Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
CLOUDGEN: Workload generation for the evaluation of cloud computing systems CLOUDGEN: Bulut Bilişim Sistemlerinin Başarim Deǧerlendirmesi icin Iş Yuku Uretimi
Date
2019-04-01
Author
Koltuk, Furkan
Yazar, Alper
Schmidt, Şenan Ece
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
301
views
0
downloads
Cite This
In this paper, we propose CLOUDGEN workflow that produces synthetic workloads for Infrastructure and Platform as a Service for the evaluation of resource management approaches in cloud computing systems. To this end, CLOUDGEN systematically processes and clusters records in a given workload trace and fits distributions for different workload parameters within the clusters. Different than the previous work, clustering is carried out to produce different virtual machine types for achieving models that are suitable for producing Infrastructure and Platform as a Service workload models. Finally, we demonstrate CLOUDGEN by modeling recent Azure traces with enough detail to enable researchers to use these models and generating synthetic traces that are statistically similar to the Azure traces.
Subject Keywords
Workload generation
,
Distribution fitting
,
Cloud computing
URI
https://hdl.handle.net/11511/43278
DOI
https://doi.org/10.1109/siu.2019.8806358
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
ACCLOUD-MAN - Power efficient resource allocation for heterogeneous clouds ACCLOUD-MAN - Heterojen bulutlarda güç etkin kaynak atamasi
Ekici, Nazim Umut; Schmidt, Klaus Werner; Yazar, Alper; Schmidt, Şenan Ece (2019-04-01)
In this paper we propose ACCLOUD-MAN, a novel resource manager for heterogeneous cloud data centers. In heterogeneous clouds a user request can be satisfied with more than one physical resource alternative. Resource manager must decide which resource alternative will be chosen, along with the decision of the server the request will be assigned to. ACCLOUD-MAN's resource management objective is to reduce the power consumption of the cloud. Manager is modeled as an Integer Linear Problem and is implemented on...
Resource Allocation for Minimized Power Consumption in Hardware Accelerated Clouds
Ekici, Nazim Umut; Schmidt, Klaus Verner; Yazar, Alper; Schmidt, Şenan Ece (2019-01-01)
In this paper we propose ACCLOUD-MAN, a novel resource manager for heterogeneous cloud data centers. In heterogeneous clouds a user request can be satisfied with more than one physical resource alternative. That is, the resource manager must decide which resource alternative will be chosen, along with the decision of the server the request will be assigned to. ACCLOUD-MAN's resource management objective is to reduce the power consumption of the cloud data center. To this end, the manager is modeled as an in...
A Novel Method for the Synthetic Generation of Non-I.I.D Workloads for Cloud Data Centers
Koltuk, Furkan; Schmidt, Şenan Ece (2020-07-01)
© 2020 IEEE.Cloud data center workloads have time- dependencies and are hence non-i.i.d (independent and identically distributed). In this paper, we propose a new model-based method for creating synthetic workload traces for cloud data centers that have similar time characteristics and cumulative distributions to those of the actual traces. We evaluate our method using the actual resource request traces of Azure collected in 2019 and the well-known Google cloud trace. Our method enables generating synthetic...
Realistic Workload Generation for Cloud Data Centers Bulut Veri Merkezleri icin Gercekci Is Yuku Uretimi
Koltuk, Furkan; Schmidt, Şenan Ece (2020-10-05)
© 2020 IEEE.This paper proposes a new method for creating synthetic workload traces in accordance with the distribution and time characteristics of a given actual workload trace. To this end, we first find the distribution that fits to the actual workload trace, then rearrange the random samples that are generated from this distribution such that the final synthetic trace has time characteristics that are similar to the actual trace. We evaluate our method using real virtual machine and task request traces ...
ACCLOUD (Accelerated CLOUD): A novel FPGA-Accelerated cloud archictecture-ACCLOUD: FPGA ile Hızlandırılmış Yeni bir BulutMimarisi
YAZAR, ALPER; EROL, AHMET; Schmidt, Şenan Ece (2018-07-09)
In this paper, we propose ACCLOUD (ACcelerated CLOUD) as a a novel architecture for cloud data centers. ACCLOUD features FPGA cards which work with the cloud servers or stand alone for hardware acceleration in the data plane. To this end, FPGA reconfigurable regions are virtualized and offered to the user together with other cloud resources including CPU, memory or disk. The cloud control is carried out within OpenStack framework incorporating the hardware resources. We propose a novel resource management a...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
F. Koltuk, A. Yazar, and Ş. E. Schmidt, “CLOUDGEN: Workload generation for the evaluation of cloud computing systems CLOUDGEN: Bulut Bilişim Sistemlerinin Başarim Deǧerlendirmesi icin Iş Yuku Uretimi,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43278.