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Cloud computing is termed as an on-demand service of computer system resources, especially CPU, memory, and I/O devices without direct access by the user. The resources are available to users through the internet via the pay-as-you-go policy. In the cloud environment, task scheduling and resource provisioning are significant problems in optimizing the system’s performance. Clients increasingly rely on data storage in handling and processing of their data. This process consumes considerable amounts of energy, which hikes the operational costs substantially. In order to discourse these challenges, the paper proposes Energy-Efficient Load Balancing model based on Markov Decision Process (MDP). The EELB model is critical to a well-balanced workload distribution as well as the minimization of power consumption The proposed model leverages MDP to make informed decisions in resource allocation, ensuring optimal power utilization across virtual machines (VMs). The proposed approach is tested and compared with existing load-balancing models using various Quality of Service (QoS) metrics. In addition, an energy-efficient task allocation system is proposed to reasonably balance task scheduling and energy saving. The simulation results show that the proposed solution significantly reduce energy consumption and achieve better performance while satisfying the deadline constraint as compared to current energy-efficient scheduling methods like RC-GA, AMTS, and E-PAGA. Extensive experimentations reveal that the proposed EELB technique enhances availability by 96%, achieves 52% in energy savings, and reduces failure rates significantly compared to existing techniques and enhances throughput. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of quality of service routing optimization within the Internet of Things networks. We particularly focus on the energy-aware and the lightweight aspects. By recognizing the relationship between lightweight and energy-aware routing, we set out to study their combined benefits. This study aims to enhance the Routing Protocol for Low Power and Lossy Networks by integrating energy-awareness and lightweight characteristics based on the Particle Swarm Optimization algorithm. Our approach addresses energy consumption, routing overhead and decision complexity in route establishment. The principal contributions include the introduction of an objective function that considers Expected Life Time, Delay and a new proposed metric Energy Aware-Expected Transmission Count. The improvement of the Long Short Term Memory predicting inertia weight based PSO with Online Gradient Descent that is used to optimize both the parent selection process and Trickle Timer mechanism. The controlled parent switching process to solve unnecessary and frequent changes. Our approach is validated through simulations in Contiki COOJA, with thorough comparisons with some existing protocols based on packet delivery ratio, average energy consumption, convergence time, control overhead, average end-to-end delay and average parent switching as performance metrics. The results reveal that our approach performs better. Depending on the protocol used for comparison, our approach reduced parent switching by 42.59–61.73%, convergence time by 20.31–66.06%, control overhead by 14.4–23.64%, energy consumption by 29.86–49.6%, end-to-end delay by 7.66–40.81% and increased packet delivery ratio by 2–42.92%. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest among researchers in Internet of Things Blockchain (IoTB). A critical aspect of IoTB is its consensus protocol, which faces challenges such as limited bandwidth, energy constraints, and storage space restrictions. To tackle these challenges, Hierarchical IoTB (HIoTB) networks have been proposed. However, determining the fair priority of block transactions and the trustworthiness of nodes remain two major open problems in the consensus protocols. This paper proposes a fair non-collateral consensus protocol named Proof of Action (PoAct), which utilizes Merkle tree-based techniques to address scalability, lightweightness, and energy efficiency in HIoTB. In the HIoTB network, we first determine the trustworthiness of nodes based on their computational resources verified through a Merkle tree proof. Then, relying on the proposed proof, we present a fair solution for selecting transactions to be included in a block, and assign a voting score to trustworthy nodes. We evaluate our proposed PoAct through various scenarios, demonstrating significant enhancements in throughput and execution time compared to puzzle-solving-based consensus protocols. Additionally, we argue that our proposed consensus protocol is scalable and energy-aware, thereby mitigating consensus attacks such as Sybil, Eclipse, and 51%, as well as addressing Fork occurrences issues. [ABSTRACT FROM AUTHOR]

Artificial Intelligence of Things (AIoT) is an emerging field within the Internet of Things (IoT) that caters to the growing demand for intelligent computing and communications. Vehicular fog computing (VFC) with AIoT enhance the processing speed of intelligent services. However, this integration also brings about significant energy wastage and high latency problems due to the large amount of data generated by intelligent tasks. To address these issues, our paper introduces EALSO, a novel approach for energy-aware and latency-sensitive task offloading in VFC. EALSO takes into account the quality of service requirements of tasks by mapping them to specific latency constraints and adjustable task priorities. To optimize the energy consumption of VFC networks while ensuring the quality of task offloading, we propose a two-stage hybrid heuristic energy consumption optimization (HHECO) algorithm. In the first stage, a stable matching algorithm is employed for fog vehicle selection. In the second stage, the heuristic algorithm is guided to reduce interference using decision tree techniques and intelligently allocate transmission power to minimize energy consumption and total network latency. We further validate our approach through real-world scenario simulations, demonstrating its superiority over state-of-the-art methods in terms of both energy consumption and latency. [ABSTRACT FROM AUTHOR]

The increasing number of Internet of Things (IoT) sensors and the need for real-time data processing have led to the emergence of Fog Computing as a promising paradigm to complement cloud services. However, optimizing energy consumption while ensuring reliable data routing remains a critical challenge. This paper proposes an Energy-aware Two-Way Trust Routing (ETWTR) scheme tailored for fog computing environments. This scheme can perform routing to improve data transmission dynamically, taking into account reliability and energy availability. ETWTR can simultaneously consider security, reliability, quality of service, and energy consumption, overcoming the limitations of fog computing. The ETWTR-based trust management system allows both requesters and service providers to check each other's reliability simultaneously to improve security. To address the challenge of energy optimization, a Deep Q-Network (DQN) is employed to predict available energy levels in nodes and optimize their usage efficiently. Our ETWTR scheme has been compared with baseline schemes to confirm its merit with various criteria. The simulation results show that ETWTR has reached higher reliability and trust with less energy consumption. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are used to collect data and detect phenomena in a real-time environment. There is considerable interest in the deployment of WSNs in remote, inaccessible and inhospitable locations; such use of WSNs throws up many challenges. WSNs come with numerous advantages, yet a notable limitation is that the battery life dictates the lifetime of the sensor node. Two critical factors that determine battery lifetime are the frequency of sensor readings and the transmission range of the sensor nodes. Some energy-efficient routing protocols have been proposed and adopted for use to extend the lifetime of sensor nodes. These protocols aim at optimizing the routes in the network. Given that multi-hop routes are energy inefficient, improving the lifetime of WSNs in a multi-hop routing environment will require the use of route optimization techniques. A modified distance-based energy-aware (mDBEA) routing protocol is proposed which is efficient and capable of minimizing the energy consumption of the sensor nodes and hence, maximizing network lifetime. Our approach addresses the problem by calculating the Euclidian distance between successive nodes to determine the shortest distance that minimizes the energy required for transmission. The simulation results indicate that the mDBEA routing protocol reduced the amount of energy consumed in the network by choosing the minimum transmission distance between the source and its neighbour nodes that significantly prolonged the network's lifetime. Our greedy approach yielded about 95% Packet delivery ratio (PDR). Our next-hop and the direct-to-sink algorithms yielded about 82% PDR. [ABSTRACT FROM AUTHOR]

Rapid developments in radio technology and processors have led to the emergence of small sensor nodes that provide communication over Wireless Sensor Networks (WSNs). The crucial issues in these networks are energy consumption management and reliable data exchange. Due to the limited resources of sensor nodes, WSNs become a vulnerable target against many security attacks. Thus, energy-aware trust-based techniques have become a powerful tool for detecting nodes' behavior and providing security solutions in WSN. Clustering-based routings are one of the most effective methods in increasing the WSN performance. In this paper, an Energy-Aware Trust algorithm based on the AODV protocol and Multi-path Routing approach (EATMR) is proposed to improve the security of WSNs. EATMR consists of two main phases: firstly, the nodes are clustered based on the Open-Source Development Model Algorithm (ODMA), and then in the second phase, clustering-based routing is applied. In this paper, the routing process follows the AODV protocol and multi-path routes approach with considering energy-aware trust. Here, the optimal and safe route is determined based on various parameters, namely energy, trust, hop-count, and distance. In this regard, we emphasize the evaluation of node trust using direct trust, indirect trust, and a multi-objective function. The simulation has been performed in MATLAB software in the presence of a Denial of Service (DoS) attack. The simulation results show that EATMR performs better than the state-of-the-art methods in terms of successfully detecting malicious nodes and enhancing network lifetime, energy consumption, and packet delivery ratio. As a conclusion, EATMR shows an average of 4.3 and 6.1% superiority over M-CSO and SQEER in different scenarios, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as the preeminent computing platform for multiple enterprises. All scales of organizations adopt cloud services to leverage cloud technology to drive their businesses ahead. It is prevalent to use the workflow paradigm in modeling a wide variety of problems to compute in distributed environments. Cloud computing is mostly adapting technology to deal with workflow applications, particularly applications with unpredictable workloads. Due to the increased demand for cloud services, excessive power utilization in cloud data centers is a serious issue that needs to be addressed. Scientific workflow applications, in particular, consume high amounts of electrical energy. Many studies have been conducted on the consumption of energy in the cloud environment, and this area of research attracts people from all fields, including both research and business. For this paper, a survey was conducted on existing energy-efficient techniques for scheduling various workflows in a cloud environment. We targeted the methods that minimize energy consumption with assured quality of service constraints. This study on energy-aware and proper workflow scheduling provide extensive knowledge about various energy-aware scheduling paradigms currently going on. The review will help in listing the future directions in this field along with other factors included. [ABSTRACT FROM AUTHOR]

Big data processing, scientific calculations, and multimedia operations are some applications that require very complex time-consuming computations which cannot be performed on personal computers. Utilizing powerful cloud resources is a common method to address this problem. The amount of energy consumption of cloud data centers is an important challenge in these complex calculations, and reducing the energy consumption of cloud data centers is one of the most important goals of the researches in this area. The proposed method of this paper, called multi-agent deep Q-network with coral reefs optimization (MDQ-CR), combines the coral reefs optimization algorithm and multi-agent deep Q-network to reduce the energy consumption of data centers and cloud resources using the dynamic voltage and frequency scaling (DVFS) technique. The MDQ-CR has two main phases. The first phase exploits coral reefs optimization algorithm with a short-term view, and the second phase uses deep Q-network with a long-term view. The Markov game model is used to lead the learning agents to converge to the global optimal solution. Since processors consume the highest amount of energy of cloud compared to the other resources, the proposed method focuses on reducing the processors' energy consumption. Reducing the voltage and frequency of processors, considering expiration times of their tasks, can reduce their energy consumption significantly. The empirical experiments show that the proposed method can save energy about 89% compared to completely randomized methods, and about 20% compared to the two recent methods of the literature. [ABSTRACT FROM AUTHOR]

Energy consumption is explosive increasing with the fast growth of big data applications. High carbon emissions from big data platforms have serious impacts on environment. In this paper, we propose an energy-aware scheduling algorithm for Spark (EASAS) to reduce energy consumption while satisfying the service level agreement (SLA). First, we present a new energy consumption model based on Spark framework. Then a strategy table for the relationship between tasks and executors is designed to record the execution time and energy consumption of tasks. The task scheduling in Spark is conducted and optimized based on the strategy table. The proposed strategy overcomes the defect of the default scheduling strategy FIFO and FAIR which cannot perceive energy consumption with the characteristics of energy consumption perception and dynamic optimization scheduling. Compared against FIFO and FAIR, Our EASAS effectively reduces on average about 25–40% of the total energy consumption of Spark applications under deadline constrains. [ABSTRACT FROM AUTHOR]

The smart home is considered as an essential domain in Internet of Things (IoT) applications, it is an interconnected home where all types of things interact with each other via the Internet. This helps to automate the home by making it smart and interconnected. However, at the same time, it raises a great concern of the privacy and security for the users due to its capability to be controlled remotely. Hence, the rapid technologically growth of IoT raises abundant challenges such as how to provide the home users with safe and secure services keeping privacy in the account and how to manage the smart home successfully under the controlled condition to avoid any further secrecy or theft of personal data. A number of the research papers are available to address these critical issues, researchers presented different approaches to overcome these stated issues. This research review will analyze smart home approaches, challenges and will suggest possible solutions for them and illustrate open issues that still need to be addressed. [ABSTRACT FROM AUTHOR]

In opportunistic networks (Oppnets), messages are transferred from one node to another when the opportunity arrives and until then, these messages are stored in their buffers. Challenges in such scenarios can arise due to multiple reasons such as buffer space, energy limitations, density of nodes and sparse networks. This paper takes into consideration these factors and proposes a novel routing protocol. Supernode routing is proposed for networks in which nodes are organised in clusters, also known as cells, and takes advantage of this property to limit flooding. Special nodes called supernodes are utilised to transmit a message from one cell to another. Nodes within cells forward their messages to the optimal supernodes based on the direction of the destination cell. The messages are propagated by flooding in intermediate cells until the message is passed over to the next cell in the path using supernodes. When the message is transferred to another cell, it is dropped from all nodes in the current cell. In this manner, messages are streamlined to an expected path to an extent, based on the sender and receiver. This is in contrast to random paths of messages found in most other protocols. The proposed model has been simulated in spatially separated scenarios using ONE simulator. It has high delivery probabilities with drastically low overhead ratios in comparison to the other existing routing protocols. [ABSTRACT FROM AUTHOR]

This paper presents a distributed, energy-aware method for the autonomous deployment and maintenance of battery-powered robots within a known or unknown region in 2D space. Our approach does not rely on a global positioning system and therefore allows for applications in GPS-denied environments such as underwater sensing or underground monitoring. After covering a region, our system maintains a formation and uses an arbitrary number of charging stations to prevent robots from fully discharging. Analyzing the topology of the network formed during robot deployment, we generate virtual recharging trees which the robots use to navigate toward a nearby charging station when needed. All robots that leave the formation are replaced by their neighbors, maximizing the effective coverage provided by the system. We demonstrate the capability of our methods using models, a physics-based simulator, and experiments with real robots. [ABSTRACT FROM AUTHOR]

Under the circumstance of big data, traditional storage systems face the big challenge of energy consumption. Switching some storage nodes, which do not experience workloads, to a low-power state is a typical approach to reduce the consumption of energy. This method divides the storage nodes into an active group and a low-power one. That is, the frequently accessed data are stored into the active group which maintains the nodes in an active state to offer service, and the cold data accessed infrequently are stored into the low-power group. The storage nodes in this low-power group are normally called cold nodes, because they can be switched to a low-power state to save energy for a certain amount of time. In cold nodes, one fact, which is often neglected, is that the placement of cold data has a significant impact on the system performance and power consumption. To some extent, switching a storage node from a low-power state to an active state incurs a crucial delay and energy consumption. This paper proposes to aggregate and store the correlated cold data in the same cold node within the low-power group. Now that the correlated data are normally accessed together, our approach can greatly reduce the number of power state transitions and lengthen the idle periods that the cold nodes experience. On the other hand, it can also minimize the performance degradation and power consumption. Experimental results demonstrate that this method effectively reduces the energy consumption while maintaining system performance at an acceptable level in contrast to some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

MapReduce and its open source implementation, Hadoop, have gained widespread adoption for parallel processing of big data jobs. Since the number of such big data jobs is also rapidly rising, reducing their energy consumption is increasingly more important to reduce environmental impact as well as operational costs. Prior work by Mashayekhy et al. (IEEE Trans. Parallel Distributed Syst. 26, 2720-2733, 2016), has tackled the problem of energy-aware scheduling of a single MapReduce job but we provide a far more efficient heuristic in this paper. We first model the problem as an Integer Linear Program to find the optimal solution using ILP solvers. Then we present a task-based greedy scheduling algorithm, TGSAVE, to select a slot for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without significant performance loss while satisfying the service level agreement (SLA). We perform several experiments on a Hadoop cluster to measure characteristics of tasks for nine different applications to evaluate our proposed algorithm. The results show that the total energy consumption of MapReduce jobs obtained by TGSAVE is up to 35% less than that achieved by EMRSA proposed in Mashayekhy et al. (IEEE Trans. Parallel Distributed Syst. 26, 2720-2733, 2016), its closest rival, for same workloads. Besides, TGSAVE is capable of finding a solution in same order of time for up to 74% tighter deadlines than the tightest deadline that EMRSA can find a feasible one. On average, TGSAVE solution is approximately 1.4% far from the optimal solution, and it can meet deadlines as tight as 12%, on average, above the energy-oblivious minimum makespan in the benchmarks we examined. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) can be used for applications which need to be quickly launched and set up. One of the most notable challenges in these networks is reliable and energy-efficient routing protocol. Since mobile nodes in MANET have limited resources, an efficient and reliable multicast routing protocol is needed to reduce the wastage of resources. In this paper, a reliable and energy-aware multicast ad hoc on demand distance vector (REA-MAODV) routing protocol is proposed which is based on MAODV (multicast ad hoc on demand distance vector) protocol. The proposed scheme achieves enhanced performance over ODMRP and finds energy-efficient multicast routes from source node to a group of destination nodes. The proposed REA-MAODV multicast routing protocol not only can update high-consumption and shorter tree branches but also can produce a multicast tree with supporting branches. For improving MAODV protocol, we used three phases: (1) selecting high-energy consumption path, (2) selecting process and adding supporting branches, and (3) maintaining mechanism of multicast tree. The simulation results in NS-2 indicated that REA-MAODV, on average, consumed 45% less energy than MAODV and it has more balance in energy consumption. Moreover, the proposed REA-MAODV protocol was able to enhance packet delivery rate (19%), and it also managed to reduce end-to-end delay in network (21%). [ABSTRACT FROM AUTHOR]

Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical hosts and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new physical machines (PMs). A heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm is proposed, which exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees. [ABSTRACT FROM AUTHOR]

We propose a unifying framework based on configuration linear programs and randomized rounding, for different energy optimization problems in the dynamic speed-scaling setting. We apply our framework to various scheduling and routing problems in heterogeneous computing and networking environments. We first consider the energy minimization problem of scheduling a set of jobs on a set of parallel speed scalable processors in a fully heterogeneous setting. For both the preemptive-nonmigratory and the preemptive-migratory variants, our approach allows us to obtain solutions of almost the same quality as for the homogeneous environment. By exploiting the result for the preemptive-nonmigratory variant, we are able to improve the best known approximation ratio for the single processor non-preemptive problem. Furthermore, we show that our approach allows to obtain a constant-factor approximation algorithm for the power-aware preemptive job shop scheduling problem. Finally, we consider the min-power routing problem where we are given a network modeled by an undirected graph and a set of uniform demands that have to be routed on integral routes from their sources to their destinations so that the energy consumption is minimized. We improve the best known approximation ratio for this problem. [ABSTRACT FROM AUTHOR]

Computer numerical control (CNC) systems are shifting to a direction of open architecture which has better flexibility, adaptability, versatility, and expansibility. Existing CNC systems tend to have a high level energy consumption. This paper introduces a new open CNC system based on the low-power embedded platform, named open and high-performance CNC (OHP-CNC). OHP-CNC is able to achieve high precision, high efficiency, and low power consumption by making use of international standards, open components such as hardware and software, and an energy-aware real-time scheduling algorithm. The proposed algorithm for mixed tasks, including periodic and aperiodic tasks, is divided into two phases. Firstly, the slack time and utilization are calculated on each processor and tasks are assigned to the processor according to the load. Secondly, because there is a trade-off between the energy-saving and the response times of the aperiodic task, the scheduling server is used to schedule aperiodic tasks in order to meet the response time constraints of aperiodic tasks. Meanwhile, periodic tasks recycle the slack time with dynamic voltage scaling technology to achieve low power consumption. Experiment results show that the energy-aware real-time algorithm yields high-performance and effective machining processes. [ABSTRACT FROM AUTHOR]

Wireless Multimedia Sensor Networks (WMSNs) consist of smart sensor nodes that retrieve multimedia data and scalar sensor data. Currently, because of their popularity, WMSNs are used in a variety of applications. Handling and routing large amount of multimedia data in resource constrained network to guarantee Quality of Service (QoS) is a challenge. In WMSNs, communication links are typically error-prone and unreliable. The presence of bad quality links on a path may cause higher packet error rate and results in low throughput. This paper presents Link Quality and Energy-Aware Routing (LQEAR) scheme, which finds energy-aware high-quality links along the path. Next forwarding node is selected based on the link quality, remaining energy and the distance between next forwarding node and the sink. The adaptive cost function can be customized for reliable and high throughput transmission, high network lifetime, and for balanced optimal QoS performance. LQEAR finds multiple node-disjoint paths between source and sink node. Simulation results show that LQEAR selects excellent quality links to offer better reliability, high throughput, and efficiently balance energy consumption of nodes to extend network lifetime. [ABSTRACT FROM AUTHOR]

Efficient utilization of energy is a hot topic of research in the field of wireless sensor networks. Limited battery resource at a sensor node coupled with the hostile multi-path fading propagation environment makes the task of the network to provide reliable data services with an enhanced lifetime, challenging. In order to achieve this goal, a number of efforts have been made by the researchers; one such key strategy is an energy-aware routing embedded with transmission power control mechanism. In this strategy, every sensor node in a network transmits at a lowest possible power level to maintain on one hand reliable wireless links with other neighboring nodes and saves the energy on the other. In this paper, we propose a novel routing technique embedded with transmission power control for wireless sensor networks while considering a realistic radio fading environment. The proposed strategy considers channel fading in the propagation environment and mitigate it through transmission power control mechanism. The main aim of the proposed protocol, APCEER, is to reduce the communication interference among sensor nodes, establish energy-efficient routes from source to sink and thus, to save energy of each and every sensor node in the network. This results in an overall increase of network lifetime, transmission throughput, energy saving and reduce communication interference and collision. Simulation and experimental results show that the proposed scheme outperforms the existing energy-aware routing strategies that are not equipped with a power control mechanism. The proposed protocol thus utilizes in urban applications of wireless sensor networks that need ultra efficient utilization of energy by power-constrained nodes operating in severe fading conditions. [ABSTRACT FROM AUTHOR]

Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users' demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial amounts of energy. This energy consumption increases the operational cost and the CO emissions. The goal of this paper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically assigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy consumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate the VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to finding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based approach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more than 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach. [ABSTRACT FROM AUTHOR]

Many existing studies just considered that nodes in a Mobile Ad Hoc NETwork (MANET) would fully cooperate to forward packets for one another. However, this assumption does not hold in many scenarios. The node selfishness in relay cooperation will certainly influence the overall performance of routing in MANETs. In this work, we investigate the energy-aware routing problem in MANETs with nodes' selfishness. We model the situation of node cooperation in energy-aware routing by using game theory. We consider the competitive and cooperative relationship between the nodes to formulate the game for MANETs. An incentive mechanism is given in order to encourage forwarding cooperation during energy-aware routing. Nodes are encouraged to forward more data packets for others in order to acquire more services from others. We also carry out a simulation for the proposed method and perform detailed analysis in this work. From the results of the simulation, we argue that it's possible to find out a suitable configuration for a MANET to support more efficient energy-aware routing through our method. [ABSTRACT FROM AUTHOR]

As the number of cores increases in current and future chip-multiprocessor (CMP) generations, coherence protocols must rely on novel hardware structures to scale in terms of performance, power, and area. Systems that use directory information for coherence purposes are currently the most scalable alternative. This paper studies the important differences between the directory behavior of private and shared blocks, which claim for a separate management of both types of blocks at the directory. We propose the PS directory, a two-level directory cache that keeps the reduced number of frequently accessed shared entries in a small and fast first-level cache, namely Shared cache, and uses a larger and slower second-level Private cache to track the large amount of private blocks. Entries in the Private cache do not implement the sharer vector, which allows important silicon area savings. Speed and area reasons suggest the use of eDRAM technology, much denser but slower than SRAM technology, for the Private cache, which in turn brings energy savings. Experimental results for a 16-core CMP show that, compared to a conventional directory, the PS directory improves performance by 14 $$\%$$ while reducing silicon area and energy consumption by 34 and 27 $$\%$$ , respectively. Also, compared to the state-of-the-art Multi-Grain Directory, the PS directory apart from increasing performance, it reduces power by 18.7 $$\%$$ , and provides more scalability in terms of area. [ABSTRACT FROM AUTHOR]

Energy management has become a significant concern in data centers for reducing operational costs. Using virtualization allows server consolidation, which increases server utilization and reduces energy consumption by turning off idle servers. This needs to consider the power state change overhead. In this paper, we investigate proactive resource provisioning in short-term planning for performance and energy management. To implement short-term planning based on workload prediction, this requires dealing with high fluctuations that are inaccurately predictable by using single value prediction. Unlike long-term planning, short-term planning can not depend on periodical patterns. Thus, we propose an adaptive range-based prediction algorithm instead of a single value. We implement and extensively evaluate the proposed range-based prediction algorithm with different days of real workload. Then, we exploit the range prediction for implementing proactive provisioning using robust optimization taking into consideration uncertainty of the demand. We formulate proactive VM provisioning as a multiperiod robust optimization problem. To evaluate the proposed approach, we use several experimental setups and different days of real workload. We use two metrics: energy savings and robustness for ranking the efficiency of different scenarios. Our approach mitigates undesirable changes in the power state of servers. This enhances servers' availability for accommodating new VMs, its robustness against uncertainty in workload change, and its reliability against a system failure due to frequent power state changes. [ABSTRACT FROM AUTHOR]

As an important field of emerging technology, wireless sensor networks (WSN) offer many new possibilities for applications such as target tracking and environmental surveillance by allowing the observer to move around freely. However, disseminating sensing data to the mobile observer raises significant design challenges for the routing scheme. In addition, WSN often operate under certain energy constraints, and therefore reducing energy dissipation in order to prolong the lifetime of the WSN is another challenge that must be faced. Most proposed routing protocols focus on achieving effective data dissemination and energy efficiency at the same time as working to satisfy the requirements of the mobile observer. However, almost all of these methods use frequent rerouting as a way of handling the mobility issue. Such rerouting increases both overheads and energy consumption, resulting in a trade-off between the need for rerouting to optimize network operations and that of maximizing network lifetime. This paper presents the Energy-aware Grid-based Routing Scheme (EAGER) for WSN with mobile observers, which is an approach that seeks to save more energy in the context of dynamic topology. In this paper, EAGER is compared to other proposed grid-based schemes by using extensive simulations. These simulations clearly show that EAGER outperforms other grid-based schemes in terms of both energy efficiency and routing performance. [ABSTRACT FROM AUTHOR]

We address a multicriteria non-preemptive energy-aware scheduling problem for computational Grid systems. This work introduces a new formulation of the scheduling problem for multicore heterogeneous computational Grid systems in which the minimization of the energy consumption, along with the makespan metric, is considered. We adopt a two-level model, in which a meta-broker agent (level 1) receives all user tasks and schedules them on the available resources, belonging to different local providers (level 2). The computing capacity and energy consumption of resources are taken from real multi-core processors from the main current vendors. Twenty novel list scheduling methods for the problem are proposed, and a comparative analysis of all of them over a large set of problem instances is presented. Additionally, a scalability study is performed in order to analyze the contribution of the best new bi-objective list scheduling heuristics when the problem dimension grows. We conclude after the experimental analysis that accurate trade-off schedules are computed by using the new proposed methods. [ABSTRACT FROM AUTHOR]

Using the available but limited resources of sensors more efficiently has been the recent interest in designing the routing protocols for Wireless Sensor Network (WSN). The major concern includes energy, storage and computing resources. Accordingly, we propose an adaptive energy-aware Multi-path routing protocol with load balance (AEMRP-LB). It introduces a concept named direction-angle to overcome the deficiency of broadcast and takes into account the tradeoff between the residual energy and hop count to establish multiple node-disjoint paths. The traffic load is balanced over the selected paths by using a weighted traffic scheduling algorithm considering their transmitting capacity, and then AEMRP-LB uses the advantages of Multi-path Source Routing to report the acquired data as saving the computing and storage resources of the sensors. In the scene with multiple Source-Sink pairs, AEMRP-LB can adaptively adjust the available residual energy of shared nodes so as to make reasonable use of them. The simulation results show that AEMRP-LB outperforms the two comparative schemes, and the sensors consume the energy in a more equitable way which ensures a more graceful degradation of service with time. [ABSTRACT FROM AUTHOR]

A distributed mobile DSP system consists of a group of mobile devices with different computing powers. These devices are connected by a wireless network. Parallel processing in the distributed mobile DSP system can provide high computing performance. Due to the fact that most of the mobile devices are battery based, the lifetime of mobile DSP system depends on both the battery behavior and the energy consumption characteristics of tasks. In this paper, we present a systematic system model for task scheduling in mobile DSP system equipped with Dynamic Voltage Scaling (DVS) processors and energy harvesting techniques. We propose the three-phase algorithms to obtain task schedules with shorter total execution time while satisfying the system lifetime constraints. The simulations with randomly generated Directed Acyclic Graphs (DAG) show that our proposed algorithms generate the optimal schedules that can satisfy lifetime constraints. [ABSTRACT FROM AUTHOR]

Most routing protocols for sensor networks try to extend network lifetime by minimizing the energy consumption, but have not taken the network reliability into account. An energy-aware, load-balancing and fault-tolerant routing scheme, termed as ELFR was propsed to adapt to the harsh environment. First a network robustness model was presented. Based on this model, the route discovery phase was designed to make the sensors to construct into a hop-leveled network which is mesh structure. A cross-layer design was adopted to measure the transmission delay so as to detect the failed nodes. The routing scheme works with acknowledge (ACK) feedback mechanism to transfer control messages to avoid producing extra control overhead messages. When nodes fail, the new healthy paths will be selected locally without rerouting. Simulation results show that our scheme is much robust, and it achieves better energy efficiency, load balancing and maintains good end-to-end delay. [ABSTRACT FROM AUTHOR]

The energy-aware MAC protocol is the basic communication protocol for device-to-device communication in sensor-cloud computing and facilitates data sensing, computing, and sharing for applications. Transmitting high-priority data to control centers quickly to manage emergencies is essential for sensor-cloud applications. In this paper, a Dynamic Wait time-based MAC (DWT-MAC) protocol is proposed for data differentiated services of a sensor-cloud system (SCS). The DWT-MAC protocol is novel in that it changes the receiver wait time, which has been fixed in previous MAC protocols, and it reduces the delay in data transmission by dynamically adjusting the wait time. In the DWT-MAC protocol, the wait time changes according to the number of senders, which can ensure that it always approaches the optimal value. A dynamic time adjustment algorithm, which causes the wait time to always trail the optimal value, is proposed to reduce delays. While the DWT-MAC protocol cannot ensure wait times of the optimal value, it can ensure that it quickly comes close to the optimal value, which is suitable for dynamically changing networks. Extensive experiments show that the DWT-MAC protocol reduces the average delay in the transmission of data of the highest priority by 49.3%. [ABSTRACT FROM AUTHOR]

Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture. However, virtual network embedding (VNE) involving the allocation of resources for heterogeneous virtual network requests (VNRs) on the substrate network (SN) is considered as NP-hard problem. VNE process may involve conflicting objectives, including energy saving and VNR acceptance rate as the most critical.In this paper, we propose a virtual network multi-objective embedding algorithm based on Q-learning and curiosity-driven (Q-CD-VNE) for improving the performance of the system by optimizing conflicting objectives, namely energy saving and acceptance rate. The proposed algorithm employs Q-learning and curiosity-driven mechanism by considering other non-deterministic factors to avoid falling into a local optimum. The major contributions of this work involve (1) modeling of the multi-objective deterministic factors as binary (0, 1) integer programming problem, (2) formulating the virtual node mapping problem using the Markov decision process (MDP), (3) solving the VNE problem using Q-learning algorithm, (4) mining non-deterministic factors using curiosity-driven mechanism for avoiding prematurely falling into the Exploration-Exploitation dilemma and local optimal. Experimental results in comparison with representative researches in the field prove that the proposed algorithm can reduce energy consumption, improve the request acceptance rate, and improve the long-term average income. [ABSTRACT FROM AUTHOR]

Load balancing is one of the main challenges in cloud computing, to dynamically distribute the workload across multiple nodes to ensure that no node is either overloaded or underloaded. This paper presents a novel energy-aware load balancing technique that uses an amalgamation of the Artificial Bee Colony and the Firefly algorithms. This technique aspires to balance the load of the cloud infrastructure while trying to maximize the energy efficiency through the efficient usage of the cloud resources. The proposed load balancing technique has been executed in the actual data center of BSNL, Chandigarh. The competence of the proposed technique is exhibited by comparing it with the three standard techniques namely RR, FFD and ACO. The experimentation results show that the proposed algorithm outperformed the existing approach, followed in the data center and the other two approaches. It saved 40.47% of the average energy consumption, which is accomplished by improving CPU utilization level by 49.68%, memory utilization level by 24.41%, reducing VM migrations by 63.10% and saving 53.21% of nodes. The improved results illustrate that the proposed technique effectively balances the load, thereby curtailing the energy consumption and enhancing the performance levels of the cloud data center. [ABSTRACT FROM AUTHOR]