mdh.sePublikationer
Ändra sökning
Länk till posten
Permanent länk

Direktlänk
BETA
Faragardi, Hamid RezaORCID iD iconorcid.org/0000-0002-1384-5323
Publikationer (10 of 20) Visa alla publikationer
Faragardi, H. R., Lisper, B., Sandström, K. & Nolte, T. (2018). A resource efficient framework to run automotive embedded software on multi-core ECUs. Journal of Systems and Software, 64-83
Öppna denna publikation i ny flik eller fönster >>A resource efficient framework to run automotive embedded software on multi-core ECUs
2018 (Svenska)Ingår i: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, s. 64-83Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The increasing functionality and complexity of automotive applications requires not only the use of more powerful hardware, e.g., multi-core processors, but also efficient methods and tools to support design decisions. Component-based software engineering proved to be a promising solution for managing software complexity and allowing for reuse. However, there are several challenges inherent in the intersection of resource efficiency and predictability of multi-core processors when it comes to running component-based embedded software. In this paper, we present a software design framework addressing these challenges. The framework includes both mapping of software components onto executable tasks, and the partitioning of the generated task set onto the cores of a multi-core processor. This paper aims at enhancing resource efficiency by optimizing the software design with respect to: 1) the inter-software-components communication cost, 2) the cost of synchronization among dependent transactions of software components, and 3) the interaction of software components with the basic software services. An engine management system, one of the most complex automotive sub-systems, is considered as a use case, and the experimental results show a reduction of up to 11.2% total CPU usage on aquad-core processor, in comparison with the common framework in the literature. 

Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-36448 (URN)10.1016/j.jss.2018.01.040 (DOI)000428493000005 ()2-s2.0-85041901291 (Scopus ID)
Tillgänglig från: 2017-09-18 Skapad: 2017-09-18 Senast uppdaterad: 2019-06-26Bibliografiskt granskad
Faragardi, H. R., Dehnavi, S., Kargahi, M., Papadopoulos, A. & Nolte, T. (2018). A Time-Predictable Fog-Integrated Cloud Framework: One Step Forward in the Deployment of a Smart Factory. In: CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18: . Paper presented at CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18, 09 May 2018, Tehran, Iran (pp. 54-62).
Öppna denna publikation i ny flik eller fönster >>A Time-Predictable Fog-Integrated Cloud Framework: One Step Forward in the Deployment of a Smart Factory
Visa övriga...
2018 (Engelska)Ingår i: CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18, 2018, s. 54-62Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper highlights cloud computing as one of the principal building blocks of a smart factory, providing a huge data storage space and a highly scalable computational capacity. The cloud computing system used in a smart factory should be time-predictable to be able to satisfy hard real-time requirements of various applications existing in manufacturing systems. Interleaving an intermediate computing layer-called fog-between the factory and the cloud data center is a promising solution to deal with latency requirements of hard real-time applications. In this paper, a time-predictable cloud framework is proposed which is able to satisfy end-to-end latency requirements in a smart factory. To propose such an industrial cloud framework, we not only use existing real-time technologies such as Industrial Ethernet and the Real-time XEN hypervisor, but we also discuss unaddressed challenges. Among the unaddressed challenges, the partitioning of a given workload between the fog and the cloud is targeted. Addressing the partitioning problem not only provides a resource provisioning mechanism, but it also gives us a prominent design decision specifying how much computing resource is required to develop the fog platform, and how large should the minimum communication bandwidth be between the fog and the cloud data center.

Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-38638 (URN)10.1109/RTEST.2018.8397079 (DOI)000467076600008 ()2-s2.0-85050457708 (Scopus ID)9781538614754 (ISBN)
Konferens
CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18, 09 May 2018, Tehran, Iran
Projekt
PREMISE - Predictable Multicore Systems
Tillgänglig från: 2018-02-12 Skapad: 2018-02-12 Senast uppdaterad: 2019-05-24Bibliografiskt granskad
Faragardi, H. R., Dehnavi, S., Nolte, T., Kargahi, M. & Fahringer, T. (2018). An energy-aware resource provisioning scheme for real-time applications in a cloud data center. Software, practice & experience, 48(10), 1734-1757
Öppna denna publikation i ny flik eller fönster >>An energy-aware resource provisioning scheme for real-time applications in a cloud data center
Visa övriga...
2018 (Engelska)Ingår i: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024X, Vol. 48, nr 10, s. 1734-1757Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Based on a pay-as-you-go model, cloud computing provides the possibility of hosting pervasive applications from both academic and business domains. However, data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and large carbon footprints to the environment. Energy-aware resource provisioning is an effective solution to diminish the energy consumption of cloud data centers. Recently, a growing trend has emerged, where cloud technology is used to run periodic real-time applications such as multimedia, telecommunication, video gaming, and industrial applications. In order for a real-time application to be able to use cloud services, cloud providers have to be able to provide timing guarantees. In this paper, we introduce an energy-aware resource provisioning mechanism for cloud data centers, which are capable of serving real-time periodic tasks following the Software as a Service model. The proposed method is compared against an energy-aware version of the RT-OpenStack. RT-OpenStack is a recently proposed approach to provide a time-predictable version of OpenStack. The experimental results manifest that our proposed resource provisioning method outperforms energy-aware version of the RT-OpenStack by 16.01%, 25.45%, and 25.45% in terms of energy consumption, number of used servers, and average utilization of used servers, respectively. Moreover, from the scalability perspective, the preference of the proposed method for large-scale data centers is more considerable.

Ort, förlag, år, upplaga, sidor
WILEY, 2018
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-40933 (URN)10.1002/spe.2592 (DOI)000443587100002 ()2-s2.0-85047733608 (Scopus ID)
Tillgänglig från: 2018-09-13 Skapad: 2018-09-13 Senast uppdaterad: 2018-11-28Bibliografiskt granskad
Faragardi, H. R. (2018). Optimizing Timing-Critical Cloud Resources in a Smart Factory. (Doctoral dissertation). Västerås: Mälardalen University
Öppna denna publikation i ny flik eller fönster >>Optimizing Timing-Critical Cloud Resources in a Smart Factory
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

This thesis addresses the topic of resource efficiency in the context of timing critical components that are used in the realization of a Smart Factory.The concept of the smart factory is a recent paradigm to build future production systems in a way that is both smarter and more flexible. When it comes to realization of a smart factory, three principal elements play a significant role, namely Embedded Systems, Internet of Things (IoT) and Cloud Computing. In a smart factory, efficient use of computing and communication resources is a prerequisite not only to obtain a desirable performance for running industrial applications, but also to minimize the deployment cost of the system in terms of the size and number of resources that are required to run industrial applications with an acceptable level of performance. Most industrial applications that are involved in smart factories, e.g., automation and manufacturing applications, are subject to a set of strict timing constraints that must be met for the applications to operate properly. Such applications, including underlying hardware and software components that are used to run the application, constitute a real-time system. In real-time systems, the first and major concern of the system designer is to provide a solution where all timing constraints are met. To do so we need a time-predictable IoT/Cloud Computing framework to deal with the real-time constraints that are inherent in industrial applications running in a smart factory. Afterwards, with respect to the time predictable framework, the number of required computing and communication resources can and should be optimized such that the deployed system is cost efficient. In this thesis, to investigate and present solutions that provide and improve the resource efficiency of computing and communication resources in a smart factory, we conduct research following three themes: (i) multi-core embedded processors, which are the key element in terms of computing components embedded in the machinery of a smart factory, (ii) cloud computing data centers, as the supplier of a massive data storage and a large computational power, and(iii) IoT, for providing the interconnection of computing components embedded in the objects of a smart factory. Each of these themes are targeted separately to optimize resource efficiency. For each theme, we identify key challenges when it comes to achieving a resource-efficient design of the system. We then formulate the problem and propose solutions to optimize the resource efficiency of the system, while satisfying all timing constraints reflected in the model. We then propose a comprehensive resource allocation mechanism to optimize the resource efficiency in the whole system while considering the characteristics of each of these research themes. The experimental results indicate a clear improvement when it comes to timing-critical IoT / Cloud Computing resources in a smart factory. At the level of multi-core embedded devices, the total CPU usage of a quad-core processor is shown to be improved by 11.2%. At the level of Cloud Computing, the number of cloud servers that are required to execute a given set of real-time applications is shown to be reduced by 25.5%. In terms of network components that are used to collect sensor data, our proposed approach reduces the total deployment cost of thesystem by 24%. In summary these results all contribute towards the realization of a future smart factory.

Ort, förlag, år, upplaga, sidor
Västerås: Mälardalen University, 2018
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 255
Nyckelord
Cloud Computing; Fog Computing; Edge Computing; Real-Time Systems; Resource Allocation
Nationell ämneskategori
Datorsystem
Forskningsämne
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-38659 (URN)978-91-7485-376-6 (ISBN)
Disputation
2018-03-08, Gamma, Mälardalens högskola, Västerås, 13:30 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-02-13 Skapad: 2018-02-12 Senast uppdaterad: 2018-06-12Bibliografiskt granskad
Mahmud, N., Rodriguez-Navas, G., Faragardi, H. R., Mubeen, S. & Seceleanu, C. (2018). Power-aware Allocation of Fault-tolerant Multi-rate AUTOSAR Applications. In: 25th Asia-Pacific Software Engineering Conference APSEC'18: . Paper presented at 25th Asia-Pacific Software Engineering Conference APSEC'18, 04 Dec 2018, Nara, Japan.
Öppna denna publikation i ny flik eller fönster >>Power-aware Allocation of Fault-tolerant Multi-rate AUTOSAR Applications
Visa övriga...
2018 (Engelska)Ingår i: 25th Asia-Pacific Software Engineering Conference APSEC'18, 2018Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper proposes an Integer Linear Programming optimization approach for the allocation of fault-tolerant embedded software applications that are developed using the AUTOSAR standard. The allocation takes into account the timing and reliability requirements of the multi-rate cause-effect chains in these applications and the heterogeneity of their execution platforms. The optimization objective is to minimize the total power consumption of the these applications that are distributed over more than one computing unit. The proposed approach is evaluated using a range of different software applications from the automotive domain, which are generated using the real-world automotive benchmark. The evaluation results indicate that the proposed allocation approach is effective and scalable while meeting the timing, reliability and power requirements in small- and medium-sized automotive software applications.

Nyckelord
autosar, software allocation, optimization, power consumption, distributed architecture, multi-rate systems, timing, reliability
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-43902 (URN)10.1109/APSEC.2018.00034 (DOI)000474770300021 ()2-s2.0-85066797560 (Scopus ID)
Konferens
25th Asia-Pacific Software Engineering Conference APSEC'18, 04 Dec 2018, Nara, Japan
Projekt
VeriSpec - Structured Specification and Automated Verification for Automotive Functional SafetyDPAC - Dependable Platforms for Autonomous systems and Control
Tillgänglig från: 2019-06-14 Skapad: 2019-06-14 Senast uppdaterad: 2019-10-11Bibliografiskt granskad
Faragardi, H. R., Fotouhi, H., Nolte, T. & Rahmani, R. (2017). A Cost Efficient Design of a Multi-Sink Multi-ControllerWSN in a Smart Factory. In: Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017: . Paper presented at 19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017; Bangkok; Thailand; 18 December 2017 through 20 December 2017 (pp. 594-602).
Öppna denna publikation i ny flik eller fönster >>A Cost Efficient Design of a Multi-Sink Multi-ControllerWSN in a Smart Factory
2017 (Engelska)Ingår i: Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017, 2017, s. 594-602Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Internet of Things (IoT), one of the key elementsof a smart factory, is dubbed as Industrial IoT (IIoT). Softwaredefined networking is a technique that benefits network managementin IIoT applications by providing network reconfigurability.In this way, controllers are integrated within the networkto advertise routing rules dynamically based on network andlink changes. We consider controllers within Wireless SensorNetworks (WSNs) for IIoT applications in such a way to providereliability and timeliness. Network reliability is addressed for thecase of node failure by considering multiple sinks and multiplecontrollers. Real-time requirements are implicitly applied bylimiting the number of hops (maximum path-length) betweensensors and sinks/controllers, and by confining the maximumworkload on each sink/controller. Deployment planning of sinksshould ensure that when a sink or controller fails, the networkis still connected. In this paper, we target the challenge ofplacement of multiple sinks and controllers, while ensuring thateach sensor node is covered by multiple sinks (k sinks) andmultiple controllers (k' controllers). We evaluate the proposedalgorithm using the benchmark GRASP-MSP through extensiveexperiments, and show that our approach outperforms thebenchmark by lowering the total deployment cost by up to24%. The reduction of the total deployment cost is fulfilled notonly as the result of decreasing the number of required sinksand controllers but also selecting cost-effective sinks/controllersamong all candidate sinks/controllers.

Nyckelord
IoT
Nationell ämneskategori
Elektroteknik och elektronik
Forskningsämne
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-36445 (URN)10.1109/HPCC-SmartCity-DSS.2017.77 (DOI)000450718900077 ()2-s2.0-85047446022 (Scopus ID)9781538625880 (ISBN)
Konferens
19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017; Bangkok; Thailand; 18 December 2017 through 20 December 2017
Tillgänglig från: 2017-09-18 Skapad: 2017-09-18 Senast uppdaterad: 2019-06-25Bibliografiskt granskad
Faragardi, H. R. (2017). Resource Optimization in Multi-processor Real-time Systems. (Licentiate dissertation). Västerås: Mälardalen University
Öppna denna publikation i ny flik eller fönster >>Resource Optimization in Multi-processor Real-time Systems
2017 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

This thesis addresses the topic of resource efficiency in multiprocessor systems in the presence of timing constraints. 

 Nowadays, almost wherever you look, you find a computing system. Most computing systems employ a multiprocessor platform. Multiprocessor systems can be found in a broad spectrum of computing systems ranging from a tiny chip hosting multiple cores to large geographically-distributed cloud data centers connected by the Internet. In multiprocessor systems, efficient use of computing resources is a substantial element when it comes to achieving a desirable performance for running software applications. 

 Most industrial applications, e.g., automotive and avionics applications, are subject to a set of real-time constraints that must be met. Such kinds of applications, along with the underlying hardware and software components running the application, constitute a real-time system. In real-time systems, the first and major concern of the system designer is to provide a solution where all timing constraints are met. Therefore, in multiprocessor real-time systems, not only resource efficiency, but also meeting all the timing requirements, is a major concern. 

 Industrie 4.0 is the current trend in automation and manufacturing when it comes to creating next generation of smart factories. Two categories of multiprocessor systems play a significant role in the realization of such a smart factory: 1) multi-core processors which are the key computing element of embedded systems, 2) cloud computing data centers as the supplier of a massive data storage and a large computational power. Both these categories are considered in the thesis, i.e., 1) the efficient use of embedded multi-core processors where multiple processors are located on the same chip, applied to execute a real-time application, and 2) the efficient use of multi-processors within a cloud computing data center. We address these two categories of multi-processor systems separately. 

 For each of them, we identify the key challenges to achieve a resource-efficient design of the system. We then formulate the problem and propose optimization solutions to optimize the efficiency of the system, while satisfying all timing constraints. Introducing a resource efficient solution for those two categories of multi-processor systems facilitates deployment of Industrie 4.0 in smart manufacturing factories where multi-core embedded processors and cloud computing data centers are two central cornerstones.

Ort, förlag, år, upplaga, sidor
Västerås: Mälardalen University, 2017
Serie
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 263
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mdh:diva-35387 (URN)978-91-7485-336-0 (ISBN)
Presentation
2017-10-05, Paros, Mälardalens högskola, Västerås, 13:30 (Engelska)
Opponent
Handledare
Tillgänglig från: 2017-09-14 Skapad: 2017-05-24 Senast uppdaterad: 2018-01-13Bibliografiskt granskad
Khalilzad, N., Faragardi, H. R. & Nolte, T. (2015). Towards Energy-Aware Placement of Real-Time Virtual Machines in a Cloud Data Center. In: Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015: . Paper presented at 17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015; New York; United States; 24 August 2015 through 26 August 2015 (pp. 1657-1662).
Öppna denna publikation i ny flik eller fönster >>Towards Energy-Aware Placement of Real-Time Virtual Machines in a Cloud Data Center
2015 (Engelska)Ingår i: Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015, 2015, s. 1657-1662Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Cloud computing is an evolving paradigm which is becoming an adoptable technology for a variety of applications. However, cloud infrastructures must be able to fulfill application requirements before adopting cloud solutions. Cloud infrastructure providers communicate the characteristics of their services to their customers through Service Level Agreements (SLA). In order for a real-time application to be able to use cloud technology, cloud infrastructure providers have to be able to provide timing guarantees in the SLAs. In this paper, we present our ongoing work regarding a cloud solution in which periodic tasks are provided as a service in the Software as a Service (SaS) model. Tasks belonging to a certain application are mapped in a Virtual Machine (VM). We also study the problem of VMplacement on a cloud infrastructure. We propose a placement mechanism which minimizes the energy consumption of the data center by consolidating VMs in a minimum number of servers while respecting the timing requirement of virtual machines.

Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-29238 (URN)10.1109/HPCC-CSS-ICESS.2015.22 (DOI)000380408100272 ()2-s2.0-84949578819 (Scopus ID)9781479989362 (ISBN)
Externt samarbete:
Konferens
17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015; New York; United States; 24 August 2015 through 26 August 2015
Projekt
ARROWS - Design Techniques for Adaptive Embedded SystemsPRESS - Predictable Embedded Software Systems
Tillgänglig från: 2015-10-06 Skapad: 2015-09-29 Senast uppdaterad: 2017-09-18Bibliografiskt granskad
Faragardi, H. R., Lisper, B., Sandström, K. & Nolte, T. (2014). A communication-aware solution framework for mapping AUTOSAR runnables on multi-core systems. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014: . Paper presented at 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014, 16 September 2014 through 19 September 2014 (pp. Article number 7005244).
Öppna denna publikation i ny flik eller fönster >>A communication-aware solution framework for mapping AUTOSAR runnables on multi-core systems
2014 (Engelska)Ingår i: 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014, 2014, s. Article number 7005244-Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

An AUTOSAR-based software application contains a set of software components, each of which encapsulates a set of runnable entities. In fact, the mission of the system is fulfilled as result of the collaboration between the runnables. Several trends have recently emerged to utilize multi-core technology to run AUTOSAR-based software. Not only the overhead of communication between the runnables is one of the major performance bottlenecks in multi-core processors but it is also the main source of unpredictability in the system. Appropriate mapping of the runnables onto a set of tasks (called mapping process) along with proper allocation of the tasks to processing cores (called task allocation process) can significantly reduce the communication overhead. In this paper, three solutions are suggested, each of which comprises both the mapping and the allocation processes. The goal is to maximize key performance aspects by reducing the overall inter-runnable communication time besides satisfying given timing and precedence constraints. A large number of randomly generated experiments are carried out to demonstrate the efficiency of the proposed solutions.

Nyckelord
Ant System, AUTOSAR, feedback-based search, mapping, multi-core, runnable, Simulated Annealing, Application programs, Factory automation, Ant systems, Feed-back based, Multi core, Microprocessor chips
Nationell ämneskategori
Data- och informationsvetenskap Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mdh:diva-27937 (URN)10.1109/ETFA.2014.7005244 (DOI)000360999100195 ()2-s2.0-84946692528 (Scopus ID)9781479948468 (ISBN)
Konferens
19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014, 16 September 2014 through 19 September 2014
Tillgänglig från: 2015-04-30 Skapad: 2015-04-30 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
Faragardi, H. R., Rajabi, A., Nolte, T. & Heidarizadeh, A. H. (2014). A Profit-aware Allocation of High Performance Computing Applications on Distributed Cloud Data Centers with Environmental Considerations. CSI Journal on Computer Science and Engineering JCSE, 2(1), 28-38
Öppna denna publikation i ny flik eller fönster >>A Profit-aware Allocation of High Performance Computing Applications on Distributed Cloud Data Centers with Environmental Considerations
2014 (Engelska)Ingår i: CSI Journal on Computer Science and Engineering JCSE, Vol. 2, nr 1, s. 28-38Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

A Set of Geographically Distributed Cloud data centers (SGDC) is a promising platform to run a large number of High Performance Computing Applications (HPCAs) in a cost-efficient manner. Energy consumption is a key factor affecting the profit of a cloud provider. In a SGDC, as the data centers are located in different corners of the world, the cost of energy consumption and the amount of CO2 emission significantly vary among the data centers. Therefore, in such systems not only a proper allocation of HPCAs results in CO2 emission reduction, but it also causes a substantial increase of the provider's profit. Furthermore, CO2 emission reduction mitigates the destructive environmental impacts. In this paper, the problem of allocation of a set of HPCAs on a SGDC is discussed where a two-level allocation framework is introduced to deal with the problem. The proposed framework is able to reach a good compromise between CO2 emission and the providers' profit subject to satisfy HPCAs deadlines and memory constraints. Simulation results based on a real intensive workload demonstrate that the proposed framework enhances the CO2 emission by 17% and the provider's profit by 9% in average.

Nyckelord
Cloud Computing, Data Center, Energy-aware allocation, CO2 emission, Multi-objective optimization, Live migration.
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-35488 (URN)
Projekt
PREMISE - Predictable Multicore Systems
Tillgänglig från: 2017-05-31 Skapad: 2017-05-31 Senast uppdaterad: 2018-02-26Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-1384-5323

Sök vidare i DiVA

Visa alla publikationer