Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic

This research introduces a new probabilistic and meta-heuristic optimization approach inspired by the Corona virus pandemic. Corona is an infection that originates from an unknown animal virus, which is of three known types and COVID-19 has been rapidly spreading since late 2019. Based on the SIR model, the virus can easily transmit from one person to several, causing an epidemic over time. Considering the characteristics and behavior of this virus, the current paper presents an optimization algorithm called Corona virus optimization (CVO) which is feasible, effective, and applicable. A set of benchmark functions evaluates the performance of this algorithm for discrete and continuous problems by comparing the results with those of other well-known optimization algorithms. The CVO algorithm aims to find suitable solutions to application problems by solving several continuous mathematical functions as well as three continuous and discrete applications. Experimental results denote that the proposed optimization method has a credible, reasonable, and acceptable performance.

     

CDA: a novel multicore scheduling for cost-aware deadline-constrained scientific workflows on the IaaS cloud

The Journal of Supercomputing - The variety of pricing models offered by cloud service providers and the availability of a wide diversity of computing resources has increased the popularity of this...     

Load dispersion-aware VM placement in favor of energy-performance tradeoff

Huge energy consumption in cloud infrastructure has turned into a challenging problem. Virtualization technology, which can be regarded as the first step in energy conservation by offering benefits like on-demand resource provisioning and live migration, creates a platform on which different resource allocation and scheduling policies can be defined on how to accumulate VMs on fewer number of hosts while respecting performance metrics. In this paper, after presenting a classification on VM placement strategies, we propose different combinatorial placement policies that take load dispersion of hosts into account to dynamically adapt their placement decisions. Simulated experiments through Cloudsim showed noteworthy results concerning energy-performance tradeoff.     

An online context-aware mechanism for computation offloading in ubiquitous and mobile cloud environments

The Journal of Supercomputing - In ubiquitous environments today, there are numerous sensors that generate a large amount of data. The results of processing this massive data can be applied through...     

Predicting Job Failures in AuverGrid Based on Workload Log Analysis

Grid systems are popular today due to their ability to solve large problems in business and science. Job failures which are inherent in any computational environment are more common in grids due to their dynamic and complex nature. Furthermore, traditional methods for job failure recovery have proven costly and thus a need to shift toward proactive and predictive management strategies is necessary in such systems. In this paper, an innovative effort has been made to predict the futurity of jobs in a production grid environment. First of all, we investigated the relationship between workload characteristics and job failures by analyzing workload traces of AuverGrid which is a part of EGEE (Enabling Grids for E-science) project. After the recognition of failure patterns, the success or failure status of jobs during 6 months of AuverGrid activity was predicted with approximately 96% accuracy. The quality of services on the grid can be improved by integrating the result of this work into management services like scheduling and monitoring.     

A proximity-aware load balancing in peer-to-peer-based volunteer computing systems

One of the main challenges in peer-to-peer-based volunteer computing systems is an efficient resource discovery algorithm. Load balancing is a part of resource discovery algorithm and aims to minimize the overall response time of the system. This paper introduces an analytical model based on distributed parallel queues to optimize the average response time of the system in a distributed manner. The proposed resource discovery algorithm consists of two phases. In the first phase, it selects peers in a load-balanced manner based on QoS constraints of request. In the second phase, a proximity-aware feature is applied to select the peer with minimum communication overhead among selected peers in the first phase. Two dispatching strategies are proposed for the load balancing based on stochastic analysis of routing in the distributed parallel queues. These policies adopt probabilistic and deterministic sequences to redirect requests to the capable peers in the system. Simulation results show that the proposed resource discovery algorithm improves the response time of user’s requests by a factor of 1.8 under a moderate load.     

Measurement of the latency parameters of the Multi-BSP model: a multicore benchmarking approach

Computer benchmarking is a common method for measuring the parameters of a computational model. It helps to measure the parameters of any computer. With the emergence of multicore computers, the evaluation of computers was brought under consideration. Since these types of computers can be viewed and considered as parallel computers, the evaluation methods for parallel computers may be appropriate for multicore computers. However, because multicore architectures seriously focus on cache hierarchy, there is a need for new and different benchmarks to evaluate them correctly. To this end, this paper presents a method for measuring the parameters of one of the most famous multicore computational models, namely Multi-Bulk Synchronous Parallel (Multi-BSP). This method measures the hardware latency parameters of multicore computers, namely communication latency (g _i ) and synchronization latency (L _i ) for all levels of the cache memory hierarchy in a bottom-up manner. By determining the parameters, the performance of algorithms on multicore architectures can be evaluated as a sequence.     

An online valuation-based sealed winner-bid auction game for resource allocation and pricing in clouds

Cloud computing is able to allocate different resources as virtual machines (VMs) to users, who need only pay for the amount of resources used. Two of the challenges in clouds are resource allocation and pricing in such a way to satisfy both cloud providers and users. Existing allocation and pricing mechanisms cannot guarantee increased profits due to various reasons. A better solution to increase the satisfaction of both parties, which is supported by economic theory, is the employment of auction-based allocation and pricing mechanisms. In these mechanisms, cloud resources and services are awarded based on the highest bids, while winners receive the quality of services expected. However, most existing auction-based mechanisms are inefficient and cannot be used in real clouds due to high computational or communication overhead, the bid function’s time complexity, and/or its inaccurate estimates. In the present paper, a lightweight mechanism is introduced which can be utilized in the real-world application of clouds. The currently proposed mechanism is a winner-bid auction game that seals users’ bids by a multi-criteria valuation-based bid function and sends them to the auctioneer. During scheduling, the auctioneer awards VMs exclusively to users with the highest bids. The presented approach is an online auction whose main aim is to increase the profits of the provider and user from different criteria. While determining the Nash equilibrium, the current study specifies the prices to be paid by users in various cases and proves the truthfulness of the proposed method. Finally, the effectiveness of the presented mechanism is examined through extensive experiments on different simulation scenarios and actual workload data.