A QoS-Enabled Load Balancing Approach for Cloud Computing Environment Join Minimum Loaded Queue (JMLQ)

Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing dedicated cloud services that ensure user's dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in cloud data centers. However, aggressive consolidation of VMs may lead to performance degradation of data centers and even Service Level Agreement (SLA) violation. Therefore, it is very important to solve the tradeoff between reduction of SLA violation level and energy costs. In this paper, we propose a Host State 3-order Markov Chain prediction model for SLA-aware and energy efficient consolidation of VMs in cloud data centers. We propose two adaptive utilization thresholds based on a robust statistical method for host state estimate, and our model uses them to build a 3-order Markov Chain Transition Matrix manually for host state detection. We implement our algorithms on Cloudsim simulator for comparative study. The experimental results show that our model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLA Violation by at most 98.69% and the metric of Energy by at most 21.88% for real world workload.

With the fast development of cloud computing methods, exponential growth is faced by several users. It is complicated for traditional data centers to perform several jobs in real-time because of inadequate bandwidth resources. Therefore, the method of fog computing is recommended for supporting and providing fast cloud services. It is not a substitute but is a powerful complement to cloud computing. The reduction of energy consumption through the notion of fog computing has certainly been a challenge for current researchers, industries, and communities. Various industries, including finance and healthcare, require a rich resource-based platform for processing large amounts of data with cloud computing across fog architecture. The consumption of energy across fog servers relies on allocating techniques for services (user requests). It facilitates processing at the edge with the probability of interacting with the cloud. This article proposed energy-aware scheduling by using Artificial Neural Network (ANN) and Modified Multiobjective Job Scheduling (MMJS) techniques. The emphasis of the work is on the reduction of energy consumption rate with less Service Level Agreement (SLA) violation in fog computing for data centers. The result shows that there is a 3.9% reduction in SLA violation when a multiobjective function with Artificial Neural Network is applied.

Switch Bandwidth Congestion Prediction in Cloud Environment

A service level agreement (SLA) is part of a contract between a cloud service provider (CSP) and a cloud service user (CSU) which defines and describes different agreed properties of the service. The negotiation of the SLA between a CSU and a CSP is a pivotal process in the SLA life cycle. An SLA may include many negotiable quality of service (QoS) parameters with each QoS parameter having multiple acceptable values. Different combinations of acceptable values for all QoS parameters grow exponentially as number of QoS parameters and their selectable values increase. Manually negotiating for all of the QoS parameters considering their various options can be an erroneous and diligent task with lower utility. In this paper, a time efficient flip-flop negotiation strategy for the SLAs in cloud services is presented. The flip-flop negotiation strategy is based on a time dependent 3D utility function and on a dynamic concession strategy using the polynomial extrapolation of opponent's concession. This negotiation strategy is useful for time-critical cloud services as it operates with a motive of reaching an agreement at earlier time without compromising over the utility level of the agreement. Moreover, a multi-provider concurrent negotiation method is presented in this work using the flip-flop negotiation strategy. Multiple experiments are performed (using the implementation of flip-flop negotiation strategy) to test the presented work. The results of the experiments enforce the validity of the flip-flop negotiation strategy for such cloud services where time is an important factor during the SLA negotiation process.

Service level agreement (SLA) in cloud computing can be defined as a contractual agreement signed between cloud user and cloud provider in order to manage the quality of service (QoS) parameters which are the fundamental part of SLAs. The current dynamic SLA monitoring approaches have considered the cloud provider's perspective only. To take into consideration the user's perspective; this research proposes a dynamic SLA monitoring approach by generating new SLA terms based on analysing the SLA penalties. A new proposed trade-off mechanism is applied between monthly cost and QoS guarantee values. Thus; we propose a dynamic model for updating QoS parameters of SLA in cloud environment. Java language is used with a QoS measurement dataset to experiment the proposed approach. The current Static SLA (SSLA) approaches are compared with the proposed Dynamic SLA (DSLA) approach. The results of this experimental study show that the number of SLA violations in DSLA are less than the number of SLA violations in SSLA.

Service level agreement (SLA) in cloud computing can be defined as a contractual agreement signed between cloud user and cloud provider in order to manage the quality of service (QoS) parameters which are the fundamental part of SLAs. The current dynamic SLA monitoring approaches have considered the cloud provider's perspective only. To take into consideration the user's perspective; this research proposes a dynamic SLA monitoring approach by generating new SLA terms based on analysing the SLA penalties. A new proposed trade-off mechanism is applied between monthly cost and QoS guarantee values. Thus; we propose a dynamic model for updating QoS parameters of SLA in cloud environment. Java language is used with a QoS measurement dataset to experiment the proposed approach. The current Static SLA (SSLA) approaches are compared with the proposed Dynamic SLA (DSLA) approach. The results of this experimental study show that the number of SLA violations in DSLA are less than the number of SLA violations in SSLA.

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

Virtual machine (VM) consolidation in Cloud computing environments 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. The main contribution of us is a Service Level Agreement (SLA) violation decision algorithm, which is used to decide a host is overload with SLA violation or not. 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, which has 11.8%~27% decrease in energy consumption, 57.9%~78.4% decrease in SLA violation, 63.2%~84.1% decrease in energy-performance metric which is a product metric of the energy consumption and SLA violation.

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective

The Cloud Computing (CC) has vast amount of data centers that consists of many computing nodes and consumes a huge amount of electrical energy. Hence the researchers found that the high service-level agreements (SLAs) violations and Energy Consumption (EC) are the major challenging issues in CC. The various traditional approaches reduced the EC, but ignored the SLA violation during the selection of Virtual Machine (VM) from overloaded hosts. In order to effectively deal with these issues, this paper proposed the Recursive Ant Colony Optimization (RACO) in 2 different workloads. The main aim of the RACO is to minimize the high EC and SLA violations by the movement of best ant, which is random and identified the distance between their movements. The algorithm consists of 3 major steps includes tracking and updating the pheromone and finally city selection. The proposed method simulated on Cloud Sim to validate the efficiency and stability of the proposed RACO and their performance compared to that of other existing techniques. The results showed that RACO reduced the EC by 40 - 42 % (approx.) which is less than the traditional ACO algorithm in Planet Lab data. HIGHLIGHTS Reduced EC and SLA Violation of Data Center are done via RACO VM Allocations are done by IQR and utilises micro, small, medium and extra-large instances Pheromone Tracking, updating and city selection are the endeavours of RACO RACO reduced EC by 40 - 42 % and SLA Violation

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

Service Level Agreements (SLA) represents the principal means to control the Quality of Service (QoS). In cloud computing, various advanced SLA strategies are used; some techniques among others try to avoid the SLA violations. Taking into account the cloud consumer contextual parameters is a promising way to predict and avoid costly SLA violations. Therefore, in this paper, we propose a context-aware system for the SLA in order to guarantee the QoS. We create a contextual ontology to introduce the semantic meaning of the cloud consumer contextual parameters. In addition, using reasoning techniques, we (1) predict SLA violations and we (2) choose and apply the corrective actions in autonomous manner. This allows getting a proactive adjustment of the cloud service execution and respecting the SLA parameters.

Offering “Computing as a utility” on pay per use plan, Cloud computing has emerged as a technology of ease and flexibility for thousands of users over last few years. Distribution of dynamic workload among available servers and efficient utilization of existing resources in datacenter is one of the major concerns in Cloud computing. The load balancing issue needs to take into consideration the utilization of servers, i.e. the resultant utilization should not exceed the preset upper limits to avoid service level agreement (SLA) violation and should not fall beneath stipulated lower limits to avoid keeping some servers in active use. Scheduling of workload is regarded as an optimization problem that considers many varying criterion such as dynamic environment, priority of incoming applications, their deadlines etc. to improve resource utilization and overall performance of Cloud computing. In this work, a Genetic Algorithm (GA) based novel load balancing mechanism is proposed. Though not done in this work, in future, we aim to compare performance of proposed algorithms with existing mechanisms such as first come first serve (FCFS), Round Robin (RR) and other search algorithms through simulations.

Cloud computing provides utility oriented services and pay-as-you-go computing model for its users. Massive adoption of Cloud services leads to build a sustainable environment and also demands for green Cloud services. With the advancement in Cloud services, the issue of carbon dioxide emission becomes significant. There is also a challenge for Cloud provider to satisfy the customer by guaranteeing the services with performance and availability in Service Level Agreement (SLA). Hence, energy consumption and SLA violation become important issues for Cloud providers. These issues can be addressed in many facets viz. efficient provisioning of resources, reducing energy consumption in datacenters, server consolidation, virtual machine (VM) migration, minimizing SLA violation or also by providing SLA with green requirements. In this paper, we survey various techniques which address green SLA with Quality of Service (QoS) requirements by using different resource management and consolidation procedures. Further, we propose our work by considering the multi-criteria method for resource management which selects the host as probable migration destination having highest predicted utilization after allocating a VM on the host. Hence, we aim to address the issue of maintaining the trade-off between energy consumption and SLA violation.