Envisioning SLO-driven Service Selection in Multi-cloud Applications

A Service Level Agreement (SLA) is a legal contract between parties to ensure the Quality of Service (QoS). It specifies one or more service level objectives (SLO), to ensure that the QoS delivered has met customer expectations. However, It becomes hard to guarantee QoS levels and detect SLA violations. Therefore, we propose to use MDE (Model Driven Engineering) to express the SLA contract requirements. This latter, created with a specific modeling language (DSML), will be used harmonically with a Multi-agent systems (MASs) in order to monitor SLA violations in real-time. Indeed, MASs are suitable tools for self-detection of failures and self-monitoring of cloud operations and services, QoS negotiation and SLA management. They are designed to operate in a dynamically changing environment. Our main motivation is firstly to use MDE technology for the creation of the SLA contract and then to integrate MASs in order to control the quality of service contract and guarantee transparency and symmetry with respect to the SLA contract between prospective signatories.

Cloud service is being adopted as a utility for large numbers of tenants by renting Virtual Machines (VMs). But for cloud storage, unpredictable IO characteristics make accurate Service-Level-Objective (SLO) enforcement challenging. As a result, it has been very difficult to support simple-to-use and technology-agnostic SLO specifying a particular value for a specific metric (e.g., storage bandwidth). This is because the quality of SLO enforcement depends on performance error and fluctuation that measure the precision of SLO enforcement . High precision of SLO enforcement is critical for user-oriented performance customization and user experiences. To address this challenge, this article presents V-Cup, a framework for VM-oriented customizable SLO and its near-precise enforcement. It consists of multiple auto-tuners, each of which exports an interface for a tenant to customize the desired storage bandwidth for a VM and enable the storage bandwidth of the VM to converge on the target value with a predictable precision. We design and implement V-Cup in the Xen hypervisor based on the fair sharing scheduler for VM-level resource management. Our V-Cup prototype evaluation shows that it achieves satisfying performance guarantees through near-precise SLO enforcement.

By choosing to use cloud services, organizations seek to reduce costs and maximize efficiency. For mission critical systems that must satisfy security constraints, this push to the cloud introduces risks associated with cloud service providers not implementing organizationally selected security controls or policies. As internal system details are abstracted away as part of the cloud architecture, the organization must rely on contractual obligations embedded in service level agreements (SLAs) to assess service offerings. Current SLAs focus on quality of service metrics and lack the semantics needed to express security constraints that could be used to measure risk. We create a framework, called SecAgreement (SecAg), that extends the current SLA negotiation standard, WS-Agreement, to allow security metrics to be expressed on service description terms and service level objectives. The framework enables cloud service providers to include security in their SLA offerings, increasing the likelihood that their services will be used. We define and exemplify a cloud service matchmaking algorithm to assess and rank SecAg enhanced WS-Agreements by their risk, allowing organizations to quantify risk, identify any policy compliance gaps that might exist, and as a result select the cloud services that best meet their security needs.

Capacity planning for IaaS cloud providers offering multiple service classes

One of the important aspects of Model-Driven Engineering (MDE) is to consider application-domain variability, which leads to creation of Domain Specific Languages (DSL). As with DSLs models are concise, easy to understand and maintain, this approach greatly increases the productivity and software quality. Usually, the DSLs in MDE are described with a metamodel and a concrete syntax definition. The models expressed in the DSL are linguistic instantiations of the language concepts found in the metamodel.However, for some of the application domains it’s not enough to consider the linguistic dimension of the instantiation. The problem arises when the domain itself contains the aspect of typing. This leads to another view on instantiation, called ontological instantiation . Since both aspects are present simultaneously, we refer to the combined approach with the term “two-dimensional metamodelling”.In the following, we will exemplify the problem with a case study based on a real challenge found in the domain of network management. The solution we propose benefits from ontology technology which is applied to enforce the semantics of ontological instantiation. Our approach presents significant differences comparing to the existing 2D metamodelling solution, although the motivations are similar. Thus, we consider our work as a case study of applying ontology enabled software engineering in the area of DSL engineering, rather than a new metamodelling technology or an application of existing 2D metamodelling architecture.The article is a result of joint work of the MOST project partners, applied within the case study provided by Comarch.KeywordsPhysical DeviceConcrete SyntaxEclipse Modelling FrameworkOntology TechnologyClose World AssumptionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The integration of domain-specific concepts in a model-driven engineering (MDE) approach raises a number of interesting research questions. There are two possibilities to represent these concepts. The first one focuses on models that contain domain-specific concepts only, i.e. domain-specific modelling languages (DSML). The second one advocates the integration of domain-specific concepts in general-purpose models, using what we will refer to in this paper as domain-specific modelling annotation languages (DSMAL). In this position paper, we argue that each approach is particularly suited for specific activities and specific actors, and show how they can be developed and used together. We also highlight the challenges created by the use of two representations, such as the evaluation of models OCL constraints and the synchronisation between the two representations. As an illustration, we present rbacUML, our approach for integrating role-based access control (RBAC) concepts into an MDE approach.

The diversity of cloud offerings motivated the proposition of cloud modelling languages (CMLs) to abstract complexities related to selection of cloud services. However, current CMLs lack the support for modelling service level objectives (SLOs) that are required for the customer applications. Consequently, we propose an application- and provider-independent SLO modelling language (SLO-ML) to enable customers to specify the required SLOs. We also sketch the architecture to realise SLO-ML.

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.

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.

Many Cloud Service Providers (CSPs) provide data storage services with datacenters distributed worldwide. These datacenters provide different Get/Put latencies and unit prices for resource utilization and reservation. Thus, when selecting different CSPs' datacenters, cloud customers of globally distributed applications (e.g., online social networks) face two challenges: i) how to allocate data to worldwide datacenters to satisfy application SLO (service level objective) requirements including both data retrieval latency and availability, and ii) how to allocate data and reserve resources in datacenters belonging to different CSPs to minimize the payment cost. To handle these challenges, we first model the cost minimization problem under SLO constraints using integer programming. Due to its NP-hardness, we then introduce our heuristic solution, including a dominant-cost based data allocation algorithm and an optimal resource reservation algorithm. We finally introduce an infrastructure to enable the conduction of the algorithms. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the effectiveness of our algorithms for SLO guaranteed services and customer cost minimization.

Recent advancements in the domain of Network Function Virtualization (NFV), and rollout of next-generation networks have necessitated the requirement for the upkeep of latency-critical application architectures in future networks and communications. While Cloud service providers recognize the evolving mission-critical requirements in latency sensitive verticals such as autonomous driving, multimedia, gaming, telecommunications, and virtual reality, there is a wide gap to bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient towards scalability in the long run. To address this, we propose a strategy to model frequent violations on the application level as a multi-output target to enable more complex decision-making in the management of virtualised communication networks. In this work, we utilize data from a real-world deployment to configure and draft a realistic set of Service Level Objectives (SLOs) for a voice based NFV application, and develop a deep neural network based multi-label classification methodology to identify and predict multiple categories of SLO breaches associated with an application state. With this, we aim to gain granular SLA and SLO violation insights, enabling us to study and mitigate their impact and inform precision in drafting proactive scaling policies. We further compare the performance against a set of multi-label compatible machine learning classifiers, and address class imbalance in a multi-label setup. We perform a comprehensive evaluation to assess the performance on example-based, label-based and ranking-based measures, and demonstrate the suitability of deep learning in such a use-case.

It is important for cloud service brokers to provide a multi-cloud storage service to minimize their payment cost to cloud service providers (CSPs) while providing service level objective (SLO) guarantee to their customers. Many multi-cloud storage services have been proposed or payment cost minimization or SLO guarantee. However, no previous works fully leverage the current cloud pricing policies (such as resource reservation pricing) to reduce the payment cost. Also, few works achieve both cost minimization and SLO guarantee. In this paper, we propose a multi-cloud Economical and SLO-guaranteed Storage Service (ES3), which determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. ES3 incorporates (1) a coordinated data allocation and resource reservation method, which allocates each data item to a datacenter and determines the resource reservation amount on datacenters by leveraging all the pricing policies; (2) a genetic algorithm based data allocation adjustment method, which reduce data Get/Put rate variance in each datacenter to maximize the reservation benefit; and (3) a dynamic request redirection method, which dynamically redirects a data request from a reservation-overutilized datacenter to a reservation-underutilized datacenter to further reduce the payment. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the superior performance of ES3 in payment cost minimization and SLO guarantee in comparison with previous methods.

Service Level Agreement in cloud computing: Taxonomy, prospects, and challenges

Software agents, modeling and simulation, and Model-Driven Engineering (MDE) are fields with their unique body of knowledge. However, developments in one area can provide new avenues for advancing the methodologies and technical strategies in other fields. This paper examines the synergies among these three fields by highlighting selected challenges in each area and delineating how methods developed in one field can mutually be beneficial. We categorize the ways in which the agent paradigm can contribute to critical activities in MDE, and similarly we describe how domain-specific languages and model transformation strategies can be instrumental in the design, implementation, and management of agent systems.

As Model-Driven Engineering (MDE) is increasingly applied to larger and more complex systems, the current generation of modelling and model management technologies are being pushed to their limits in terms of capacity and efficiency. Additional research and development is imperative in order to enable MDE to remain relevant with industrial practice and to continue delivering its widely recognised productivity, quality, and maintainability benefits. Achieving scalability in modelling and MDE involves being able to construct large models and domain-specific languages in a systematic manner, enabling teams of modellers to construct and refine large models in a collaborative manner, advancing the state of the art in model querying and transformations tools so that they can cope with large models (of the scale of millions of model elements), and providing an infrastructure for efficient storage, indexing and retrieval of large models. This paper attempts to provide a research roadmap for these aspects of scalability in MDE and outline directions for work in this emerging research area.