scholarly journals Optimal Resource Provisioning and the Impact of Energy-Aware Load Aggregation for Dynamic Temporal Workloads in Data Centers

2014 ◽  
Vol 11 (4) ◽  
pp. 486-503 ◽  
Author(s):  
Haiyang Qian ◽  
Fu Li ◽  
Ravishankar Ravindran ◽  
Deep Medhi
Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3980 ◽  
Author(s):  
Paris Charalampou ◽  
Efstathios D. Sykas

There is a lot of effort to limit the impact of CO2 emissions from the information communication technologies (ICT) industry by reducing the energy consumption on all aspects of networking technologies. In a service provider network, data centers (DCs) are the major power consumer and considerable gains are expected by regulating the operation network devices. In this context, we developed a mixed integer programming (MIP) algorithm to optimize the power consumption of network devices via energy aware traffic engineering. We verified our approach by simulating DC network topologies and demonstrated that clear benefits can be achieved for various network sizes and traffic volumes. Our algorithm can be easily implemented as an application in the software-defined networking (SDN) paradigm, making quite feasible its deployment in a production environment.


Author(s):  
Marcelo Amaral ◽  
Jordà Polo ◽  
David Carrera ◽  
Nelson Gonzalez ◽  
Chih-Chieh Yang ◽  
...  

AbstractModern applications demand resources at an unprecedented level. In this sense, data-centers are required to scale efficiently to cope with such demand. Resource disaggregation has the potential to improve resource-efficiency by allowing the deployment of workloads in more flexible ways. Therefore, the industry is shifting towards disaggregated architectures, which enables new ways to structure hardware resources in data centers. However, determining the best performing resource provisioning is a complicated task. The optimality of resource allocation in a disaggregated data center depends on its topology and the workload collocation. This paper presents DRMaestro, a framework to orchestrate disaggregated resources transparently from the applications. DRMaestro uses a novel flow-network model to determine the optimal placement in multiple phases while employing best-efforts on preventing workload performance interference. We first evaluate the impact of disaggregation regarding the additional network requirements under higher network load. The results show that for some applications the impact is minimal, but other ones can suffer up to 80% slowdown in the data transfer part. After that, we evaluate DRMaestro via a real prototype on Kubernetes and a trace-driven simulation. The results show that DRMaestro can reduce the total job makespan with a speedup of up to ≈1.20x and decrease the QoS violation up to ≈2.64x comparing with another orchestrator that does not support resource disaggregation.


Author(s):  
Mohammad Shojafar ◽  
Nicola Cordeschi ◽  
Enzo Baccarelli

The pervasive use of cloud computing and the resulting growing number of Internet data centers have brought forth many concerns, including electrical energy cost, energy dissipation, cooling and carbon emission. Therefore, the need for efficient workload schedulers which are capable of minimizing the consumed energy becomes increasingly important. Green computing, a new trend for high-end computing, attempts to approach this problem by delivering both high performance and reduced energy consumption. Motivated by these considerations, in this chapter, we propose a joint computation-and-communication adaptive resource-provisioning scheduler for virtualized data centers, e.g., the Internet Data Center (IDC) scheduler, which exploits the DVFS-enabled reconfiguration capability of the underlying virtualized computing/communication platform. Specifically, we present and test a dynamic resource provisioning scheduler, which adaptively controls the execution time and bandwidth usage of each input job, as well as the internal and external switching costs on per-Virtual Machine (VM) basis.


2021 ◽  
Vol 11 (9) ◽  
pp. 3870
Author(s):  
Jeongsu Kim ◽  
Kyungwoon Lee ◽  
Gyeongsik Yang ◽  
Kwanhoon Lee ◽  
Jaemin Im ◽  
...  

This paper investigates the performance interference of blockchain services that run on cloud data centers. As the data centers offer shared computing resources to multiple services, the blockchain services can experience performance interference due to the co-located services. We explore the impact of the interference on Fabric performance and develop a new technique to offer performance isolation for Hyperledger Fabric, the most popular blockchain platform. First, we analyze the characteristics of the different components in Hyperledger Fabric and show that Fabric components have different impacts on the performance of Fabric. Then, we present QiOi, component-level performance isolation technique for Hyperledger Fabric. The key idea of QiOi is to dynamically control the CPU scheduling of Fabric components to cope with the performance interference. We implement QiOi as a user-level daemon and evaluate how QiOi mitigates the performance interference of Fabric. The evaluation results demonstrate that QiOi mitigates performance degradation of Fabric by 22% and improves Fabric latency by 2.5 times without sacrificing the performance of co-located services. In addition, we show that QiOi can support different ordering services and chaincodes with negligible overhead to Fabric performance.


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