Toward an analytical method for SLA validation

Metric-based Evaluation of Implemented Software Architectures

Cloud is a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). It provides an on-demand, pay-as-you-go computing resources and had become an alternative to traditional IT Infrastructure. As more and more consumers delegate their task to cloud providers, Service Level Agreement (SLA) between consumer and provider becomes an important aspect. Due to the dynamic nature of cloud the matching of SLA templates need to be dynamic and continuous monitoring of Quality of Service (QoS) is necessary to enforce SLAs. SLA template contains many parameters like cloud's resources (physical memory, main memory, processor speed etc.) and properties (availability, response time etc.). This work addresses the issue of matching SLA parameters to find suitable cloud provider for particular application.

In the cloud computing context, Service Level Agreements (SLAs) are contracts between Cloud Service Providers (CSPs) and Cloud Service Customers (CSCs), stating the guaranteed quality level of the services offered by CSPs. Existing cloud SLAs focus only on few service terms, completely ignoring all security related aspects. They are often reported in a way that is hardly understandable for customers. Moreover, they offer guarantees uniform for all offered services and all customers, regardless of particular service characteristics or customers specific needs. This paper presents a framework that enables the adoption of a per-service SLA model, by supporting the automatic implementation of cloud Security SLAs tailored to the needs of each customer for specific service instances. In particular, the process and the software architecture for per-service SLA implementation are shown. A case study application demonstrates the feasibility and effectiveness of the proposed solution.

In this paper, the work is carried upon the security of cloud computing environment from the economic denial of service (EDOS) attacks. The cloud computing environments have become popular in the past decade, as number of businesses has shifted their work online to the cloud. The cloud service providers offer the maximum uptime for their users, which is committed in the form of service level agreements (SLAs). The uptime is the term used to indicate the availability of the cloud computing resources for client's application to run its processes. If at any point, the client application is not able to run its operations due to non-availability of resources, it is considered as the downtime, which is negative to the uptime and considered as SLA violation. The SLAs keep the information about the SLA violations and their settlements, which is considered as the loss of the cloud service providers. For example, the uptime of 99.99% is committed by cloud service provider, and due to any internal or external reason, achieved uptime is lower than committed value, the cloud service provider may face a penalty as per defined in the SLA, which is considered direct profit loss for the service provider. In order to reduce the financial losses due to the EDOS attacks, the proposed model is designed by combining the periodic authentication, pattern analysis and data flow control mechanisms to prevent the cloud from attacks. The proposed Multi-Layered Attack Recognition (MLAR) Model has outperformed the existing controlled access based EDOS (CA-EDOS) prevention model on the basis of resource utilization and response delay parameters.

Introduced in the early stages of software development, the Charmy framework assists the software architect in making and evaluating architectural choices. Rarely, the software architecture of a system can be established once and forever. Most likely poorly defined and understood architectural constraints and requirements force the software architect to accept ambiguities and move forward to the construction of a suboptimal software architecture. Charmy aims to provide an easy and practical tool for supporting the iterative modeling and evaluation of software architectures. From an UML-based architectural design, an executable prototype is automatically created. Charmy simulation and model checking features help in understanding the functioning of the system and discovering potential inconsistencies of the design. When a satisfactory and stable software architecture is reached, Java code conforming to structural software architecture constraints is automatically generated through suitable transformations. The overall approach is tool supported.

Cloud Computing provides higher level services and shared pools of configurable resources with minimal management effort over the internet. Cloud Computing achieves coherence and economic of scale by means of shared resources. The growth of cloud computing depends on the high availability of low cost computers, storage devices and networks as well as hardware virtualization and utility computing. Service Level Agreement (SLA) is a service commitment tool between a cloud service provider and a cloud customer. Using SLA in cloud computing, reduce the chances of disappointing the cloud customer and manage the expectation between the service provider and the customer. The challenge is, it is very difficult to handle all request by the cloud providers at a time during peak hours and to keep up SLA. So when there is an uneven request arrival pattern, the cloud resources may either be underutilized or over-utilized. In order to balance the load, the load balancer plays the major role in cloud computing. The load balancing algorithm equalizes the workload and computing resources in a cloud computing environment. It allows an organization to manage their workload demands by allocating resources among multiple servers and thus it minimizes the response time, minimize the waiting time, maximize the throughput time, maximize the resource utilization and minimize the communication delays of the server by meeting the SLA. The major goal of this study is to review both the existing static and dynamic load balancing algorithms proposed till now and to design and implement a Load balancer that uses a Meta Heuristics approach – Ant Colony Optimization technique to perform balancing the load so that the SLA is met evenly without any issues. Each of the load balancing algorithms is compared with other algorithms theoretically and experimentally one of it is tested with the proposed system using AWS Cloud PaaS (Platform as a Service).

This research recognizes the limitation and challenges of adapting and applying Process Mining as a powerful tool and technique in the Hypothetical Software Architecture (SA) Evaluation Framework with the features and factors of lightweightness. Process mining deals with the large-scale complexity of security and performance analysis, which are the goals of SA evaluation frameworks. As a result of these conjectures, all Process Mining researches in the realm of SA are thoroughly reviewed, and nine challenges for Process Mining Adaption are recognized. Process mining is embedded in the framework and to boost the quality of the SA model for further analysis, the framework nominates architectural discovery algorithms Flower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductive and compares them vs. twelve quality criteria. Finally, the framework’s testing on three case studies approves the feasibility of applying process mining to architectural evaluation. The extraction of the SA model is also done by the best model discovery algorithm, which is selected by intensive benchmarking in this research. This research presents case studies of SA in service-oriented, Pipe and Filter, and component-based styles, modeled and simulated by Hierarchical Colored Petri Net techniques based on the cases’ documentation. Process mining within this framework deals with the system’s log files obtained from SA simulation. Applying process mining is challenging, especially for a SA evaluation framework, as it has not been done yet. The research recognizes the problems of process mining adaption to a hypothetical lightweight SA evaluation framework and addresses these problems during the solution development.

Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.

As the expanding of cloud datacenter and the soaring demand for various types of services and resources, the energy consumption problem is continuously mushrooming. Unfortunately, existing scheduling algorithms mainly pursue timeliness, but take little account the energy factor. On the other hand, Service Level Agreement (SLA) plays a key role in guaranteeing the QoS for users. In SLA, deadline (response time) is one of the QoS metrics that users are mostly concerned about. However, the deadline set in SLA is likely to be violated when there are real-time tasks with different tight deadline-constrained. Therefore, it is a valuable research issue to propose a scheduling algorithm which can save energy while meeting the deadline. In view of this challenge, an energy-aware tasks scheduling method with deadline-constrained is proposed in this paper by exploiting the computing parallelism of divisible task. In our method, the urgency level is defined to determine the scheduling order of the real-time tasks waiting to be processed. Accordingly, two energy-aware tasks scheduling algorithms are proposed by considering whether the load of task is divisible. Finally, experimental evaluations are presented for validating the method.

In the cloud computing context, Service Level Agreements (SLAs) 'tailored' to specific Cloud Service Customers (CSCs) seem to be still an utopia, and things are even worse as regards the security terms to be guaranteed. In fact, existing cloud SLAs focus only on a few service terms, and Cloud Service Providers (CSPs) mainly provide uniform guarantees for all offered services and for all customers, regardless of any particular service characteristics or of customer-specific needs. This paper presents a framework that enables the adoption of a per-service SLA model, supporting the automatic implementation of cloud security SLAs tailored to the needs of each customer for specific service instances. In particular, the process and the software architecture for per-service SLA implementation are shown. A case study application, related to the provisioning of a secure web container service, is presented and discussed, to demonstrate the feasibility and effectiveness of the proposed solution.

Cloud computing is a successful model for hosting web-facing applications that are accessed by their users as services. While clouds currently offer Service Level Agreements (SLAs) containing guarantees of availability, they do not make performance guarantees for deployed applications.

Today, companies are required to be in control of their IT assets, and to provide proof of this in the form of independent IT audit reports. However, many companies have outsourced various parts of their IT systems to other companies, which potentially threatens the control they have of their IT assets. To provide proof of being in control of outsourced IT systems, the outsourcing client and outsourcing provider need a written service level agreement (SLA) that can be audited by an independent party. SLAs for availability and response time are common practice in business, but so far there is no practical method for specifying confidentiality requirements in an SLA. Specifying confidentiality requirements is hard because in contrast to availability and response time, confidentiality incidents cannot be monitored: attackers who breach confidentiality try to do this unobserved by both client and provider. In addition, providers usually do not want to reveal their own infrastructure to the client for monitoring or risk assessment. Elsewhere, we have presented an architecture-based method for confidentiality risk assessment in IT outsourcing. In this paper, we adapt this method to confidentiality requirements specification, and present a case study to evaluate this new method.

: Software architecture is critical to the quality of a software-intensive system. For an acquisition organization, such as the Department of Defense (DoD), the ability to evaluate software architectures early in an acquisition can have a favorable impact on the delivered system. This technical note discusses the role of software architecture evaluations in a system acquisition and describes the contractual elements that are needed to accommodate architecture evaluations in an acquisition. The note then provides an example of contractual language that incorporates the Architecture Tradeoff Analysis Method (ATAM) as a software architecture evaluation method in a system acquisition.

Serverless computing brings another revolution to cloud computing as function-as-a-service (FaaS) where the applications are abstracted as a group of functions. Serverless applications are cost-effective and manage resources efficiently but the lack of performance modeling and energy optimization affects the potential users' broad adoption of serverless computing. Performance enhancement and energy optimization are necessary to guarantee the serverless applications' service level agreement (SLA). This review paper presents various performance metrics in serverless computing, including cost, scalability, latency, energy consumption, resource utilization, fault tolerance, and response time. Based on these metrics, various performance modeling and energy optimization techniques have been explored to reduce energy consumption and improve system efficiency. Furthermore, the review investigates software platforms for implementing serverless computing, including AWS Lambda, Apache OpenWhisk, Azure Functions, and Google Cloud Functions, highlighting key findings and limitations. This comprehensive review serves as a guide for researchers, directing them toward new and promising research directions in the field.

As the use of virtualization and partitioning grows, it becomes possible to deploy a multi-tier web-based application with a variable amount of computing power. This introduces the possibility of provisioning only for a minimum workload, with the intention of renting more resources as necessary, but it also creates the problem of quickly and accurately identifying when more resources are needed or unneeded resources are being paid for. This paper presents a machine learning based approach to handling this problem. An autonomous adaptive agent learns to predict the gain (or loss) that would result from more (or less) resources; this agent uses only low-level system statistics, rather than relying on custom instrumentation of the operating system or middleware. Our agent is fully implemented and evaluated on a publicly available multi-machine, multi-process distributed system (the online transaction processing benchmark TPC-W). We show that our adaptive agent is competitive with any static choice of computing resources over a variety of test workloads. We also show that the agent outperforms each static choice in at least one case, implying that it is well suited for a situation where the workload is unknown 1.