Showing papers on "Service-level agreement published in 2016"
TL;DR: The concept of hierarchical NSaaS is introduced, helping operators to offer customized end-to-end cellular networks as a service and enabling operators to build network slices for vertical industries more agilely.
Abstract: With the blossoming of network functions virtualization and software-defined networks, networks are becoming more and more agile with features like resilience, programmability, and open interfaces, which help operators to launch a network or service with more flexibility and shorter time to market. Recently, the concept of network slicing has been proposed to facilitate the building of a dedicated and customized logical network with virtualized resources. In this article, we introduce the concept of hierarchical NSaaS, helping operators to offer customized end-to-end cellular networks as a service. Moreover, the service orchestration and service level agreement mapping for quality assurance are introduced to illustrate the architecture of service management across different levels of service models. Finally, we illustrate the process of network slicing as a service within operators by typical examples. With network slicing as a service, we believe that the supporting system will transform itself to a production system by merging the operation and business domains, and enabling operators to build network slices for vertical industries more agilely.
223 citations
TL;DR: The communication cost minimization problem for BDSP is formulated into a mixed-integer linear programming (MILP) problem and proved to be NP-hard, and a computation-efficient solution based on MILP is proposed.
Abstract: With the explosion of big data, processing large numbers of continuous data streams, i.e., big data stream processing (BDSP), has become a crucial requirement for many scientific and industrial applications in recent years. By offering a pool of computation, communication and storage resources, public clouds, like Amazon's EC2, are undoubtedly the most efficient platforms to meet the ever-growing needs of BDSP. Public cloud service providers usually operate a number of geo-distributed datacenters across the globe. Different datacenter pairs are with different inter-datacenter network costs charged by Internet Service Providers (ISPs). While, inter-datacenter traffic in BDSP constitutes a large portion of a cloud provider's traffic demand over the Internet and incurs substantial communication cost, which may even become the dominant operational expenditure factor. As the datacenter resources are provided in a virtualized way, the virtual machines (VMs) for stream processing tasks can be freely deployed onto any datacenters, provided that the Service Level Agreement (SLA, e.g., quality-of-information) is obeyed. This raises the opportunity, but also a challenge, to explore the inter-datacenter network cost diversities to optimize both VM placement and load balancing towards network cost minimization with guaranteed SLA. In this paper, we first propose a general modeling framework that describes all representative inter-task relationship semantics in BDSP. Based on our novel framework, we then formulate the communication cost minimization problem for BDSP into a mixed-integer linear programming (MILP) problem and prove it to be NP-hard. We then propose a computation-efficient solution based on MILP. The high efficiency of our proposal is validated by extensive simulation based studies.
99 citations
16 May 2016
TL;DR: This paper devise a methodology, referred to as MEdia FOg Resource Estimation (MeFoRE), to provide resource estimation on the basis of service give-up ratio, also called Relinquish Rate (RR), and enhance QoS on the based of previous Quality of Experience (QoE) and Net Promoter Score (NPS) records.
Abstract: Internet of Things (IoT) is now transitioning from theory to practice. This means that a lot of data will be generated and the management of this data is going to be a big challenge. To transform IoT into reality and build upon realistic and more useful services, better resource management is required at the perception layer. In this regard, Fog computing plays a very vital role. With the advent of Vehicular Ad hoc Networks (VANET) and remote healthcare and monitoring, quick response time and latency minimization are required. However, the receiving nodes have a very fluctuating behavior in resource consumption especially if they are mobile. Fog, a localized cloud placed close to the underlying IoTs, provides the means to cater such issues by analyzing the behavior of the nodes and estimating resources accordingly. Similarly, Service Level Agreement (SLA) management and meeting the Quality of Service (QoS) requirements also become issues. In this paper, we devise a methodology, referred to as MEdia FOg Resource Estimation (MeFoRE), to provide resource estimation on the basis of service give-up ratio, also called Relinquish Rate (RR), and enhance QoS on the basis of previous Quality of Experience (QoE) and Net Promoter Score (NPS) records. The algorithms are implemented using CloudSim and applied on real IoT traces on the basis of Amazon EC2 resource pricing.
84 citations
01 Dec 2016
TL;DR: This paper introduces VNF placement and provisioning optimization strategies over an edge- central carrier cloud infrastructure taking into account Quality of Service (QoS) requirements and using queuing and QoS models.
Abstract: Cloud computing and Network Function Virtualization (NFV) represent together a promising solution for wireless network operators to improve business agility and cope with the continuing growth in data traffic. Furthermore, the use of edge clouds in association with a centralized cloud, referred to as the edge-central cloud, notably improves user experience while ensuring scalability and load balancing. In such carrier cloud environment, efficient management mechanisms for the Virtualized Network Functions (VNFs) are of crucial importance. In this paper, we introduce VNF placement and provisioning optimization strategies over an edge- central carrier cloud infrastructure taking into account Quality of Service (QoS) requirements (i.e., response time, latency constraints and real-time requirements) and using queuing and QoS models. Our main design goals are to optimize resource utilization, to prevent cloudlet overload, and to avoid violation of Service Level Agreement (SLA) requirements. Through extensive simulations, we show how a trade-off can be achieved between these conflicting objectives.
82 citations
TL;DR: The proposed architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing and new algorithm that is decision rules of linearized decision tree based on three conditions (services size, completion time, and VMs capacity) for managing and delegating user request in order to balance workload are proposed.
Abstract: Despite the wide utilization of cloud computing (e.g., services, applications, and resources), some of the services, applications, and smart devices are not able to fully benefit from this attractive cloud computing paradigm due to the following issues: (1) smart devices might be lacking in their capacity (e.g., processing, memory, storage, battery, and resource allocation), (2) they might be lacking in their network resources, and (3) the high network latency to centralized server in cloud might not be efficient for delay-sensitive application, services, and resource allocations requests. Fog computing is promising paradigm that can extend cloud resources to edge of network, solving the abovementioned issue. As a result, in this work, we propose an architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing. We provide new algorithm that is decision rules of linearized decision tree based on three conditions (services size, completion time, and VMs capacity) for managing and delegating user request in order to balance workload. Moreover, we propose algorithm to allocate resources to meet service level agreement (SLA) and quality of services (QoS) as well as optimizing big data distribution in fog and cloud computing. Our simulation result shows that our proposed approach can efficiently balance workload, improve resource allocation efficiently, optimize big data distribution, and show better performance than other existing methods.
77 citations
01 Dec 2016
TL;DR: An optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically assigns VMs to Physical Machines (PMs) in cloud data centers is developed, which co-optimizes energy consumption and service level agreement (SLA) violations.
Abstract: Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users' demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial amounts of energy. This energy consumption increases the operational cost and the CO2 emissions. The goal of this paper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically assigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy consumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate the VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to finding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based approach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more than 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach.
74 citations
TL;DR: It is shown how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed datacenters.
Abstract: Recently, datacenter carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of datacenters to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed datacenters. Specifically, we first verify that electricity cost minimization conflicts with carbon emission minimization, based on an empirical study of several representative geo-distributed cloud services. We then jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analysis and real-world trace-driven evaluation demonstrate the effectiveness of our framework in reducing both electricity cost and carbon emission.
64 citations
TL;DR: This work proposes to use a G/G/c-like model to represent a cloud system and assess expected performance indices, and extensively validated its approximation against discrete-event simulation for several QoS performance metrics such as task response time and blocking probability with excellent results.
Abstract: Cloud providers need to size their systems to determine the right amount of resources to allocate as a function of customer's needs so as to meet their SLAs (Service Level Agreement), while at the same time minimizing their costs and energy use. Queueing theory based tools are a natural choice when dealing with performance aspects of the QoS (Quality of Service) part of the SLA and forecasting resource utilization. The characteristics of a cloud center lead to a queueing system with multiple servers (nodes) in which there is potentially a very large number of servers and both the arrival and service process can exhibit high variability. We propose to use a G/G/c -like model to represent a cloud system and assess expected performance indices. Given the potentially high number of servers in a cloud system, we present an efficient, fast and easy-to-implement approximate solution. We have extensively validated our approximation against discrete-event simulation for several QoS performance metrics such as task response time and blocking probability with excellent results. We apply our approach to examples of system sizing and our examples clearly demonstrate the importance of taking into account the variability of the tasks arrivals and thus expose the risk of under- or over-provisioning if one relies on a model with Poisson assumptions.
58 citations
TL;DR: A resource provisioning and scheduling framework has been presented which caters to provisioned resource distribution and scheduling of resources and results show that the framework provisions and schedules resource efficiently by considering energy consumption, execution cost and execution time as QoS parameters.
Abstract: Resource provisioning of appropriate resources to cloud workloads depends on the quality of service (QoS) requirements of cloud applications and is a challenging task. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounter a problem of allocation of resources, which cannot be addressed with existing resource management frameworks. Resource scheduling, if done after efficient resource provisioning, will be more effective and the cloud resources would be scheduled as per the user requirements (QoS) on provisioned resources. Execution of cloud workloads should be as per QoS parameters to fully satisfy the cloud consumer. Therefore, based on QoS parameters, it is mandatory to predict and verify the resource provisioning before actual resource scheduling. In this paper, a resource provisioning and scheduling framework has been presented which caters to provisioned resource distribution and scheduling of resources. Cloud workloads have been re-clustered using k-means-based clustering algorithm after firstly clustering them through workload patterns to identify the QoS requirements of a workload, and then based on identified QoS requirements resources are provisioned before actual scheduling. Further, scheduling has been done based on different scheduling policies. Finally, the performance of the proposed framework has been evaluated in both real and simulated cloud environment and experimental results show that the framework provisions and schedules resource efficiently by considering energy consumption, execution cost and execution time as QoS parameters.
57 citations
TL;DR: A combinatorial auction system that determines winners at each bidding round according to the job's urgency based on execution time deadline in order to efficiently allocate resources and reduce the penalty cost.
Abstract: Combinatorial auction is a popular approach for resource allocation in cloud computing. One of the challenges in resource allocation is that QoS Quality of Service constraints are satisfied and provider’s profit is maximized. In order to increase the profit, the penalty cost for SLA Service Level Agreement violations needs to be reduced. We consider execution time constraint as SLA constraint in combinatorial auction system. In the system, we determine winners at each bidding round according to the job’s urgency based on execution time deadline, in order to efficiently allocate resources and reduce the penalty cost. To analyze the performance of our mechanism, we compare the provider’s profit and success rate of job completion with conventional mechanism using real workload data.
50 citations
TL;DR: This paper proposes an autonomic resource provisioning approach that is based on the concept of the control monitor-analyze-plan-execute (MAPE) loop, and designs a resource Provisioning framework for cloud environments.
Abstract: Recently, there has been a significant increase in the use of cloud-based services that are offered in software as a service (SaaS) models by SaaS providers, and irregular access of different users to these cloud services leads to fluctuations in the demand workload. It is difficult to determine the suitable amount of resources required to run cloud services in response to the varying workloads, and this may lead to undesirable states of over-provisioning and under-provisioning. In this paper, we address improvements to resource provisioning for cloud services by proposing an autonomic resource provisioning approach that is based on the concept of the control monitor-analyze-plan-execute (MAPE) loop, and we design a resource provisioning framework for cloud environments. The experimental results show that the proposed approach reduces the total cost by up to 35 %, the number of service level agreement (SLA) violations by up to 40 %, and increases the resource utilization by up to 25 % compared with the other approaches.
TL;DR: An adaptive fuzzy threshold-based algorithm has been proposed to detect overloaded and under-loaded hosts and results demonstrate that the proposed algorithm significantly outperforms the other competitive algorithms.
Abstract: Dynamic consolidation of virtual machines (VMs) is an effective technique, which can lead to improvement of energy efficiency and resource utilization in cloud data centers. However, due to varying workloads in applications, consolidating the virtual machines can cause a violation in Service Level Agreement. The main goal of the dynamic VM consolidation is to optimize the energy-performance trade-off. Detecting when a host is being overloaded or underloaded are two substantial sub-problems of dynamic VM consolidation, which directly affects the utilization of resources, Quality of Service, and energy efficiency as well. In this paper, an adaptive fuzzy threshold-based algorithm has been proposed to detect overloaded and under-loaded hosts. The proposed algorithm generates rules dynamically and updates membership functions to adapt to changes in workload. It is validated with real-world PlanetLab workload. Simulation results demonstrate that the proposed algorithm significantly outperforms the other competitive algorithms.
TL;DR: This paper explores the use of Information-Centric Networking to support management operations in IoT deployments, presenting the design of a flexible architecture that allows the appropriate operation of IoT devices within a delimited ICN network domain.
TL;DR: New prediction algorithm for determination of overloaded hosts as well as novel multi-criteria decision making techniques to select virtual machines to optimize energy, SLA, and number of migrations in cloud data centers are proposed.
Abstract: Increasing demand for acquiring diverse range of services has led to the establishment of huge energy hungry cloud data centers all around the world. Cloud providers face with major concerns to reduce their energy consumption while ensuring high quality of service based on the Service Level Agreement (SLA). Consolidation is proposed as one of the most effective techniques for online energy saving in cloud environments with dynamic workloads. This paper proposes novel proactive online resource management policies to optimize energy, SLA, and number of migrations in cloud data centers. More precisely, this paper proposes new prediction algorithm for determination of overloaded hosts as well as novel multi-criteria decision making techniques to select virtual machines. The results of simulations using CloudSim simulator shows up to 98.11 % reduction in the output metric which is representative of energy consumption, SLA violation, and number of migrations, in comparison with state of the art.
TL;DR: This work separates the agreement's fault-tolerance concerns and strategies into multiple autonomous layers that can be hierarchically combined into an intuitive, parallelized, effective and efficient management structure for the automated management of the complete SLA lifecycle.
TL;DR: Some major security issues of current cloud computing environments are going to be going to, including access control, service continuity and privacy while protecting together the service provider and the user.
TL;DR: A novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Back Propagation Neural Network) based on BPNN (Backpropagation neural Network) algorithm, which achieves an improved prediction accuracy compared to the HMM and Naive Bayes Classifier models by a considerable margin.
Abstract: Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naive Bayes Classifier models by a considerable margin.
TL;DR: A model which will help the cloud service users in finding out the efficient and trustworthy cloud service provider is proposed and the proposed model is flexible enough to be customized according to the precedence level of aforementioned parameters for the cloud Service users.
Abstract: Cloud computing is one of those technologies which have revolutionized the modern world. Through this, people can start their businesses without huge investments required for infrastructure like servers, technical staff for maintenance, and purchasing of expensive software, etc. With many advantages, there are few risks involved with cloud computing. There are issues like unavailability of service, i.e., they are down when required. Another issue is of outdated service/stuff provision to the clients by the cloud service providers. Similarly, lacking of effective and quality support services to their customers is another important concern. Moreover, non-capability of cloud service provider in honoring the service level agreement is an additional prong in this list. All such issues make cloud service users frustrated. Hence, there is a need of a system which may help the cloud service user to select good cloud service provider. Hence, for the above mentioned issues, in this article, we have proposed a model which will help the cloud service users in finding out the efficient and trustworthy cloud service provider. In put data to the model can be collected from regulatory authorities, performance of cloud service provider in the last one year, and feedback taken from the customers. Moreover, the proposed model is flexible enough to be customized according to the precedence level of aforementioned parameters for the cloud service users, i.e., educational institutes. We have also given a comparative analysis of proposed model with general existing model to portray the importance and requirement of the designed model for the said application domain.
TL;DR: This paper considers the case of a cloud service provider (CSP) who owns multiple geographically distributed data centers, with collocated sources of renewable energy, and proposes and evaluated a novel strategy for mitigating the risk associated with electricity cost.
01 Oct 2016
TL;DR: The goal is the minimization of the migration-induced communication energy under service level agreement (SLA)-induced hard constrains on the total migration time, downtime and overall available bandwidth.
Abstract: Live virtual machine migration aims at enabling the dynamic balanced use of the networking/computing physical resources of virtualized data-centers, so to lead to reduced energy consumption. Here, we analytically characterize, prototype in software and test an optimal bandwidth manager for live migration of VMs in wireless channel. In this paper we present the optimal tunable-complexity bandwidth manager (TCBM) for the QoS live migration of VMs under a wireless channel from smartphone to access point. The goal is the minimization of the migration-induced communication energy under service level agreement (SLA)-induced hard constrains on the total migration time, downtime and overall available bandwidth.
TL;DR: This paper addresses the problem of the revenue maximisation through the SLA-aware resource allocation through the TSF Time Service Factor based pricing models and proposes an optimal solution that considers various Quality of Service QoS parameters.
Abstract: Cloud computing is distinguished from such conventional computing paradigms as grid computing and cluster computing in that it provides a practical business model for customers to use the resources remotely. It is natural for service providers to allocate the pooled cloud resources dynamically among the differentiated customers to maximise their revenue. This paper addresses the problem of the revenue maximisation through the SLA-aware resource allocation. Firstly, two TSF Time Service Factor based pricing models are proposed since TSF is a widely used metric to determine the billings of internet services with variable performance. Then the resource allocation problem is formulised with queuing theory and its optimal solutions are proposed. The optimal solution considers various Quality of Service QoS parameters such as pricing, arrival rates, service rates and available resources. Finally, the experiment results, both with the synthetic dataset and traced dataset, are presented. They have validated our optimal resource allocation solutions and shown that our algorithms outperform the related work.
TL;DR: A novel triangular data privacy-preserving (TDPP) model that supports public auditing with the capability of auditing all the key stakeholders (i.e., CSU, TPA, and CSP) for achieving optimal security in a cloud environment is presented.
TL;DR: An energy-aware consolidation strategy based on predictive control is introduced, in which virtual machines are properly migrated among physical machines to reduce the amount of active units in the cloud.
Abstract: Infrastructure-as-a-Service is one of the most used paradigms of cloud computing and relies on large-scale datacenters with thousands of nodes. As a consequence of this success, the energetic demand of the infrastructure may lead to relevant economical costs and environmental footprint. Thus, the search for power optimization is of primary importance. In this perspective, this paper introduces an energy-aware consolidation strategy based on predictive control, in which virtual machines are properly migrated among physical machines to reduce the amount of active units. To this aim, a discrete-time dynamic model and suitable constraints are introduced to describe the cloud. The migration strategies are obtained by solving finite-horizon optimal control problems involving integer variables. The proposed method allows one to trade among power savings and violations of the service level agreement. To prove its effectiveness, a simulation campaign is conducted in different scenarios using both synthetic and real workloads, also by performing a comparison with three heuristics selected from the reference literature.
TL;DR: This study provides a holistic brokerage model to manage on-demand and advance service reservation, pricing, and reimbursement, and a mechanism of incentive and penalties is provided, which helps in trust build-up for the customers and service providers, prevention of resource underutilization, and profit gain for the involved entities.
Abstract: Media content in its digital form has been rapidly scaling up, resulting in popularity gain of cloud computing. Cloud computing makes it easy to manage the vastly increasing digital content. Moreover, additional features like, omnipresent access, further service creation, discovery of services, and resource management also play an important role in this regard. The forthcoming era is interoperability of multiple clouds, known as cloud federation or inter-cloud computing. With cloud federation, services would be provided through two or more clouds. Once matured and standardized, inter-cloud computing is supposed to provide services which would be more scalable, better managed, and efficient. Such tasks are provided through a middleware entity called cloud broker. A broker is responsible for reserving resources, managing them, discovering services according to customer's demands, Service Level Agreement (SLA) negotiation, and match-making between the involved service provider and the customer. So far existing studies discuss brokerage in a narrow focused way. In the research outcome presented in this paper, we provide a holistic brokerage model to manage on-demand and advance service reservation, pricing, and reimbursement. A unique feature of this study is that we have considered dynamic management of customer's characteristics and historical record in evaluating the economics related factors. Additionally, a mechanism of incentive and penalties is provided, which helps in trust build-up for the customers and service providers, prevention of resource underutilization, and profit gain for the involved entities. For practical implications, the framework is modeled on Amazon Elastic Compute Cloud (EC2) On-Demand and Reserved Instances service pricing. For certain features required in the model, data was gathered from Google Cluster trace.
TL;DR: In this work, energy consumption is investigated and measure of a number of virtual machines running the Hadoop system, over an OpenNebula Cloud, based on sentiment analysis undertaken over Twitter messages to understand the tradeoff between energy efficiency and performance.
TL;DR: A system architecture for supporting QoS for concurrent data streams to be composed of self-regulating nodes that features an envelope process for regulating and controlling data access and a resource manager to enable resource allocation, and selective SLA violations, while maximizing revenue.
01 Mar 2016
TL;DR: A framework in order to improve the cloud service selection by taking into account services capabilities, quality attributes, level of user's knowledge and service level agreements is proposed.
Abstract: With the growing popularity of cloud computing the number of cloud service providers and services have significantly increased. Thus selecting the best cloud services becomes a challenging task for prospective cloud users. The process of selecting cloud services involves various factors such as characteristics and models of cloud services, user requirements and knowledge, and service level agreement (SLA), to name a few. This paper investigates into the cloud service selection tools, techniques and models by taking into account the distinguishing characteristics of cloud services. It also reviews and analyses academic research as well as commercial tools in order to identify their strengths and weaknesses in the cloud services selection process. It proposes a framework in order to improve the cloud service selection by taking into account services capabilities, quality attributes, level of user's knowledge and service level agreements. The paper also envisions various directions for future research.
TL;DR: This paper shows the considerable impact that RAM can have on the total energy consumption, particularly in servers with large amounts of this memory, and proposes two new approaches for dynamic consolidation of virtual machines in cloud data centers that take into account both CPU and RAM usage.
26 Jun 2016
TL;DR: The demonstration will show three families of dynamic scaling algorithms --feedback control, reinforcement learning, and online machine learning--and will enable attendees to change tuning parameters, performance thresholds, and workloads to compare and contrast the algorithms in different settings.
Abstract: We demonstrate PerfEnforce, a dynamic scaling engine for analytics services. PerfEnforce automatically scales a cluster of virtual machines in order to minimize costs while probabilistically meeting the query runtime guarantees offered by a performance-oriented service level agreement (SLA). The demonstration will show three families of dynamic scaling algorithms --feedback control, reinforcement learning, and online machine learning--and will enable attendees to change tuning parameters, performance thresholds, and workloads to compare and contrast the algorithms in different settings.
TL;DR: In this paper, the authors proposed dynamic threshold based auto-scaling algorithms that predict required resources using Long Short-Term Memory Recurrent Neural Network and auto-scale virtual resources based on predicted values.
Abstract: Scalability is an important characteristic of cloud computing. With scalability, cost is minimized by provisioning and releasing resources according to demand. Most of current Infrastructure as a Service (IaaS) providers deliver threshold-based auto-scaling techniques. However, setting up thresholds with right values that minimize cost and achieve Service Level Agreement is not an easy task, especially with variant and sudden workload changes. This paper has proposed dynamic threshold based auto-scaling algorithms that predict required resources using Long Short-Term Memory Recurrent Neural Network and auto-scale virtual resources based on predicted values. The proposed algorithms have been evaluated and compared with some of existing algorithms. Experimental results show that the proposed algorithms outperform other algorithms.