Online Resource Scheduling Under Concave Pricing for Cloud Computing

Abstract

With the booming cloud computing industry, computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. The diverse pricing schemes among different IaaS service providers or even in the same provider form a complex economic landscape that nurtures the market of cloud brokers. By strategically scheduling multiple customers' resource requests, a cloud broker can fully take advantage of the discounts offered by cloud service providers. In this paper, we focus on how a broker can help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. We present a randomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower bound of its competitive ratio. Three special cases of the offline concave cost scheduling problem and the corresponding optimal algorithms are introduced. Our simulation shows that ROSA achieves a competitive ratio close to the theoretical lower bound under the special cases. Trace-driven simulation using Google cluster data demonstrates that ROSA is superior to the conventional online scheduling algorithms in terms of cost saving.

A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration

“…Still, the lengthy services have miss-mapped to low resource capacity ESs that influence the system's execution makespan (MS) and cost. In [10], Randomized Online Stack-centric Scheduling algorithm (ROSA) designed for effective service allocation based on cost-effective resource usage analysis. In [11], task-graph template has been designed for TS called Nondominated Sorting Genetic Algorithm (NSGA).…”

TRS Scheduling for Improved QoS Performance in Cloud System

“…A randomised online stack algorithm (ROSA) based on booking calculation is presented in [21], which uses a certain proportion of resources at the lower bound and finds resources with the lowest cost in resource allocation. A policy-based load-balancing approach is presented that uses the ant colony to perform optimization to reduce the makespan [22].…”

Resource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms

“…From the investigation, the results demonstrate that this mixture is superior to unreserved RR. Panda with Bhoi [20] suggests compelling RR algorithms using the min-max scattering ratio of residual CPU burst time. This calculation outperforms RR as far as normal turnaround time, normal holding time interval and particular switch setting techniques.…”

“…In the current section, the issue of cloud scheduling [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][41][42][43][44] is structured to match the reinforcement learning representation, so the cloud assets in our paper are usually formed as states in distinct images as demonstrated in Figure 3. The additional images, job slots, will be the needed jobs to be scheduled and their resource requirements.…”