Computation Requests Research Articles

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

With the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

Multi-Level Resource Sharing Framework Using Collaborative Fog Environment for Smart Cities

Fog computing has proved its importance over legacy cloud architectures for computation, storage, and communication where edge devices are used to facilitate the delay-sensitive applications. The inception of fog nodes has brought computing intelligence close to the end-devices. Many fog computing frameworks have been proposed where edge devices are used for computation. In this paper, we proposed a simulation framework for fog devices that can use end devices to handle the peak computation load to provide better Quality of Services (QoS). The regional fog nodes are deployed at network edge locations which are used as an intelligent agent to handle the computation requests by either scheduling them on local servers, cloud data centers, or at the under-utilized end-user devices. The proposed device-to-device resource sharing model relies on Ant Colony Optimization (ACO) and Earliest Deadline First(EDF) Algorithm to provide a better quality of service using device available at multi-layer design. The concept of using IoT devices as fog nodes has improved the performance of legacy fog based systems. The proposed work is benchmarked in terms of system cost, efficiency, energy, and quality of service. Further, the proposed framework is with xFogSim in terms of task efficiency.

Numerical Analysis of the Rudder–Propeller Interaction

The proper evaluation of the Rudder–Propeller interactions is mandatory to correctly predict the manoeuvring capability of a modern ship, in particular considering the commonly adopted ship layout (rudder often works in the propeller slipstream). Modern Computational Fluid Dynamics (CFD) solvers can provide, not only the performance of the whole system but also an insight into the flow problem. In the present paper, an open-source viscous flow solver has been validated against available literature experimental measurements in different conditions. After an extensive analysis of the numerical influence of the mesh arrangement and the turbulent quantities on the rudder provided forces, the study focused its attention on the forces generated by the rudder varying the propeller loading conditions and the mutual position between the two devices. These analyses give a hint to describe and improve a commonly-used semi-empirical method based on the actuator disk theory. These analyses also demonstrate the ability of these numerical approaches to correctly predict the interaction behaviour in pre-stall conditions with quite reasonable computational requests (proper also for a design stage), giving additional information on the sectional forces distribution along the span-wise rudder direction, useful to further develop a new semi-empirical rudder model.

Blockchain-based decentralized workload and energy management of geo-distributed data centers

Minimizing the costs of cloud services is important for cloud service providers to grow their business. In cost calculation of cloud services, power consumption cost is a major factor. The variability present in renewable power generation, electricity price, and power demand variates the power utilization expenditures. Due to high computational requests, datacenters consume enormous energy every day, thereby necessitating the need for energy efficient designs. Therefore, an energy consumption cost reduction mechanism is required to optimize the data center’s power consumption cost locally and to have the option to redistribute the workload on geo-distributed data centers in peak hours for further reduction in power consumption cost. In this paper, the authors investigate the energy consumption cost optimization problem in cloud datacenters and propose a blockchain-based decentralized workload distribution and management model. Moreover, we minimize request scheduler time for transferring any job from one data center to another and secure the energy cost optimization process due to shut down/ communication failure. The proposed work is the primitive to present the energy cost optimization problem in geo-distributed data centers by introducing blockchain-based secure workload scheduling method considering both the temporal and spatial disparities associated with electricity tariffs and workload arrival process. The simulations are evaluated on real-world workload of the Google data center and associated electricity tariffs. The blockchain-based decentralized workload management framework is evaluate based on privacy and security analysis, update overhead, latency, and throughput. Moreover, the results show blockchain model migrate workload in minimum 46 % reduced time compared to conventional model.

Performance Analysis of a Delay Constrained Data Offloading Scheme in an Integrated Cloud-Fog-Edge Computing System

The recent growth in intensive services and applications demand has triggered the functional integration of cloud computing with edge computing capabilities. One of the main goals is to allow a fast processing to tasks with strict real time constraints in order to lower the task dropping probability due to expiration of the associated deadlines. This paper deals with the performance evaluation and optimization of a three layers cloud-fog-edge computing infrastructure by resorting to the use of queueing theory results. In particular, a Markov queueing system model with reneging is proposed for the cloud subsystem, in order to consider the premature computation requests departure due to their deadline expiration. Furthermore, a computational resources allocation method is proposed with the aim at maximizing the social welfare metric, constrained to specific quality of service requirements. Finally, the proposed queueing theory analysis as well as of the computational resources allocation approach is validated by comparing the obtained analytical predictions with simulation results.

A Power-of-Two Choices Based Algorithm for Fog Computing

The fog computing paradigm brings together storage, communication, and computation resources closer to users’ end-devices. Therefore, fog servers are deployed at the edge of the network, offering low latency access to users. With the expansion of such fog computing services, different providers will be able to deploy multiple resources within a restricted geographical proximity. In this paper, we investigate an incentive-based cooperation scheme across fog providers. We propose a distributed cooperative algorithm amongst fog computing providers where fully collaborative fog nodes are subject to different loads. The proposed algorithm leverages the power-of-two result and exploits a cooperation probability, namely the probability that a given provider collaborates by accepting a computation request from another provider, as a mean to achieve a fair cooperation. We adopt an analytical approach based on exploiting a simplified performance model to demonstrate numerically that a set of optimal accepting probabilities exits when the number of server nodes goes to infinity. This result then drives the design of our distributed algorithm. Second, in our experimental approach, we perform a set of simulation analysis to verify the validity of the proposed solution when the number of servers is limited.

Multi-matrix verifiable computation

The problem of securely outsourcing computation to cloud servers has attracted a large amount of attention in recent years. The verifiable computation of Gennaro, Gentry, Parno (Crypto'10) allows a client to verify the server's computation of a function with substantially less time than performing the outsourced computation from scratch. In a multi-function model (Parno, Raykova, Vaikuntanathan; TCC'12) of verifiable computation, the process of encoding function and the process of preparing input are decoupled such that any client can freely submit a computation request on its input, without having to generate an encoding of the function in advance. In this paper, we propose a multi-matrix verifiable computation scheme that allows the secure outsourcing of the matrix functions over a finite field. Our scheme is outsourceable. When it is used to outsource $m$ linear functions, the scheme is roughly $m$ times faster and has less communication cost than the previously best known scheme by Fiore and Gennaro (CCS'12), both in the client-side computation and in the server-side computation. We also show the cost saving with detailed implementations.

A Novel Coordinate Rotation Digital Computer Method for Energy and Latency Saving by Trigonometric Operations Spatial Locality Principle

In this work, we propose an approximate and energy-efficient CORDIC method, based on a trigonometric function spatial locality principle derived from benchmarks profiling. Successive sine/cosine computation requests cover more than 50% when the absolute phase difference is at most ten degrees. Consequently, this property suggests an optimized circuit implementation, both iterative or a succession of microrotation modules, where the last CORDIC requires fewer iterations, reducing the latency and the total energy budget at the same precision of two separate and independent instances. Thus, this simple design strategy allows significant area and energy dissipation in general-purpose VLSI architectures, but it introduces also dramatically optimizations in applicationspecific embedded systems used in the area of signal processing and radio frequency communication. In this contribution, we introduce a method, the hardware overhead and the energy budget per single cycle. Simulation results show the total energy saving in considered benchmarks is 40% in pipelined and iterative general purposes CORDIC. Furthermore, our application-specific systems (fast Fourier transform and digital oscillators for radiofrequency down conversions) show remarkable cycle savings when the successive sine/cosine computation requests are more than 70%. Finally, in this work, we extend the proposed approach to whichever phase difference less than 26.56° , as a variable for the second CORDIC number of angle rotations.

Two-Stage Computation Offloading Scheduling Algorithm for Energy-Harvesting Mobile Edge Computing

Recently, mobile edge computing (MEC) technology was developed to mitigate the overload problem in networks and cloud systems. An MEC system computes the offloading computation tasks from resource-constrained Internet of Things (IoT) devices. In addition, several convergence technologies with renewable energy resources (RERs) such as photovoltaics have been proposed to improve the survivability of IoT systems. This paper proposes an MEC integrated with RER system, which is referred to as energy-harvesting (EH) MEC. Since the energy supply of RERs is unstable due to various reasons, EH MEC needs to consider the state-of-charge (SoC) of the battery to ensure system stability. Therefore, in this paper, we propose an offloading scheduling algorithm considering the battery of EH MEC as well as the service quality of experience (QoE). The proposed scheduling algorithm consists of a two-stage operation, where the first stage consists of admission control of the offloading requests and the second stage consists of computation frequency scheduling of the MEC server. For the first stage, a non-convex optimization problem is designed considering the computation capability, SoC, and request deadline. To solve the non-convex problem, a greedy algorithm is proposed to obtain approximate optimal solutions. In the second stage, based on Lyapunov optimization, a low-complexity algorithm is proposed, which considers both the workload queue and battery stability. In addition, performance evaluations of the proposed algorithm were conducted via simulation. However, this paper has a limitation in terms of verifying in a real-world scenario.

Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks

Mobile Edge Computing (MEC) is a promising technology to extend the diverse services to the edge of Internet of Things (IoT) system. However, the static edge server deployment may cause “service hole” in IoT networks in which the location and service requests of the User Equipments (UEs) may be dynamically changing. In this paper, we firstly explore a vehicle edge computing network architecture in which the vehicles can act as the mobile edge servers to provide computation services for nearby UEs. Then, we propose as vehicle-assisted offloading scheme for UEs while considering the delay of the computation task. Accordingly, an optimization problem is formulated to maximize the long-term utility of the vehicle edge computing network. Considering the stochastic vehicle traffic, dynamic computation requests and time-varying communication conditions, the problem is further formulated as a semi-Markov process and two reinforcement learning methods: Q-learning based method and deep reinforcement learning (DRL) method, are proposed to obtain the optimal policies of computation offloading and resource allocation. Finally, we analyze the effectiveness of the proposed scheme in the vehicular edge computing network by giving numerical results.

Socially Aware Dynamic Computation Offloading Scheme for Fog Computing System With Energy Harvesting Devices

Fog computing is considered as a promising technology to meet the ever-increasing computation requests from a wide variety of mobile applications. By offloading the computation-intensive requests to the fog node or the central cloud, the performance of the applications, such as energy consumption and delay, are able to be significantly enhanced. Meanwhile, utilizing the recent advances of social network and energy harvesting (EH) techniques, the system performance could be further improved. In this paper, we take the social relationships of the EH mobile devices (MDs) into the design of computational offloading scheme in fog computing. With the objective to minimize the social group execution cost, we advocate game theoretic approach and propose a dynamic computation offloading scheme designing the offloading process in fog computing system with EH MDs. Different queue models are applied to model the energy cost and delay performance. It can be seen that the proposed problem can be formulated as a generalized Nash equilibrium problem (GNEP) and we can use exponential penalty function method to transform the original GNEP into a classical Nash equilibrium problem and address it with semi-smooth Newton method with Armijo line search. The simulation results demonstrate the effectiveness of the proposed scheme.

Numerical Evaluation of Rudder Performance Behind a Propeller in Bollard Pull Condition

Correct evaluation of rudder performance is a key issue in assessing ship maneuverability. This paper presents a simplified approach based on a viscous flow solver to address propeller and rudder interactions. Viscous flow solvers have been applied to this type of problems, but the large computational requests limit (or even prevent) their application at a preliminary ship design stage. Based on this idea, a simplified approach to include the propeller effect in front of the rudder is considered to speed up the solution. Based on the concept of body forces, this approach enables sufficiently fast computation for a preliminary ship design stage, thereby maintaining its reliability. To define the limitations of the proposed procedure, an extensive analysis of the simplified method is performed and the results are compared with experimental data presented in the literature. Initially, the reported results show the capability of the body-force approach to represent the inflow field to the rudder without the full description of the propeller, also with regard to the complex bollard pull condition. Consequently, the rudder forces are satisfactorily predicted at least with regard to the lift force. However, the drag force evaluation is more problematic and causes higher discrepancies. Nevertheless, these discrepancies may be accepted due to their lower influence on the overall ship maneuverability performance.

Drop computing: Ad-hoc dynamic collaborative computing

Mobile applications nowadays generally consist of a frontend component running on the device, and a backend component running on the cloud that performs the larger computations. However, this usage model leads to high costs for developing the application (since a cloud infrastructure that should scale to the number of users must be maintained), and to a potentially bad user experience (if the latency is high or the users employ mobile broadband they pay for). Thus, we introduce the Drop Computing paradigm, which proposes the concept of decentralized computing over multilayered networks, combining cloud and wireless technologies over a social crowd formed between mobile and edge devices. Mobile devices and people interconnect to form ad-hoc dynamic collaborations to support the equivalent of a crowd-based edge multilayered cloud of clouds, where the capabilities of any mobile device are extended beyond the local technology barriers, to accommodate external resources available in the crowd of other devices. Thus, instead of every data or computation request going directly to the cloud, Drop Computing employs the mobile crowd formed of devices in close proximity for quicker and more efficient access. Devices in the mobile crowd are leveraged for requesting already downloaded data or performing computations, and the cloud acts as the second (or even third) option. We present a proof-of-concept implementation of Drop Computing and show, through simulations, that it is feasible for real-life usage, since it is able to drastically reduce costs while not affecting or even improving the user experience.

Intelligent Resource Management in Blockchain-Based Cloud Datacenters

Nowadays, more and more companies migrate business from their own servers to the cloud. With the influx of computational requests, datacenters consume tremendous energy every day, attracting great attention in the energy efficiency dilemma. In this paper, we investigate the energy-aware resource management problem in cloud datacenters, where green energy with unpredictable capacity is connected. Via proposing a robust blockchain-based decentralized resource management framework, we save the energy consumed by the request scheduler. Moreover, we propose a reinforcement learning method embedded in a smart contract to further minimize the energy cost. Because the reinforcement learning method is informed from the historical knowledge, it relies on no request arrival and energy supply. Experimental results on Google cluster traces and real-world electricity price show that our approach is able to reduce the datacenters cost significantly compared with other benchmark algorithms.

MLBox: Machine learning box for asymptotic scheduling

As the usability of Cloud-based solutions has increased for various types of users with different needs, from scientists that want to process big data sets collected from sensors or business analysts that want to take decisions based on the huge amount of gathered data to simple users that store or share documents via a Cloud platform, the generated data is increasing more and more. For example, the ATLAS and other detectors at CERN generate petabytes of data and Facebook stores data with a rate of around 600 TB daily. In the current context, efficient scheduling for Big Data applications is a challenge and an appropriate scheduling technique is required for different types of incoming requests. In this paper we propose a scheduling algorithm for different types of computation requests: independent tasks, like bag of tasks (BoT) model or tasks with dependencies modeled as directed acyclic graphs (DAG), and they will be scheduled for execution in a Cloud datacenter. The tasks in the requests are scheduled on the available resources using the suitable scheduling algorithm for each request. We rely on a machine learning toolbox, named as MLBox, to find what algorithm should be used for a certain request. We implemented four heuristics for scheduling BoTs and four heuristics for DAGs scheduling and generated the training data for the machine learning algorithm by running multiple traditional scheduling algorithms and selecting the ‘best’ one for a given request. We evaluate the performance by comparing the scheduling of different tasks requests using some of the traditional algorithms and our machine learning based scheduling algorithm.

Evaluating load balancing policies for performance and energy-efficiency

Nowadays, more and more increasingly hard computations are performed in challenging fields like weather forecasting, oil and gas exploration, and cryptanalysis. Many of such computations can be implemented using a computer cluster with a large number of servers. Incoming computation requests are then, via a so-called load balancing policy, distributed over the servers to ensure optimal performance. Additionally, being able to switch-off some servers during low period of workload, gives potential to reduced energy consumption. Therefore, load balancing forms, albeit indirectly, a trade-off between performance and energy consumption. In this paper, we introduce a syntax for load-balancing policies to dynamically select a server for each request based on relevant criteria, including the number of jobs queued in servers, power states of servers, and transition delays between power states of servers. To evaluate many policies, we implement two load balancers in: (i) iDSL, a language and tool-chain for evaluating service-oriented systems, and (ii) a simulation framework in AnyLogic. Both implementations are successfully validated by comparison of the results.

CPS: a dynamic and distributed pricing policy in cyber foraging systems for fixed state cloudlets

The deployment of cyber foraging systems includes deployment of Cloudlets as computational agents, which receive heavy computational demands during peak hours based on the context of service being offered. We introduce an intelligent pricing policy mechanism for cloudlets-services for mobile cloud users. The dynamic distributed pricing policy CPS (cyber-foraging pricing scheme) also introduces methods which can optimize the computational service requests fired by the mobile cloud users. In CPS, each fixed cloudlet optimizes its revenue using the supply-demand curve and sets a dynamic cost for the home and foreign users. CPS considers two types of mobile-cloud users i.e., foreign users and home users and computational requests are fired from both home and foreign users. The proposed work uses working day movement mobility model for the mobile cloud users. This work uses response time by a cloudlet to evaluate its real time home and foreign prices. We also propose a hostile environment pricing scheme for the service providers. We implemented the proposed pricing scheme on NS-3 and collected the results. The results also indicate high increase in the utility of the corresponding mobile-cloud user.

An Integer-Linear Algorithm for Optimizing Energy Efficiency in Data Centers

Nowadays, to meet the enormous computational requests, energy consumption, the largest part which is related to idle resources, is strictly increased as a great part of a data center's budget. So, minimizing energy consumption is one of the most important issues in the field of green computing. In this paper, we present a mathematical model formed as integer-linear programming which minimizes energy consumption and maximizes user’s satisfaction, simultaneously. However, migration variables, as principal decision variables of the model, can be relaxed to continuous activities in some practical problems. This constraint relaxation helps a decision maker to find faster solutions that are usually good approximations for optimum. Near feasible solutions (infeasible solutions that are desirably close to the feasible region) have been investigated as another relaxation considering the kind of solutions. For this purpose, we initially present a measure to evaluate the amount of infeasibility of solutions and then let the model consider an extended region including solutions with remissible infeasibility, if necessary.

High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis.

The High-performance Integrated Virtual Environment (HIVE) is a distributed storage and compute environment designed primarily to handle next-generation sequencing (NGS) data. This multicomponent cloud infrastructure provides secure web access for authorized users to deposit, retrieve, annotate and compute on NGS data, and to analyse the outcomes using web interface visual environments appropriately built in collaboration with research and regulatory scientists and other end users. Unlike many massively parallel computing environments, HIVE uses a cloud control server which virtualizes services, not processes. It is both very robust and flexible due to the abstraction layer introduced between computational requests and operating system processes. The novel paradigm of moving computations to the data, instead of moving data to computational nodes, has proven to be significantly less taxing for both hardware and network infrastructure.The honeycomb data model developed for HIVE integrates metadata into an object-oriented model. Its distinction from other object-oriented databases is in the additional implementation of a unified application program interface to search, view and manipulate data of all types. This model simplifies the introduction of new data types, thereby minimizing the need for database restructuring and streamlining the development of new integrated information systems. The honeycomb model employs a highly secure hierarchical access control and permission system, allowing determination of data access privileges in a finely granular manner without flooding the security subsystem with a multiplicity of rules. HIVE infrastructure will allow engineers and scientists to perform NGS analysis in a manner that is both efficient and secure. HIVE is actively supported in public and private domains, and project collaborations are welcomed.Database URL: https://hive.biochemistry.gwu.edu

Energy Conservation Using Dynamic Voltage Frequency Scaling for Computational Cloud.

Cloud computing is a new technology which supports resource sharing on a “Pay as you go” basis around the world. It provides various services such as SaaS, IaaS, and PaaS. Computation is a part of IaaS and the entire computational requests are to be served efficiently with optimal power utilization in the cloud. Recently, various algorithms are developed to reduce power consumption and even Dynamic Voltage and Frequency Scaling (DVFS) scheme is also used in this perspective. In this paper we have devised methodology which analyzes the behavior of the given cloud request and identifies the associated type of algorithm. Once the type of algorithm is identified, using their asymptotic notations, its time complexity is calculated. Using best fit strategy the appropriate host is identified and the incoming job is allocated to the victimized host. Using the measured time complexity the required clock frequency of the host is measured. According to that CPU frequency is scaled up or down using DVFS scheme, enabling energy to be saved up to 55% of total Watts consumption.