# energy minimization in cloud computing IEEE PAPER 2021

One of the main ways cloud providers can cut down on energy consumption is through saving energy from their data centers. Data centers play a significant role in the reduction of energy consumption as they are also the leading powerhouse for cloud resources

Cloud computing is the well-known technology for scaling of extensive data and complex computation. … The Power Consumption (PC) rate will increase simultaneously, with DC (Data Center)s size expansion or increase in the number of DCs to fulfill the needs of data storage, processing and hosting demands

**Multiagent-based resource allocation for energy minimization in cloud computing systems**

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Cloud computing has emerged as a very flexible service paradigm by allowing users to require virtual machine (VM) resources on-demand and allowing cloud service providers (CSPs) to provide VM resources via a pay-as-you-go model. This paper addresses the CSPs problem of efficiently allocating VM resources to physical machines (PMs) with the aim of minimizing the energy consumption. Traditional energy-aware VM allocations either allocate VMs to PMs in a centralized manner or implement VM migrations for energy

**Multiagent-based resource allocation for energy minimization in cloud computing systems **

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Cloud computing has emerged as a very flexible service paradigm by allowing users to require virtual machine (VM) resources on-demand and allowing cloud service providers (CSPs) to provide VM resources via a pay-as-you-go model. This paper addresses the

** Towards green cloud computing an algorithmic approach for energy minimization in cloud data centers**

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Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data efficient and environment-friendly cloud architecture which is called Green cloud computing 2013; Siddiqui; Wadhwa Verma; John), energy management of

** A game theoretic resource allocation for overall energy minimization in mobile cloud computing system **

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Cloud computing and virtualization techniques provide mobile devices with battery energy saving opportunities by allowing them to offload computation and execute code remotely. When the cloud infrastructure consists of heterogeneous servers, the mapping between

** OKRA: optimal task and resource allocation for energy minimization in mobile edge computing systems **

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bad, then the offloading policy could prioritize local CPU resources for energy saving resource allocation, network stability and latency guarantee to formulate the power minimization problem for It aims to minimize the power consumption of mobile devices subject to constraints

** Energy efficient utilization of resources in cloud computing systems **

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The main objective is to minimize energy consumption while maintaining a specified service quality, eg, time Moreover, they both leverage DVFS technique in their energy minimization core-0705.pdf) 4. Barroso L, Holzle U (2007) The case for energyproportional computing

**Joint energy minimization and resource allocation in C-RAN with mobile cloud **

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0 is a weight to trade off between the energy consumptions in the mobile cloud and the C-RAN, and it can be also explained In this section, we provide the energy minimization prob- lem formulation Our design aims to minimize the energy cost while satisfying the time constraints and is shown to be near-optimal in the sense that it minimizes CPU and clock speed control and network interface selection scheme so as to minimize CPU and ALGORITHM In this section, we formulate an optimization problem con- sidering energy minimization with queue RF components for offloading tasks, is the objective function to be minimized Lu [20] proposed a straightforward offloading decision strategy to minimize the energy SCHEDULING WITH DYNAMIC VOLTAGE AND FREQUENCY SCALING FOR ENERGY MINIMIZATION IN THE

**Energy Minimization for Cloud Services with Stochastic Requests**

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In this paper, we present a novel approach to minimize the energy consumption by An energy minimization (EM) algorithm is proposed to select the most suitable servers to achieve experimental studies show that the EM algorithm saves up to 61.95% energy consumption than

** A taxonomy and survey of energyefficient data centers and cloud computing systems **

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3 A Taxonomy and Survey of EnergyEfficient Data Centers and Cloud Computing Systems of standardized methods and techniques for the reduction of the energy consumption in They include Climate Savers Computing Initiative (CSCI), Green Computing Impact Organization

** A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems **

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The foremost objective of cloud service providers is to have a cost effective and energy efficient solution for allocating virtualized ICT resources to end-users application while meeting the On the other hand, end-users are interested in minimizing the cloud investment while

** Approximation algorithms for energy minimization in cloud service allocation under reliability constraints**

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Our goal in this paper is to find allocations that minimize energy consumption while enforcing RELi, (iii) the overall energy consumption ∑j∈ON Estat(j)+ ej CAPA j is minimized of obtained solutions is reasonably high, especially in the case of energy minimization and even

** Multi-antenna NOMA for computation offloading in multiuser mobile edge computing systems **

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under different setups for binary and partial offloading, respectively, with the objective of minimizing the users For both cases, we aim to minimize the weighted sum- energy at all users while For the partial offloading case, the weighted sum- energy minimization is formulated as

** A parallel bi-objective hybrid metaheuristic for energyaware scheduling for cloud computing systems **

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Most of these works propose algorithms to minimize the makespan Although the minimization of makespan is crucial, tasks of a DAG in our study are not associated The linear programming system mainly deals with energy reduction for a given pre-generated schedule with a

** Energyefficient cloud computing **

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2.4. Power minimization in wireless and wired networks Routing for wireless ad hoc networks with battery-powered nodes [47] have also been considered in the selection paths so as to satisfy QoS constraints and minimize power Page 7. EnergyEfficient Cloud Computing 1051

**Slack allocation algorithm for energy minimization in cluster systems **

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Many studies on cluster and cloud computing use DVFS technique in the context of Service to assign different frequencies to each task to try to minimize energy consumption without Our algorithm for energy minimization is an iterative approach that allocates a certain amount of

** Mobile cloud computing energyaware task offloading (MCC: ETO)**

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scheduling for increasing resources of multiple mobile devices to minimize energy and computational DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems Partial mobile application offload- ing to the cloud for energyefficiency with RRH-BBU association and energy sharing problem towards brown energy usage minimization in green a green energy generation aware RRH-BBU association relationship, intending to minimize the number of we solve the LP in line 17 to obtain the optimal energy sharing for

** Dual-side dynamic controls for cost minimization in mobile cloud computing systems **

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at time slot t. This is a user-dependent factor decided by sensitiveness on price and energy ALGORITHM In this section, we formulate two optimization problems considering the cost minimization with queuing t) + Qi c(t))2 means the CSP and user i try to minimize the processing

** Computation offloading and resource allocation in mixed fog/ cloud computing systems with min-max fairness guarantee**

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Different from , , and , a fairness-aware cost minimization problem is and offloading decision making were jointly optimized in so as to conserve energy while satisfying We propose to minimize the maximum cost among all UEs while meeting the maximum delay

** Multiagent-based resource allocation for energy minimization in cloud computing systems **

free download

Cloud computing has emerged as a very flexible service paradigm by allowing users to require virtual machine (VM) resources on-demand and allowing cloud service providers (CSPs) to provide VM resources via a pay-as-you-go model. This paper addresses the

** Towards green cloud computing an algorithmic approach for energy minimization in cloud data centers**

free download

Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data efficient and environment-friendly cloud architecture which is called Green cloud computing 2013; Siddiqui; Wadhwa Verma; John), energy management of

** A game theoretic resource allocation for overall energy minimization in mobile cloud computing system **

free download

Cloud computing and virtualization techniques provide mobile devices with battery energy saving opportunities by allowing them to offload computation and execute code remotely. When the cloud infrastructure consists of heterogeneous servers, the mapping between

** OKRA: optimal task and resource allocation for energy minimization in mobile edge computing systems **

free download

bad, then the offloading policy could prioritize local CPU resources for energy saving resource allocation, network stability and latency guarantee to formulate the power minimization problem for It aims to minimize the power consumption of mobile devices subject to constraints

** Energy efficient utilization of resources in cloud computing systems **

free download

The main objective is to minimize energy consumption while maintaining a specified service quality, eg, time Moreover, they both leverage DVFS technique in their energy minimization core-0705.pdf) 4. Barroso L, Holzle U (2007) The case for energyproportional computing

** Joint energy minimization and resource allocation in C-RAN with mobile cloud **

free download

0 is a weight to trade off between the energy consumptions in the mobile cloud and the C-RAN, and it can be also explained In this section, we provide the energy minimization prob- lem formulation Our design aims to minimize the energy cost while satisfying the time constraints and is shown to be near-optimal in the sense that it minimizes CPU and clock speed control and network interface selection scheme so as to minimize CPU and ALGORITHM In this section, we formulate an optimization problem con- sidering energy minimization with queue RF components for offloading tasks, is the objective function to be minimized Lu [20] proposed a straightforward offloading decision strategy to minimize the energy SCHEDULING WITH DYNAMIC VOLTAGE AND FREQUENCY SCALING FOR ENERGY MINIMIZATION IN THE

** Energy Minimization for Cloud Services with Stochastic Requests**

free download

In this paper, we present a novel approach to minimize the energy consumption by An energy minimization (EM) algorithm is proposed to select the most suitable servers to achieve experimental studies show that the EM algorithm saves up to 61.95% energy consumption than

** A taxonomy and survey of energyefficient data centers and cloud computing systems **

free download

3 A Taxonomy and Survey of EnergyEfficient Data Centers and Cloud Computing Systems of standardized methods and techniques for the reduction of the energy consumption in They include Climate Savers Computing Initiative (CSCI), Green Computing Impact Organization

** A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems **

free download

The foremost objective of cloud service providers is to have a cost effective and energy efficient solution for allocating virtualized ICT resources to end-users application while meeting the On the other hand, end-users are interested in minimizing the cloud investment while

**Approximation algorithms for energy minimization in cloud service allocation under reliability constraints**

free download

Our goal in this paper is to find allocations that minimize energy consumption while enforcing RELi, (iii) the overall energy consumption ∑j∈ON Estat(j)+ ej CAPA j is minimized of obtained solutions is reasonably high, especially in the case of energy minimization and even

** Multi-antenna NOMA for computation offloading in multiuser mobile edge computing systems **

free download

under different setups for binary and partial offloading, respectively, with the objective of minimizing the users For both cases, we aim to minimize the weighted sum- energy at all users while For the partial offloading case, the weighted sum- energy minimization is formulated as

** A parallel bi-objective hybrid metaheuristic for energyaware scheduling for cloud computing systems **

free download

Most of these works propose algorithms to minimize the makespan Although the minimization of makespan is crucial, tasks of a DAG in our study are not associated The linear programming system mainly deals with energy reduction for a given pre-generated schedule with a

** Energyefficient cloud computing **

free download

2.4. Power minimization in wireless and wired networks Routing for wireless ad hoc networks with battery-powered nodes [47] have also been considered in the selection paths so as to satisfy QoS constraints and minimize power Page 7. EnergyEfficient Cloud Computing 1051

** Slack allocation algorithm for energy minimization in cluster systems **

free download

Many studies on cluster and cloud computing use DVFS technique in the context of Service to assign different frequencies to each task to try to minimize energy consumption without Our algorithm for energy minimization is an iterative approach that allocates a certain amount of

** Mobile cloud computing energyaware task offloading (MCC: ETO)**

free download

scheduling for increasing resources of multiple mobile devices to minimize energy and computational DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems Partial mobile application offload- ing to the cloud for energyefficiency with RRH-BBU association and energy sharing problem towards brown energy usage minimization in green a green energy generation aware RRH-BBU association relationship, intending to minimize the number of we solve the LP in line 17 to obtain the optimal energy sharing for

**Dual-side dynamic controls for cost minimization in mobile cloud computing systems **

free download

at time slot t. This is a user-dependent factor decided by sensitiveness on price and energy ALGORITHM In this section, we formulate two optimization problems considering the cost minimization with queuing t) + Qi c(t))2 means the CSP and user i try to minimize the processing

** Computation offloading and resource allocation in mixed fog/ cloud computing systems with min-max fairness guarantee**

free download

Different from , , and , a fairness-aware cost minimization problem is and offloading decision making were jointly optimized in so as to conserve energy while satisfying We propose to minimize the maximum cost among all UEs while meeting the maximum delay

** Efficient resource allocation for mobile-edge computing networks with NOMA: Completion time and energy minimization **

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To minimize the total energy of all users, joint time allocation and power control was optimized for a For the special case with infinite edge cloud capacity, the orig- inal minimization problem is shown to It is also proved that transmitting with maximal time is always energy efficient

** Reliability and energy efficiency in cloud computing systems : Survey and taxonomy**

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systems have been discussed in 4 Reliable cloud computing services, 5 Energy management in To make cloud services reliable, both service providers and service users have their own Good design following the given principles will minimize the effect of failures and enhance of the three types of control knobs in the server farm that minimizes power consumption chose it because the MIMO controller they used is directly applicable to our energy minimization application 2.3.1 Controller Design Since the goal of the controller is to minimize the control

**Joint optimization of computing and cooling energy : Analytic model and a machine room case study**

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form optimal solution for load distribution across machines (in the sense of total energy minimization ) We present a solution that minimizes energy consumption in one computer room by holistically The goal of the optimization is to minimize the sum of computing and cooling

** Energy aware scheduling using genetic algorithm in cloud data centers**

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to be assigned to the virtual machines in the host of cloud data centres with reference to minimization of the EXPERIMENTAL ANALYSIS The aim of the proposed model is to find a solution to minimize the energy consumption while assigning Optimizing Energy Consumption in

**Downlink and uplink energy minimization through user association and beamforming in C-RAN**

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vectors and the active set of APs are jointly optimized to minimize the power of this paper are summarized as follows: First, to optimize the energy consumption tradeoffs and UL MU-AP association and beamforming design by solving a weighted sum-power minimization problem Three-phase time-aware energy minimization with DVFS and unrolling for Chip Multiprocessors The authors of used nonlinear programming and mixed integer linear programming to minimize energy consumption of N i k and V k such that the total energy dissipation, ( ∑ i

** Computation offloading strategies based on energy minimization under computational rate constraints**

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STRATEGY IN THE MULTIUSER CASE In the multiuser case, the optimization can be cast as the minimization of the sum of the energies spent by the In particular note that user 5 entails the maximum energy consumption since it must transmit a large program state, with aformulated a power-constrained delay minimization problem for mobile-edge computing systems with Markov decision the users utility obtained by task execution while minimizing the energy consumption, task designed a selective offloading scheme to minimize the energy con

**Energy minimization for D2D-assisted mobile edge computing networks**

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Edge Cloud Access Point Fig A supplementary algorithm is then proposed in the second stage to minimize the total energy consumption while maintaining the maximum The energy minimization problem is then studied with a supplementary algorithm de- veloped in section IV [HTML]

** Dynamic thermal and IT resource management strategies for data center energy minimization **

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L Fu, J Wan, J Yang, D Cao Computing journalofcloudcomputing To address this issue, joint optimization techniques [10 12], which aim to minimize the overall we elaborate state-of-the-art IT and thermal management techniques for data center energy minimization To meet the Quality of Service (QoS) required by cloud users in the context ofof this module is essential to take the most appropriate energy minimization decisions, it into our new models in differing environmental impact and to minimize the total A. Kayssi, CloudESE: Energy efficiency model for cloud computing environments, Energy Aware Computing

** Delay-optimal computation task scheduling for mobile-edge computing systems **

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To handle this issue, an initial investigation for two- timescale computation offloading policy design was conducted in , which, however, only considered to minimize the energy consumption of We then formulate the power- constrained delay minimization problem a dynamic MCC system and proposed an offloading mechanism for cost minimization for both of delay-tolerant applications during poor network connectivity in order to minimize the energy the task offloading decisions, even if offloading is beneficial in terms of energy efficiency [HTML]

** Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment**

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Minimization Approach with EPO Algorithm for Cloud Computing Environment Ruan et al. [28] have presented a PPRGear strategy for VM placement and migration with energy efficiency in the Feng et al. [29] have developed a two-step SAG algo- rithm to minimize the energy

**Enabling low-latency applications in LTE-A based mixed fog/ cloud computing systems **

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In [18], in order to minimize system energy consumption, the authors performed a joint optimization of computation offloading, sub- carrier assignment, and computation The authors in [19] formulated a system energy consumption minimization problem with the

** Migrate or not Exploiting dynamic task migration in mobile cloud computing systems **

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of experience (QoE), which for a particular task translates into minimization of execution source (eg wind turbine or photovoltaic) with the objective to minimize dependence on the migration of tasks among different datacenters modifies the instantaneous energy consumption as

**A robust optimization technique for energy cost minimization of cloud data centers**

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The authors formulate a stochastic problem to minimize the long-term operational cost of the data center by consider- ing the data center workload, the techniques proposed in and [15] are combined and extended to develop an energy cost minimization algorithm for

** Energy consumption in cloud computing data centers**

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other hand, several research effort has been also been made to minimize energy consumption in consumption The CRAH unit energy consumption dominates the total cooling system energy consumption, it CFD) to model the complex flow and CRAHs to minimized recirculation

** Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems **

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the scheduling algorithms of grid computing was presented toward performance and makespan minimization It minimized the cost of deadline constrained applications without considering the communication proposed a critical-greedy (CG) algorithm to minimize the end-to-end

** A study of efficient energy management techniques for cloud computing environment**

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Minimize energy consumption and task execution time Implement a Scheduler using COMPS programming model Server workload analysis for power minimization using consolidation Energy efficient scheduling and management for large-scale services computing systems In this work, we consider a cross-layer approach to minimize the holistic energy B. Energy Minimization Techniques Energy minimization techniques for data centers can be gen- erally The first category directly minimizes energy consumption by setting it as the control ob

**Towards energyaware resource scheduling to maximize reliability in cloud computing systems **

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As minimizing power consumption leads to a minimization of energy we concentrate Optimization According to the stated constraints and the reliability model, we can define our problem as an Integer Linear Programming (ILP) problem as follows: Maximize and Minimize (26) However, system reliability and energy minimization are correlated when DVFS is used to address the problem about how to schedule mixed-criticality task sets to minimize systems energy consumption while when the system is in the LO-mode so that the energy consumption is

** An efficient energy saving task consolidation algorithm for cloud computing systems **

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2) The algorithm also minimizes the energy consumption in contrast to by choosing the best The problem is to map the tasks and the VMs such that the energy consumption is minimized In the first step, it aims to minimize energy by properly utilizing the idle period of the

**Exploiting VM migration for the automated power and performance management of green cloud computing systems **

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The purpose of the Migration Manager is to find the placement of the currently running VMs on the PMs that results in the minimization of both SLO violations minimize J(k Beloglazov, A., et al.: A taxonomy and survey of energyefficient data centers and cloud computing systemsthere are some tasks that perform better in Sparc processors, and uses them to minimize energy The objective will be to develop an integrated energy minimization solution that takes into RTSS.2007.29 Q. Tang, SKS Gupta, and G. Varsamopoulos, Energyefficient thermal

**Joint offloading and computing optimization in wireless powered mobile-edge computing systems **

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In this case, we develop an optimal resource allocation scheme that minimizes the APs total energyefficient MEC-WPT design by considering a computation latency con- strained energy minimization problem to Ri, ie, we have qi = Ri − li, ∀i ∈ K . We aim to minimize the energy

**Maximizing profit in cloud computing system via resource allocation**

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An economic approach to manage shared resources and minimize the energy consumption in hosting centers By changing the problem to minimization form, the hessian matrix of the objective function is 3] LA Barroso and U. H lzle, The Case for EnergyProportional Computing

** Energy minimization in multi-task software-defined sensor networks**

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ZENG ET AL.: ENERGY MINIMIZATION IN MULTI-TASK SOFTWARE-DEFINED SENSOR NETWORKS can see that as 1 if 9t 2 T;ast ¼ 1 and as 0 only if 8t 2 T; ast ¼ 0. Our objective to minimize the total sensing power con- sumption is equivalent to minimizing the number We formulate two optimization problems to minimize energy consumption and completion time of the UAV UAV energy minimization solution is different from the completion time minimization solution in 1) We study the completion time and energy optimization issues in the UAV First, Optimizing Data Placement of Loops for Energy Minimization with Multiple Types of Memories , by Zhuge et al place array data in loops to multiple types of memory units such that the energy and time costs of memory accesses can be minimized and proposes In this background, we conceive a PLS-aided and energy minimization computational offloading scheme for the UAV- MEC system with an To achieve an energyefficient UAV-MEC system we minimize the total energyconsumption of UAV for The total energyconsumption of

** Energyefficient distributed computing systems **

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Chapter 17: Multiple Frequency Selection in DVFS- Enabled Processors to Minimize Energy Consumption 17.1 Rodero, Ivan, NSF Cloud and Autonomic Computing Center and Rutgers Discovery Computer systems consume tremendous amount of energy and natural resources

** Joint user selection and energy minimization for ultra-dense multi-channel C-RAN with incomplete CSI**

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the joint optimization of RRH selection, RRH-UE associations and transmit beamforming to minimize the NPC PAN et al.: JOINT USER SELECTION AND ENERGY MINIMIZATION technique in [27] that aimed at rate maximization problem to the NPC minimization problem with [HTML]

** Multi-objective approach for energyaware workflow scheduling in cloud computing environments**

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Energy : Minimize 𝐸total (𝑀) As given in (15), there are three conflicting objectives: minimizing execution time, minimizing execution cost and minimizing energy consumption used in MOP as follows: (without loss of generality we suppose that the objectives are to be minimized )

** Power-delay tradeoff in multi-user mobile-edge computing systems **

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A power consumption minimization problem with task buffer stability constraints is formulated offloading policy was developed for MEC systems with energy harvesting devices Besides, a decentralized computation offloading algorithm was proposed to minimize the computation process of allocating a setN ofntasks to a setP ofpprocessors without violating precedence constraints aiming to minimize ma- kespan Since the two objectives ( minimization of execution can be at most partitioned into a series of on-chip and off-chip tasks, energy savings can

** Energyefficient virtual machine replication and placement in a cloud computing system **

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In this paper, a VM placement problem is considered with the objective of minimizing the total The objective in this problem is to minimize the number (or a weighted summation) of the This means we consider energy minimization with a fixed set of VMs instead of maximizing the

** Energy minimization of mobile edge computing networks with finite retransmissions in the finite blocklength regime**

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Energy Minimization of Mobile Edge Computing retransmissions and the blocklength for a single transmission/retransmission, where the objective is to minimize the expected energy cost in the task offloading is investigated in , following which the total energy consumption is