

- Mohammad Shojafar
- |
Università degli Studi "La Sapienza" di Roma, Department of Information Engineering, Electronics and Telecommunications, PhD student/ Associated Researcher -CESPOL-
Università degli Studi "La Sapienza" di Roma, Department of Information Engineering, Electronics and Telecommunications, PhD student/ Associated Researcher -CESPOL- | Mohammad Shojafar (S'13-M'17) is an Intel Innovator and senior researcher in SPRITZ Security and Privacy Research Group at the University of Padua, Italy. He was CNIT Senior Researcher at the University of Rome Tor Vergata contributed on European H2020 Superfluidity project. Also, he completed some Italian projects named "SAMMClouds", "V-FoG", "PRIN15" projects aim to address some of the open issues related to the Software as a Service (SaaS) and Infrastructure as a Service (IaaS) systems In Cloud and Fog computing which are supported by the University of Sapienza Rome and University of Modena and Reggio Emilia, Italy, respectively. He received the Ph.D. degree from Sapienza University of Rome, Rome, Italy, in 2016 with an "Excellent" degree.
Supervisors: Mauro Conti, Luca Chiaraviglio, Mukesh Singhal, Rajmukar Buyya, Enzo Baccarelli, and Jemal Abawajy
Address: Department of Mathematics and Computer Science,
University of Padua,
Via Trieste, 63,
35131, Padua, Italy.
Supervisors: Mauro Conti, Luca Chiaraviglio, Mukesh Singhal, Rajmukar Buyya, Enzo Baccarelli, and Jemal Abawajy
Address: Department of Mathematics and Computer Science,
University of Padua,
Via Trieste, 63,
35131, Padua, Italy.
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Papers
Energy management systems are designed to monitor, optimize, and control the smart grid energy ma... more Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management.
Views PaperRank
The hierarchical routing algorithm is categorized as a kind of routing method using node clusteri... more The hierarchical routing algorithm is categorized as a kind of routing method using node clustering to create a hierarchical structure in large‐scale mobile ad hoc network (LMANET). In this paper, we proposed a new hierarchical clustering algorithm (HCAL) and a corresponded protocol for hierarchical routing in LMANET. The HCAL is designed based on a cost metric in the form of the link expiration time and node's relative degree. Correspondingly, the routing protocol for HCAL adopts a reactive protocol to control the existing cluster head (CH) nodes and handle proactive nodes to be considered as a cluster in LMANET. Hierarchical clustering algorithm jointly utilizes table‐driven and on‐demand routing by using a combined weight metric to search dominant set of nodes. This set is composed by link expiration time and node's relative degree to establish the intra/intercommunication paths in LMANET. The performance of the proposed algorithm and protocol is numerically evaluated in average end‐ to‐end delay, number of CH per round, iteration count between the CHs, average CH keeping time, normalized routing overhead, and packet delivery ratio over a number of randomly generated benchmark scenarios. Furthermore, to corroborate the actual effectiveness of the HCAL algorithm, extensive performance comparisons are carried out with some state‐of‐the‐art routing algorithms, namely, Dynamic Doppler Velocity Clustering, Signal Characteristic‐Based Clustering, Dynamic Link Duration Clustering, and mobility‐based clustering algorithms. KEYWORDS cluster head (CH), clustering, cost metrics, hierarchical routing algorithm, mobile ad hoc networks (MANET)
Views PaperRank
—Fog Computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms tha... more —Fog Computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, up to date, have been considered standing-alone. However, due to their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the Future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the Internet of Energy, Smart City, Industry 4.0 and Big Data Streaming, while, at the same time, introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE, and, then, we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterwards, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only Device-to-Device (D2D) links, in order to establish inter-thing " ad hoc " communication. Lastly, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.
Views PaperRank
The emerging utilization of Software-as-a-Service (SaaS) Fog computing centers as an Internet vir... more The emerging utilization of Software-as-a-Service (SaaS) Fog computing centers as an Internet virtual computing commodity is raising concerns over the energy consumptions of networked data centers for the support of delay-sensitive applications. In addition to the energy consumed by the servers, the energy wasted by the network devices that support TCP/IP reliable inter-Virtual Machines (VMs) connections is becoming a significant challenge. In this paper, we propose and develop a framework for the joint characterization and optimization of TCP/IP SaaS Fog data centers that utilize a bank of queues for increasing the fraction of the admitted workload. Our goal is twofold: (i) we maximize the average workload admitted by the data center; and, (ii) we minimize the resulting networking-plus-computing average energy consumption. For this purpose, we exploit the Lyapunov stochastic optimization approach, in order to design and analyze an optimal (yet practical) online joint resource management framework, which dynamically performs: (i) admission control; (ii) dispatching of the admitted workload; (iii) flow control of the inter-VM TCP/IP connections; (iv) queue control; (v) up/down scaling of the processing frequencies of the instantiated VMs; and, (vi) adaptive joint consolidation of both physical servers and TCP/IP connections. The salient features of the resulting scheduler (e.g., the Q * sched-uler) are that: (i) it admits distributed and scalable implementation; (ii) it provides deterministic bounds on the instantaneous queue backlogs; (iii) it avoids queue overflow phenomena; and, (iv) it effectively tracks the (possibly unpredictable) time-fluctuations of the input workload, in order to perform joint resource consolidation without requiring any a priori information and/or forecast of the input workload. Actual energy and delay performances of the proposed scheduler are numerically evaluated and compared against the corresponding ones of some competing and state-of-the-art schedulers, under: (i) Fast-Giga-10Giga Ethernet switching technologies; (ii) various settings of the reconfiguration-consolidation costs; and, (iii) synthetic, as well as real-world workloads. The experimental results support the conclusion that the proposed scheduler can achieve over 30 percent energy savings.
Views PaperRank
by Mohammad Shojafar and Enzo Baccarelli
In this paper, we propose a dynamic resource provisioning scheduler to maximize the application t... more In this paper, we propose a dynamic resource provisioning scheduler to maximize the application throughput and minimize the computing-plus-communication energy consumption in virtualized networked data centers. The goal is to maximize the energy-efficiency, while meeting hard QoS requirements on processing delay. The resulting optimal resource scheduler is adaptive, and jointly performs: i) admission control of the input traffic offered by the cloud provider; ii) adaptive balanced control and dispatching of the admitted traffic; iii) dynamic reconfiguration and consolidation of the Dynamic Voltage and Frequency Scaling (DVFS)-enabled virtual machines instantiated onto the virtualized data center. The proposed scheduler can manage changes of the workload without requiring server estimation and prediction of its future trend. Furthermore, it takes into account the most advanced mechanisms for power reduction in servers, such as DVFS and reduced power states. Performance of the proposed scheduler is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces. The results confirm the delay-vs.-energy good performance of the proposed scheduler.
Views PaperRank
Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitt... more Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.
Views PaperRank
GLOBECOM 2015
The expected pervasive use of mobile cloud computing and the growing number of Internet data cent... more The expected pervasive use of mobile cloud computing and the growing number of Internet data centers have brought forth many concerns, such as, energy costs and energy saving management of both data centers and mobile connections. Therefore, the need for adaptive and distributed resource allocation schedulers for minimizing the communication-plus-computing energy consumption has become increasingly important. In this paper, we propose and test an efficient dynamic resource provisioning scheduler that jointly minimizes computation and communication energy consumption, while guaranteeing user Quality of Service (QoS) constraints. We evaluate the performance of the proposed dynamic resource provisioning algorithm with respect to the execution time, goodput and bandwidth usage and compare the performance of the proposed scheduler against the exiting approaches. The attained experimental results show that the proposed dynamic resource provisioning algorithm achieves much higher energy-saving than the traditional schemes.
Views PaperRank
In Cloud computing environments, computing resources are
available for users, and they only pay f... more In Cloud computing environments, computing resources are
available for users, and they only pay for used resources The most important issues in cloud computing are scheduling and energy consumption which many researchers worked on them. In these systems a scheduling mechanism has two phases: task prioritization and processor selection. Different priorities may cause to different makespan and for each processor which assigned to the task, the energy consumption is different. So
a good scheduling algorithm must assign priority to each task and select the best processor for them, in such a way that makespan and energy consumption be minimized. In this paper, we proposed a two phase's algorithm for scheduling, named TETS, the first phase is task prioritization and the second phase is processor assignment.We use three prioritization methods for prioritize the tasks and produce optimized initial chromosomes and assign the tasks to processors which is an energy-aware model. Simulation results indicate that our algorithm is better than previous algorithms in terms of energy consumption and makespan. It can improve
the energy consumption by 20% and makespan by 4%.
Views PaperRank
Energy management systems are designed to monitor, optimize, and control the smart grid energy ma... more Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management.
Views PaperRank
The hierarchical routing algorithm is categorized as a kind of routing method using node clusteri... more The hierarchical routing algorithm is categorized as a kind of routing method using node clustering to create a hierarchical structure in large‐scale mobile ad hoc network (LMANET). In this paper, we proposed a new hierarchical clustering algorithm (HCAL) and a corresponded protocol for hierarchical routing in LMANET. The HCAL is designed based on a cost metric in the form of the link expiration time and node's relative degree. Correspondingly, the routing protocol for HCAL adopts a reactive protocol to control the existing cluster head (CH) nodes and handle proactive nodes to be considered as a cluster in LMANET. Hierarchical clustering algorithm jointly utilizes table‐driven and on‐demand routing by using a combined weight metric to search dominant set of nodes. This set is composed by link expiration time and node's relative degree to establish the intra/intercommunication paths in LMANET. The performance of the proposed algorithm and protocol is numerically evaluated in average end‐ to‐end delay, number of CH per round, iteration count between the CHs, average CH keeping time, normalized routing overhead, and packet delivery ratio over a number of randomly generated benchmark scenarios. Furthermore, to corroborate the actual effectiveness of the HCAL algorithm, extensive performance comparisons are carried out with some state‐of‐the‐art routing algorithms, namely, Dynamic Doppler Velocity Clustering, Signal Characteristic‐Based Clustering, Dynamic Link Duration Clustering, and mobility‐based clustering algorithms. KEYWORDS cluster head (CH), clustering, cost metrics, hierarchical routing algorithm, mobile ad hoc networks (MANET)
Views PaperRank
—Fog Computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms tha... more —Fog Computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, up to date, have been considered standing-alone. However, due to their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the Future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the Internet of Energy, Smart City, Industry 4.0 and Big Data Streaming, while, at the same time, introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE, and, then, we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterwards, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only Device-to-Device (D2D) links, in order to establish inter-thing " ad hoc " communication. Lastly, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.
Views PaperRank
The emerging utilization of Software-as-a-Service (SaaS) Fog computing centers as an Internet vir... more The emerging utilization of Software-as-a-Service (SaaS) Fog computing centers as an Internet virtual computing commodity is raising concerns over the energy consumptions of networked data centers for the support of delay-sensitive applications. In addition to the energy consumed by the servers, the energy wasted by the network devices that support TCP/IP reliable inter-Virtual Machines (VMs) connections is becoming a significant challenge. In this paper, we propose and develop a framework for the joint characterization and optimization of TCP/IP SaaS Fog data centers that utilize a bank of queues for increasing the fraction of the admitted workload. Our goal is twofold: (i) we maximize the average workload admitted by the data center; and, (ii) we minimize the resulting networking-plus-computing average energy consumption. For this purpose, we exploit the Lyapunov stochastic optimization approach, in order to design and analyze an optimal (yet practical) online joint resource management framework, which dynamically performs: (i) admission control; (ii) dispatching of the admitted workload; (iii) flow control of the inter-VM TCP/IP connections; (iv) queue control; (v) up/down scaling of the processing frequencies of the instantiated VMs; and, (vi) adaptive joint consolidation of both physical servers and TCP/IP connections. The salient features of the resulting scheduler (e.g., the Q * sched-uler) are that: (i) it admits distributed and scalable implementation; (ii) it provides deterministic bounds on the instantaneous queue backlogs; (iii) it avoids queue overflow phenomena; and, (iv) it effectively tracks the (possibly unpredictable) time-fluctuations of the input workload, in order to perform joint resource consolidation without requiring any a priori information and/or forecast of the input workload. Actual energy and delay performances of the proposed scheduler are numerically evaluated and compared against the corresponding ones of some competing and state-of-the-art schedulers, under: (i) Fast-Giga-10Giga Ethernet switching technologies; (ii) various settings of the reconfiguration-consolidation costs; and, (iii) synthetic, as well as real-world workloads. The experimental results support the conclusion that the proposed scheduler can achieve over 30 percent energy savings.
Views PaperRank
by Mohammad Shojafar and Enzo Baccarelli
In this paper, we propose a dynamic resource provisioning scheduler to maximize the application t... more In this paper, we propose a dynamic resource provisioning scheduler to maximize the application throughput and minimize the computing-plus-communication energy consumption in virtualized networked data centers. The goal is to maximize the energy-efficiency, while meeting hard QoS requirements on processing delay. The resulting optimal resource scheduler is adaptive, and jointly performs: i) admission control of the input traffic offered by the cloud provider; ii) adaptive balanced control and dispatching of the admitted traffic; iii) dynamic reconfiguration and consolidation of the Dynamic Voltage and Frequency Scaling (DVFS)-enabled virtual machines instantiated onto the virtualized data center. The proposed scheduler can manage changes of the workload without requiring server estimation and prediction of its future trend. Furthermore, it takes into account the most advanced mechanisms for power reduction in servers, such as DVFS and reduced power states. Performance of the proposed scheduler is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces. The results confirm the delay-vs.-energy good performance of the proposed scheduler.
Views PaperRank
Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitt... more Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.
Views PaperRank
GLOBECOM 2015
The expected pervasive use of mobile cloud computing and the growing number of Internet data cent... more The expected pervasive use of mobile cloud computing and the growing number of Internet data centers have brought forth many concerns, such as, energy costs and energy saving management of both data centers and mobile connections. Therefore, the need for adaptive and distributed resource allocation schedulers for minimizing the communication-plus-computing energy consumption has become increasingly important. In this paper, we propose and test an efficient dynamic resource provisioning scheduler that jointly minimizes computation and communication energy consumption, while guaranteeing user Quality of Service (QoS) constraints. We evaluate the performance of the proposed dynamic resource provisioning algorithm with respect to the execution time, goodput and bandwidth usage and compare the performance of the proposed scheduler against the exiting approaches. The attained experimental results show that the proposed dynamic resource provisioning algorithm achieves much higher energy-saving than the traditional schemes.
Views PaperRank
In Cloud computing environments, computing resources are
available for users, and they only pay f... more In Cloud computing environments, computing resources are
available for users, and they only pay for used resources The most important issues in cloud computing are scheduling and energy consumption which many researchers worked on them. In these systems a scheduling mechanism has two phases: task prioritization and processor selection. Different priorities may cause to different makespan and for each processor which assigned to the task, the energy consumption is different. So
a good scheduling algorithm must assign priority to each task and select the best processor for them, in such a way that makespan and energy consumption be minimized. In this paper, we proposed a two phase's algorithm for scheduling, named TETS, the first phase is task prioritization and the second phase is processor assignment.We use three prioritization methods for prioritize the tasks and produce optimized initial chromosomes and assign the tasks to processors which is an energy-aware model. Simulation results indicate that our algorithm is better than previous algorithms in terms of energy consumption and makespan. It can improve
the energy consumption by 20% and makespan by 4%.
Views PaperRank
One of the major challenges that cloud providers face is minimizing power consumption of their da... more One of the major challenges that cloud providers face is minimizing power consumption of their data centers. To this point, majority of current research focuses on energy efficient management of resources in the Infrastructure as a Service model using virtualization and through virtual machine consolidation. However, current virtualized data centers are not designed for supporting communication–computing intensive real-time applications, such as, info-mobility applications, real-time video co-decoding. In fact, imposing hard-limits on the overall per-job delay forces the overall networked computing infrastructure to adapt quickly its resource utilization to the (possibly, unpredictable and abrupt) time fluctuations of the offered workload. Jointly, a promising approach for making networked data centers more energy-efficient is the use of traffic engineering-based method to dynamically adapt the number of active servers to match the current workload. Therefore, it is desirable to develop a flexible and robust resource allocation algorithm that automatically adapts to time-varying workload and pays close attention to the consumed energy in computing and communication in virtualized networked data centers (VNetDCs). In this thesis, we propose three new dynamic and adaptive energy-aware algorithms scheduling policies that model and manage VNetDCs. Our focuses are to propose i) admission control of the offered input traffic; ii) balanced control and dispatching of the admitted workload; iii) dynamic reconfiguration and consolidation of the Dynamic Voltage and Frequency Scaling (DVFS)-enabled Virtual Machines (VMs) instantiated onto the parallel computing platform; and, iv) rate control of the traffic injected into the TCP/IP mobile connection. Necessary and sufficient conditions for the feasibility and optimality of the proposed schedulers are also provided in closed-form. Specifically, the first approach, called VNetDC, the optimal minimum-energy scheduler for the joint adaptive load balancing and provisioning of the computing-plus-communication resources. VNetDC platforms have been considered which operate under hard real-time constraints. VNetDC has capability to adapt to the time-varying statistical features of the offered workload without requiring any a priori assumption and/or knowledge about the statistics of the processed data. Green-NetDC is the second scheduling policy that is a flexible and robust resource allocation algorithm that automatically adapts to time-varying workload and pays close attention to the consumed energy in computing and communication in VNetDCs. GreenNetDC not only ensures users the Quality of Service (through Service Level Agreements) but also achieves maximum energy saving and attains green cloud computing goals in a fully distributed fashion by utilizing the DVFS-based CPU frequencies. Finally, the last algorithm tested an
efficient dynamic resource provisioning scheduler which applied in Networked Data Centers (NetDCs). This method is connected to (possibly, mobile) clients through TCP/IP-based vehicular backbones The salient features of this algorithm is that: i) It is adaptive and admit distributed scalable implementation; ii) It is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rate of the traffic delivered to the client, instantaneous goodput and total processing delay; and, iii) It explicitly accounts for the dynamic interaction between computing and networking resources, in order to maximize the resulting energy efficiency. Actual performance of the proposed scheduler in the presence of :i) client mobility; ii)wireless fading; iii)reconfiguration and two-thresholds consolidation costs of the underlying networked computing platform; and, iv)abrupt changes of the transport quality of the available TCP/IP mobile connection, is numerically tested and compared against the corresponding ones of some state-of-the-art static schedulers, under both synthetically generated and measured real-world workload traces.
Views PaperRank