Service Level Agreement Constraints Research Articles

A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

The demand for cloud computing has increased manifold in the recent past. More specifically, on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs. The cloud service provider fulfills different user requirements using virtualization - where a single physical machine can host multiple Virtual Machines. Each virtual machine potentially represents a different user environment such as operating system, programming environment, and applications. However, these cloud services use a large amount of electrical energy and produce greenhouse gases. To reduce the electricity cost and greenhouse gases, energy efficient algorithms must be designed. One specific area where energy efficient algorithms are required is virtual machine consolidation. With virtual machine consolidation, the objective is to utilize the minimum possible number of hosts to accommodate the required virtual machines, keeping in mind the service level agreement requirements. This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host. The online algorithm is analyzed using a competitive analysis approach. In addition, an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms. Our proposed online algorithm consumed 25% less energy and performed 43% fewer migrations than the benchmark algorithms.

Performance-Cost Trade-Off in Auto-Scaling Mechanisms for Cloud Computing.

Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.

Enabling Cloud Applications to Negotiate Multiple Resources in a Cost-Efficient Manner

Cloud applications can achieve similar performance with diverse multi-resource configurations, allowing cloud service providers to benefit from optimal resource allocation for reducing their operation cost. This paper aims to solve the problem of multi-resource negotiation with considerations of both the service-level agreement (SLA) and the cost efficiency so that the performance requirement for cloud services is satisfied and the cost of resource usage is also minimized. The performance and resource demand are usually application-dependent, making the optimization problem complicated, especially when the dimension of multi-resource configurations is large. To this end, we use reinforcement learning to solve the optimal problem of multi-resource configuration with simultaneous optimization of the learning efficiency and performance guarantee. The developed prototype named SmartYARN is an extended Apache YARN equipped with our learning algorithm which can enable cloud applications to negotiate multiple resources cost-effectively. The extensive evaluations with four typical benchmarks show that SmartYARN performs well in reducing the cost of resource usage while maintaining compliance with the SLA constraints of cloud service simultaneously.

Research on the design of analytical communication and information model for teaching resources with cloud‐sharing platform

AbstractThe recent growth in the adoption by several organizations of cloud computing services has created the challenge to determine their performance of information model. The ability of cloud computing systems to provide the required services in line with the quality of service and service level agreement constraints is a major problem for cloud service providers and consumers. To that end, it is possible to develop the means of ensuring that the desired levels of success are achieved in simulated environments. In the eventual stage of commissioning and running cloud computing, this will reduce the disrupted service and performance degradation problems using analytical communication and information model of cloud technology. However, several modelers and simulators for simulating cloud computing systems are available in conventional research. This paper, therefore, provides a critical assessment with state of the art cloud computing modeling and simulation frameworks. It compares the prominent frameworks of simulation regarding application programming interface features, programming flexibility, operating system requirements, services supported, and licensing needs with 95% reliability. It offers recommendations to the researchers, administrators, and managers of cloud computing systems regarding the choice of the most appropriate framework.

Customer satisfaction model in cloud computing environment

Service level agreements are widely accepted mechanism for gaining enterprise-level information technology (IT) resources. The IT infrastructure provided by the Cloud provider must meet SLA established with the customer. SLA is generally used mechanism to determine contracts accessing requirements in business content. It defines a set of non-operating requirements of the service and also, incorporates penalties for unsatisfied requirements. The numbers ofcloud customers are increasing day-by-day resulting in the increased loads on cloud service providers, thus, making Service Level Agreement (SLA) a prime need of time for ensuring the service quality. This helps in meeting the challenges presented by enterprise-oriented application software's towards traditional sales models. This paper is an attempt to devise a mathematical model for better handling of cloud's resources and increasing the satisfaction level of cloud customers by meeting SLA constraints.

DDMTS: A novel dynamic load balancing scheduling scheme under SLA constraints in cloud computing

Cloud computing is a computing method based on the Internet designed to share resources through virtualization technology. For a large number of requests waiting to be processed, task scheduling is used to reasonably allocate computing resources to requests. With the rapid development of computer hardware and software, deep reinforcement learning (DRL) provides a new direction for better solving task scheduling problems. In this paper, we propose a novel DRL-based dynamic load balancing task scheduling algorithm under service-level agreement (SLA) constraints to reduce the load imbalance of virtual machines (VMs) and task rejection rate. First, we use the DRL method to select a suitable VM for the task and then determine whether to execute the task on the selected VM violates the SLA. If the SLA is violated, the task is refused and feedback a negative reward for DRL training; otherwise, the task is received and executed, and feedback a reward according to the balance of the VMs load after the task is executed. Compared with three other task scheduling algorithms applied to randomly generated benchmark and Google real user workload trace benchmark, the proposed algorithm exhibits the best performance in balancing VMs load and reducing the task rejection rate, improving the overall level of cloud computing services.

Dynamic Scalability Model for Containerized Cloud Services

Cloud computing has become an important research area in large-scale computing systems and is being employed by many organizations in government, businesses, and industry. Schemes and appropriate models for dynamic resources provisioning in the cloud environment have been extensively studied. To date, the research literature is lacking schemes and models that offer dynamic scalability in which Quality of Service (QoS) and high performance are provided to customers with the usage of the least number of cloud resources, especially for containerized services hosted on the cloud. With dynamic scalability, cloud computing can offer on-demand, timely, and dynamically adjustable computing resources to services hosted on the cloud. This paper presents a dynamic scaling model based on queueing theory to scale containers virtual resources and satisfy the customer Service Level Agreements (SLA) while guarding costs of scaling very low. The aim is to improve the virtual computing resources utilization and satisfy SLA constraints in terms of CPU utilization, system response time, system drop rate, system number of tasks, and system throughput. Simulation results are provided using Java Modelling Tools simulation tool, which shows that our proposed model can determine under any offered workload the needed containers instances to satisfy the required QoS parameters.

Novel QoE monitoring and management architecture with eTOM for SDN-based 5G networks

The 5th Generation (5G) network design is still under exploration and will require an innovative approach to network monitoring and management with a relentless focus on customer needs. Among the recent technologies, software-defined networking (SDN) and network functions virtualization (NFV), which are already offering network programmability and automation, have been proposed to improve resource management in 5G systems. Furthermore, the enhanced telecom operations map (eTOM) can be used to manage both End-to-End (E2E) service and end-customer experience. In this paper, we illustrate a novel SDN-based architecture for 5G networks that will be able to enhance quality-of-experience (QoE) monitoring and management by integrating SDN and eTOM. This architecture is deployed and evaluated by simulations of a service-level agreement (SLA) verification scenario. As a result, the implemented platform is capable of identifying SLA violation and can successfully enforce suitable mechanisms to dynamically adjust network parameters, thereby satisfying the SLA constraints.

A Genetic-Based Adaptive Approach for Reliable and Efficient Service Composition

Composite services often run in dynamically changing environments where new composition requirements can be added and Web services may disappear or their quality of service may decline. The service composition requirements are classified into three groups: functional, nonfunctional, and transactional requirements. The functional computing requirements concern functional capabilities of Web services. The nonfunctional computing requirements are defined as service-level agreements (SLA) constraints. The transactional computing requirements define behavioral constraints and are expressed by designers using the acceptable termination states (ATS) concept. In this work, we proposed an adaptive service composition (ASC) approach. Such an approach is adaptive as it can be used to either define or reconfigure composite services according to SLA and ATS constraints. It introduces a new recovery capability (RC) concept for capturing the recoverability level supported by Web services. We adopted a genetic-based algorithm to model the ASC approach. The choice of such an algorithm is justified by the nature of the problem of our ASC process which belongs to the NP-hard class. We experimentally showed the effectiveness of our ASC approach and empirically studied the service composition RC in particular.

Time and activity sequence prediction of business process instances

The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies.

FCMS: A fuzzy controller for CPU and memory consolidation under SLA constraints

SummaryCloud providers (CPs) rely on server consolidation (the allocation of several virtual machines [VMs] on the same physical server) to minimize their costs. Maximizing the consolidation level is thus become 1 of the major goals of cloud providers. This is a challenging task because it requires the ability of estimating, in a resource contention scenario, multidimensional resource demands for multitier cloud applications that must meet service‐level agreements (SLAs) in face of nonstationary workloads. In this paper, we cope with the problem of jointly allocating CPU and memory capacity to (a) precisely estimate their capacity required by each VM to meet its SLAs and (b) coordinate their allocation to limit the negative effects due to the interactions of dynamic allocation mechanisms, which, if ignored, can lead to SLA violations. We tackle this problem by devising FCMS, a feedback fuzzy controller that is able to dynamically adjust the CPU and memory capacity allocated to each VM in a coordinated way, to precisely match the needs induced by the incoming workload. By means of an extensive experimental evaluation, we show that FCMS is able to achieve the above goals and works better than existing state‐of‐the‐art alternative solution in all the considered experimental scenarios.

Hierarchical SLA-Driven Resource Management for Peak Power-Aware and Energy-Efficient Operation of a Cloud Datacenter

In this work, a hierarchical, service level agreement (SLA) based resource management solution for cloud datacenters is presented, which considers the energy non-proportionality of existing servers, peak power constraints, and cooling power consumption. The goal of this resource manager is to minimize the operational cost of the data center. The hierarchical structure of the proposed solution makes the resource management scalable. The proposed resource management solution simultaneously considers server and cooling power consumption, guarantee-based SLA and complexity of the decision making in the resource management of the cloud computing systems. Considering SLA and state of the datacenter in determining the amount of resource that needs to be allocated to applications results in significant reduction of the operational cost in datacenter. The effectiveness of the proposed management scheme compared to previous work is demonstrated using a comprehensive cloud computing simulation tool. The proposed resource management algorithms reduce the operational cost of a datacenter by about 40 percent while satisfying SLA constraints and decrease the run-time of the management algorithms by up to 86 percent with respect to the state of the art centralized management solution.

Optimization Approach for Resource Allocation on Cloud Computing for IoT

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.

Evaluating the cooling and computing energy demand of a datacentre with optimal server provisioning

As cloud computing continues to gain significance across fields, the energy consumption of datacentres creates new challenges in the design and operation of computer systems, with cooling remaining a key part of the total energy expenditure. We investigate the implications of increasing the room temperature setpoint in datacentres to save energy. For this, we develop a holistic model for the energy consumption of the server room that depends on user workload and service level agreement constraints, and that considers both cooling and computing energy dissipation. The model is applicable to a steady-state analysis of the system and brings insight into the impact of the most relevant parameters that affect the net energy consumption, such as the outside temperature, room temperature setpoint, and user demand. We analyse both static and dynamic server provisioning cases. In the latter case, a global power management scheme determines the optimal number of servers required to handle the incoming user demand to fulfil a target service level objective. Finally, we consider the extra energy needed to maintain service continuity under the expected higher server mortality rate due to warmer operational temperatures. Energy and temperature measurements acquired from a server machine running scientific benchmark programs allow to realistically fix model parameters for the study and to obtain pragmatic conclusions.

Backtracking Algorithm for Virtual Cluster Migration in Cloud Computing

Objectives: The aim of the paper is to propose a novel approach for Virtual Cluster (VC) migration in cloud computing which achieves better performance in less time and incorporating all Service Level Agreements. Method: Cloud Computing (CC) is rapidly gaining momentum and has spread its flavors and dimensions in wide discipline of Information Technology (IT) industry. When a new technology is spreading its wings to various places the functionalities realized by it throws challenges too. In an attempt to tap the whole potential of CC these challenges are addresses by professionals from IT industry and academia. Out of the myriad number of challenges, one is the need for migration of Virtual Clusters (VC) from one physical machine to another physical machine in spite of fault tolerance, load balancing, complying with Service Level Agreements (SLA). After studying and analyzing the various existing approaches for VC migration it is proposed that Backtracking algorithm outperforms the existing ones in terms of time and performance. Findings: The implementation of Backtracking algorithm proves the fact that the above objectives are achieved and the test results through line graphs below clarifies the fact further. The novelty of the algorithm and the compact lines of coding achieve the optimal solution for VC migration with satisfying SLA and time constraints. The comparison chart on feature analysis clearly list down the additional characteristics of the backtracking algorithm which adds value and uniqueness to the optimal solution. The flowchart aids to the development of algorithm and its easy implementation. The test data is run for various memory sizes of VC and VM therefore checked for all type of situations arising at real time. All the solutions are optimal in case of time and efficiency when compared with other exciting techniques like serial and greedy approaches. Application/Improvement: Though a handful of approaches are there in CC field for VC migration, not all techniques provides a balance between performance and time while the proposed backtracking algorithm does it with a unique solution. Also it is one of the few algorithms which satisfy all the SLA constraints and one of a kind to accommodate any number of SLA into the algorithm.

Shared execution of recurring workloads in MapReduce

With the increasing complexity of data-intensive MapReduce workloads, Hadoop must often accommodate hundreds or even thousands of recurring analytics queries that periodically execute over frequently updated datasets, e.g., latest stock transactions, new log files, or recent news feeds. For many applications, such recurring queries come with user-specified service-level agreements (SLAs), commonly expressed as the maximum allowed latency for producing results before their merits decay. The recurring nature of these emerging workloads combined with their SLA constraints make it challenging to share and optimize their execution. While some recent efforts on multi-job optimization in MapReduce have emerged, they focus on only sharing work among ad-hoc jobs on static datasets. Unfortunately, these sharing techniques neither take the recurring nature of the queries into account nor guarantee the satisfaction of the SLA requirements. In this work, we propose the first scalable multi-query sharing engine tailored for recurring workloads in the MapReduce infrastructure, called "Helix" . Helix deploys new sliced window-alignment techniques to create sharing opportunities among recurring queries without introducing additional I/O overheads or unnecessary data scans. And then, Helix introduces a cost/benefit model for creating a sharing plan among the recurring queries, and a scheduling strategy for executing them to maximize the SLA satisfaction. Our experimental results over real-world datasets confirm that Helix significantly outperforms the state-of-art techniques by an order of magnitude.

A SLA-based cloud resource provisioning optimisation mechanism for multi-tenant applications

As cloud resources exhibit the characteristics of dynamic and scalability, an increasing number of service providers deploy their multi-tenant applications to Cloud. For cloud service providers, how to arrange the allocation of application resources reasonably, in order to not only satisfy the service level agreements (SLAs) of diverse tenants, but also maximise their resource utilisation, has become a major challenge. In this paper, we propose a SLA-based resource provisioning optimisation mechanism for multi-tenant applications. Based on the invoking relationships among services and the workload of service instances, the mechanism divides the tenants' SLA into SLA constraints, and maps each SLA constraint to the corresponding service. In addition, the mechanism uses an optimised round-robin scheduling policy to improve the resource utilisation of each service instance, and employs a defragmentation optimisation algorithm to control the scale of service instances and increase the overall resource utilisation. The experimental results show that the proposed mechanism is feasible and effective.

Adaptive Smoothed Functional Algorithms for Optimal Staffing Levels in Service Systems

Service systems are people-centric. The service providers employ a large workforce to service many clients, aiming to meet the service-level agreements (SLAs) and deliver a satisfactory client experience. A challenge is that the volumes of service requests change dynamically and the types of such requests are unique to each client. The task of adapting the staffing levels to the workloads in such systems while complying with aggregate SLA constraints is nontrivial. We formulate this problem as a constrained parametrized Markov process with a discrete parameter and propose two multi-timescale smoothed functional (SF)-based stochastic optimization SASOC (staff allocation using stochastic optimization with constraints) algorithms—SASOC-SF-N and SASOC-SF-C—for its solution. Whereas SASOC-SF-N uses a Gaussian-based smoothed functional approach, SASOC-SF-C uses the Cauchy smoothed functional algorithm for primal descent. Further, all SASOC algorithms incorporate a generalized projection operator that extends the system to a continuous setting with suitably defined transition probabilities. We validate these optimization schemes on five real-life service systems and compare their performance with a previous SASOC algorithm and the commercial optimization software OptQuest. Our algorithms are observed to be 25 times faster than OptQuest and have proven convergence guarantees to the optimal staffing levels, whereas OptQuest fails to find feasible solutions in some cases even under a reasonably high threshold on the number of search iterations. From the optimization experiments, we observe that our algorithms find better solutions than OptQuest in many cases, and among our algorithms, SASOC-SF-C performs marginally better than SASOC-SF-N.

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load.

Provisioning for large scale loss network systems with applications in cloud computing

Provisioning for large loss networks is a classic problem in performance, due to the fat that loss network is an important mathematical model for many applications, notably those in telephony. Lately, loss network models are utilized and extended to provide performance analysis and control for exciting new applications in statistical physics [3], workforce management [9] and cloud computing [4]. In these new studies, a loss network often serves as a crucial element in characterizing system dynamics and producing calculation for vital performance metrics. This can be seen from the application in resource provision for cloud computing. Cloud computing is rapidly gaining momentum as a new paradigm for offering computing as services via the Internet. Service provider usually offers a menu of service instances, which require the commitment of distinct resources(CPU, Memory,etc) at various amounts. Along with purchase of these instances, service level agreements (SLA) will specify the desired targets on various performance metrics that the service provider should meet. A common performance metric is service availability, defined as the percentage of time at which new service requests can be admitted into the system with their desired amount of resources fulfilled. Violation of the SLAs typically results in significant penalty. The objective of resource provisioning is to seek the balance between the resource costs and SLA penalty so that service availability can be guaranteed efficiently. We develop an integrated optimization framework to search for the optimal resource provision with SLA constraints. First, we develop a Markovian model to capture users’ flexibility on upgrade/downgrade services on demand and characterize the steady-state behavior of the offered load. Then, the multi-class multi-resource provisioning problem can be naturally mapped to a stochastic loss network model, and SLA constraints are mapped to constraints on loss probabilities. Based on Kelly’s approach for capacity planning in a loss network [6, 7, 8], we propose an optimization framework to determine resource levels that minimize the combined costs of resource and violation penalty. Since computing the exact loss probabilities is a ]P complete problem thus prohibitive for large service loads, we consider the Erlang fixed-point approximation for the blocking probabilities that has been proven to be asymptotically exact in the limiting regime as the traffic intensities and the resource capacities grow together in proportion [10, 6, 7]. We further