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Showing papers on "Service-level agreement published in 2014"


Journal ArticleDOI
01 Dec 2014
TL;DR: This work proposes a classification of techniques for automating application scaling in the cloud into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis, and uses this classification to carry out a literature review of proposals.
Abstract: Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

688 citations


Journal ArticleDOI
TL;DR: A Cloud management platform to optimize VM consolidation along three main dimensions, namely power consumption, host resources, and networking is proposed for the open-source OpenStack Cloud.

252 citations


Journal ArticleDOI
TL;DR: This paper proposes an admission control and scheduling mechanism which not only maximizes the resource utilization and profit, but also ensures that the QoS requirements of users are met as specified in SLAs.

209 citations


Proceedings ArticleDOI
01 Feb 2014
TL;DR: This study aims to identify an efficient resource allocation strategy that utilizes resources effectively in the resource constrained environment of cloud computing.
Abstract: Cloud computing provides user-requested services that are reliable, dynamic, flexible and efficient. In order to offer such guaranteed services to cloud users, effective resource allocation strategies must be implemented. The methodology used should also confirm to the Service Level Agreement (SLA) drawn between the customer and the service provider. This work presents a study of such resource allocation strategies in cloud computing. The strategies include resource requirements prediction algorithms and resource allocation algorithms. This works studies the various resource allocation techniques utilized in cloud computing and makes a comparative study of the merits and demerits of each technique. This study aims to identify an efficient resource allocation strategy that utilizes resources effectively in the resource constrained environment of cloud computing.

166 citations


Posted Content
TL;DR: Various load balancing schemes in different cloud environment based on requirements specified in Service Level Agreement (SLA) are presented.
Abstract: Cloud Computing is a new trend emerging in IT environment with huge requirements of infrastructure and resources. Load Balancing is an important aspect of cloud computing environment. Efficient load balancing scheme ensures efficient resource utilization by provisioning of resources to cloud users on demand basis in pay as you say manner. Load Balancing may even support prioritizing users by applying appropriate scheduling criteria. This paper presents various load balancing schemes in different cloud environment based on requirements specified in Service Level Agreement (SLA).

161 citations


Book ChapterDOI
01 Jan 2014
TL;DR: This chapter provides a comprehensive overview on the Cloud’s anatomy, definition, characteristic, affects, architecture, and core technology, and clearly classifies the Cloud's deployment and service models, providing a full description of the Cloud services vendors.
Abstract: Cloud Computing has recently emerged as a compelling paradigm for managing and delivering services over the internet. It is rapidly changing the landscape of information technology, and ultimately turning the long-held promise of utility computing into a reality. With such speedy progressing and emerging, it becomes crucial to understand all aspects about this technology. This chapter provides a comprehensive overview on the Cloud’s anatomy, definition, characteristic, affects, architecture, and core technology. It clearly classifies the Cloud’s deployment and service models, providing a full description of the Cloud services vendors. The chapter also addresses the customer-related aspects such as the Service Level Agreement, service cost, and security issues. Finally, it covers detailed comparisons between the Cloud Computing paradigm and other existing ones in addition to its significant challenges. By that, the chapter provides a complete overview on the Cloud Computing and paves the way for further research in this area.

154 citations


Journal ArticleDOI
TL;DR: This paper proposes customer driven SLA-based resource provisioning algorithms to minimize cost by minimizing resource and penalty cost and improve CSL by minimizing SLA violations.
Abstract: Cloud computing is a solution for addressing challenges such as licensing, distribution, configuration, and operation of enterprise applications associated with the traditional IT infrastructure, software sales and deployment models. Migrating from a traditional model to the Cloud model reduces the maintenance complexity and cost for enterprise customers, and provides on-going revenue for Software as a Service (SaaS) providers. Clients and SaaS providers need to establish a Service Level Agreement (SLA) to define the Quality of Service (QoS). The main objectives of SaaS providers are to minimize cost and to improve Customer Satisfaction Level (CSL). In this paper, we propose customer driven SLA-based resource provisioning algorithms to minimize cost by minimizing resource and penalty cost and improve CSL by minimizing SLA violations. The proposed provisioning algorithms consider customer profiles and providers' quality parameters (e.g., response time) to handle dynamic customer requests and infrastructure level heterogeneity for enterprise systems. We also take into account customer-side parameters (such as the proportion of upgrade requests), and infrastructure-level parameters (such as the service initiation time) to compare algorithms. Simulation results show that our algorithms reduce the total cost up to 54 percent and the number of SLA violations up to 45 percent, compared with the previously proposed best algorithm.

134 citations


Journal ArticleDOI
TL;DR: A novel service cloud architecture is presented, and an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds that can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.
Abstract: Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.

114 citations


Journal ArticleDOI
TL;DR: Results show that a mixed negotiation approach for cloud service negotiation can achieve a higher utility than a concession approach, while incurring fewer failures than a tradeoff approach.
Abstract: Internet of Things (IoT) allows connected objects to communicate via the Internet. IoT can benefit from the unlimited capabilities and resources of cloud computing. Also, when coupled with IoT, cloud computing can in turn deal with real world things in a more distributed and dynamic manner. As the cloud market becomes more open and competitive, Quality of Service (QoS) will be more important. However, cloud providers and cloud consumers have different, and sometimes opposite, preferences. If such a conflict occurs, a Service Level Agreement (SLA) cannot be reached without negotiation. A tradeoff negotiation approach can outperform a concession approach in terms of utility, but may incur more failures if information is incomplete. To balance utility and success rate, we propose a mixed approach for cloud service negotiation, which is based on the “game of chicken.” In particular, if one is uncertain about the strategy of its counterpart, it is best to mix concession and tradeoff strategies in negotiation. To evaluate the effectiveness of this approach, we conduct extensive simulations. Results show that a mixed negotiation approach can achieve a higher utility than a concession approach, while incurring fewer failures than a tradeoff approach.

106 citations


Journal ArticleDOI
16 Jan 2014
TL;DR: An ontology-based approach to analyze Cloud service compatibility by applying reasoning on the expert knowledge and a combination of evolutionary algorithms and fuzzy logic for composition optimization are developed to minimize effort of users in expressing their preferences.
Abstract: When a single Cloud service (i.e., a software image and a virtual machine), on its own, cannot satisfy all the user requirements, a composition of Cloud services is required. Cloud service composition, which includes several tasks such as discovery, compatibility checking, selection, and deployment, is a complex process and users find it difficult to select the best one among the hundreds, if not thousands, of possible compositions available. Service composition in Cloud raises even new challenges caused by diversity of users with different expertise requiring their applications to be deployed across difference geographical locations with distinct legal constraints. The main difficulty lies in selecting a combination of virtual appliances (software images) and infrastructure services that are compatible and satisfy a user with vague preferences. Therefore, we present a framework and algorithms which simplify Cloud service composition for unskilled users. We develop an ontology-based approach to analyze Cloud service compatibility by applying reasoning on the expert knowledge. In addition, to minimize effort of users in expressing their preferences, we apply combination of evolutionary algorithms and fuzzy logic for composition optimization. This lets users express their needs in linguistics terms which brings a great comfort to them compared to systems that force users to assign exact weights for all preferences.

103 citations


Journal ArticleDOI
TL;DR: This paper addresses the problem of trust management in multi-cloud environments based on a set of distributed Trust Service Providers (TSPs), and proposes a framework that is effective and relatively stable in differentiating trustworthy and untrustworthy CSPs in a multi- cloud environment.
Abstract: In this paper, we address the problem of trust management in multi-cloud environments based on a set of distributed Trust Service Providers (TSPs). These are independent third-party providers/trust agents, trusted by Cloud Providers (CPs), Cloud Service Providers (CSPs) and Cloud Service Users (CSUs), that provide trust related services to cloud participants. TSPs are distributed over the clouds, and they elicit raw trust evidence from different sources and in different formats. This evidence is information regarding the adherence of a CSP to a Service Level Agreement (SLA) for a cloud-based service and the feedback sent by CSUs. Using this information, they evaluate an objective trust and a subjective trust of CSPs. TSPs communicate among themselves through a trust propagation network that permits a TSP to obtain trust information about a CSP from other TSPs. Experiments show that our proposed framework is effective and relatively stable in differentiating trustworthy and untrustworthy CSPs in a multi-cloud environment.

Proceedings ArticleDOI
06 Jul 2014
TL;DR: This work builds a process model which is augmented by time and data information in order to enable remaining time prediction, and proposes a new approach where both the control and the data flow perspectives are jointly used to improve the prediction quality.
Abstract: Accurate prediction of the completion time of a business process instance would constitute a valuable tool when managing processes under service level agreement constraints. Such prediction, however, is a very challenging task. A wide variety of factors could influence the trend of a process instance, and hence just using time statistics of historical cases cannot be sufficient to get accurate predictions. Here we propose a new approach where, in order to improve the prediction quality, both the control and the data flow perspectives are jointly used. To achieve this goal, our approach builds a process model which is augmented by time and data information in order to enable remaining time prediction. The remaining time prediction of a running case is calculated combining two factors: (a) the likelihood of all the following activities, given the data collected so far; and (b) the remaining time estimation given by a regression model built upon the data.

Journal ArticleDOI
TL;DR: CloudExp is introduced, a modeling and simulation environment for cloud computing that can be used to evaluate a wide spectrum of cloud components such as processing elements, data centers, storage, networking, Service Level Agreement constraints, web-based applications, Service Oriented Architecture (SOA), virtualization, management and automation, and Business Process Management.

Journal ArticleDOI
TL;DR: An improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment is proposed that offers significant improvement in the aspects of response time and makespan and demonstrates high potential for the improvement in energy efficiency of the data center.
Abstract: Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.

Journal ArticleDOI
TL;DR: A comprehensive solution is proposed, from a conceptual reference model to its design and implementation, that overcomes drawbacks of existing SLAs and is satisfactory enough to consider SALMonADA for SLA supervision because of its low intrusiveness.
Abstract: Service Level Agreements (SLAs) establish the Quality of Service (QoS) agreed between service-based systems consumers and providers. Since the violation of such SLAs may involve penalties, quality assurance techniques have been developed to supervise the SLAs fulfillment at runtime. However, existing proposals present some drawbacks: 1) the SLAs they support are not expressive enough to model real-world scenarios, 2) they couple the monitoring configuration to a given SLA specification, 3) the explanations of the violations are difficult to understand and even potentially inaccurate, 4) some proposals either do not provide an architecture, or present low cohesion within their elements. In this paper, we propose a comprehensive solution, from a conceptual reference model to its design and implementation, that overcomes these drawbacks. The resulting platform, SALMonADA, receives the SLA agreed between the parties as input and reports timely and comprehensive explanations of SLA violations. SALMonADA performs an automated monitoring configuration and it analyses highly expressive SLAs by means of a constraint satisfaction problems based technique. We have evaluated the impact of SALMonADA over the resulting service consumption time performance. The results are satisfactory enough to consider SALMonADA for SLA supervision because of its low intrusiveness.

Journal ArticleDOI
TL;DR: The introduction of Cloudcompaas, a SLA-aware PaaS Cloud platform that manages the complete resource lifecycle and provides a framework for general Cloud computing applications that could be dynamically adapted to correct the QoS violations by using the elasticity features of Cloud infrastructures.

Posted Content
TL;DR: In this article, the authors proposed an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment, and analyzed the performance of their approach using the CloudSim toolkit.
Abstract: Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.

Journal ArticleDOI
TL;DR: A redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff and the results show that the framework outperforms previous work.
Abstract: Virtual machine (VM) consolidation in Cloud computing 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. There are two main contributions in the framework: (1) establish a service level agreement (SLA) violation decision algorithm to decide whether a host is overload with SLA violation; (2) minimum power and maximum utilization policy is then proposed to improve the Minimum Power policy in previous work. 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. Specifically, it guarantees 21---34 % decrease in energy consumption, 84---92 % decrease in SLA violation, 87---94 % decrease in energy-performance metric, and 63 % decrease in execution time. And we further discuss why the redesigned framework outperforms the previous design.

Patent
31 Jul 2014
TL;DR: In this paper, a QoS provisioning policy that permits the network node to process the packets in a manner that complies with the QoS model or SLA is proposed.
Abstract: A method may include receiving a request to establish a quality of service (QoS) policy that identifies a desired QoS associated with traffic being transported by a network; generating a QoS model based on the identified desired QoS, where the QoS model includes a class of service (CoS) and corresponding forwarding priorities associated with the traffic; retrieving a service level agreement (SLA), associated with a client device that is interconnected to a network node associated with the network, where the SLA includes a particular CoS and corresponding other forwarding priorities for packets associated with the client device; creating a QoS provisioning policy based on the QoS model and the SLA, where the creating includes mapping the CoS to the particular CoS or mapping the forwarding priorities to the other forwarding priorities; and transmitting, to the network node, the QoS provisioning policy that permits the network node to process the packets in a manner that complies with the QoS model or the SLA.

Journal ArticleDOI
01 Jul 2014
TL;DR: A novel algorithm is presented for determining a cooperation strategy that tells providers whether to satisfy users' resource requests locally or outsource them to a certain provider, and yields the optimal cooperation structure from which no provider unilaterally deviates to gain more revenue.
Abstract: Having received significant attention in the industry, the cloud market is nowadays fiercely competitive with many cloud providers. On one hand, cloud providers compete against each other for both existing and new cloud users. To keep existing users and attract newcomers, it is crucial for each provider to offer an optimal price policy which maximizes the final revenue and improves the competitive advantage. The competition among providers leads to the evolution of the market and dynamic resource prices over time. On the other hand, cloud providers may cooperate with each other to improve their final revenue. Based on a service level agreement, a provider can outsource its users’ resource requests to its partner to reduce the operation cost and thereby improve the final revenue. This leads to the problem of determining the cooperating parties in a cooperative environment. This paper tackles these two issues of the current cloud market. First, we solve the problem of competition among providers and propose a dynamic price policy. We employ a discrete choice model to describe the user’s choice behavior based on his obtained benefit value. The choice model is used to derive the probability of a user choosing to be served by a certain provider. The competition among providers is formulated as a non-cooperative stochastic game where the players are providers who act by proposing the price policy simultaneously. The game is modelled as a Markov Decision Process whose solution is a Markov Perfect Equilibrium. Then, we address the cooperation among providers by presenting a novel algorithm for determining a cooperation strategy that tells providers whether to satisfy users’ resource requests locally or outsource them to a certain provider. The algorithm yields the optimal cooperation structure from which no provider unilaterally deviates to gain more revenue. Numerical simulations are carried out to evaluate the performance of the proposed models.

Journal ArticleDOI
TL;DR: The novelty of the proposed scheme is to integrate timing analysis, queuing theory, integer programming, and control theory techniques to achieve energy efficiency and desired service level agreements in cloud data centers.

Proceedings ArticleDOI
08 Dec 2014
TL;DR: This paper defines a language, named SLAC, devised for specifying SLA for the cloud computing domain, and illustrates potentialities and effectiveness of the SLAC language and its management framework by experimenting with an Open Nebula cloud system.
Abstract: The need of mechanisms to automate and regulate the interaction amongst the parties involved in the offered cloud services is exacerbated by the increasing number of providers and solutions that enable the cloud paradigm. This regulation needs to be defined through a contract, the so-called Service Level Agreement (SLA). We argue that the current solutions for SLA specification cannot cope with the distinctive characteristics of clouds. Therefore, in this paper we define a language, named SLAC, devised for specifying SLA for the cloud computing domain. The main differences with respect to the existing specification languages are: SLAC is domain specific, its semantics are formally defined in order to avoid ambiguity, it supports the main cloud deployment models, and it enables the specification of multi-party agreements. Moreover, SLAC supports the business aspects of the domain, such as pricing schemes, business actions and metrics. Furthermore, SLAC comes with an open-source software framework which enables the specification, evaluation and enforcement of SLAs for clouds. We illustrate potentialities and effectiveness of the SLAC language and its management framework by experimenting with an Open Nebula cloud system.

Journal ArticleDOI
TL;DR: In this article, a virtualized GPU resource adaptive scheduling algorithm for cloud games is proposed, which interposes scheduling algorithms in the graphics API of the operating system, and hence the host graphic driver or the guest operating system remains unmodified.
Abstract: As the virtualization technology for GPUs matures, cloud gaming has become an emerging application among cloud services. In addition to the poor default mechanisms of GPU resource sharing, the performance of cloud games is inevitably undermined by various runtime uncertainties such as rendering complex game scenarios. The question of how to handle the runtime uncertainties for GPU resource sharing remains unanswered. To address this challenge, we propose vGASA, a virtualized GPU resource adaptive scheduling algorithm in cloud gaming. vGASA interposes scheduling algorithms in the graphics API of the operating system, and hence the host graphic driver or the guest operating system remains unmodified. To fulfill the service level agreement as well as maximize GPU usage, we propose three adaptive scheduling algorithms featuring feedback control that mitigates the impact of the runtime uncertainties on the system performance. The experimental results demonstrate that vGASA is able to maintain frames per second of various workloads at the desired level with the performance overhead limited to 5-12 percent.

Journal ArticleDOI
29 Aug 2014-PLOS ONE
TL;DR: The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects and offers rational recommendations based on user preferences and practical cloud provisioning.
Abstract: Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI).

Proceedings ArticleDOI
03 Nov 2014
TL;DR: A proactive Markov Decision Process (MDP)-based load balancing algorithm is proposed, which handles the challenges of allying MDP in virtual resource management in cloud datacenters, and outperforms both previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency, and long-term load balance maintenance.
Abstract: To provide robust infrastructure as a service (IaaS), clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. Previous reactive load balancing algorithms migrate VMs upon the occurrence of load imbalance, while previous proactive load balancing algorithms predict PM overload to conduct VM migration. However, both methods cannot maintain long-term load balance and produce high overhead and delay due to migration VM selection and destination PM selection. To overcome these problems, in this paper, we propose a proactive Markov Decision Process (MDP)-based load balancing algorithm. We handle the challenges of allying MDP in virtual resource management in cloud datacenters, which allows a PM to proactively find an optimal action to transit to a lightly loaded state that will maintain for a longer period of time. We also apply the MDP to determine destination PMs to achieve long-term PM load balance state. Our algorithm reduces the numbers of Service Level Agreement (SLA) violations by long-term load balance maintenance, and also reduces the load balancing overhead (e.g., CPU time, energy) and delay by quickly identifying VMs and destination PMs to migrate. Our trace-driven experiments show that our algorithm outperforms both previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency and long-term load balance maintenance.

Proceedings ArticleDOI
30 Sep 2014
TL;DR: A system that proactively and systematically manages the risk throughout the lifecycle phases of automation, consisting of an authorization mechanism that guarantees the right level of eligibility and privilege of accessing the automation content, and an execution validator that controls the risk of human error which may cause massive damage to the infrastructure.
Abstract: Endpoint management, including patching, health checking, configuration etc., is a key function for data center and cloud management. Managing multiple nodes through automation tools or scripts significantly increases efficiency. However, the risks of adverse impact due to excessive privilege or human error may propagate to a large pool of endpoints and lead to massive service disruptions and SLA (Service Level Agreement) violations. In this paper, we present a system that proactively and systematically manages the risk throughout the lifecycle phases of automation. We present a prototype implementation consisting of an authorization mechanism that guarantees the right level of eligibility and privilege of accessing the automation content (during the deployment stage), and an execution validator that controls the risk of human error which may cause massive damage to the infrastructure (during execution of the automation content). Our current implementation has been deployed to more than a dozen customer environments and achieved an efficiency gain of 58% with high execution accuracy.

Proceedings ArticleDOI
27 Jun 2014
TL;DR: RLC provides a reasonable solution for high-efficiency and less disruptive migration scheme by utilizing the three phases of the process migration, and introduces a learning phase to estimate the writable working set (WWS) prior to the migration, resulting in an almost single time transfer of the pages.
Abstract: Today, IaaS cloud providers are dynamically minimizing the cost of data centers operations, while maintaining the Service Level Agreement (SLA). Currently, this is achieved by the live migration capability, which is an advanced state-of-the-art technology of Virtualization. However, existing migration techniques suffer from high network bandwidth utilization, large network data transfer, large migration time as well as the destination's VM failure during migration. In this paper, we propose Reliable Lazy Copy (RLC) - a fast, efficient and a reliable migration technique. RLC provides a reasonable solution for high-efficiency and less disruptive migration scheme by utilizing the three phases of the process migration. For effective network bandwidth utilization and reducing the total migration time, we introduce a learning phase to estimate the writable working set (WWS) prior to the migration, resulting in an almost single time transfer of the pages. Our approach decreases the total data transfer by 1.16 x - 12.21x and the total migration time by a factor of 1.42x - 9.84x against the existing approaches, thus providing a fast and an efficient, reliable VM migration of the VMs in the cloud.

Journal ArticleDOI
TL;DR: This article provides a conflict classification for SLAs that includes new conflicts derived from the use of conditional and optional term sets; and a novel language-agnostic technique based on constraint satisfaction problems to automatically detect and explain these conflicts.
Abstract: WS-Agreement is one of the most widely used SLA specifications. An advantage of WS-Agreement over other agreement metamodels is that it allows one to define conditional and optional term sets inside an agreement document, which are commonly found features in real-world agreements. Unfortunately, they increase the complexity of the automated detection and explanation of conflicts between SLA terms, leading to new kinds of conflicts that are not supported by current techniques. Furthermore, creating a general-purpose conflict analyser in WS-Agreement is a hard task since it should understand the semantics of an unbounded number of languages that can be used in the eight extension points that WS-Agreement includes for the sake of flexibility. In this article, we address these issues by providing a conflict classification for SLAs that includes new conflicts derived from the use of conditional and optional term sets; and a novel language-agnostic technique based on constraint satisfaction problems to automatically detect and explain these conflicts. In pursuing these results, we defined some WS-Agreement concepts as well as a fully-fledged WS-Agreement-compliant language. The developed technique and its reference implementation have been thoroughly validated.

Journal ArticleDOI
TL;DR: A prediction-based dynamic resource scheduling algorithms to dynamically consolidate the VMs with adaptive resource allocation to reduce the number of physical machines and is able to realize automatic elastic resource allocation with acceptable effect on SLAs.
Abstract: Virtualization and cloud computing technologies now make it possible to consolidate multiple online services, which are packed in virtual machines (VMs), into a smaller number of physical servers. However, it is still a challenging scheduling problem for cloud provider to dynamically manage the resource for VMs in order to handle variable workloads without service level agreement (SLA) violation. In this paper, we introduce a Prediction-based Dynamic Resource Scheduling (PDRS) solution to automate elastic resource scaling for virtualized cloud systems. Unlike traditional static consolidation or threshold-driven reactive scheduling, we both consider the dynamic workload fluctuations of each VM and the resource conflict handling problem. PDRS first employs an online resource prediction, which is a VM resource demand state predictor based on the Autoregressive Integrated Moving Average (ARIMA) model, to achieve adaptive resource allocation for cloud applications on each VM. Then we propose our prediction-based dynamic resource scheduling algorithms to dynamically consolidate the VMs with adaptive resource allocation to reduce the number of physical machines. Extensive experimental results show that our scheduling is able to realize automatic elastic resource allocation with acceptable effect on SLAs.

Patent
10 Dec 2014
TL;DR: In this paper, the authors describe techniques for allocating computing resources to a task from a shared hardware structure, which may involve receiving a request to execute a task for a tenant on shared hardware resources, and determining a set of computing resources for allocation to the task based on a service level agreement associated with the tenant.
Abstract: Techniques are described for allocating computing resources to a task from a shared hardware structure. The techniques may involve receiving a request to execute a task for a tenant on shared hardware resources, and determining a set of computing resources for allocation to the task based on a service level agreement associated with the tenant. The set of computing resources can be allocated to the task based on the service level agreement associated with the tenant. In some aspects, one or more performance counters associated with one or more of the computing resources can be monitored to determine an activity level for the one or more computing resources during execution of the task, and one or more allocations of the computing resources for execution of the task can be adjusted based on the activity level for the one or more computing resources.