Service-level agreement

About: Service-level agreement is a research topic. Over the lifetime, 4358 publications have been published within this topic receiving 75333 citations. The topic is also known as: SLA.

Optimization Approach for Resource Allocation on Cloud Computing for IoT

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.

BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting

Abstract: Cloud computing promises elasticity, flexibility and cost-effectiveness to satisfy service level agreement conditions. The cloud service providers should plan and provision the computing resources rapidly to ensure the availability of infrastructure to match the demands with closed proximity. The workload prediction has become critical as it can be helpful in managing the infrastructure effectively. In this paper, we present a workload forecasting framework based on neural network model with supervised learning technique. An improved and adaptive differential evolution algorithm is developed to improve the learning efficiency of predictive model. The algorithm is capable of optimizing the best suitable mutation operator and crossover operator. The prediction accuracy and convergence rate of the learning are observed to be improved due to its adaptive behavior in pattern learning from sampled data. The predictive model’s performance is evaluated on four real-world data traces including Google cluster trace and NASA Kennedy Space Center logs. The results are compared with state-of-the-art methods, and improvements up to 91%, 97% and 97.2% are observed over self-adaptive differential evolution, backpropagation and average-based workload prediction techniques, respectively.

Microscaler: Automatic Scaling for Microservices with an Online Learning Approach

Abstract: Recently, the microservice becomes a popular architecture to construct cloud native systems due to its agility. In cloud native systems, autoscaling is a core enabling technique to adapt to workload changes by scaling out/in. However, it becomes a challenging problem in a microservice system, since such a system usually comprises a large number of different micro services with complex interactions. When bursty and unpredictable workloads arrive, it is difficult to pinpoint the scaling-needed services which need to scale and evaluate how much resource they need. In this paper, we present a novel system named Microscaler to automatically identify the scaling-needed services and scale them to meet the service level agreement (SLA) with an optimal cost for micro-service systems. Microscaler collects the quality of service metrics (QoS) with the help of the service mesh enabled infrastructure. Then, it determines the under-provisioning or over-provisioning services with a novel criterion named service power. By combining an online learning approach and a step-by-step heuristic approach, Microscaler could achieve the optimal service scale satisfying the SLA requirements. The experimental evaluations in a micro-service benchmark show that Microscaler converges to the optimal service scale faster than several state-of-the-art methods.

DualFog-IoT: Additional Fog Layer for Solving Blockchain Integration Problem in Internet of Things

Abstract: Integration of blockchain and Internet of Things (IoT) to build a secure, trusted and robust communication technology is currently of great interest for research communities and industries. But challenge is to identify the appropriate position of blockchain in current settings of IoT with minimal consequences. In this article we propose a blockchain-based DualFog-IoT architecture with three configuration filter of incoming requests at access level, namely: Real Time, Non-Real Time, and Delay Tolerant Blockchain applications. The DualFog-IoT segregate the Fog layer into two: Fog Cloud Cluster and Fog Mining Cluster. Fog Cloud Cluster and the main cloud datacenter work in a tandem similar to existing IoT architecture for real-time and non-real-time application requests, while the additional Fog Mining Cluster is dedicated to deal with only Delay Tolerant Blockchain application requests. The proposed DualFog-IoT is compared with existing centralized datacenter based IoT architecture. Along with the inherited features of blockchain, the proposed model decreases system drop rate, and further offload the cloud datacenter with minimal upgradation in existing IoT ecosystem. The reduced computing load from cloud datacenter doesn't only help in saving the capital and operational expenses, but it is also a huge contribution for saving energy resources and minimizing carbon emission in environment. Furthermore, the proposed DualFog-IoT is also being analyzed for optimization of computing resources at cloud level, the results presented shows the feasibility of proposed architecture under various ratios of incoming RT and NRT requests. However, the integration of blockchain has its footprints in terms of latent response for delay tolerant blockchain applications, but real-time and non-real-time requests are gracefully satisfying the service level agreement.

Automatic SLA Matching and Provider Selection in Grid and Cloud Computing Markets

Abstract: Cloud computing is a novel computing paradigm that offers data, software, and hardware services in a manner similar to traditional utilities such as water, electricity, and telephony. Usually, in Cloud and Grid computing, contracts between traders are established using Service Level Agreements (SLAs), which include objectives of service usage. However, due to the rapidly growing number of service offerings and the lack of a standard for their specification, manual service selection is a costly task, preventing the successful implementation of ubiquitous computing on demand. In order to counteract these issues, automatic methods for matching SLAs are necessary. In this paper, we introduce a method for finding semantically equal SLA elements from differing SLAs by utilizing several machine learning algorithms. Moreover, we utilize this method to enable automatic selection of optimal service offerings for Cloud and Grid users. Finally, we introduce a framework for automatic SLA management, present a simulation-based evaluation, and demonstrate several significant benefits of our approach for Cloud and Grid users.