Abstract
Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73–43.44%, 44.06–92.11%, and 16.38–24.17%, respectively, leading to a cost-efficient resource allocation for cloud infrastructures.
Highlights
Nowadays, data centers are growing exponentially due to cloud services’ popularization [1]
Cloud Service Providers (CSPs) use this kind of infrastructure to offer different tools and resources. They are mostly grouped into several types of services: Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Storage as a Service (STaaS), Communications as a Service (CaaS), Network as a Service (NaaS), Monitoring as a Service (MaaS), a rapidly grooving Serverless computing, etc
Researchers have focused on CPU utilization, where the Virtual Machines (VMs) placement problem is usually solved by NP-hard bin-packing
Summary
Data centers are growing exponentially due to cloud services’ popularization [1]. The efficient use of the data center infrastructures is fundamental for users and CSPs. For example, the low utilization of the servers is a critical factor for energy consumption inefficiency. Several contention-aware resource allocation strategies to reduce energy consumption and increase performance are proposed [12,13,14,15,16]. Resource contention emphasizes avoiding cohosted applications that contend for shared resources These works study the power consumption in environments with bare metal and VMs infrastructure. We present job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. They improve the performance of well-known heuristics and provide a good compromise between QoS, makespan, and energy We demonstrate that they are suitable for containers in cloud environments as more efficient and valuable solutions.
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