In cloud architectures, the microservice model divides an application into a set of loosely coupled and collaborative fine-grained services. As a lightweight virtualization technology, the container supports the encapsulation and deployment of microservice applications. Despite a large number of solutions and implementations, there remain open issues that have not been completely addressed in the deployment and management of the microservice containers. An effective method for container resource scheduling not only satisfies the service requirements of users but also reduces the running overhead and ensures the performance of the cluster. In this paper, a multi-objective optimization model for the container-based microservice scheduling is established, and an ant colony algorithm is proposed to solve the scheduling problem. Our algorithm considers not only the utilization of computing and storage resources of the physical nodes but also the number of microservice requests and the failure rate of the physical nodes. Our algorithm uses the quality evaluation function of the feasible solutions to ensure the validity of pheromone updating and combines multi-objective heuristic information to improve the selection probability of the optimal path. By comparing with other related algorithms, the experimental results show that the proposed optimization algorithm achieves better results in the optimization of cluster service reliability, cluster load balancing, and network transmission overhead.

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Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud

In recent years, the growth rate of Cloud computing technology is increasing exponentially, mainly for its extraordinary services with expanding computation power, the possibility of massive storage, and all other services with the maintained quality of services (QoSs). The task allocation is one of the best solutions to improve different performance parameters in the cloud, but when multiple heterogeneous clouds come into the picture, the allocation problem becomes more challenging. This research work proposed a resource-based task allocation algorithm. The same is implemented and analyzed to understand the improved performance of the heterogeneous multi-cloud network. The proposed task allocation algorithm (Energy-aware Task Allocation in Multi-Cloud Networks ( ETAMCN )) minimizes the overall energy consumption and also reduces the makespan. The results show that the makespan is approximately overlapped for different tasks and does not show a significant difference. However, the average energy consumption improved through ETAMCN is approximately 14%, 6.3%, and 2.8% in opposed to the random allocation algorithm, Cloud Z-Score Normalization ( CZSN ) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. An observation of the average SLA-violation of ETAMCN for different scenarios is performed.

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Energy-Aware Task Allocation for Multi-Cloud Networks

Containerization has been used in many applications for isolation purposes due to its lightweight, scalable, and highly portable properties. However, to apply containerization in large-scale Internet data centers faces a big challenge. Services in data centers are always instantiated as a group of containers, which often generate heavy communication workloads and therefore resulting in inefficient communications and downgraded service performance. Although assigning the containers of the same service to the same server can reduce the communication overhead, this may cause heavily imbalanced resource utilization since containers of the same service are usually intensive to the same resource. To reduce communication cost as well as balance the resource utilization in large-scale data centers, we further explore the container distribution issues in a real industrial environment and find that such conflict lies in two phases—container placement and container reassignment. The objective of this paper is to address the container distribution problem in these two phases. For the container placement problem, we propose an efficient communication aware worst fit decreasing algorithm to place a set of new containers into data centers. For the container reassignment problem, we propose a two-stage algorithm called Sweep&Search to optimize a given initial distribution of containers by migrating containers among servers. We implement the proposed algorithms in Baidu’s data centers and conduct extensive evaluations. Compared with the state-of-the-art strategies, the evaluation results show that our algorithms perform better up to 70% and increase the overall service throughput up to 90% simultaneously.

Communication-Aware Container Placement and Reassignment in Large-Scale Internet Data Centers

Enterprise application providers are increasingly moving their workloads to the cloud for technical and economic benefits. Multi-cloud environment makes it possible to orchestrate multiple cloud resources. With the increasing number of available cloud resources provided by multiple cloud providers at different locations with different prices, application providers face the challenge to select proper cloud resources to deploy their applications in the form of a workflow of component service units. Existing studies usually consider minimizing execution time or/and deployment cost. From the perspective of application providers, however, they also pay huge attention to application response time, including particularly network latency between deployed services and users. Meanwhile, application deployment is often subject to stringent budgetary control to ensure financial viability. This article studies a new type of composite application deployment problem that jointly considers both the performance optimization and budget control in multi-cloud at the global scale. To find solutions with minimal response time without running into the risk of over-spending, we propose a hybrid GA-based approach, featuring new design of domain-tailored service clustering, repair algorithm, solution representation, population initialization, and genetic operators. Extensive experiments using the real-world dataset demonstrate that our proposed hybrid GA approach outperforms some recently proposed approaches.

Location-Aware and Budget-Constrained Service Deployment for Composite Applications in Multi-Cloud Environment

A survey of data center consolidation in cloud computing systems

In the last few years, the container-based Cloud computing paradigm has gradually emerged as a flexible approach for energy efficient resource utilization. Cloud providers aim to optimize resource utilization and energy consumption while performing container consolidation, which involves virtual machine (VM) selection and VM placement. Most of the related works focus either on VM selection or VM placement, neglecting the interplay of the two optimization problems. This paper proposes a Two-stage Multi-type Particle Swarm Optimization approach, named TMPSO, to energy-aware container consolidation in Cloud data centers. Our experimental evaluation based on benchmark datasets demonstrates that our proposed algorithm shows further energy saving comparing with some existing approaches.

Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers

Various enterprises are increasingly gaining technical and economic benefits from global cloud marketplace. Multi-cloud environment makes it possible to coordinate access and utilize multiple cloud resources. Cloud service brokers can be regarded as the intermediation to discover, integrate, aggregate, customize, and optimize cloud services, e.g. Infrastructure as a Service (IaaS), from different providers. However, the existing cloud brokering schemes rarely consider the optimization techniques of deploying cloud services with respect to various criteria, such as cost and performance. In this paper, we propose a genetic-based approach to addressing the multi-cloud service brokering problem, i.e. selecting and leasing virtual machines (VMs) for cloud users with minimal cost and network latency. Experimental results show that our approach with problem-specific chromosome representation, fitness measurement, and population initialization strategy outperforms the existing approaches proposed in the literature.

A Genetic-Based Approach to Location-Aware Cloud Service Brokering in Multi-Cloud Environment

In recent years, container-based clouds are becoming increasingly popular for their lightweight nature. Existing works on container consolidation mainly focus on reducing the energy consumption of cloud data centers. However, reducing energy consumption often results in container migrations which have big impact on the performance (i.e. availability) of applications in the containers. In this paper, we consider container consolidation as one multi-objective optimization problem with the objectives of minimizing the total energy consumption and minimizing the total number of container migrations within the certain period of time and present an NSGA-II based algorithm to find solutions for the container consolidation problem. Our experimental evaluation based on the real-world workload demonstrates that our proposed approach can lead to further energy saving and significant reduction of container migrations at the same time compared with some existing approaches.

Multi-objective Container Consolidation in Cloud Data Centers

To gain technical and economic benefits, enterprise application providers are increasingly moving their workloads to the cloud. With the increasing number of cloud resources from multiple cloud providers at different locations with differentiated prices, application providers face the challenge to select proper cloud resources to replicate and deploy applications to maintain low response time and high quality of user experience without running into the risk of over-spending. In this paper, we study the global-wide cloud application replication and deployment problem considering the application average response time, including particularly application execution time and network latency, subject to the budgetary control. To address the problem, we propose a GA-based approach with domain-tailored solution representation, fitness measurement, and population initialization. Extensive experiments using the real-world datasets demonstrate that our proposed GA-based approach significantly outperforms common application placement strategies, i.e., NearData and NearUsers, and our recently proposed hybrid GA approach.

Tao Shi

Papers

Location-Aware and Budget-Constrained Service Deployment for Composite Applications in Multi-Cloud Environment

Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers

A Genetic-Based Approach to Location-Aware Cloud Service Brokering in Multi-Cloud Environment

Multi-objective Container Consolidation in Cloud Data Centers

Location-Aware and Budget-Constrained Application Replication and Deployment in Multi-Cloud Environment