cloud SLAs are text documents that require significant manual effort to parse and determine if providers meet the SLAs. Moreover, due to the lack of standardization, providers differ in the way they define the terms and metrics, making it more difficult to compare different provider SLAs. We have developed a novel framework to significantly automate the process of extracting knowledge embedded in cloud SLAs and representing it in a semantically rich knowledge graph helping the user to make a calculated decision in choosing a provider. Our framework captures the key terms, measures, and deontic rules, in the form of obligations and permissions present in the cloud SLAs. In this paper, we discuss our framework, technique, and challenges in automating the cloud services agreement. We also describe our results and their validation against well-established standards.

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.

Hybrid Cloud SLAs for Industry 4.0: Bridging the Gap

Hybrid Cloud Service Level Agreements (SLA) comprises of the legal terms and conditions for the cloud contract. Even though all the service level objectives, metrics and service descriptions are clearly outlined in the cloud SLA contract, sometimes vendors fail to meet the promised services and confusing terms lead to tenant-vendor cloud legal battles. Hybrid Cloud involves two different cloud models (public and private) working together, applications running under the hybrid cloud are subject to different availability sets, functionality and parameters developing SLA complexity and ambiguity. The new manufacturing environment (Industry 4.0 concept) is based on a fully connected, intelligent and automated factory, which will highly be dependent on cloud computing and IoT-based solutions for data analytics, storage and computational needs. In situations where Hybrid cloud services are not defined and managed properly may result in Industrial-IoT data security issues leading to financial and data losses. This paper discusses various aspects of the cloud service level agreement in Industry 4.0 for better understanding and implementation and puts a light on the issues that arise out of imprecise statements.

Enhancing service maintainability by monitoring and auditing SLA in cloud computing

Enforcing Service Level Agreements (SLA) on service provisioning is a challenge in cloud computing environments. This paper proposes an architecture for multiparty (provider and client) auditing in cloud computing to identify SLA deviations. The architecture uses inspectors (software agents) and an independent auditor (third party) to collect SLA metrics from these parties. Privacy is preserved by using the separation of duties for all associated entities (inspectors and auditors). Additionally, service computing surges are automatically detected and handled using machine learning, avoiding performance bottlenecks and misinterpretation of measured SLA items. Thus, this paper improves service maintainability by avoiding service design changes when the service faces performance issues.

Analyzing CSP Trustworthiness and Predicting Cloud Service Performance.

Analytics firm Cyence estimated Amazon's four-hour cloud computing outage in 2017 "cost S&P 500 companies at least $150 million" and traffic monitoring firm Apica claimed "54 of the top 100 online retailers saw site performance slump by at least 20 percent". According to Ponemon, 2015 data center outages cost Fortune 1000 companies between $1.25 and $2.5 billion. Despite potential risks, the cloud computing industry continues to grow. For example, Internet of Things, which is projected to grow 266% between 2013 and 2020, will drive increased demand on cloud computing as data across multiple industries is collected and sent back to cloud data centers for processing. RightScale estimates enterprises will continue to increase cloud demand with 85% having multi-cloud strategies. This growth and dependency will influence risk exposure and potential for impact (e.g. availability, performance, security, financial). The research in this paper and proposed solution calculates cloud service provider (CSP) trustworthiness levels and predicts cloud service and cloud service level agreement (SLA) availability performance. Evolving industry standards (e.g. NIST, ISO/IEC) for cloud SLAs and existing work regarding CSP trustworthiness will be leveraged as regression-based predictive models are constructed to analyze CSP cloud computing services, SLA performance and CSP trustworthiness.

Cloud Marginal Resource Allocation: A Decision Support Model

One of the significant challenges for cloud providers is how to manage resources wisely and how to form a viable service level agreement (SLA) with consumers to avoid any violation or penalties. Some consumers make an agreement for a fixed amount of resources, these being the required resources that are needed to execute its business. Consumers may need additional resources on top of these fixed resources, known as– marginal resources that are only consumed and paid for in case of an increase in business demand. In such contracts, both parties agree on a pricing model in which a consumer pays upfront only for the fixed resources and pays for the marginal resources when they are used. A marginal resource allocation is a challenge for service provider particularly small- to medium-sized service providers as it can affect the usage of their resources and consequently their profits. This paper proposes a novel marginal resource allocation decision support model to assist cloud providers to manage the cloud SLAs before its execution, covering all possible scenarios, including whether a consumer is new or not, and whether the consumer requests the same or different marginal resources. The model relies on the capabilities of the user-based collaborative filtering method with an enhanced top-k nearest neighbor algorithm and a fuzzy logic system to make a decision. The proposed framework assists cloud providers manage their resources in an optimal way and avoid violations or penalties. Finally, the performance of the proposed model is shown through a cloud scenario which demonstrates that our proposed approach can assists cloud providers to manage their resources wisely to avoid violations.

Enforcing trustworthy cloud SLA with witnesses: A game theory-based model using smart contracts.

SummaryThere lacks trust between the cloud customer and provider to enforce traditional cloud SLA (Service Level Agreement) where the blockchain technique seems a promising solution. However, current explorations still face challenges to prove that the off‐chain SLO (Service Level Objective) violations really happen before recorded into the on‐chain transactions. In this paper, a witness model is proposed implemented with smart contracts to solve this trust issue. The introduced role, “Witness”, gains rewards as an incentive for performing the SLO violation report, and the payoff function is carefully designed in a way that the witness has to tell the truth, for maximizing the rewards. This fact that the witness has to be honest is analyzed and proved using the Nash Equilibrium principle of game theory. For ensuring the chosen witnesses are random and independent, an unbiased selection algorithm is proposed to avoid possible collusions. An auditing mechanism is also introduced to detect potential malicious witnesses. Specifically, we define three types of malicious behaviors and propose quantitative indicators to audit and detect these behaviors. Moreover, experimental studies based on Ethereum blockchain demonstrate the proposed model is feasible, and indicate that the performance, ie, transaction fee, of each interface follows the design expectations.

ESNemble: an Echo State Network-based ensemble for workload prediction and resource allocation of Web applications in the cloud

Workload prediction is an essential prerequisite to allocate resources efficiently and maintain service level agreements in cloud computing environment. However, the best solution for a prediction task may not be a single model due to the challenge of varied characteristics of different systems. Thus, in this work, we propose an ensemble model, namely ESNemble, based on echo state network (ESN) for workload time series forecasting. ESNemble consists of four main steps, including features selection using ESN reservoirs, dimensionality reduction using kernel principal component analysis, features aggregation using matrices concatenation, and regression using least absolute shrinkage and selection operator for final predictions. In addition, necessary hyperparameters for ESNemble are optimized using genetic algorithm. For experimental evaluation, we have used ESNemble to combine five different prediction algorithms on three recent logs extracted from real-world web servers. Through our experimental results, we have shown that ESNemble outperforms all component models in terms of accuracy and resource allocation and presented the running time of our model to show the feasibility of our model in real-world applications.

Response time-based resource allocation according to service level agreements in cloud computing

Response time-based resource allocation according to service level agreements in cloud computing

Cloud Service Level Agreements and Resource Management

Cloud Service Level Agreements and Resource Management

Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs

In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider’s perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties.

Dynamic Search Engine Platform for Cloud Service Level Agreements Using Semantic Annotation

This article describes how cloud applications are negotiated, deployed, monitored, evaluated and terminated through the service level agreements (SLA). The service definition & their objectives, performance measures, pricing, roles of the involved parties are stated as part of the SLA. Searching for SLA templates from the provider's place is considered as a cumbersome process for the consumer. Also, it is not guaranteed that retrieved SLAs always match with the consumer requirements. Hence, semantic search engine platforms for cloud SLA using a novel architecture are introduced here. SLA agreements are crawled from the web and annotation is performed in the agreement terms using SLA ontologies to fasten and improve the accuracy of the search process. In the proposed architecture, 3 ontologies are developed for SaaS, PaaS and IaaS as well as 140 SLA documents are gathered. Results revealed that the search efficacy is almost 90% in finding the desired SLA for the consumer to ease negotiation. Moreover, the performance is compared with similar search engine GoNTogle, and it was observed that proposed model produced good results.

Cloud SLA Terms Analysis Based On Ontology

Services in cloud computing run under specified constraints defined in Service Level Agreement (SLA) which becomes the basic core that guarantee the Quality of Service (QoS). However, the ambiguity in agreement terms makes the consumer face new challenges especially in understanding and analyzing SLA document. These challenges increasingly rise when the consumer uses services from multiple providers; since each one has its own SLA terminology. In this paper, we have significantly automated the process of managing and analyzing cloud SLA using semantic web technologies like OWL (Ontology Web Language), SWRL (Semantic Web Rule Language) and SQWRL (Semantic Query-Enhanced Web Rule Language). We describe the cloud SLA modeling and analyzing approach that assists consumers automatically analyze terms of various SLA documents. A prototype implementation demonstrates the feasibility and the efficiency of our approach.

A Three-Dimensional Performance Measurement Model for Cloud Computing

New paradigms for processing and storing data such as cloud computing require new approaches for the measurement of cloud service performance. To establish a Service Level Agreement (SLA) between a cloud service provider and its customers, the cloud services and their service level objectives need to be identified. An additional challenge in the performance measurement of cloud services is the lack of models that integrate the different perspectives of providers, maintainers and customers within the same model in order to define the concepts commonly used in cloud SLA contracts. This work proposes a three-dimensional Performance Measurement Model for Cloud Computing (P2M2C-3D) which consolidates performance measurement from the perspectives of providers, maintainers and customers for the different types of cloud services.