Mapping the SLA Landscape for High Performance Clouds

By Dr. Ivona Brandic

February 7, 2011

Cloud computing represents the convergence of several concepts in IT, ranging from virtualization, distributed application design, grid computing, and enterprise IT management–resulting to be a promising paradigm for on demand provision of ICT infrastructures.

During the past few years significant effort has been made in the sub-fields of cloud research, including the development of various federation mechanisms, cloud security, virtualization and service management techniques.

While a wealth of work has been accomplished to suit the technological development of clouds, there has yet been very little work done in the area of the market mechanisms that support them.

As we learned in the past, however (consider the case of grid technologies) appropriate market models for virtual goods, ease of use of those markets, low thresholds for entering the market for traders and buyers, and the appropriate processes for the definition and management of virtual goods have remained challenging issues. The way these topics are addressed will decide whether cloud computing will take root as a self-sustaining state-of-the-art technology.
The current cloud landscape is characterized by two market mechanisms: either users can select products from one of the big players with their sets of well-defined, but rigid offerings; or they rely on off-line relationships to cloud providers with niche products.

This division is especially marked in the area of HPC given the comprehensive special requirements needed, including specific security infrastructures, compliance to legal guidelines, massive scalability or support for parallel code execution, among others. HPC thus suffers from a low number of comparable choices, thus resulting in low liquidity of current cloud markets and provider/vendor lock in.

Sufficient market liquidity is essential for dynamic and open cloud markets. Liquid markets are characterized by a high number of matches for bids and offers. With the low market liquidity traders have the high risk of not being able to trade resources, while users have the risks of not being able to find suitable products.

A crucial factor in achieving high market liquidity is the existence of standardized goods. Virtual goods, as this is the case in clouds, however, exhibit high variability in product description. That means, that very similar or almost identical goods can be described in various ways with different attributes and parameters.

As shown in Table 1 below, computing resources traded in a PaaS fashion can be described through different non-standardized attributes, e.g., CPU cores, incoming bandwidth, processor types, required storage. Thus, high variability in the description of goods results again in low market liquidity. Another important characteristic of virtual goods is that they change and evolve over time following various technological trends. For example the attribute number of cores appeared just with the introduction of multi core architectures.

Table 1: Example SLA parameters

Incoming Bandwidth >10 MBit/s
Outgoing Bandwidth >12 MBit/s
Storage >1024 GB
Availability >99%
CPU Cores >16

Based on aforementioned observations, two challenging questions have been identified:

  • How can users’ demand and traders’ offers be channeled towards standardized products, which can evolve and adapt over time and reflect users’ needs and traders’ capabilities?
  • Which mechanism do we need to achieve sufficient market liquidity, where traders have high probability to sell their products and where users have sufficient probability to buy products they require.

To counteract this problem we make use of Service Level Agreements (SLAs), which are traditionally used to establish contracts between cloud traders and buyers.

Table 1 shows a typical SLA with the parameters and according values expressing non-functional requirements for the service usage. SLA templates represent popular SLA formats containing all attributes and parameters but without any values and are usually used to channel demand and offer of a market. Private templates are utilized at the buyers and traders infrastructures and reflect the needs of the particular stakeholder in terms of SLA parameters they use to establish a contract. Typical SLA parameters used at the PaaS level are depicted in Table 1 and include availability, inbound bandwidth, outgoing bandwidth, etc. Considering the high variability of virtual goods in cloud markets, the probability is high that public templates used in marketplaces to attract buyers and sellers and private templates of cloud stakeholders do not match.

One could think that traditional approaches like semantic technologies, e.g., ontologies, can be used to channel variety of SLA templates. Also public templates, which can be downloaded and utilized within the local business / scientific applications could counter act the problem of the variety of SLA templates. However, usage of ontologies is a highly static approach where the dynamics of the changing demand / supply of the market and evolving products cannot be captured. Moreover, utilization of public SLA templates in private business processes or scientific applications is in many cases not possible since it requires changes of the local applications.

In the context of the Austrian national FoSII project (DSG group, Vienna University of Technology), we are investigating self-governing cloud Computing infrastructures necessary for the attainment of established Service Level Agreements (SLAs). Timely prevention of SLA violations requires advanced resource monitoring and knowledge management. In particular, we are developing novel techniques for mapping low-level resource metrics to high-level SLAs and bridging the gap between metrics monitored by the arbitrary monitoring tools and SLA metrics guaranteed to the user, which are usually application based.

We apply various knowledge management techniques, as for example Case Based Reasoning for the prevention of SLA violations before they occur while reducing energy consumption. In collaboration with the Seoul National University we are exploring novel models for SLA mapping to counteract the problem of heterogeneous public and private templates in cloud markets. SLA mapping approach facilitates market participants to define translations from their private templates to public SLA templates while keeping their private temples unchanged. The effects of the SLA mapping approach are twofold:

  • It increases market liquidity since slightly different private templates are channeled towards few publicly available public templates. Consequently, public templates can be frequently adapted based on the supplied, aggregated, and analyzed SLA mappings. Thus, publicly available SLA temples reflect the demand and supply of the markets and can be easily adapted.
  • By clustering supplied SLA mappings different groups of cloud buyers with similar demand can be identified. Thus, based on the information obtained from the clustering information, products for a specific group of users can be tailored. This includes also generation of product niches, which are usually neglected in traditional markets.

SLA mapping is used to bridge the gap between inconsistent parts of two SLA templates – usually between the publicly available template and the private template. For the implementation of the SLA mappings we use XSLT, a declarative XML-based language for the transformation of XML documents. Thereby the original document is not changed, rather the new document is created based on the content of the original document. Thus, if the original document is the private template of the cloud user, which differs from the public template, transformations based on the XSLT can be defined transforming the private into the public template.

Thereby we distinguish two different types of mappings:

1. Ad-hoc SLA mapping. Such mappings define translations between a parameter existing in both, private and public SLA template. We differ simple ad-hoc mapping i.e., mapping of different values for an SLA attribute or an SLA element, e.g., mapping between the names CPU Cores and Number Of Cores of an SLA parameter, and complex ad-hoc mapping, i.e., mapping between different functions for calculating a value of an SLA parameter. An example for the complex mapping would be a unit for expressing a value of an SLA parameter Price from EUR to USD, where translation have to be defined from one function for calculating price to another one. Although, simple and complex mappings appear to be rather trivial, contracts cannot be established between non-matching templates without human intervention of without the overhead of the semantic layer – which anyway has to be managed manually.

2. Future SLA mapping defines a wish for adding a new SLA parameter supported by the application to a public SLA template, or a wish for deleting an existing SLA parameter from a public template. Unlike ad-hoc mapping, future mapping cannot be applied immediately, but possibly in the future. For example a buyer could express the need for a specific SLA parameter, which does not exist yet, but can be integrated into the public templates after the observation of the supplied SLA mappings.

So far we have implemented the first prototype of the VieSLAF (Vienna Service Level Agreement) middleware for the management of SLA mappings allowing users and traders to define, manage, and apply their mappings. In our recent work we developed simulation models for the definition of market settings suitable for the evaluation of the SLA mapping approach in a real world scenario. Based on the applied SLA mappings we defined utility and cost models for users and providers. Thereafter, we applied three different methods for the evaluation of the supplied SLA mappings during a specific time span. We simulated market conditions with a number of market participants entering and leaving the market with different distributions of SLA parameters, thus, requiring different SLA mapping scenarios.

Our first observations show promising results where we achieve good high net utilities considering utilities and costs of doing SLA mappings vs. doing nothing (i.e., not achieving a match in the market). Moreover, in our simulations we applied clustering algorithms where we isolated clusters of SLA templates, which can be used as a starting point for the definition of various cloud products. Utilities achieved when applying clustering algorithms outperforms the costs for doing SLA mappings and doing nothing.

However, those are only preliminary results and the whole potential of SLA mappings is still not fully exploited. Integration into IDEs like Eclipse, where cloud stakeholders can define SLA mapping using suitable Domain Specific Languages, e.g., visual modeling languages, is an open research issue and could facilitate definition of SLA mapping by domains specialists.

The process of defining SLA mapping fully is still in the early stages; for now, these mappings are defined manually by the end users. However, with the development of the appropriate infrastructures and middleware mapping could be done in an automatic way. For example, if the attribute Price has to be translated to Euro a third party service delivering the current USD/Euro exchange rate could be included in an autonomic way facilitating not only mapping between different attributes, but also the proper generation of the according attribute values.

Aggregated and analyzed SLA maps can deliver important information about the demand and structure of the market, thus, facilitating development of open and dynamic cloud markets. Thereby, market rules and structures can be adapted on demand based on the current developments of the products and market participants.

About the Author

Dr. Ivona Brandic is Assistant Professor at the Distributed Systems Group, Information Systems Institute, Vienna University of Technology (TU Wien).

Prior to that, she was Assistant Professor at the Department of Scientific Computing, Vienna University. She received her PhD degree from Vienna University of Technology in 2007. From 2003 to 2007 she participated in the special research project AURORA (Advanced Models, Applications and Software Systems for High Performance Computing) and the European Union’s GEMSS (Grid-Enabled Medical Simulation Services) project.

She is involved in the European Union’s SCube project and she is leading the Austrian national FoSII (Foundations of Self-governing ICT Infrastructures) project funded by the Vienna Science and Technology Fund (WWTF). She is Management Committee member of the European Commission’s COST Action on Energy Efficient Large Scale Distributed Systems. From June-August 2008 she was visiting researcher at the University of Melbourne. Her interests comprise SLA and QoS management, Service-oriented architectures, autonomic computing, workflow management, and large scale distributed systems (cloud, grid, cluster, etc.).

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