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Dr. Muhammad Zakarya
Abdul Wali Khan University

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0 Cloud Computing IaaS
0 Energy Efficiency
0 Internet of Things
0 Resource Management
0 fog computing

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Energy Efficiency
Resource Management
Internet of Things
fog computing

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Preprint content
Published: 21 June 2021
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In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.

ACS Style

Muhammad Zakarya; Lee Gillam; Khaled Salah; Omer F. Rana; Santosh Tirunagari; Rajkumar Buyya. CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds. 2021, 1 .

AMA Style

Muhammad Zakarya, Lee Gillam, Khaled Salah, Omer F. Rana, Santosh Tirunagari, Rajkumar Buyya. CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds. . 2021; ():1.

Chicago/Turabian Style

Muhammad Zakarya; Lee Gillam; Khaled Salah; Omer F. Rana; Santosh Tirunagari; Rajkumar Buyya. 2021. "CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds." , no. : 1.

Preprint content
Published: 21 June 2021
Reads 0
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In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.

ACS Style

Muhammad Zakarya; Lee Gillam; Khaled Salah; Omer F. Rana; Santosh Tirunagari; Rajkumar Buyya. CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds. 2021, 1 .

AMA Style

Muhammad Zakarya, Lee Gillam, Khaled Salah, Omer F. Rana, Santosh Tirunagari, Rajkumar Buyya. CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds. . 2021; ():1.

Chicago/Turabian Style

Muhammad Zakarya; Lee Gillam; Khaled Salah; Omer F. Rana; Santosh Tirunagari; Rajkumar Buyya. 2021. "CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds." , no. : 1.

Journal article
Published: 27 May 2021 in Neural Computing and Applications
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The accurate prediction of cardiovascular disease is an essential and challenging task to treat a patient efficiently before occurring a heart attack. In recent times, various intelligent healthcare frameworks have been designed with different machine learning and swarm optimization techniques for cardiovascular disease prediction. However, most of the existing strategies failed to achieve higher accuracy for cardiovascular disease prediction due to the lack of data-recognized techniques and proper prediction methodology. Motivated by the existing challenges, in this paper, we propose an intelligent healthcare framework for predicting cardiovascular heart disease based on Swarm-Artificial Neural Network (Swarm-ANN) strategy. Initially, the proposed Swarm-ANN strategy randomly generates predefined numbers of Neural Networks (NNs) for training and evaluating the framework based on their solution consistency. Additionally, the NN populations are trained by two stages of weight changes and their weight is adjusted by a newly designed heuristic formulation. Finally, the weight of the neurons is modified by sharing the global best weight with other neurons and predicts the accuracy of cardiovascular disease. The proposed Swarm-ANN strategy achieves 95.78% accuracy while predicting the cardiovascular disease of the patients from a benchmark dataset. The simulation results exhibit that the proposed Swarm-ANN strategy outperforms the standard learning techniques in terms of various performance matrices.

ACS Style

Sudarshan Nandy; Mainak Adhikari; Venki Balasubramanian; Varun G. Menon; Xingwang Li; Muhammad Zakarya. An intelligent heart disease prediction system based on swarm-artificial neural network. Neural Computing and Applications 2021, 1 -15.

AMA Style

Sudarshan Nandy, Mainak Adhikari, Venki Balasubramanian, Varun G. Menon, Xingwang Li, Muhammad Zakarya. An intelligent heart disease prediction system based on swarm-artificial neural network. Neural Computing and Applications. 2021; ():1-15.

Chicago/Turabian Style

Sudarshan Nandy; Mainak Adhikari; Venki Balasubramanian; Varun G. Menon; Xingwang Li; Muhammad Zakarya. 2021. "An intelligent heart disease prediction system based on swarm-artificial neural network." Neural Computing and Applications , no. : 1-15.

Journal article
Published: 08 April 2021 in Future Generation Computer Systems
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In the densely populated Internet of Things (IoT) applications, sensing range of the nodes might overlap frequently. In these applications, the nodes gather highly correlated and redundant data in their vicinity. Processing these data depletes the energy of nodes and their upstream transmission towards remote datacentres, in the fog infrastructure, may result in an unbalanced load at the network gateways and edge servers. Due to heterogeneity of edge servers, few of them might be overwhelmed while others may remain less-utilized. As a result, time-critical and delay-sensitive applications may experience excessive delays, packet loss, and degradation in their Quality of Service (QoS). To ensure QoS of IoT applications, in this paper, we eliminate correlation in the gathered data via a lightweight data fusion approach. The buffer of each node is partitioned into strata that broadcast only non-correlated data to edge servers via the network gateways. Furthermore, we propose a dynamic service migration technique to reconfigure the load across various edge servers. We assume this as an optimization problem and use two meta-heuristic algorithms, along with a migration approach, to maintain an optimal Gateway-Edge configuration in the network. These algorithms monitor the load at each server, and once it surpasses a threshold value (which is dynamically computed with a simple machine learning method), an exhaustive search is performed for an optimal and balanced periodic reconfiguration. The experimental results of our approach justify its efficiency for large-scale and densely populated IoT applications.

ACS Style

Mian Ahmad Jan; Muhammad Zakarya; Muhammad Khan; Spyridon Mastorakis; Varun G. Menon; Venki Balasubramanian; Ateeq Ur Rehman. An AI-enabled lightweight data fusion and load optimization approach for Internet of Things. Future Generation Computer Systems 2021, 122, 40 -51.

AMA Style

Mian Ahmad Jan, Muhammad Zakarya, Muhammad Khan, Spyridon Mastorakis, Varun G. Menon, Venki Balasubramanian, Ateeq Ur Rehman. An AI-enabled lightweight data fusion and load optimization approach for Internet of Things. Future Generation Computer Systems. 2021; 122 ():40-51.

Chicago/Turabian Style

Mian Ahmad Jan; Muhammad Zakarya; Muhammad Khan; Spyridon Mastorakis; Varun G. Menon; Venki Balasubramanian; Ateeq Ur Rehman. 2021. "An AI-enabled lightweight data fusion and load optimization approach for Internet of Things." Future Generation Computer Systems 122, no. : 40-51.

Review article
Published: 09 March 2021 in Computer Science Review
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In major Information Technology (IT) companies such as Google, Rackspace and Amazon Web Services (AWS), virtualization and containerization technologies are usually used to execute customers’ workloads and applications — as part of their cloud computing services offering. The computational resources are provided through large-scale datacentres, which consume substantial amount of energy and, consequently, affect our environment with global warming. Cloud datacentres have become a backbone for today’s business and economy, which are the fastest-growing electricity consumers, globally. Numerous studies suggest that ∼30% of the US datacentres are comatose and the others are grossly less-utilized, which make it possible to save energy through technologies like virtualization and containerization. These technologies provide support for allocation and consolidation of workloads on appropriate resources. However, consolidation comprises migrations of virtual machines (VMs), containers and/or applications, depending on the underlying virtualization method; that are expensive in terms of energy consumption, performance degradation, and therefore, costs which is mostly not accounted for in many existing models, and, possibly, it could be more energy and performance efficient not to consolidate. This paper describes energy consumption and performance, therefore, cost issues of large-scale datacentres. Besides, we cover various methods for energy and performance efficient distributed systems, clouds and datacentres. We elaborate energy efficiency methods at three different levels: hardware; resource management; and applications. Besides these, different performance management techniques are mapped onto taxonomies and described in details. In last, energy, performance and cost management techniques, at geographically distributed and multi-access edge computing platforms, are described along with critical discussion.

ACS Style

Ayaz Ali Khan; Muhammad Zakarya. Energy, performance and cost efficient cloud datacentres: A survey. Computer Science Review 2021, 40, 100390 .

AMA Style

Ayaz Ali Khan, Muhammad Zakarya. Energy, performance and cost efficient cloud datacentres: A survey. Computer Science Review. 2021; 40 ():100390.

Chicago/Turabian Style

Ayaz Ali Khan; Muhammad Zakarya. 2021. "Energy, performance and cost efficient cloud datacentres: A survey." Computer Science Review 40, no. : 100390.

Journal article
Published: 19 January 2021 in Multimedia Tools and Applications
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Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how to model multiple complicated spatial dependencies between different regions, dynamic temporal laws among different time intervals with external factors such as holidays, events, and weather. Some existing work leverage the long short-term memory (LSTM) and convolutional neural network (CNN) to explore temporal relations and spatial relations, respectively; which have outperformed the classical statistical methods. However, it is difficult for these approaches to jointly model spatial and temporal correlations. To address this problem, we propose a dynamic deep hybrid spatio-temporal neural network namely DHSTNet, to predict traffic flows in every region of a city with high accuracy. In particular, our DSHTNet model comprises four properties i.e., closeness volume, daily volume, trend volume, and external branch, respectively. Moreover, the projected model dynamically assigns different weights to various branches and, then, integrate outputs of four properties to produce final prediction outcomes. The model has been evaluated, both for offline and online predictions, using an edge/fog infrastructure where training happens on the remote cloud and prediction occurs at the edge i.e. in the proximity of users. Extensive experiments and evaluation on two real-world datasets demonstrate the advantage of the proposed model, in terms of high accuracy over prevailing state-of-the-art baseline methods. Moreover, we apply the exaggeration approach based on an attention mechanism to the above model, called as AAtt-DHSTNet; to predict citywide short-term traffic crowd flows; and show its notable performance in the traffic flows prediction. The aggregation method collects information from the related time series, remove redundancy and, thus, increases prediction speed and accuracy. Our empirical evaluation suggests that the AAtt-DHSTNet model is approximately 20.8% and 8.8% more accurate than the DHSTNet technique, for two different real-world traffic datasets.

ACS Style

Ahmad Ali; Yanmin Zhu; Muhammad Zakarya. A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimedia Tools and Applications 2021, 1 -33.

AMA Style

Ahmad Ali, Yanmin Zhu, Muhammad Zakarya. A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimedia Tools and Applications. 2021; ():1-33.

Chicago/Turabian Style

Ahmad Ali; Yanmin Zhu; Muhammad Zakarya. 2021. "A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing." Multimedia Tools and Applications , no. : 1-33.

Journal article
Published: 19 January 2021 in Journal of Systems and Software
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With rapid availability of renewable energy sources and growing interest in their use in the datacenter industry presents opportunities for service providers to reduce their energy related costs, as well as, minimize the ecological impact of their infrastructure. However, renewables are largely intermittent and can, negatively affect users’ applications and their performance, therefore, the profit of the service providers. Furthermore, services could be offered from those geographical locations where electricity is relatively cheaper than other locations; which may degrade the applications’ performance and potentially increase users’ costs. To ensure larger providers’ profits and lower users’ costs, certain non-interactive workloads could be either: moved and executed in geographical locations offering the lowest energy prices; or could be queued and delayed to execute later (in day or night time) when renewables, such as solar and wind energies, are at peak. However, these may have negative impacts on the energy consumption, workloads performance, and users’ costs. Therefore, to ensure energy, performance and cost efficiencies, appropriate workload scheduling, placement, migration, and resource management techniques are required to mange the infrastructure resources, workloads, and energy sources. In this paper, we propose a workload placement and three different migration policies that maximize the providers’ revenues, ensure the workload performance, reduce energy consumption, along with reducing ecological impacts and users’ costs. Using real workload traces and electricity prices for several geographical locations and distributed, heterogeneous, datacenters, our experimental evaluation suggest that the proposed approaches could save significant amount of energy (∼15.26%), reduces service monetary costs (∼0.53% - ∼19.66%), improves (∼1.58%) or, at least, maintains the expected level of applications’ performance, and increases providers’ revenue along with environmental sustainability, against the well-known first fit (FF), best fit (BF) heuristic algorithms, and other closest rivals.

ACS Style

Hashim Ali; Muhammad Zakarya; Izaz Ur Rahman; Ayaz Ali Khan; Rajkumar Buyya. [email protected]: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters. Journal of Systems and Software 2021, 175, 110907 .

AMA Style

Hashim Ali, Muhammad Zakarya, Izaz Ur Rahman, Ayaz Ali Khan, Rajkumar Buyya. [email protected]: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters. Journal of Systems and Software. 2021; 175 ():110907.

Chicago/Turabian Style

Hashim Ali; Muhammad Zakarya; Izaz Ur Rahman; Ayaz Ali Khan; Rajkumar Buyya. 2021. "[email protected]: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters." Journal of Systems and Software 175, no. : 110907.

Journal article
Published: 31 October 2020 in Energies
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Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

ACS Style

Muhammad Qureshi; Muhammad Qureshi; Muhammad Fayaz; Muhammad Zakarya; Sheraz Aslam; Asadullah Shah. Time and Cost Efficient Cloud Resource Aallocation for Real-Time Data-Intensive Smart Systems. Energies 2020, 13, 5706 .

AMA Style

Muhammad Qureshi, Muhammad Qureshi, Muhammad Fayaz, Muhammad Zakarya, Sheraz Aslam, Asadullah Shah. Time and Cost Efficient Cloud Resource Aallocation for Real-Time Data-Intensive Smart Systems. Energies. 2020; 13 (21):5706.

Chicago/Turabian Style

Muhammad Qureshi; Muhammad Qureshi; Muhammad Fayaz; Muhammad Zakarya; Sheraz Aslam; Asadullah Shah. 2020. "Time and Cost Efficient Cloud Resource Aallocation for Real-Time Data-Intensive Smart Systems." Energies 13, no. 21: 5706.

Journal article
Published: 28 October 2020 in Journal of Network and Computer Applications
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In major Information Technology (IT) companies such as Google, Rackspace and Amazon Web Services (AWS), virtualisation and containerisation technologies are usually used to execute customers' workloads and applications. The computational resources are provided through large-scale datacenters, which consume substantial amount of energy and have, therefore, ecological impacts. Since long, Google runs users' applications in containers, Rackspace offers bare-metal hardware, whereas AWS runs them either in VMs (EC2), containers (ECS) and/or containers inside VMs (Lambda); therefore, making resource management a tedious activity. The role of a resource management system is of the greatest importance, principally, if IT companies practice various kinds of sand-boxing technologies, for instance, bare-metal, VMs, containers, and/or nested containers in their datacenters (hybrid platforms). The absence of centralised, workload-aware resource managers and consolidation policies produces questions on datacenters energy efficiency, workloads performance, and users' costs. In this paper, we demonstrate, through several experiments, using the Google workload data for 12,583 hosts and approximately one million tasks that belong to four different kinds of workload, the likelihood of: (i) using workload-aware resource managers in hybrid clouds; (ii) achieving energy and cost savings, in heterogeneous hybrid datacenters such that the workload performance is not affected, negatively; and (iii) how various allocation policies, combined with different migration approaches, will impact on datacenter's energy and performance efficiencies. Using plausible assumptions for hybrid datacenters set-up, our empirical evaluation suggests that, for no migration, a single scheduler is at most 16.86% more energy efficient than distributed schedulers. Moreover, when migrations are considered, our resource manager can save up to 45.61% energy and can improve up to 17.9% workload performance.

ACS Style

Ayaz Ali Khan; Muhammad Zakarya; Izaz Ur Rahman; Rahim Khan; Rajkumar Buyya. HeporCloud: An energy and performance efficient resource orchestrator for hybrid heterogeneous cloud computing environments. Journal of Network and Computer Applications 2020, 173, 102869 .

AMA Style

Ayaz Ali Khan, Muhammad Zakarya, Izaz Ur Rahman, Rahim Khan, Rajkumar Buyya. HeporCloud: An energy and performance efficient resource orchestrator for hybrid heterogeneous cloud computing environments. Journal of Network and Computer Applications. 2020; 173 ():102869.

Chicago/Turabian Style

Ayaz Ali Khan; Muhammad Zakarya; Izaz Ur Rahman; Rahim Khan; Rajkumar Buyya. 2020. "HeporCloud: An energy and performance efficient resource orchestrator for hybrid heterogeneous cloud computing environments." Journal of Network and Computer Applications 173, no. : 102869.

Article
Published: 10 September 2020 in The Journal of Supercomputing
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The major reason for using a simulator, instead of a real test-bed, is to enable repeatable evaluation of large-scale cloud systems. CloudSim, the most widely used simulator, enables users to implement resource provisioning, and management policies. However, CloudSim does not provide support for: (i) interactive online services; (ii) platform heterogeneities; (iii) virtual machine migration modelling; and (iv) other essential models to abstract a real datacenter. This paper describes modifications needed in the classical CloudSim to support realistic experimentations that closely match experimental outcomes in a real system. We extend, and partially re-factor CloudSim to “PerficientCloudSim” in order to provide support for large-scale computation over heterogeneous resources. In the classical CloudSim, we add several classes for workload performance variations due to: (a) CPU heterogeneities; (b) resource contention; and (c) service migration. Through plausible assumptions, our empirical evaluation, using real workload traces from Google and Microsoft Azure clusters, demonstrates that “PerficientCloudSim” can reasonably simulate large-scale heterogeneous datacenters in respect of resource allocation and migration policies, resource contention, and platform heterogeneities. We discuss statistical methods to measure the accuracy of the simulated outcomes.

ACS Style

Muhammad Zakarya; Lee Gillam; Ayaz Ali Khan; Izaz Ur Rahman. PerficientCloudSim: a tool to simulate large-scale computation in heterogeneous clouds. The Journal of Supercomputing 2020, 77, 3959 -4013.

AMA Style

Muhammad Zakarya, Lee Gillam, Ayaz Ali Khan, Izaz Ur Rahman. PerficientCloudSim: a tool to simulate large-scale computation in heterogeneous clouds. The Journal of Supercomputing. 2020; 77 (4):3959-4013.

Chicago/Turabian Style

Muhammad Zakarya; Lee Gillam; Ayaz Ali Khan; Izaz Ur Rahman. 2020. "PerficientCloudSim: a tool to simulate large-scale computation in heterogeneous clouds." The Journal of Supercomputing 77, no. 4: 3959-4013.

Journal article
Published: 01 September 2020 in IEEE Access
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Datacentres provide the foundations for cloud computing, but require large amounts of electricity for their operation. Approaches that promise to reduce power use by minimizing execution time, for example using different scheduling and resource management techniques, are discussed in the literature. This paper summarizes some of the most important scheduling techniques in clouds focusing on power consumption, covering VM-level, host-level and task-level scheduling where the most promising approach is task level scheduling, with energy savings by means of load filtering, consolidation, adapted CPU throughput, or host power control. We explore use of the rate monotonic (RM) and backfilling algorithms for real-time task scheduling in cloud environment because RM is the simplest fixed priority scheduling technique, and thus the choice for modern real-time systems, and prior uses of RM in task scheduling have demonstrated power efficiency with optimal results. We specifically consider deadline-based tasks scheduling for real-time clouds which, to the best of our knowledge, has not been employed previously. RM with backfilling is experimentally evaluated and results show that, compared to the classical algorithms, all tasks were scheduled with minimum power consumption (5.5% - 29.3%), on minimum resources (3.9% - 25.2% less) while majority were meeting their deadlines (93.21% - 94.7%). The approach can guarantee deadline oriented Software as a Service (SaaS) in cloud if arrival rate i.e. network transfer time can be estimated in advance. We subsequently provided an extension of the proposed approach to task-based load balancing for almost balanced resource utilization and approximately 1.0% to 1.6% energy efficiency.

ACS Style

Hashim Ali; Muhammad Shuaib Qureshi; Ayaz Ali Khan; Muhammad Zakarya; Muhammad Fayaz. An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres. IEEE Access 2020, 8, 161288 -161303.

AMA Style

Hashim Ali, Muhammad Shuaib Qureshi, Ayaz Ali Khan, Muhammad Zakarya, Muhammad Fayaz. An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres. IEEE Access. 2020; 8 (99):161288-161303.

Chicago/Turabian Style

Hashim Ali; Muhammad Shuaib Qureshi; Ayaz Ali Khan; Muhammad Zakarya; Muhammad Fayaz. 2020. "An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres." IEEE Access 8, no. 99: 161288-161303.

Journal article
Published: 28 July 2020 in IEEE Sensors Journal
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The scarcity of water resources throughout the world demands its optimum utilization in various sectors. Smart Sensing-enabled irrigation management systems are the ideal solutions to ensure the optimum utilization of water resources in the agriculture sector. This paper presents a Wireless Sensor Network (WSN)-enabled Decision Support System (DSS) for developing a need-based irrigation schedule for the orange orchard. For efficient monitoring of various in-field parameters, our proposed approach uses the latest smart sensing technology such as soil moisture, leaf-wetness, temperature and humidity. The proposed smart sensing-enabled test-bed was deployed in the orange orchard of our institute for approximately one year and successfully adjusted its irrigation schedule according to the needs and demands of the plants. Moreover, a modified Longest Common SubSequence (LCSS) mechanism is integrated with the proposed DSS for distinguishing multi-valued noise from the abrupt changing scenarios. To resolve the concurrent communication problem of two or more wasp-mote sensor boards with a common receiver, an enhanced RTS/CTS handshake mechanism is presented. Our proposed DSS compares the most recently refined data with pre-defined threshold values for efficient water management in the orchard. Irrigation activity is scheduled if water deficit criterion is met and the farmer is informed accordingly. Both the experimental and simulation results show that the proposed scheme performs better in comparison to the existing schemes.

ACS Style

Rahim Khan; Muhammad Zakarya; Venki Balasubramanian; Mian Ahmad Jan; Varun G. Menon. Smart Sensing-Enabled Decision Support System for Water Scheduling in Orange Orchard. IEEE Sensors Journal 2020, 21, 17492 -17499.

AMA Style

Rahim Khan, Muhammad Zakarya, Venki Balasubramanian, Mian Ahmad Jan, Varun G. Menon. Smart Sensing-Enabled Decision Support System for Water Scheduling in Orange Orchard. IEEE Sensors Journal. 2020; 21 (16):17492-17499.

Chicago/Turabian Style

Rahim Khan; Muhammad Zakarya; Venki Balasubramanian; Mian Ahmad Jan; Varun G. Menon. 2020. "Smart Sensing-Enabled Decision Support System for Water Scheduling in Orange Orchard." IEEE Sensors Journal 21, no. 16: 17492-17499.

Journal article
Published: 26 June 2020 in IEEE Transactions on Services Computing
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Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud, due to long distances. Fog/edge computing is a new model for analysing and acting on time-sensitive data, adjacent to where it is produced. Further, cloud services provided by large companies such as Google, can also be localised to improve response time and service agility. This is accomplished through deploying small-scale datacentres in various locations, where needed in proximity of users; and connected to a centralised cloud that establish a multi-access edge computing (MEC). The MEC setup involves three parties, i.e. service-providers (IaaS), application-providers (SaaS), network-providers (NaaS); which might have different goals, therefore, making resource management difficult. Unlike existing literature, we consider resource management with-respect-to all parties; and suggest game-theoretic resource management techniques to minimise infrastructure energy consumption and costs while ensuring applications' performance. Our empirical evaluation, using Google's workload traces, suggests that our approach could reduce up to 11.95% energy consumption, and ~17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up-to 20.27% energy bills and NaaS can increase their costs-savings up-to 18.52% as compared to other methods.

ACS Style

Muhammad Zakarya; Lee Gillam; Hashim Ali; Izaz Rahman; Khaled Salah; Rahim Khan; Omer Rana; Rajkumar Buyya. epcAware: A Game-based, Energy, Performance and Cost Efficient Resource Management Technique for Multi-access Edge Computing. IEEE Transactions on Services Computing 2020, PP, 1 -1.

AMA Style

Muhammad Zakarya, Lee Gillam, Hashim Ali, Izaz Rahman, Khaled Salah, Rahim Khan, Omer Rana, Rajkumar Buyya. epcAware: A Game-based, Energy, Performance and Cost Efficient Resource Management Technique for Multi-access Edge Computing. IEEE Transactions on Services Computing. 2020; PP (99):1-1.

Chicago/Turabian Style

Muhammad Zakarya; Lee Gillam; Hashim Ali; Izaz Rahman; Khaled Salah; Rahim Khan; Omer Rana; Rajkumar Buyya. 2020. "epcAware: A Game-based, Energy, Performance and Cost Efficient Resource Management Technique for Multi-access Edge Computing." IEEE Transactions on Services Computing PP, no. 99: 1-1.

Foundations
Published: 15 June 2020 in Soft Computing
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Particle swarm optimization (PSO) is an optimization method that is most widely used to solve a number of problems in various fields such as engineering, economics and computer systems. However, due to its scalability and unsatisfying performance particularly for large-scale optimization problems; numerous PSO variants have been suggested so far, in the literature. This paper also proposes a new variant of the canonical PSO algorithm (‘N-state switching PSO—NS-SPSO’) that uses the evolutionary factor information to update particles velocities and, therefore, further enhance its performance. The evolutionary factor is derived by using the population distribution and the mean distance of each particle from the global best. The population distribution and the mean distance are determined through Euclidean distance. Moreover, algorithmic parameters such as inertia weight, and acceleration coefficients are assigned appropriate values at N stages (derived from exploration, exploitation, convergence and jumping out states) that improves the search efficiency and convergence speed. The proposed algorithm is applied to 12 widely used mathematical benchmark functions that demonstrate its best performance in terms of minimum evaluation error, fast convergence and low computational time. Besides these, seven high-dimensional functions and few other algorithms for large-scale optimization were considered to test the scalability of NS-SPSO algorithm. Our comparative results show that NS-SPSO performs well on low-dimensional problems and is promising for solving large-scale optimization problems. Furthermore, the proposed NS-PSO algorithm almost outperforms its closest rivals for various benchmarks.

ACS Style

Izaz Ur Rahman; Muhammad Zakarya; Mushtaq Raza; Rahim Khan. An n-state switching PSO algorithm for scalable optimization. Soft Computing 2020, 24, 11297 -11314.

AMA Style

Izaz Ur Rahman, Muhammad Zakarya, Mushtaq Raza, Rahim Khan. An n-state switching PSO algorithm for scalable optimization. Soft Computing. 2020; 24 (15):11297-11314.

Chicago/Turabian Style

Izaz Ur Rahman; Muhammad Zakarya; Mushtaq Raza; Rahim Khan. 2020. "An n-state switching PSO algorithm for scalable optimization." Soft Computing 24, no. 15: 11297-11314.

Journal article
Published: 04 March 2020 in IEEE Access
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Wireless sensor networks (WSNs) is an infrastructure free organization of various operational devices. Due to their overwhelming characteristics, these networks are used in different applications. For WSNs, it is necessary to collect real time and precise data as critical decisions are based on these readings in different application scenarios. In WSNs, authentication of the operational devices is one the challenge issue to the research community as these networks are dynamic and self-organizing in nature. Moreover, due to the constraint oriented nature of these devices a generalized light-weight authentication scheme is needed to be developed. In this paper, a light-weight anonymous authentication techniques is presented to resolve the black-hole attack issue associated with WSNs. In this scheme, Medium Access Control (Mac) address is used to register every node in WSNs with its nearest cluster head(CH) or base station module(s). The registration process is performed in an off-line phase to ensure authenticity of both legitimate nodes and base stations in an operational network. The proposed technique resolves the black-hole attack issue as an intruder node needs to be registered with both gateway and neighbouring nodes which is not possible. Moreover, a hybrid data encryption scheme, elliptic curve integrated encryption standard (ECIES) and elliptic curve deffi-hellman problem (ECDDHP), is used to improve authenticity, confidentiality and integrity of the collected data. Simulation results show the exceptional performance of the proposed scheme against field proven techniques in terms of minimum possible end-to-end delay & communication cost, maximum average packet delivery ratio and throughput in presence of malicious node(s).

ACS Style

Muhammad Adil; Rahim Khan; Mohammed Amin Almaiah; Mohammed Al-Zahrani; Muhammad Zakarya; Muhammad Saeed Amjad; Rehan Ahmed. MAC-AODV Based Mutual Authentication Scheme for Constraint Oriented Networks. IEEE Access 2020, 8, 44459 -44469.

AMA Style

Muhammad Adil, Rahim Khan, Mohammed Amin Almaiah, Mohammed Al-Zahrani, Muhammad Zakarya, Muhammad Saeed Amjad, Rehan Ahmed. MAC-AODV Based Mutual Authentication Scheme for Constraint Oriented Networks. IEEE Access. 2020; 8 (99):44459-44469.

Chicago/Turabian Style

Muhammad Adil; Rahim Khan; Mohammed Amin Almaiah; Mohammed Al-Zahrani; Muhammad Zakarya; Muhammad Saeed Amjad; Rehan Ahmed. 2020. "MAC-AODV Based Mutual Authentication Scheme for Constraint Oriented Networks." IEEE Access 8, no. 99: 44459-44469.

Journal article
Published: 21 January 2020 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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ACS Style

Izaz Ur Rahman; Zidong Wang; Weibo Liu; Baoliu Ye; Muhammad Zakarya; Xiaohui Liu. An N-State Markovian Jumping Particle Swarm Optimization Algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020, 1 -13.

AMA Style

Izaz Ur Rahman, Zidong Wang, Weibo Liu, Baoliu Ye, Muhammad Zakarya, Xiaohui Liu. An N-State Markovian Jumping Particle Swarm Optimization Algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2020; ():1-13.

Chicago/Turabian Style

Izaz Ur Rahman; Zidong Wang; Weibo Liu; Baoliu Ye; Muhammad Zakarya; Xiaohui Liu. 2020. "An N-State Markovian Jumping Particle Swarm Optimization Algorithm." IEEE Transactions on Systems, Man, and Cybernetics: Systems , no. : 1-13.

Journal article
Published: 20 January 2020 in IEEE Access
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Multivariate data sets (MDSs), with enormous size and certain ratio of noise/outliers, are generated routinely in various application domains. A major issue, tightly coupled with these MDSs, is how to compute their similarity indexes with available resources in presence of noise/outliers - which is addressed with the development of both classical and non-metric based approaches. However, classical techniques are sensitive to outliers and most of the non-classical approaches are either problem/application specific or overlay complex. Therefore, the development of an efficient and reliable algorithm for MDSs, with minimum time and space complexity, is highly encouraged by the research community. In this paper, a non-metric based similarity measure algorithm, for MDSs, is presented that solves the aforementioned issues, particularly, noise and computational time, successfully. This technique finds the similarity indexes of noisy MDSs, of both equal and variable sizes, through utilizing minimum possible resources i.e., space and time. Experiments were conducted with both benchmark and real time MDSs for evaluating the proposed algorithm‘s performance against its rival algorithms, which are traditional dynamic programming based and sequential similarity measure algorithms. Experimental results show that the proposed scheme performs exceptionally well, in terms of time and space, than its counterpart algorithms and effectively tolerates a considerable portion of noisy data.

ACS Style

Rahim Khan; Muhammad Zakarya; Ayaz Ali Khan; Izaz Ur Rahman; Mohd Amiruddin Abd Rahman; Muhammad Khalis Abdul Karim; Mohd Shafie Mustafa. A Heuristic Approach for Finding Similarity Indexes of Multivariate Data Sets. IEEE Access 2020, 8, 21759 -21769.

AMA Style

Rahim Khan, Muhammad Zakarya, Ayaz Ali Khan, Izaz Ur Rahman, Mohd Amiruddin Abd Rahman, Muhammad Khalis Abdul Karim, Mohd Shafie Mustafa. A Heuristic Approach for Finding Similarity Indexes of Multivariate Data Sets. IEEE Access. 2020; 8 (99):21759-21769.

Chicago/Turabian Style

Rahim Khan; Muhammad Zakarya; Ayaz Ali Khan; Izaz Ur Rahman; Mohd Amiruddin Abd Rahman; Muhammad Khalis Abdul Karim; Mohd Shafie Mustafa. 2020. "A Heuristic Approach for Finding Similarity Indexes of Multivariate Data Sets." IEEE Access 8, no. 99: 21759-21769.

Journal article
Published: 23 November 2019 in Journal of Network and Computer Applications
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Datacenters are the principal electricity consumers for cloud computing that provide an IT backbone for today's business and economy. Numerous studies suggest that most of the servers, in the US datacenters, are idle or less-utilized, making it possible to save energy by using resource consolidation techniques. However, consolidation involves migrations of virtual machines, containers and/or applications, depending on the underlying virtualisation method; that can be expensive in terms of energy consumption and performance loss. In this paper, we: (a) propose a consolidation algorithm which favours the most effective migration among VMs, containers and applications; and (b) investigate how migration decisions should be made to save energy without any negative impact on the service performance. We demonstrate through a number of experiments, using the real workload traces for 800 hosts, approximately 1516 VMs, and more than million containers, how different approaches to migration, will impact on datacenter's energy consumption and performance. We suggest, using reasonable assumptions for datacenter set-up, that there is a trade-off involved between migrating containers and virtual machines. It is more performance efficient to migrate virtual machines; however, migrating containers could be more energy efficient than virtual machines. Moreover, migrating containerised applications, that run inside virtual machines, could lead to energy and performance efficient consolidation technique in large-scale datacenters. Our evaluation suggests that migrating applications could be ∼5.5% more energy efficient and ∼11.9% more performance efficient than VMs migration. Further, energy and performance efficient consolidation is ∼14.6% energy and ∼7.9% performance efficient than application migration. Finally, we generalise our results using several repeatable experiments over various workloads, resources and datacenter set-ups.

ACS Style

Ayaz Ali Khan; Muhammad Zakarya; Rahim Khan; Izaz Ur Rahman; Mukhtaj Khan; Atta Ur Rehman Khan. An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters. Journal of Network and Computer Applications 2019, 150, 102497 .

AMA Style

Ayaz Ali Khan, Muhammad Zakarya, Rahim Khan, Izaz Ur Rahman, Mukhtaj Khan, Atta Ur Rehman Khan. An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters. Journal of Network and Computer Applications. 2019; 150 ():102497.

Chicago/Turabian Style

Ayaz Ali Khan; Muhammad Zakarya; Rahim Khan; Izaz Ur Rahman; Mukhtaj Khan; Atta Ur Rehman Khan. 2019. "An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters." Journal of Network and Computer Applications 150, no. : 102497.

Research article
Published: 17 October 2019 in International Journal of Communication Systems
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Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

ACS Style

Rahim Khan; Muhammad Zakarya; Zhiyuan Tan; Muhammad Usman; Mian Ahmad Jan; Mukhtaj Khan. PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power-aware fusion, aggregation, and routing scheme. International Journal of Communication Systems 2019, 32, e4144 .

AMA Style

Rahim Khan, Muhammad Zakarya, Zhiyuan Tan, Muhammad Usman, Mian Ahmad Jan, Mukhtaj Khan. PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power-aware fusion, aggregation, and routing scheme. International Journal of Communication Systems. 2019; 32 (18):e4144.

Chicago/Turabian Style

Rahim Khan; Muhammad Zakarya; Zhiyuan Tan; Muhammad Usman; Mian Ahmad Jan; Mukhtaj Khan. 2019. "PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power-aware fusion, aggregation, and routing scheme." International Journal of Communication Systems 32, no. 18: e4144.

Journal article
Published: 05 June 2019 in IEEE Transactions on Cloud Computing
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Cloud datacenters have become a backbone for today's business and economy, which are the fastest-growing electricity consumers, globally. Numerous studies suggest that ~30% of the US datacenters are comatose and the others are grossly less-utilized, which make it possible to save energy through resource consolidation techniques. However, consolidation comprises migrations that are expensive in terms of energy consumption and performance degradation, which is mostly not accounted for in many existing models, and, possibly, it could be more energy and performance efficient not to consolidate. In this paper, we investigate how migration decisions should be taken so that the migration cost is recovered, as only when migration cost has been recovered and performance is guaranteed, will energy start to be saved. We demonstrate through several experiments, using the Google workload data for 12,583 hosts and approximately one million tasks that belong to three different kinds of workload, how different allocation policies, combined with various migration approaches, will impact on datacenter's energy and performance efficiencies. Using several plausible assumptions for containerised datacenter set-up, we suggest, that a combination of the proposed energy-performance-aware allocation (Epc-Fu) and migration (Cper) techniques, and migrating relatively long-running containers only, offers for ideal energy and performance efficiencies.

ACS Style

Ayaz Ali Khan; Muhammad Zakarya; Rajkumar Buyya; Rahim Khan; Mukhtaj Khan; Omer Rana. An energy and performance aware consolidation technique for containerized datacenters. IEEE Transactions on Cloud Computing 2019, PP, 1 -1.

AMA Style

Ayaz Ali Khan, Muhammad Zakarya, Rajkumar Buyya, Rahim Khan, Mukhtaj Khan, Omer Rana. An energy and performance aware consolidation technique for containerized datacenters. IEEE Transactions on Cloud Computing. 2019; PP (99):1-1.

Chicago/Turabian Style

Ayaz Ali Khan; Muhammad Zakarya; Rajkumar Buyya; Rahim Khan; Mukhtaj Khan; Omer Rana. 2019. "An energy and performance aware consolidation technique for containerized datacenters." IEEE Transactions on Cloud Computing PP, no. 99: 1-1.