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A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
TLDR
This study discusses causes and problems of high power/energy consumption, and presents a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization, and data center levels. Expand
A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis
TLDR
Concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as a comparison, both from a theoretical and an empirical perspective are introduced. Expand
Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions
TLDR
This work addresses the problem of scheduling precedence-constrained parallel applications on multiprocessor computer systems and presents two energy-conscious scheduling algorithms using dynamic voltage scaling (DVS) and a novel objective function and a variant from that. Expand
Observations on Using Genetic Algorithms for Dynamic Load-Balancing
TLDR
This work investigates how a genetic algorithm can be employed to solve the dynamic load-balancing problem whereby optimal or near-optimal task allocations can "evolve" during the operation of the parallel computing system. Expand
Federated Learning over Wireless Networks: Optimization Model Design and Analysis
TLDR
This work formulates a Federated Learning over wireless network as an optimization problem FEDL that captures both trade-offs and obtains the globally optimal solution by charactering the closed-form solutions to all sub-problems, which give qualitative insights to problem design via the obtained optimal FEDl learning time, accuracy level, and UE energy cost. Expand
Energy efficient utilization of resources in cloud computing systems
TLDR
Two energy-conscious task consolidation heuristics are presented, which aim to maximize resource utilization and explicitly take into account both active and idle energy consumption and demonstrate their promising energy-saving capability. Expand
Genetic Scheduling for Parallel Processor Systems: Comparative Studies and Performance Issues
TLDR
This paper investigates an alternative paradigm, based on genetic algorithms, to efficiently solve the scheduling problem without the need to apply any restricted assumptions that are problem-specific, such is the case when using heuristics. Expand
An overview of Channel Assignment methods for multi-radio multi-channel wireless mesh networks
TLDR
An in-depth survey of some of the CA approaches in the literature, with a classification that captures their essentials proposed and the future research directions for CA discussed at length. Expand
Tradeoffs Between Profit and Customer Satisfaction for Service Provisioning in the Cloud
TLDR
This paper uses utility theory leveraged from economics and develops a new utility model for measuring customer satisfaction in the cloud based on the utility model, and designs a mechanism to support utility-based SLAs in order to balance the performance of applications and the cost of running them. Expand
Minimizing Energy Consumption for Precedence-Constrained Applications Using Dynamic Voltage Scaling
TLDR
This paper addresses the problem of scheduling precedence-constrained parallel applications on high-performance computing systems—specifically with heterogeneous resources—accounting for both application completion time and energy consumption by adopting dynamic voltage scaling (DVS) to minimize energy consumption. Expand
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