Aware Resource Management Research Articles

Self‐attention convolutional neural network optimized with Remora Optimization Algorithm for energy aware resource management in Non‐orthogonal Multiple Access networks

SummaryNon‐orthogonal multiple access (NOMA) is an efficient technology for future wireless communication systems. The resource management on NOMA networks is flattering for improving energy efficiency (EE) of systems. Therefore, self‐attention convolutional neural network (SACNN) optimized with Remora Optimization Algorithm (ROA) for energy aware resource management in NOMA network (SACNN‐ROA‐EE) is proposed in this paper for solving user allocation, channel allocation, and power allocation problems. The Matching‐Coalition approach is considered to face the user allocation issue, and the SACNN is considered to face the channel allocation and power allocation problems. SACNN does not adopt any optimization methods to determine the optimal parameters in channel allocation and power allocation. Therefore, the ROA is applied to optimize the SACNN weight parameters. The proposed technique is activated in MATLAB, and its efficacy is analyzed with under performances metrics, such as minimum user rate, sum rate, energy, and spectral performance. The proposed SACNN‐ROA‐EE attains 24.35%, 27.84%, and 38.58% better sum rate and 27.45%, 43.28%, 30.56% better EE compared with existing methods, such as EE utilizing communication deep neural network optimized with power allocation optimization (PAO‐CDNN‐EE), EE under deep transfer deterministic policy gradient along deep deterministic policy gradient approach (DPPGA‐DTDPPG‐EE), and EE utilizing deep reinforcement learning with baseline algorithms (BA‐DRL‐EE), respectively.

Multi-resource predictive workload consolidation approach in virtualized environments

The revolution of virtualization technologies and Cloud computing solutions has emphasized the need for energy-efficient and Service level agreement (SLA)-aware resource management techniques in cloud data centers. Workload consolidation in Infrastructure-as-a-Service (IaaS) providers allows for efficient utilization of hardware resources and reduced energy consumption by consolidating workloads onto fewer physical servers. To ensure successful workload consolidation, it is crucial for IaaS providers to carefully estimate the host state and identify overloaded and underloaded hosts, thereby avoiding overly aggressive consolidation. Existing proposals determine the host state depending on its current resource utilization or a single anticipated resource utilization value, and often consider only a single resource type of the host, such as CPU. These limitations may lead to unreliable host state estimations, resulting in excessive and needless service migrations between physical machines (PMs). This, in turn, can lead to extra delays in service execution, degraded performance, increased power consumption, and SLA violations. To address these challenges, we propose a workload consolidation approach that leverages a multi-resource and multi-step resource utilization prediction model. Based on this model, our overload and underload decision-making algorithms consider the forecasted future trend (sequence of future value) of each host resource's utilization, including CPU, memory, and bandwidth. Through extensive experimentations conducted with two real-world datasets, we demonstrate that our approach can significantly reduce power consumption, SLA violation rate, and the number of migrations compared to existing benchmarks.

Bankruptcy approach to integrity aware resource management in a cloud federation

Bankruptcy approach to integrity aware resource management in a cloud federation

FollowMe@LS: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters

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.

Energy and Latency Aware Resource Management for Solar Powered Cellular Networks

There is an increasing need to power cellular base stations (BSs) using solar energy in many parts of the globe. This is primarily because of the high cost of running these base stations on traditional power sources such as diesel due to a lack of reliable grid availability in those areas. Apart from the high cost, increasing diesel consumption also causes harm to the environment due to its increasing global carbon footprint. Using solar energy powered base stations is a highly promising solution to address these issues. One of the main areas of concern for solar powered cellular networks is to precisely manage the resources, namely, the available spectrum and energy so as to avoid power outages and to maintain an acceptable QoS for the end users. This article gives an overview of the challenges faced in resource management for solar powered base stations and presents state-of-the-art resource management strategies for both grid-connected and off-grid solar powered base stations.

Service aware resource management into cloudlets for data offloading towards IoT

Task offloading in edge cloud and self-organized distributed cloudlet design are two significant research areas to explore internet of things (IoT) setup in mobile cloud computing diligence through network function virtualization. The cloud service providers already set up metropolitan level cloudlet connectivity. In proposed senario by applying a virtual network function to execute various user demands with different IoT concerned service-related difficulties have presented. The objective is to exploit the number of self-proclaimed requests via load harmonizing to reduce the cohesive cost of entries within a period of time. A problem function is designed to offload a set of tasks in a metropolitan area network, where a particular network related function with a maximum bearable delay is requested by each sensor data computation through different network services according to different offloading requests. An algorithm is designed to make a reduced bipartite graph with maximum matching and minimum weight. To save cost, an effective prediction offloading algorithm has been developed where instances of network functions get created and/or released in different cloudlets considering that the task offloading request patterns dynamically changes. Later an analysis concludes that the possible outcome of the proposed algorithm is efficient in term of delay reduced while distributing the load among the cloudlets into the edge-cloud. The effective cost has been reduced up to approx. one-third and execution time has reduced up to one-tenth with the help of proposed queue management system design which helps to utilize the cloudlet instances ineffective way.

Content and buffer status aware packet scheduling and resource management framework for video streaming over LTE system

With the development of the video encoding and wireless communication technologies, DASH (Dynamic Adaptive Streaming over HTTP) services have an increasing and great share of all the mobile services. However, we find some problems which still need to be addressed for DASH service optimization: (1) the limitation of the video segment representations cannot keep pace with the change of wireless channel states; (2) the characteristics of the video transmission have been not fully utilized in current DASH services; and (3) long interruption time will yield poor QoE (Quality of Experience). To solve these problems, we build a new transmission architecture by modifying the traditional TCP transmission flow. We first add a TCP (Transmission Control Protocol) proxy responsible for discarding the expiry packets automatically before putting them into the TCP sending window and then build a “ACK (acknowledgement) reconstruction module” to reconstruct the received ACK for concealing the packets discarded by the packet scheduler. Based on the new framework, we explore the interdependence among all the packets from the encoder to indicate the importance of each packet and update the interdependence relationship of every scheduling period based on the feedback ACK information. At MAC (Media Access Control) layer, a buffer status estimation module is employed to estimate the client buffer and playback information, which can be used to calculate the packet urgency. Then, a cross-layer design, which consists of an application layer of media server and client, TCP layer, MAC layer, and physical layer, is formulated and the packet scheduling and resource allocation can be jointly optimized. At client, through the analysis of the status of client buffer and MAC queue, an adaptive segment request scheme is developed to determine how and when to send the segment request. Simulation results show that the proposed algorithms can efficiently improve the received video quality as well as the playback continuity compared with other existing algorithms.

Energy Aware Resource Management of Cloud Data Centers

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Virtualization technology forms a key concept for new cloud computing architectures. The data centers are used to provide cloud services burdening a significant cost due to high energy consumption. Data centers are provisioned to accommodate peak demand rather than average demand and cloud applications consume much more electrical energy than they need. Thus, it necessitates that cloud computing solutions not only minimize operational costs, but also reduce the power consumption. In this paper, we investigate load balancing and power saving methods in virtualized cloud infrastructures. Imbalanced distribution of workloads across resources can lead to performance degradation and much electrical power consumption in such data centers. We present an architectural framework and principles for energy-efficient cloud computing environments. Resource provisioning and allocation algorithms, named Load-Power-aware, are proposed in this architecture. The algorithm employs a heuristic to dynamically improve the energy efficiency in data center, while guarantees the Quality of Service (QoS). The efficiency of the proposed approach is evaluated by using the most common cloud computing simulation toolkit, CloudSim. The performance modeling and simulation results are depicted the proposed approach significantly improves the energy efficiency in a given dynamic scenario, while a small amount of service level agreements (SLA) is missed.

Cost and fault-tolerant aware resource management for scientific workflows using hybrid instances on clouds

Cloud service providers are offering computing resources at a reasonable price as a pay-per-use model. Further, cloud service providers have also introduced different pricing models like spot, blockspot and spotfleet instances that are cost effective and user’s have to go through the bidding to balance the reliability and monetary costs. Henceforth, Scientific Workflows (SWf) that are used to model applications of high throughput, computation and complex large-scale data analysis are significantly adopting these computing resources. Nevertheless, spot instances are terminated when the market spot price exceeds the users bid price. Moreover, failures are inevitable in such a large distributed systems and often pose a challenge to design a fault-tolerant scheduling algorithm for SWf. This paper presents an efficient, low-cost and fault-tolerant scheduling algorithm and a bidding strategy to minimize the volatility and cost of resource provisioning for SWf. The proposed algorithm uses spot and blockspot instances as hybrid instances in comparison with on-demand instance to reduce the execution cost and fault-tolerant while meeting the SWf deadline. The results obtained reveal the promising potential of the proposed scheduling algorithm and are demonstrated through empirical simulation study that is robust under short deadlines with minimal makespan and cost.

Energy Aware Resource Management and Job Scheduling in Cloud Datacenter

Energy Aware Resource Management and Job Scheduling in Cloud Datacenter

Rate-based thermal, power, and co-location aware resource management for heterogeneous data centers

Today’s data centers contain large numbers of compute nodes that require substantial power, and therefore require a large amount of cooling resources to operate at a reliable temperature. The high power consumption of the computing and cooling systems produces extraordinary electricity costs, requiring some data center operators to be constrained by a specified electricity budget. In addition, the processors within these systems contain a large number of cores with shared resources (e.g., last-level cache), heavily affecting the performance of tasks that are co-located on cores and contend for these resources. This problem is only exacerbated as processors move to the many-core realm. These issues lead to interesting performance-power tradeoffs; by considering resource management in a holistic fashion, the performance of the computing system can be maximized while satisfying power and temperature constraints. In this work, the performance of the system is quantified as the total reward earned from completing tasks by their individual deadlines. By designing three resource allocation techniques, we perform a rigorous analysis on thermal, power, and co-location aware resource management using two different facility configurations, three different workload environments, and a sensitivity analysis of the power and thermal constraints.

Delay Aware Resource Management for Grid Energy Savings in Green Cellular Base Stations With Hybrid Power Supplies

Base stations equipped with resources to harvest renewable energy are not only environment-friendly but can also reduce the grid energy consumed, thus bringing cost savings for the cellular network operators. Intelligent management of the harvested energy can further increase the cost savings. Such management of energy savings has to be carefully coupled with managing the quality of service so as to ensure customer satisfaction. In such a process, there is a trade-off between the energy drawn from grid and the quality of service. Unlike prior studies which mainly focus on network energy minimization, this paper proposes a framework for jointly managing the grid energy savings and the quality of service (in terms of the network latency), which is achieved by downlink power control and user association reconfiguration. We use a real BS deployment scenario from London, U.K., to show the performance of our proposed framework and compare it against existing benchmarks. We show that the proposed framework can lead to around 60% grid energy savings as well as better network latency performance than the traditionally used scheme.

Energy and Traffic Aware Full-Duplex Communications for 5G Systems

In this paper, we consider the problem of resource allocation in a dense small-cell network. Each small-cell base station is powered by a renewable energy source and operates in the full-duplex mode. We account for the rate-dependent energy term for data decoding into the total energy consumption at the small-cell base station. Owing to this new energy term, the transmitter and receiver operations now draw the energy from a common source. For a new energy consumption model and high interference scenario, which arises due to full-duplex communications, we formulate an energy and load aware resource management optimization problem under the energy causality and total transmit power constraints of the small-cell base station and uplink user equipments. In particular, the problem minimizes the data queue length of each network user equipment by jointly designing the beamformers, power, and sub-carrier allocation and their scheduling. Owing to the non-convexity of the problem, a global solution is inefficient; thus, we opt for the successive parametric convex approximation method to obtain a sub-optimal solution. This method solves for the convex approximate of the non-convex problem in each iteration and leads to faster convergence. For practical implementation, we further develop a distributed algorithm by using the dual decomposition framework, which relies on limited exchange of information between the involved base stations. Numerical simulations compare the network scenario which accounts for uplink channel rate-dependent energy consumption with that which ignores it. Results advocate the need for redesigning of the resource allocation scheme. In addition, numerical simulations also validate the usefulness of full-duplex communications over the half-duplex communications in terms of minimizing the sum data queue length of the users.

Leakage aware resource management approach with machine learning optimization framework for partially reconfigurable architectures

Shrinking size of transistors has enabled us to integrate more and more logic elements into FPGA chips leading to higher computing power. However, it also brings a serious concern to the leakage power dissipation of the FPGA devices. One of the major reasons for leakage power dissipation in FPGA is the utilization of prefetching technique to minimize the reconfiguration overhead (delay) in Partially Reconfigurable (PR) FPGAs. This technique creates delays between the reconfiguration and execution parts of a task, which may lead up to 38% leakage power of FPGA since the SRAM-cells containing reconfiguration information cannot be powered down. In this work, a resource management approach (RMA) containing scheduling, placement and post-placement stages has been proposed to address the aforementioned issue. In scheduling stage, a leakage-aware priority function is derived to cope with the leakage power. The placement stage uses a cost function that allows designers to determine the desired trade-off between performance and leakage-saving. The post-placement stage employs a heuristic approach to close the gaps between reconfiguration and execution of tasks, hence further reduce leakage waste. To further examine the trade-off between performance (schedule length) and leakage waste, we propose a framework to utilize the Genetic Algorithm (GA) for exploring the design space and obtaining Pareto optimal design points. Addressing the time-consuming limitation of GA, we apply Regression technique and Clustering algorithm to build predictive models for the Pareto fronts using a training task graph dataset. Experiments show that our approach can achieve large leakage savings for both synthetic and real-life applications with acceptable extended deadline. Furthermore, different variants of the proposed approach can reduce leakage power by 40–65% when compared to a performance-driven approach and by 15–43% when compared to state-of-the-art works. It’s also proven that our Machine Learning Optimization framework can estimate the Pareto front for new coming task graphs 10x faster than well-established GA approach with only 10% degradation in quality.

Energy Aware Resource Management in Distributed Cloud Computing with Overload Avoidance

Energy Aware Resource Management in Distributed Cloud Computing with Overload Avoidance

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

IARJSET - Science, Engineering and Technology

Cloud computing is the current technology used for sharing and accessing resources using web services or internet. It provides a scalable and cost effective environment. Large number of servers in the datacentres leads to huge consumption of power in the cloud computing scenario. Energy-related costs have become one of the major economic factors in IT datacentres, and companies and the research community are currently working on new efficient power aware resource management strategies. Optimization of power consumption is a key challenge for effectively operating a datacentre.

Load aware resource management in mobile agent

In this paper, a fuzzy-based Resource Management for Load Balancing (fuzzy-RMLB) in mobile agents is presented for growth of service types and user numbers in mobile networks to attain the higher performance required in service provision and throughput. The estimation of workload is difficult and time consuming, and the result causes an unstable system and unnecessary message passing overhead. The structure of the load balancing system is composed of three design agents: the Load Index Agent (LIA), the Resource Management Agent (RMA) and the Load Transfer Agent (LTA). It is observed that the fuzzy-RMLB exhibits better adaptability, robustness, and fault-tolerant capability, thus yielding better performance compared to other existing algorithms.

CHERUB: power consumption aware cluster resource management

CHERUB: power consumption aware cluster resource management

Enhancing virtual environments with QoS aware resource management

Nowadays, the consolidation of application servers is the most common use for current virtualization solutions. Each application server takes the form of a virtual machine (VM) that can be hosted into one physical machine. In a default Xen implementation, the scheduler is configured to handle equally all of the VMs that run on a single machine. As a consequence, the scheduler shares equally all of the available physical CPU resources among the running VMs. However, when the applications that run in the VM dynamically change their resource requirements, a different solution is needed. Furthermore, if the resource usage is associated with service-level agreements, a predefined equal share of the processor power is insufficient for the VMs. Within the Xen’s primitives, even though it is possible to tune the scheduler parameters, there is no tool to achieve the dynamic change of the share of the processor power assigned to each VM. A combination of a number of primitives, however, appears to be suited as a base for achieving this. In this paper, we present an approach to efficiently manage the quality of service (QoS) of virtualized resources in multicore machines. We evaluate different alternatives within Xen for building an enhanced management of virtual CPU resources. We compare these alternatives in terms of performance, flexibility, and ease of use. We devise an architecture to build a high-level service that combines interdomain communication mechanisms with monitoring and control primitives for local resource management. We achieve this by our solution, a local resource manager (LRM), which adjusts the resources needed by each VM according to an agreed QoS. The LRM has been implemented as a prototype and deployed on Xen-virtualized machines. By means of experiments, we show that the implemented management component can meet the service-level objectives even under dynamic conditions by adapting the resources assigned to the virtualized machines according to demand. With the LRM, we therefore achieve both fine-grain resource allocation and efficient assignment.