Total Execution Cost Research Articles

Deadline Constrained based Resource Allocation in Cloud Environment

Cloud computing faces a challenge of handling huge amounts of data. The users keep on pushing the data without knowing the challenge in increased storage. Task Scheduling deals with allocating the task to a respective resource pool on a demand basis. Approaches have been built that handle requests from users with deadlines on the amount of request that can be handled. It is important to understand that the mechanism is available to handle the deadlines. The experimental results show that the proposed algorithm produces remarkable performance improvement rate on the total execution cost and total transfer time under meeting the deadline constraint. In view of the experimental results, the proposed algorithm provides a better-quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment.

Workflow load Balancing using soft computing base novel framework with Qos Parameters

Cloud computing represents a new era of computing network, where the resources of the system are dispersed and shared among its users in the network premises. The user of this system is able to use such resources through the technology of internet based on system of Pay-As-Per-Use. If a service is used by any type of user, it helps in production of wide variety of data. So, the cost of data transfer between two of the dependent resources will be extremely high. Additionally, an application of complex nature involves large number of tasks boosting the process of total cost of execution with respect to the used application, if the process is not scheduled in an optimized manner. In order to overcome such issues, a hybrid approach of water cycle optimization is proposed with particle swarm optimization. This method is divided into two steps of working determining under and over utilized virtual machines. In experimental analysis, the proposed approach on different scientific workflows is done where significant performance in all the workflows is based on total execution time and total execution cost.

Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing

The problem of allocating real-time tasks to cloud computing resources minimizing the makespan is defined as a NP-hard problem. This work studies the same problem with two realistic multi-objective criteria; the makespan and the total cost of execution and communication between tasks. A mathematical model including objective functions and constraints is proposed. In addition, a theoretical lower bound for the makespan which served later as a baseline to benchmark the experimental results is theoretically determined and proven. To solve the studied problem, a multi-objective genetic algorithm is proposed in which new crossover and mutation operators are proposed. Pareto-optimal solutions are retrieved using the genetic algorithm. The experimental results show that genetic algorithm provides efficient solutions in term of makespan for different-size problem instances with reference to the lower bound. Moreover, the proposed genetic algorithm produces many Pareto optimal solutions that dominate the solution given by greedy algorithm for both criteria.

Transient fault aware application partitioning computational offloading algorithm in microservices based mobile cloudlet networks

Mobile Cloudlet Computing paradigm (MCC) allows execution of resource-intensive mobile applications using computation cloud resources by exploiting computational offloading method for resource-constrained mobile devices. Whereas, computational offloading needs the mobile application to be partitioned during the execution in the MCC so that total execution cost is minimized. In the MCC, at the run-time network contexts (i.e., network bandwidth, signal strength, latency, etc.) are intermittently changed, and transient failures (due to temporary network connection failure, services busy, database disk out of storage) often occur for a short period of time. Therefore, transient failure aware partitioning of the mobile application at run-time is a challenging task. Since, existing MCC offers computational monolithic services by exploiting heavyweight virtual machines, which incurs with long VM startup time and high overhead, and these cannot meet the requirements of fine-grained microservices applications (e.g., E-healthcare, E-business, 3D-Game, and Augmented Reality). To cope up with prior issues, we propose microservices based mobile cloud platform by exploiting containerization which replaces heavyweight virtual machines, and we propose the application partitioning task assignment (APTA) algorithm which determines application partitioning at run-time and adopts the fault aware (FA) policy to execute microservices applications robustly without interruption in the MCC. Simulation results validate that the proposed microservices mobile cloud platform not only shrinks the setup time of run-time platform but also reduce the energy consumption of nodes and improve the application response time by exploiting APTA and FA to the existing VM based MCC and application partitioning strategies.

Workflow scheduling using Jaya algorithm in cloud

SummaryCloud computing is an on‐demand service that can be accessed by a user according to his requirements through the Internet. Multiple users can request any amount of services, so scheduling of those services is a crucial task in cloud computing. Scheduling is a way of assigning the work to a computer resource. We have multiple tasks at a time that are waiting to be allotted to multiple computer resources. Various optimization algorithms have been used to do task scheduling so that total execution cost is minimized. In this paper, we have implemented Jaya optimization algorithm for workflow scheduling and have compared it with four nature‐inspired algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), honey bee, and cat swarm optimization (CSO), keeping the fitness function same for all of them using CloudSim. Previously, work has been done on PSO, GA, ACO, honey bee, and CSO using different criteria. The results are compared on the basis of execution cost and makespan of the algorithm on both an independent set of tasks and a set of tasks that follow a workflow schedule. Benchmark functions such as Montage, CyberShake, Inspiral, and Sipht are used for workflow scheduling. It has been observed that Jaya outperforms the other algorithms as it produces similar results in the least amount of time as it converges very quickly.

A deadline constrained scheduling algorithm for cloud computing system based on the driver of dynamic essential path.

To solve the problem of the deadline-constrained task scheduling in the cloud computing system, this paper proposes a deadline-constrained scheduling algorithm for cloud computing based on the driver of dynamic essential path (Deadline-DDEP). According to the changes of the dynamic essential path of each task node in the scheduling process, the dynamic sub-deadline strategy is proposed. The strategy assigns different sub-deadline values to every task node to meet the constraint relations among task nodes and the user’s defined deadline. The strategy fully considers the dynamic sub-deadline affected by the dynamic essential path of task node in the scheduling process. The paper proposed the quality assessment of optimization cost strategy to solve the problem of selecting server for each task node. Based on the sub-deadline urgency and the relative execution cost in the scheduling process, the strategy selects the server that not only meets the sub-deadline but also obtains much lower execution cost. In this way, the proposed algorithm will make the task graph complete within its deadline, and minimize its total execution cost. Finally, we demonstrate the proposed algorithm via the simulation experiments using Matlab tools. The experimental results show that, the proposed algorithm produces remarkable performance improvement rate on the total execution cost that ranges between 10.3% and 30.8% under meeting the deadline constraint. In view of the experimental results, the proposed algorithm provides better-quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than IC-PCP, DCCP and CD-PCP.

Dynamic Data Allocation and Task Scheduling on Multiprocessor Systems With NVM-Based SPM

Low-power and short-latency memory access is critical to the performance of chip multiprocessor (CMP) system devices, especially to bridge the performance gap between memory and CPU. Together with increased demand for low-energy consumption and high-speed memory, scratch-pad memory (SPM) has been widely adopted in multiprocessor systems. In this paper, we employ a hybrid SPM, composed of a static random-access memory and a nonvolatile memory (NVM), to replace the cache in CMP. However, there are several challenges related to the CMP that need to be addressed, including how to dynamically assign processors to application tasks and dynamically allocate data to memories. To solve these problems based on this architecture, we propose a novel dynamic data allocation and task scheduling algorithm, i.e., dynamic greedy data allocation and task scheduling (DGDATS). Experiments on DSP benchmarks demonstrate the effectiveness and efficiency of our proposed algorithms; namely, our proposed algorithm can generate a highly efficient dynamic data allocation and task scheduling approach to minimize the total execution cost and produce the least amount of write operations on NVMs. Our extensive simulation study demonstrates that our proposed algorithm exhibits an excellent performance compared with the heuristic allocation (HA) and adaptive genetic algorithm for data allocation (AGADA) algorithms. Based on the CMP systems with hybrid SPMs, DGDATS reduces the total execution cost by 22.18% and 51.37% compared with those of the HA and AGADA algorithms, respectively. Additionally, the average number of write operations on NVM is 19.82% lower than that of HA.

A fuzzy-based method for task scheduling in the cloud environments using inverted ant colony optimisation algorithm

Cloud computing is the latest emerging trend of distributed computing, in which distributed resources are delivered based on user's demand. In the cloud environment, computing resources need to be scheduled so that providers make the most use of the resources and users will find the applications they need at the lowest cost. In this paper, an inverted ant colony optimisation (IACO) algorithm has been used to solve the task scheduling problem in the cloud environment with the goal of reducing runtime and increasing load balancing. In the proposed method, pheromone repellent is used instead of pheromone gravity, so the effect of pheromone prevents the wrong choice. In order to observe load balance and the effect of pheromone repulsion, fuzzy logic and weight definition have been used. The results have shown that the proposed algorithm while reducing the total time and cost of execution, could also increase load balance.

Fairness-based heuristic workflow scheduling and placement in cloud computing

Cloud computing has become a commercial business environment for small and large scale industries to avail the resources. Cloud application requests are processed dynamically to access resource utilisation based on the diverse demand for cloud resources. The expected allocation of resources to the application is inequitable. This paper aims to analyse the QoS metrics of cloud services based on user satisfaction, and proposes Fairness-based Heuristic Workflow Scheduling and Placement (FHWSP) algorithm with the new multi-objective function. It minimises the overall profit and time of execution with respect to characteristics of tasks and physical servers in the datacentre. The evaluation of the proposed FHWSP algorithm with the existing traditional algorithms is simulated in a CloudSim simulated environment. The experimental results significantly improve the QoS parameters in terms of 5% in makespan and 3% in total execution cost of the scheduling and placement algorithm on CloudSim environment.

An Efficient Rule-Based Distributed Reasoning Framework for Resource-bounded Systems

Over the last few years, context-aware computing has received a growing amount of attention among the researchers in the IoT and ubiquitous computing community. In principle, context-aware computing transforms a physical environment into a smart space by sensing the surrounding environment and interpreting the situation of the user. This process involves three major steps: context acquisition, context modelling, and context-aware reasoning. Among other approaches, ontology-based context modelling and rule-based context reasoning are widely used techniques to enable semantic interoperability and interpreting user situations. However, implementing rich context-aware applications that perform reasoning on resource-bounded mobile devices is quite challenging. In this paper, we present a context-aware systems development framework for smart spaces, which includes a lightweight efficient rule engine and a wide range of user preferences to reduce the number of rules while inferring personalized contexts. This shows rules can be reduced in order to optimize the inference engine execution speed, and ultimately to reduce total execution time and execution cost.

Time-Cost Efficient Scheduling Algorithms for Executing Workflow in Infrastructure as a Service Clouds

Cloud Computing enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. The cloud infrastructures can be used as an appropriate environment for executing of workflow applications. To execute workflow applications in this environment, it is necessary to develop the workflow scheduling algorithms that consider different QoS parameters such as execution time and cost. Therefore, in this paper we focus on two criteria: total completion time (makespan) and execution cost of workflow, and propose two heuristic algorithms: MTDC (Minimum Time and Decreased Cost) which aims to create a schedule that minimizes the makespan and decreases execution cost, and CTDC (Constrained Time and Decreased Cost) which is based on the first algorithm (MTDC) and aims to create a schedule that decreases the execution cost while satisfying the deadline constraint of the workflow application. The proposed algorithms are evaluated by a simulation process using WorkflowSim. To evaluate the proposed algorithms, the results of MTDC are compared with the results of HEFT (Heterogeneous Earliest Finish Time), and the results of CTDC are compared with the results of heuristic based algorithms [such as IC-PCP (IaaS Cloud Partial Critical Paths), IC-PCPD2 (Deadline Distribution) and BDHEFT (Budget and Deadline HEFT)] and meta-heuristic based algorithms [such as PSO (Particle Swarm Optimization) and CGA2 (Coevolutionary Genetic Algorithm with Adaptive penalty function)]. The results show that the proposed algorithms perform better than the mentioned algorithms in most cases.

A strategy for joint service offloading and scheduling in heterogeneous cloud radio access networks

Cloud radio access network is one of the most promising cellular networks for the next generation of mobile networks. The basic idea of cloud RAN (radio access network) is virtualizing and centralizing the intelligent part of the base station, the base band unit, and keeping remote radio heads on cell site enabling a centralized processing and management. Offloading data computation to edge cloud was proposed as a solution to deal with resource limitation while keeping a good quality of service. In this paper, we propose a strategy to jointly handle offloading decision and offloading request scheduling in cloud RAN. We aim to improve network quality of service while reducing the scheduling cost expressed in terms of overload, network delay, and migration cost. Numerical results show that the proposed approach is able to reduce the response time of the applications, mobile terminal energy consumption, and total execution cost.

FFBAT: A security and cost‐aware workflow scheduling approach combining firefly and bat algorithms

SummaryCloud computing is distributed computing on a large scale driven by practical and effective operations, in which a pay‐per‐use framework provides dynamic scaling in response to the needs of workflow applications. Many existing cloud computing environments do not effectively employ security measures to counter security threats in task scheduling. To improve the scheduling system, we include security service to the scheduling process. However, adding security services to applications inevitably causes overhead in terms of computation time. The tradeoff between achieving high computing performance and providing the desired level of security protection imposes a big challenge for task scheduling. To solve this problem, we propose a security and cost aware scheduling algorithm for heterogeneous tasks in scientific workflow executed in a cloud. Our proposed algorithm is based on the hybrid optimization approach, which combines Firefly and Bat algorithms. The coding strategy is to minimize the total execution cost while meeting the deadline and risk rate constraints. The proposed system uses a multi‐objective function, and the results indicate that our algorithm always outperforms the traditional algorithms.

Architecting Feasible Deployment Alternatives for Publish-Subscribe Systems

Publish-Subscribe is one of the important patterns for developing scalable distributed systems. Usually, the deployment of the publishers and subscribers to the nodes can be done in many different ways, whereby each deployment alternative will have a different impact on the performance. The many possible architecture design alternatives tend to trade-off with respect to execution cost and communication cost. Unfortunately, for the human engineer it is not tractable to define a feasible configuration in case of large number of nodes and participants. In this paper we propose a generic method to assist the architect by automatically deriving feasible deployment alternatives of Publish-Subscribe based distributed systems. The approach is based on the so-called capacitated task allocation problem (CTAP) in which constraints on memory capacity and processing power are applied to manage the trade-offs between the total execution cost and total communication cost to derive feasible design alternatives. The method is supported by our tool framework (Deploy-PS) that provides an integrated development environment for modeling the Publish-Subscribe deployment architecture, modeling the physical resources and the performance requirements, and the selection and generation of the feasible deployment architecture alternatives.

Task and Motion Coordination for Heterogeneous Multiagent Systems With Loosely Coupled Local Tasks

We consider a multiagent system that consists of heterogeneous groups of homogeneous agents. Instead of defining a global task for the whole team, each agent is assigned a local task as syntactically cosafe linear temporal logic formulas that specify both motion and action requirements. Interagent dependence is introduced by collaborative actions, of which the execution requires multiple agents’ collaboration. To ensure the satisfaction of all local tasks without central coordination, we propose a bottom–up motion and task coordination strategy that contains an off-line initial plan synthesis and an online coordination scheme based on real-time exchange of request and reply messages. It facilitates not only the collaboration among heterogeneous agents but also the task swapping between homogeneous agents to reduce the total execution cost. It is distributed as any decision is made locally by each agent based on local computation and communication within neighboring agents. It is scalable and resilient to agent failures as the dependence is formed and removed dynamically based on agent capabilities and their plan execution status, instead of preassigned agent identities. The overall scheme is demonstrated by a simulated scenario of 20 agents with loosely coupled local tasks.

Deadline‐constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing

SummaryThe cloud infrastructures provide a suitable environment for the execution of large‐scale scientific workflow application. However, it raises new challenges to efficiently allocate resources for the workflow application and also to meet the user's quality of service requirements. In this paper, we propose an adaptive penalty function for the strict constraints compared with other genetic algorithms. Moreover, the coevolution approach is used to adjust the crossover and mutation probability, which is able to accelerate the convergence and prevent the prematurity. We also compare our algorithm with baselines such as Random, particle swarm optimization, Heterogeneous Earliest Finish Time, and genetic algorithm in a WorkflowSim simulator on 4 representative scientific workflows. The results show that it performs better than the other state‐of‐the‐art algorithms in the criterion of both the deadline‐constraint meeting probability and the total execution cost.

Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures

In this study, we describe the ​further development of Elastic Cloud Computing Cluster (EC3), a tool ​for creating self-managed cost-efficient virtual hybrid elastic clusters on top of Infrastructure as a Service (IaaS) clouds. By using spot ​instances and checkpointing techniques, EC3 can significantly reduce the total ​execution cost as well as facilitating automatic fault tolerance. Moreover, EC3 can deploy and manage hybrid clusters across on-premises and public ​cloud resources, thereby introducing ​cloud bursting capabilities. ​We present the results of a case study that we conducted to assess the effectiveness of the tool ​based on the structural dynamic analysis of buildings. In addition, we evaluated the checkpointing algorithms in a real ​cloud environment with existing workloads to study their effectiveness. The results ​demonstrate the feasibility and benefits of this type of ​cluster for computationally intensive applications.

A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds

Security is increasingly critical for various scientific workflows that are big data applications and typically take quite amount of time being executed on large-scale distributed infrastructures. Cloud computing platform is such an infrastructure that can enable dynamic resource scaling on demand. Nevertheless, based on pay-per-use and hourly-based pricing model, users should pay attention to the cost incurred by renting virtual machines (VMs) from cloud data centers. Meanwhile, workflow tasks are generally heterogeneous and require different instance series (i.e., computing optimized, memory optimized, storage optimized, etc.). In this paper, we propose a security and cost aware scheduling (SCAS) algorithm for heterogeneous tasks of scientific workflow in clouds. Our proposed algorithm is based on the meta-heuristic optimization technique, particle swarm optimization (PSO), the coding strategy of which is devised to minimize the total workflow execution cost while meeting the deadline and risk rate constraints. Extensive experiments using three real-world scientific workflow applications, as well as CloudSim simulation framework, demonstrate the effectiveness and practicality of our algorithm.

BF-PSO-TS: Hybrid Heuristic Algorithms for Optimizing Task Schedulingon Cloud Computing Environment

Task Scheduling is a major problem in Cloud computing because the cloud provider has to serve many users. Also, a good scheduling algorithm helps in the proper and efficient utilization of the resources. So, task scheduling is considered as one of the major issues on the Cloud computing systems. The objective of this paper is to assign the tasks to multiple computing resources. Consequently, the total cost of execution is to be minimum and load to be shared between these computing resources. Therefore, two hybrid algorithms based on Particle Swarm Optimization (PSO) have been introduced to schedule the tasks; Best-Fit-PSO (BFPSO) and PSO-Tabu Search (PSOTS). According to BFPSO algorithm, Best-Fit (BF) algorithm has been merged into the PSO algorithm to improve the performance. The main principle of the modified BFSOP algorithm is that BF algorithm is used to generate the initial population of the standard PSO algorithm instead of being initiated randomly. According to the proposed PSOTS algorithm, the Tabu-Search (TS) has been used to improve the local research by avoiding the trap of the local optimality which could be occurred using the standard PSO algorithm. The two proposed algorithms (i.e., BFPSO and PSOTS) have been implemented using Cloudsim and evaluated comparing to the standard PSO algorithm using five problems with different number of independent tasks and resources. The performance parameters have been considered are the execution time (Makspan), cost, and resources utilization. The implementation results prove that the proposed hybrid algorithms (i.e., BFPSO, PSOTS) outperform the standard PSO algorithm

맵리듀스에서 빅데이터 분석을 위한 다중 Group-by 질의의 효율적인 처리 기법

MapReduce is a framework used to process large data sets in parallel on a large cluster. A group-by query is a query that partitions the input data into groups based on the values of the specified attributes, and then evaluates the value of the specified aggregate function for each group. In this paper, we propose an efficient method for processing multiple group-by queries using MapReduce. Instead of computing each group-by query independently, the proposed method computes multiple group-by queries in stages with one or more MapReduce jobs in order to reduce the total execution cost. We compared the performance of this method with the performance of a less sophisticated method that computes each group-by query independently. This comparison showed that the proposed method offers better performance in terms of execution time.