A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users’ requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This article provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions

Efficient Energy Management in Cloud Data center using VM Consolidation

Cloud computing is a model which can fast provisioned and released the computing resources by using minimum number of management effort. This can be done by the user without doing any communication with the cloud service providers. Cloud provide the computing resources, on-demand network access which is pooled together and it can be provisioned dynamically according to the user needs. Due to the large application, more number of computing nodes are required. A large amount of electrical energy is consumed due to the establishment of the data center. There is a problem of carbon dioxide emissions and increasing cost of operation due to the formation of large data center. A consolidation of virtual machines technique is proposed in our thesis to reduce the energy consumption and to maximize the utilization of the computing resources in the data center. Several virtual machines are taken together into a single physical machine in the consolidation technique and it helps to decrease the consumption of energy by putting idle server into inactive mode. A number of active hosts is minimized by continuously reallocating VMs using live migration. In each migration, Service Level Agreement(SLA) violations may occur, hence it is required to reduce the number of migrations.In order to satisfy quality of services in cloud computing environment, our proposed techniques mainly performs the following functions:(i)reducing the consumption of energy, (ii) minimize the number of migrations and (iii) minimize the percentage of SLA violations. Initially we detect whether any host is overloaded or not. The Overloaded host is detected by considering CPU utilization as a threshold Value. If an overloaded host is detected then some virtual machines are migrated from it by using VM selection policy. After selection of the VMs, the next step is to place the new VMs. For VM placement, the greedy algorithms such as Best Fit Decreasing(BFD) and Modified First Fit Decreasing(MFFD) are used in this thesis. The proposed techniques are compared with the existing EEDVM and PALVM techniques. Using proposed AUTREC technique there is 8% improved in energy consumption, 3% in number of migrations, 10% in SLA violation and 12% in host shutdown as compared to EEDVM technique. Using proposed DUTREC technique there is 9% improved in energy consumption, 6% in number of migrations, 20% in SLA violation and 13% in host shutdown as compared to PALVM technique

Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utilization under workload independent quality of service constraints. These approaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performance degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth contribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of servers with energy efficiency. Our sixth contribution is a Utilization Prediction-aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

An improved dynamic load balancing for virtualmachines in cloud computing using hybrid bat and bee colony algorithms

Cloud technology is a utility where different hardware and software resources are accessed on pay-per-user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization. In virtualization, a physical server changes into the virtual machine (VM) and acts as a physical server. Due to the large number of users sometimes the task sent by the user to cloud causes the VM to be under loaded or overloaded. This system state happens due to poor task allocation process in VM and causes the system failure or user tasks delayed. For the improvement of task allocation, several load balancing techniques are introduced in a cloud but stills the system failure occurs. Therefore, to overcome these problems, this study proposed an improved dynamic load balancing technique known as HBAC algorithm which dynamically allocates task by hybridizing Artificial Bee Colony (ABC) algorithm with Bat algorithm. The proposed HBAC algorithm was tested and compared with other stateof-the-art algorithms on 200 to 2000 even tasks by using CloudSim on standard workload format (SWF) data sets file size (200kb and 400kb). The proposed HBAC showed an improved accuracy rate in task distribution and reduced the makespan of VM in a cloud data center. Based on the ANOVA comparison test results, a 1.25 percent improvement on accuracy and 0.98 percent reduced makespan on task allocation system of VM in cloud computing is observed with the proposed HBAC algorithm

Applicability of MMRR load balancing algorithm in cloud computing

Cloud computing is now a modern model for managing, configuring, and accessing distributed computing systems around the network on a full scale. One of cloud computing's fundamental problems is the balancing of loads, which is essential for evenly distributing the workload across all nodes. Over the years, scholars have proposed various approaches in order to resolve this problem. Nevertheless, optimizations of task execution time, completion time, response time, and virtual machine resources (VMs) are still posing tremendous challenges. This study proposes a new load balancing algorithm, which combines maximum minimum and round robin algorithm (MMRR), so that tasks with long execution time can be allocated using maximum minimum and tasks with lowest execution task will be assigned using round robin. Cloud analyst tool was used to introduce the new load balancing techniques and a comparative analysis with existing algorithm was conducted to optimize cloud services to clients. The study findings indicate that Maximum Minimum Round Robin (MMRR) has brought significant changes to cloud services. The data center’s loading time is good from both Throttled and MMRR, but Round Robin was worst. MMRR performed better from the algorithms tested based on the whole response time and cost-effectiveness (89%). The study suggested that MMRR be implemented for enhancing user satisfaction in the cloud service

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area