Multi-Layered Attack Recognition (MLAR) Model to Protect Cloud From EDOS Attacks

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

Cloud computing is very promising technology to deliver computing resources like infrastructure (IaaS), platform (PaaS), software (SaaS) etc. in form of services over the Internet. Server consolidation is the mechanism to minimize active (running) physical servers in the data center of cloud service provider. Consolidation helps the service provider to make efficient usage data center resources and in turn to minimize running cost of data center. Users have to pay as per service usage. SLA is like contract between cloud service provider and users. Service Level Agreement (SLA) is the mechanism of providing guaranteed services to cloud users. SLA Violation may cause high penalties to service provider. In this paper it is shown that if consolidation is maximized, it may cause more number of SLA violation. A balanced approach of server consolidation with minimal SLA violations is suggested in this paper.

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in cloud data centers. However, aggressive consolidation of VMs may lead to performance degradation of data centers and even Service Level Agreement (SLA) violation. Therefore, it is very important to solve the tradeoff between reduction of SLA violation level and energy costs. In this paper, we propose a Host State 3-order Markov Chain prediction model for SLA-aware and energy efficient consolidation of VMs in cloud data centers. We propose two adaptive utilization thresholds based on a robust statistical method for host state estimate, and our model uses them to build a 3-order Markov Chain Transition Matrix manually for host state detection. We implement our algorithms on Cloudsim simulator for comparative study. The experimental results show that our model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLA Violation by at most 98.69% and the metric of Energy by at most 21.88% for real world workload.

This work presents a novel scheme for Service Level Agreements (SLAs) in a service-oriented architecture (SOA). An SLA is a contract that guarantees quality of service (QoS) between service providers and consumers. An SLA can be split into several parts for an SOA system which is consisting of different services. A split SLA is used to guarantee quality of services for one of services in an SOA system. However, these contract rules do not regulate follow-up process in services. SLA violations may occur consequently. For instance, interactions between services are not encrypted because the encryption parameter isn't included in the split SLA contract. Confidential data would be transmitted through unsafe channels. There would be a security issue of SLA Management for follow-up processes in an SOA system. The novel scheme in this work addresses these problems. Using SLAs management proposed in this work can identify the services that consumer needs and split the contracts for follow-up services. SLAs are not only guaranteed between service providers and consumers but also assured in follow-up processes. The proposed method improves the security and completeness of SLA using in an SOA system.

Virtual machine (VM) consolidation in Cloud computing environments provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. The main contribution of us is a Service Level Agreement (SLA) violation decision algorithm, which is used to decide a host is overload with SLA violation or not. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work, which has 11.8%~27% decrease in energy consumption, 57.9%~78.4% decrease in SLA violation, 63.2%~84.1% decrease in energy-performance metric which is a product metric of the energy consumption and SLA violation.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

Service level agreement (SLA) is a contract between the service provider (SP) and the customer. Among various parameters, SLA imposes penalties on the SP when the customer's quality-of-service (QoS) requirements are violated. Hence, it is desirable to avoid SLA violations and such penalties. SLA also states the maximum amount of downtime a connection may tolerate during its lifetime. In this study, we present a dynamic provisioning scheme called Preemption-Oriented SLA-Aware Provisioning (POSAP) for telecom mesh networks, which considers a connection's (1) availability requirements, (2) penalty, and (3) state. In addition to the connection's various parameters (availability, penalty, etc.), the amount of affordable downtime is used as a metric to determine the risk of violating the connection's SLA, and hence, its priority. Connection's priority is denoted by urgency level. Our scheme allows resource preemption among connections, i.e., high-priority connections may preempt backup resources from low-priority connections. Also, new connection requests may preempt resources from existing connections. Urgency level determines how resources can be preempted. Our scheme is applied to a US-wide network. Our results show the improved performance in terms of (1) reduced SLA violations, (2) increased network utilization, and (3) increased network revenue, compared to a dedicated primary-backup (P-B) scheme.

Service Level Agreements (SLA) represents the principal means to control the Quality of Service (QoS). In cloud computing, various advanced SLA strategies are used; some techniques among others try to avoid the SLA violations. Taking into account the cloud consumer contextual parameters is a promising way to predict and avoid costly SLA violations. Therefore, in this paper, we propose a context-aware system for the SLA in order to guarantee the QoS. We create a contextual ontology to introduce the semantic meaning of the cloud consumer contextual parameters. In addition, using reasoning techniques, we (1) predict SLA violations and we (2) choose and apply the corrective actions in autonomous manner. This allows getting a proactive adjustment of the cloud service execution and respecting the SLA parameters.

Cloud computing is a promising technology for the realization of large, scalable, and on-demand provisioned computing infrastructures. Currently, many enterprises are adopting Clouds to achieve high performance and scalability for their applications while maintaining low costs. Service provisioning in the Cloud is based on a set of predefined nonfunctional properties specified and negotiated by means of Service Level Agreements (SLAs). Cloud workloads are dynamic and change constantly. Thus, in order to reduce steady human interactions, self-manageable Cloud techniques are required to comply with the agreed customers' SLAs. Flexible and reliable management of SLAs is of paramount importance for both, Cloud providers and consumers. On one hand, the prevention of SLA violations avoids penalties that are costly to providers. On the other hand, based on flexible and timely reactions to possible SLA violation threats, user interaction with the system can be minimized enabling Cloud computing to take roots as a flexible and reliable form of on-demand computing. Furthermore, a trade-off has to be found between proactive actions that prevent SLA violations and those that reduce energy consumption, i.e., increase energy efficiency. In this talk we discuss how the application of Cloud computing technologies can support the work of scientists working in the field of high-throughput sequencing, while at the same time optimizing utilization of resource and increasing energy efficiency.

Service level agreement (SLA) is a formal contract between a service provider and a service consumer to guarantee the Quality of service (QoS) expectations, it is used in all areas of information technology, such as Cloud Computing, Internet of Things (IoT), networks and Web services. For IoT applications, the main challenges are: (1) how to describe the SLA terms, such as QoS properties, service levels, penalties in SLA violation, (2) how to monitor these terms, and (3) how to integrate an SLA into all the IoT application layers. Therefore, in this paper, we propose ML-SLA-IoT, a framework for SLA specification and monitoring. It covers the entire layers of an IoT application. It describes precisely QoS levels, provided services, and obligations. It differs from other SLA specification languages in the specification of user preferences, the use of microservices (for reusability, dynamism and ease of integration) and ML-SLA (multi-level metrics and QoS according to existing constraints and user preferences). Furthermore, ML-SLA-IoT monitors SLA terms in an automatic and decentralised manner using smart contracts and blockchain technologies without the intervention of the third-party. Finally, we present a comparative study and experiments with existing solutions regarding to some criteria for SLA specification and monitoring. Our results show that ML-SLA-IoT gives better performance in terms of dynamic pricing, obligation combination, and SLA monitoring.

With the fast development of cloud computing methods, exponential growth is faced by several users. It is complicated for traditional data centers to perform several jobs in real-time because of inadequate bandwidth resources. Therefore, the method of fog computing is recommended for supporting and providing fast cloud services. It is not a substitute but is a powerful complement to cloud computing. The reduction of energy consumption through the notion of fog computing has certainly been a challenge for current researchers, industries, and communities. Various industries, including finance and healthcare, require a rich resource-based platform for processing large amounts of data with cloud computing across fog architecture. The consumption of energy across fog servers relies on allocating techniques for services (user requests). It facilitates processing at the edge with the probability of interacting with the cloud. This article proposed energy-aware scheduling by using Artificial Neural Network (ANN) and Modified Multiobjective Job Scheduling (MMJS) techniques. The emphasis of the work is on the reduction of energy consumption rate with less Service Level Agreement (SLA) violation in fog computing for data centers. The result shows that there is a 3.9% reduction in SLA violation when a multiobjective function with Artificial Neural Network is applied.

Currently, from the customer's opinion, the establishment and monitoring of Service Level Agreements (SLA) between cloud service consumers (CSC) and cloud service providers (CSP) is far to be ideal. This contacts require crucial importance for consumers having companies whose success is conditioned by the announced Quality of Service (QoS) that the service provider delivers. Inflexible term and price conditions, nontransparent verification of advertised service quality, and inflexible expression of SLAs represent today the most gap in terms of SLAs on the cloud market. This paper presents an approach of an autonomous framework based Agreement-Broker as a third party in the cloud market that provides a way to source out tasks regarding the management of SLAs. Thus the Agreement-Broker adds an additional transitive connection between consumers and providers without affecting any existing one. The Agreement-Broker's main purposes are: –Registering declarations in the form of established SLAs; –Giving customers the opportunity to discover provided SLAs and to easily compare them; –Notifying participating parties about agreementrelated events like creation, violation or termination Thus, the overhead for managing SLAs which causes congestion between consumers and providers is minimized for both and a higher trust is established by broker of an external entity.

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) metrics like reliability, availability, performance, and cost determine which resources and services are available in a cloud computing scenario. Provider and the user-specified performance characteristics, such as, rejection rate, throughput, response time, financial cost, and energy consumption, form the basis for QoS. To fulfil the needs of its customers, cloud computing must ensure that its services are given with the appropriate quality of service QoS. A “A legally enforceable agreement known as a “Service Level Agreement” (SLA) between a service provider and a customer that outlines service objectives, quality of service requirements, and any associated financial penalties for falling short. We, therefore, presented “A Proactive Resource Supply based Run-time Monitoring of SLA in Cloud Computing”, which allows for the proactive management of SLAs during run-time via the provisioning of cloud services and resources. Within the framework of the proposed work, SLAs are negotiated between cloud users and providers at run-time utilizing SLA Manager. Resources are proactively allocated via the Resource Manager to cut down on SLA violations and misdetection costs. As metrics of performance, we looked at the frequency with which SLAs were broken and the money lost due to false positives. We compared the proposed PRP-RM-SLA model’s simulated performance to the popular existing SLA-based allocation strategy SCOOTER. According to simulation data, the suggested PRP-RM-SLA model is 25% more effective than the current work SCOOTER at reducing SLA breaches and the cost of misdetection.

Service Level Agreements (SLA) are commonly used to specify the quality attributes between cloud service providers and the customers. A violation of SLAs can result in high penalties. To allow the analysis of SLA compliance before the services are deployed, we describe in this paper an approach for SLA-aware deployment of services on the cloud, and illustrate its workflow by means of a case study. The approach is based on formal models combined with static analysis tools and generated runtime monitors. As such, it fits well within a methodology combining software development with information technology operations (DevOps).

Service level agreements (SLAs) gain more and more importance in the area of cloud computing. An SLA is a contract between a customer and a cloud service provider (CSP) in which the CSP guarantees functional and non-functional quality of service parameters for cloud services. Since CSPs have to pay for the hardware used as well as penalties for violating SLAs, they are eager to fulfill these agreements while at the same time optimizing the utilization of their resources.In this paper we examine SLA-aware VM scheduling strategies for cloud data centers. The service level objectives considered are resource usage and availability. The sample resources are CPU and RAM. They can be overprovisioned by the CSPs which is the main leverage to increase their revenue. The availability of a VM is affected by migrating it within and between data centers. To get realistic results, we simulate the effect of the strategies using the FederatedCloudSim framework and real-world workload traces of business-critical VMs.Our evaluation shows that there are considerable differences between the scheduling strategies in terms of SLA violations and the number of migrations. From all strategies considered, the combination of the Minimization of Migrations strategy for VM selection and the Worst Fit strategy for host selection achieves the best results.