SILK+ Preventing Latency Spikes in Log-Structured Merge Key-Value Stores Running Heterogeneous Workloads

2020 ◽  
Vol 36 (4) ◽  
pp. 1-27
Author(s):  
Oana Balmau ◽  
Florin Dinu ◽  
Willy Zwaenepoel ◽  
Karan Gupta ◽  
Ravishankar Chandhiramoorthi ◽  
...  
Keyword(s):  
Heterogeneous Workloads

A Novel Methodology for Benchmarking the Hypervisors over Heterogeneous Workloads - Multi Vari Approach

2010 ◽  
Vol 4 (4) ◽  
pp. 11-16 ◽  
Author(s):  
Devi Prasad Bhukya ◽  
S Ramachandram ◽  
E Chaitra ◽  
Reeta Sony A.L
Keyword(s):  
Heterogeneous Workloads

scholarly journals QoS-Based Service-Time Scheduling in the IoT-Edge Cloud

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5797
Author(s):  
Briytone Mutichiro ◽  
Minh-Ngoc Tran ◽  
Young-Han Kim
Keyword(s):  
Service Time ◽  
Traffic Patterns ◽  
Performance Requirements ◽  
Resource Requirements ◽  
Time Scheduling ◽  
Improved Performance ◽  
Container Orchestration ◽  
Different Characteristics ◽  
Heterogeneous Workloads

In edge computing, scheduling heterogeneous workloads with diverse resource requirements is challenging. Besides limited resources, the servers may be overwhelmed with computational tasks, resulting in lengthy task queues and congestion occasioned by unusual network traffic patterns. Additionally, Internet of Things (IoT)/Edge applications have different characteristics coupled with performance requirements, which become determinants if most edge applications can both satisfy deadlines and each user’s QoS requirements. This study aims to address these restrictions by proposing a mechanism that improves the cluster resource utilization and Quality of Service (QoS) in an edge cloud cluster in terms of service time. Containerization can provide a way to improve the performance of the IoT-Edge cloud by factoring in task dependencies and heterogeneous application resource demands. In this paper, we propose STaSA, a service time aware scheduler for the edge environment. The algorithm automatically assigns requests onto different processing nodes and then schedules their execution under real-time constraints, thus minimizing the number of QoS violations. The effectiveness of our scheduling model is demonstrated through implementation on KubeEdge, a container orchestration platform based on Kubernetes. Experimental results show significantly fewer violations in QoS during scheduling and improved performance compared to the state of the art.

scholarly journals Towards Efficient Resource Allocation for Heterogeneous Workloads in IaaS Clouds

2018 ◽  
Vol 6 (1) ◽  
pp. 264-275 ◽  
Author(s):  
Lei Wei ◽  
Chuan Heng Foh ◽  
Bingsheng He ◽  
Jianfei Cai
Keyword(s):  
Resource Allocation ◽  
Efficient Resource ◽  
Heterogeneous Workloads

Elastic Power-Aware Resource Provisioning of Heterogeneous Workloads in Self-Sustainable Datacenters

2016 ◽  
Vol 65 (2) ◽  
pp. 508-521 ◽  
Author(s):  
Dazhao Cheng ◽  
Jia Rao ◽  
Changjun Jiang ◽  
Xiaobo Zhou
Keyword(s):  
Resource Provisioning ◽  
Heterogeneous Workloads
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