Optimized Resource Provisioning based on SLAs in Cloud Infrastructures

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1 Optimized Resource Provisioning based on SLAs in Cloud Infrastructures Leonidas Katelaris: Department of Digital Systems University of Piraeus, Greece Marinos Themistocleous: Department of Digital Systems University of Piraeus, Greece Konstantinos Koumaditis: Department of Digital Systems University of Piraeus, Greece George Pittas: Department of Digital Systems University of Piraeus, Greece Abstract Resource allocation in Cloud Data Centres plays an important role for providers in fulfilling their obligations to their customers. An efficient system for resource management in Cloud Data Centres requires the automatic sharing of resources according to the individual requirements of each service offered, in order to comply with the minimum requirements outlined in the agreed Service Level Agreements (SLAs). In order to investigate further this area this paper aims to: (a) report on the research outcomes stemming from efforts on resource allocation in Cloud infrastructures, (b) present how these outcomes address the OpEx in Cloud Environments and (c) propose a model to achieve optimized resource provisioning to reduce OpEx in Cloud infrastructures. Keywords: Cloud Computing, Service Level Agreements, Resource Provisioning. 1. Introduction There is no doubt that we are in the midst of rapid technological change. The speed at which technology is changing is unimaginable. This makes the adoption of new technology products from companies and organizations a real challenge in an effort to keep on top while maintaining their competitiveness. The Technological Environment is now more dynamic which favors the continuous emergence of new technology trends and models. Cloud Computing, namely one of the most important in the field of Information Systems the last decade(foster et al., 2008). Even more services are turning into the Cloud, in order to gain from the many advantages Cloud offers. This advantages had a great impact in the IS market. Major players(ang et al., 2010, Prodan and Ostermann, 2009) sharing IS market had to invest large funds in an effort to gain competitive advantages. Yet, the cost of the provided services through the Cloud it remains high. The reasons for this vary and can be approached from various directions. The Operational Expenses (OpEx) and the Capital Expenses (CapEx) playing important role in the direction of decreasing the total cost of Cloud services. In this paper we will focus on OpEx(Zhang et al., 2010a). The authors choose OpEx because we believe that the initial investment has been done (CapEx), therefore reducing the OpEx will have greater impact in the total cost of a Cloud service. 1

2 Over the last years Data Centers have evolved increasingly popular for the provisioning of computing resources (Kliazovich et al., 2012). Operational costs of Data Centers have exponential increment along with the expansion of compute needs. Energy consumption in today s Data Centers plays a key role for Cloud Providers. Capacity of today s Data centers became enormous alongside with the total needs for energy. There have been many attempts from the Cloud Providers in order to minimize the expenses in Cloud infrastructures. Starting with this section being the introduction, in the next paragraphs we present the structure of the paper. Section 1 (Background Theory): In this section we are introducing the Cloud Computing and SLAs domain. In section 1 we present definitions and terms about Cloud Computing and SLAs. Also in this section we introduce into the OpEx in Cloud Environments. Section 2 (Related Work in Resource Provisioning): In this section we discuss previous works in the field of Resource Provisioning in Cloud Computing. Due to limitations in the length of this paper we discuss three of the many we studied during our research. Yet, in our future publications we wil include further findings. Section 3 (Research Methodology): In this section we discuss the methodology used according by our research specifying an optimized model for resource provisioning. 2. Background Theory In this section, we present definitions and basic terms relevant to the research domain of Cloud Computing. These definitions and basic terms are necessary in order to introduce the reader in the Cloud Computing subject, such as (a) Cloud Computing, (b) Service Level Agreements, (c) OpEx, (d) Cloud Infrasturcure. 2.1 Cloud Computing and SLAs In an attempt to give a definition for Cloud Computing, it is obvious that there is not only one definition for Cloud Computing. The National Institute of Standards and Technology defines Cloud Computing as (NIST): Cloud Computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models. (Mell and Grance, 2009). A Cloud Service Consumer is looking through a list provided by the Cloud Service Provider, for a service he/she needs to leverage. In order to determine the performances specifications of the Services one or more contracts are needed. These contracts in the domain of IS called Service Level Agreements (SLAs). A definition for SLAs follows as provided by ITIL (Official-Site, 2011): 2

3 Service Level Agreement is an agreement between an IT service provider and a customer. A service level agreement describes the service, documents service level targets, and specifies the responsibilities of the IT service provider and the customer. A single agreement may cover multiple IT services or multiple customers (Official-Site, 2011). 2.2 Operational Expenses (OpEx) in Cloud Infrastructures In this section we will give a brief description of Cloud Infrastructure and their OpEx in order to introduce another objective of this paper, the resource provisioning. 2.3 What referred as Cloud infrastructure As stated energy consumption plays a key role in today s Data Centres for the Cloud Providers. According to this we have to define what Cloud infrastructure is. As Cloud infrastructure, primarily referrers to the Data Centers, where the most of the hardware is hosted. A Data Center as seen in, Figure 1 includes (Greenberg et al., 2008): Servers Power Energy Infrastructure Network Infrastructure related to Cooling and Distribution of Energy in the Data Center. Figure 1. Cloud Data Center Infrastructure Cloud Data Center differs from existing enterprise Data Centers. This differentiation depends on many reasons. In enterprise Data Center the operating staff, is usually less than 5% of the total operating cost of the Data Center. This is mainly due to the high rate of automation in infrastructure (corresponding to one worker per 100 servers) (Greenberg et al., 2008). In the other hand in a Cloud Data Center, 3

4 automation infrastructure is an essential element in order to conserve one of the fundamental characteristics of Cloud the, elasticity. In this case corresponds one worker per 1000 servers (Greenberg et al., 2008). In combination with the enterprise Data Center the rate is quite less as about ten times. Yet total operational cost for the staff is much bigger. The explanation for this, is given in the enormous size of modern Data Centers as previously mentioned. As an example the Microsoft s Data Center in Illinois spanning more than 700,000 square feet (Miller, 2009). In Cloud Data Center when we refer to energy consumption we are talking about large economies of scale that not present in an enterprise Data Center. Moving forward to the next section of this paper, we introduce into the area of resource provisioning in Cloud infrastructures, by referring related works. 3. Related Work in Resource Provisioning Introduction In this section we present relevant efforts on resource provisioning since There are many attempts in the resource provisioning area. In paper we present three of them. Our selection is based on the differentiation between their approaches. 3.1 Agile Resource Management In more detail Zhang et al., (2010b) provide an approach based on Ghost Virtual Machines (VMs). The thought behind this is that VMs are enabled in a Service Cluster, but they are not available to accept application requests until needed. The approach provided by Zhang et al., (2010b) uses the specificity namely agility (Qian et al., 2007) of the Cloud to quickly reassign resources in a Data Center. Based on agility they built a utility computing platform with ghost VMs for quick management on service changes demands. The test bed from their implementation showed that in a sudden increase of workload a time slot of 18 seconds is needed for a ghost VM to handle client load. In order to achieve this they develop an algorithm which define when a VM is overloaded or under loaded. To develop their approach they have been based on a central problem of utility computing. The problem of managing the resources for a various applications. Furthermore, they based on the nature of todays web applications, where web application cluster contains application servers within the web applications run (Qian et al., 2007). Number of contributions have been made through their work on agile resource management by Zhang et al.,: A utility computing platform based on Ghost VMs, which provides agility to applications demand changes. An architecture technology and resource allocation algorithm, which is relevant across different virtualization technologies. An effective resource re-allocation between applications, with changing demands on resources, based on an external benchmark application (TPC-W). 4

5 3.2 Resource Provisioning via Multiplexing Additionally Meng et al., (2010), propose a method for resource provisioning through multiplexing VMs in the Cloud. As opposed to the conventional practice of evaluating the measure of VMs separately, they propose a provisioning approach. In this provisioning approach different VMs are solidified and provisioned together, in light of an assessment of their total limit needs. The method presented by Meng et al., (2010) has primary contributions as described below: SLA management model which is able to map application performance demands to resource demand requirements. The SLA management model is able to determine the capacity of each VM, but also ensures that the SLAs for individual VMs are still well-kept. Binding algorithm, which is used to calculate the total resources needed for the VMs that will be connected via multiplexing. The algorithm seeks to VMs with the most well-suited demand patterns. These combinations lead to resource saving. They present effective and realistic applications compatible with the proposed method for capacity planning and ready to provide resource guarantees via VM reservations. 3.3 SLA-based Resource Provisioning Furthermore Garg et al. (2011) propose admission control and scheduling mechanism for dynamic resource provisioning in Cloud Data Centers. This mechanism is based on a policy they named Mixed Workload Aware Policy (MWAP). Though their mechanism of new requests acceptance they achieve utilization maximizing alongside with ensuring the signed SLAs between customers. Based on MWAP, they achieve a 60% increasing in utilization, according to other similar methods used like consolidation and migration (Garg et al., 2011). Their proposed method is based on an admission control and scheduling policy, which in each scheduling cycle perform three functions(garg et al., 2011): An admission control which is responsible for the acceptance or rejection of new applications, according to resource availability. The idea before accepting new requests is to take into account requirements of two different application workloads (Garg et al., 2011). A consideration of multiple SLAs types which monitor each individual application resource demand based on agreed QoS level guaranteed SLAs. An auto-scale function which assigns new VM instances to applications that exceed their reserved capacity in the SLA. This function takes into account auto-scaling thresholds 5

6 3.4 Previous Research Efforts Outcomes The outcomes stemming from the efforts analysed above, must be taken into account in order to develop a resource provisioning model for Cloud Data Centres. Some of the major outcomes summarized below: VM sizing: In order to achieve efficient resource allocation, we have to take into account the given features to VM during initialization. Based on the research efforts analysed before, this is a key feature. Also Meng et al.(2010) although not calculate the size of the virtual machines that are initially created. Through their multiplexing method they connect different VMs in a Super VM. To achieve this they compute the requirements of the different VMs to be connected. This attempt is on the basis of good resource allocation and management in Cloud Data Centres. VM integration time in the request serving infrastructure: Special attention is given to the time needed until the infrastructure is able to accept new service requests in a workload increment scenario. Specifically, Zhang et al., (2010a) in their proposed method which is based on Ghost VMs, they are able to identify the needs and requirements to promote Ghost VMs to be involved in servicing new requests in just 18 seconds (Zhang et al., 2010b). Maximize utilization of infrastructure: Maximizing the uutilization οf Cloud Computing infrastructure, is a common goal for providers. Garg et al. (Garg et al., 2011), in their approach they calculate the ability for a Cloud infrastructure to accept a service request, otherwise rejected. Based on the above findings that emerged from the review of previous research efforts on resource allocation in Cloud Computing infrastructures, the need for resource provisioning model in Cloud Data Centres surfaces. This is analysed in the next section. 4. Proposed Resource Provisioning Model based on SLAs In this section we present a model for resource provisioning. This model is based on SLAs. According to previous related works (Soundararajan and Anderson, 2010, Meng et al., 2010, Qian et al., 2007, Garg et al., 2011, Calheiros et al., 2011) in the area of resource provisioning and also based on their outcomes discussed in the previous section (Section 3.4), our model will be derived from. The Optimized Resource Provisioning Model as see in Figure 2 can be used by the Cloud Infrastructure Provider and Broker. The idea behind this model is to be able to predict the demand for a Cloud service, based on previously signed SLAs for a distinct period. Our belief is that, through this prediction will be possible for Cloud Infrastructure Providers to provide more efficient infrastructures based on current trend needs or specific type of Cloud services. Accordingly will result in a more dynamic resource provisioning and finally will result in the decrease of the OpEx. Decreasing the OpEx in Cloud Data Centers will result in a cheaper Cloud, enable to provide its services in a wider range of customers and businesses. 6

7 Our proposed model will consist from three phases such as (a) Data Loading, (b) Attributes Gravity, (c) Service Classification (Figure 2). Figure 2: Optimised Resource Provisioning Model The process starts with the SLA parameters loading derived from customer s service request in a phase we name Data Loading. This SLA parameters referred to high level metrics given by the customer for leveraging a service which will be translated to low level metrics in the Cloud Infrastructure Provider. In the next phase namely Attributes Gravity, through an algorithm it could be possible to give gravities to the requested attributes, sorting them from the most important to the less. This phase of the optimized resource provisioning model is very important because, the algorithm could be able to take into account many parameters in order to give the right gravity to each requested attribute. Such parameters could be the type of service the attribute is requested. For example, when a customer leverages a VM for video processing, RAM and CPU must be consider as the most important attributes for the provider, in order to fulfill customer s needs. In another example a customer leverages a Cloud service for video sharing. In this case the most important attributes will be bandwidth and storage. The previous examples raise another important research issue in the Cloud Computing domain, which refers to Cloud services categorizing, but will not be discussed in this paper. Another parameter the algorithm has to take into account in order to assign weights to the requested attributes is the expected workload for similar services leveraged before (monitoring data) by other customers. The prediction of the actual expected workload is very useful in order to prevent over-provisioning or under-provisioning situations (Greenberg et al., 2008). Finally proceeding to the last phase of the model named Service Classification, the service requested by the customer will be classified according to a provider s policy in order to be given to a suitable VM for execution. In the previous phases the same processes are executed in both Provider and Broker scenario. For this phase the broker despite the Provider, classifies the Providers and not the services, according to the services can accept in a Providers Classification Policy. Based on the classification the Broker choose the right Provider to execute the service. 7

8 5. Research Methodology This section outlines how the literature review will be approached in detail, including a retrieval of literature in order to complete our goal. As part of our research we will use qualitative research method (Merriam, 1998, Hennink et al., 2010). According to Benbasat et al., (1987) qualitative research can offer significant benefits such as: Allows the researcher to understand the nature and complexity of what will be investigated. Provides information on new research efforts Supports research of a phenomenon in its natural environment. Therefore this research will be use interviews model which is an important tool for data collecting (Mack et al., 2005) in qualitative research method, according to Myers and Newman (2007). There are various types of interviews, according to Robson (2002) these types are classified as below: Structured interviews: In this type of interview the questions are developed in advance and asked in a previously planned order. This type of interview is similar to questionnaire-based survey (Koumadits K., 2014) Semi-structured interviews: In this type of interview the order of the questions can change during the interview, according to interview flow and the researcher. The flexibility of this type of interview allows exploration of the studied issue and improvisation from the researcher. Unstructured interviews: In this type of interview the questions are formulated as general disquiets from the researcher. The questions are developed based on the interest of the subject and the researcher. 6. Conclusions In this paper we presented a model for an optimized resource provisioning, which can be used to reduce the OpEx of Cloud infrastructures. Ongoing our research on resource provisioning is based on this model and through the research methodology discussed in previous sections we will try to give a complete model for optimized resource provisioning. Firstly we have examine various related works in the area of resource allocation in Cloud Data Centers, three of most significant presented in the current paper. This help us have a clear view of the research area. Moving forward, we plan a rigorous examination of the literature for parameters that have to be included in the design of the algorithm. This will be used in our model to calculate the weights for the requested attributes. We also have to research various issues arising like the collection of monitoring data from Cloud Providers and Brokers. Another issue we have to take over during our research is the classification policy we propose which have to be followed by Providers and Brokers in order to classify services and Providers accordingly. 8

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