Showing papers in "Sustainable Computing: Informatics and Systems in 2018"

TL;DR: A new system for Cloud Computing integrated with Internet of Things as a base scenario for Big Data and an architecture relaying on the security of the network are proposed in order to improve their security issues.

TL;DR: A review on thermal management in data centers and various potential cooling technologies developed respecting energy saving constraint, which includes free cooling, liquid cooling, two-phase technologies and building envelope is presented.

TL;DR: This work proposes an energy-aware and QoS-aware multi-objective Ant Colony Optimization (MACO) approach for VM placement and consolidation that outperforms the other tested approaches in terms of energy savings, reduction of resource wastage in term of CPU, and minimization of the number of VM migrations and SLA violations.

TL;DR: An improved LEACH clustering protocol called enhanced multi-hop LEACH is proposed to reduce and balance energy consumption in order to allow increased packet delivery and network lifetime in WSNs.

TL;DR: The proposed algorithm reduces the energy consumption by depreciating the number of active PMs, while also minimizes the makespan and task rejection rate and demonstrates the effectiveness of the proposed algorithm over some existing standard algorithms.

TL;DR: A resource allocation model for processing the applications efficiently and Particle Swarm Optimization based scheduling (PSO) algorithm named as PSO-COGENT algorithm that not only optimize execution cost and time but also reduces the energy consumption of cloud data centers, considering deadline as constraint is proposed.

TL;DR: The results gained from the experiments demonstrate a superior performance of Cloud-SEnergy which ranged from an average energy consumption reduction of 4.3% when compared to Based Cloud technique, to an average reduction of 43.2% whenCompared to All Clouds technique.

TL;DR: The design and development of Cloud-WBAN is designed and developed, an experimental framework that not only brings the cloud computing system closer to the WBANs user (edge-of-things computing) but also automatically adjust the computing resources (at cloudlet) so as to maintain the service level agreements (SLAs) of theWBANs users on the basis of its sensory data volume and application's type.

TL;DR: The problems of the implementation of intelligent transport systems in developing countries like sub-Saharan Africa are presented and solutions, which provide methodologies for the modeling of a system and monitor traffic on inter-urban road transport networks in developed countries are proposed.

TL;DR: A distributed approach to an energy-efficient dynamic virtual machine consolidation mechanism that determines, based on novel algorithms, which virtual machines to migrate, and when, and the results of the performance evaluation demonstrate that the proposed new algorithms are able to enhance the energy efficiency in cloud data centers.

TL;DR: Results show that self-regulated agent systems can give high performance if an appropriate self-regulation model is used and a complete architecture for building future complex smart cities based on the systems thinking paradigm and using self-regulating MAS integrated with fog computing for implementation is currently under preparation.

TL;DR: This work has validated proposed approach with Microsoft Learning to Rank dataset, and it has been found in the experiments performed that 3403 dangling pages out of 12211 dangling pages have been degraded using the proposed scheme.

TL;DR: This paper proposes a new VM placement algorithm called multi-objective virtual machine placement (MOVMP) for IaaS cloud with a resource usage factor (RUF) to maximize the resource usage of the PMs during placement of the VMs and presents a multi-dimensional resource usage model.

TL;DR: This paper proposes to combine two good-quality algorithms and design a new algorithm named Energy-Performance Trade-Off Multi-Resource Cloud Task Scheduling Algorithm (namely ETMCTSA), which can flexibly manage and control the energy and performance of a cloud system via tuning the probability parameter of the algorithm.

TL;DR: The novel concept of Virtual Medical Device is proposed as a sustainable and efficient mechanism deployed in the network edge to ease and provide dynamism and low latency for achieving security in MCPS.

TL;DR: A general host overloading detection algorithm based on logistic regression model and median absolute derivation is proposed that outperforms all other known host status prediction techniques and can be used with any VM placement and migration algorithms.

TL;DR: This paper's scheduler meets power capping constraints and minimizes quality-of-service (QoS) disruption through smart planning of the job execution order, based on constraint programming in conjunction with a machine learning module predicting the power consumptions of HPC applications.

TL;DR: This paper proposes a DVFS enabled proportional fair strategy called Energy Aware Frame Based Fair Scheduling (EAFBFS) for executing a set of real-time QoS aware tasks on multicores that is able to deliver high and tunable fairness accuracy, low scheduling related overheads along with resource utilisation optimality.

TL;DR: This paper proposes the Dynamic Tasks Scheduling algorithm based on Weighted Bi-graph model (DTSWB), which takes the dynamics of both tasks and providers into consideration and achieves better performance than existing scheduling algorithms.

TL;DR: This article proposes a generic adaptable and reusable model of large scale workload which comes from the Google Cluster Workload Traces and proposes along with the model a reference implementation in order for other studies using the results to produce comparable experiments.

TL;DR: This survey paper review the various techniques available for CPU-GPU cooperative computing to improve the resource utilization and reduce the energy consumption of heterogeneous systems by cooperatively performing the computation on both multicore CPUs and GPUs.

TL;DR: Average energy savings of 7% have been achieved by using the optimal frequency determined by the proposed model for the NAS benchmarks, showing that a simplistic strategy of just lowering the operating frequency does not always yield a reduced energy consumption.

TL;DR: A greedy energy-efficient (GEE) algorithm is proposed to improve the energy efficiency under the constraint of reliability and a series of constraints to reduce the frequency of preemption are proposed.

TL;DR: This study presents a method to minimize energy consumption while processing the same workload, i.e., ultimately reducing the energy consumed by operating servers.

TL;DR: This material is based upon work supported by the U.S. government under contract number DE-AC02-06CH11357 (DOE Catalog project) and in part by the European Union FEDER funds under contract TIN2015-65316-P.

TL;DR: More research is needed to design interventions using a socio-technical approach rather than focusing on individuals as the unit of analysis for design and evaluation, and there is a need to expand the research beyond user considerations to include societal and political ones.

TL;DR: A simulator is developed that takes DC computational devices as input and provide models that allow designers to analyze and visualize DC thermal conditions, and enables a DC user/administrator to maintain thermal balance in DC by detecting the root cause of hot spots under different workload.

TL;DR: Key considerations in the design of mobile applications are studied from the energy consumption point of view, and strategies for energy saving are studied, preventing energy consumption considerations from affecting other aspects of the application design, such as GUI adaptability and information management.

TL;DR: This work studies the impact of scaling the voltage and frequency of the cache subsystem in a multicore system on establishing the energy-time Pareto frontier and compares the performance of three heuristic-based energy-efficient DVFS algorithms to demonstrate the differences between heuristics performances and the LP-based optimal Pare to frontier.

TL;DR: This study introduces two models that extend the maximum lifetime coverage problem to incorporate the battery recovery effect, and proposes two efficient algorithms that work for both models, adapting greedy and Garg–Konemann-based algorithms for the original MLCP.