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Grid computing : techniques and applications
Based on the author's own course, this textbook is designed for a first course on grid computing at the senior undergraduate and first- year graduate levels. Unlike other texts on the subject, this book includes a lecture structure and student programming assignments.
Book
High-performance parallel database processing and grid databases
The latest techniques and principles of parallel and grid database processing The growth in grid databases, coupled with the utility of parallel query processing, presents an important opportunity to understand and utilize high-performance parallel database processing within a major database management system (DBMS). This important new book provides readers with a fundamental understanding of parallelism in data-intensive applications, and demonstrates how to develop faster capabilities to support them. It presents a balanced treatment of the theoretical and practical aspects of high-performance databases to demonstrate how parallel query is executed in a DBMS, including concepts, algorithms, analytical models, and grid transactions. High-Performance Parallel Database Processing and Grid Databases serves as a valuable resource for researchers working in parallel databases and for practitioners interested in building a high-performance database. It is also a much-needed, self-contained textbook for database courses at the advanced undergraduate and graduate levels.
eBook
Clouds Meet Agents: Toward Intelligent Cloud Services
Cloud computing systems provide large-scale infrastructures for high-performance computing that can adapt to user and application needs. Multi-agent systems (MASs) comprise interacting agents capable of intelligent behavior. Integrating these two technologies could enable high-performance, complex systems and intelligent applications, making clouds more flexible and autonomic and providing MASs with a reliable and scalable computing infrastructure on which to execute large-scale applications.
Journal Article
Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models
Cloud computing is a paradigm that provides services through the Internet. The paradigm has been influenced by previously available technologies (for example cluster, peer-to-peer, and grid computing) and has now been adopted by almost all large organizations. Companies such as Google, Amazon, Microsoft and Facebook have made significant investments in cloud computing, and now provide services with high levels of dependability. The efficient and accurate assessment of cloud-based infrastructure is fundamental in guaranteeing both business continuity and uninterrupted public services, as much as is possible. This paper presents an approach for selecting cloud computing infrastructures, in terms of dependability and cost that best suits both company and customer needs. We use stochastic models to calculate dependability-related metrics for different cloud infrastructures. We then use a Multiple-Criteria Decision-Making (MCDM) method to rank the best cloud infrastructures, taking customer service constraints such as reliability, downtime, and cost into consideration. A case study demonstrates the practicability and usefulness of the proposed approach.
Journal Article
Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems
We address a multicriteria non-preemptive energy-aware scheduling problem for computational Grid systems. This work introduces a new formulation of the scheduling problem for multicore heterogeneous computational Grid systems in which the minimization of the energy consumption, along with the makespan metric, is considered. We adopt a two-level model, in which a meta-broker agent (level 1) receives all user tasks and schedules them on the available resources, belonging to different local providers (level 2). The computing capacity and energy consumption of resources are taken from real multi-core processors from the main current vendors. Twenty novel list scheduling methods for the problem are proposed, and a comparative analysis of all of them over a large set of problem instances is presented. Additionally, a scalability study is performed in order to analyze the contribution of the best new bi-objective list scheduling heuristics when the problem dimension grows. We conclude after the experimental analysis that accurate trade-off schedules are computed by using the new proposed methods.
Journal Article
Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions
Data-intensive systems encompass terabytes to petabytes of data. Such systems require massive storage and intensive computational power in order to execute complex queries and generate timely results. Further, the rate at which this data is being generated induces extensive challenges of data storage, linking, and processing. A data-intensive cloud provides an abstraction of high availability, usability, and efficiency to users. However, underlying this abstraction, there are stringent requirements and challenges to facilitate scalable and resourceful services through effective physical infrastructure, smart networking solutions, intelligent software tools, and useful software approaches. This paper analyzes the extensive requirements which exist in data-intensive clouds, describes various challenges related to the paradigm, and assess numerous solutions in meeting these requirements and challenges. It provides a detailed study of the solutions and analyzes their capabilities in meeting emerging needs of widespread applications.
Journal Article
On scheduling transaction in grid computing using cuckoo search-ant colony optimization considering load
Scheduling of transactions in the grid computing system is known to be an NP-hard problem. In order to solve this problem, this paper introduces a hybrid approach named cuckoo search-ant colony optimization. The approach is to dynamically generate an optimal schedule by clustering the resources considering their load so as to complete the transactions within their deadlines as well as utilizing the resources in an efficient way. The approach also balances the load of the system before scheduling the transactions. We use cuckoo search method for making clusters of resources based on their load. We use ant colony optimization for selecting the appropriate and optimal resources. We evaluate the performance of the proposed algorithm with six existing algorithms. The results illustrate that an important advantage of the cuckoo search-ant colony optimization algorithm is its speed of clustering and ability to obtain faster and feasible load balanced schedules.
Journal Article
A novel vehicular task deployment method in hybrid MEC
With the skyrocketing need for low-latency services on the Internet of Vehicles (IoV) and elastic cross-layer resource provisioning, multi-access edge computing (MEC) is considered a high-potent solution, which evolves from cloud and grid computing to meet the above needs in IoV scenarios. Instead of considering single-point and monolithic IoV tasks, in this paper, we consider the IoV applications to be with structural properties and the supporting environment to be with a hybrid cloud-edge architecture. We develop a scheduling method that offloads tasks to the eNode or cloud according to their estimations of latest starting time. Simulative results clearly demonstrate that our method beat existing solutions in terms of average completion time, average waiting time, and in-time completion rate.
Journal Article
Meta-heuristic Approaches for Effective Scheduling in Infrastructure as a Service Cloud: A Systematic Review
Cloud computing involves a large number of shared virtual servers that are accessible from both public and private networks. It has provided scalable and multitenant computing approaches for Infrastructure as a Service, Software as a Service, and Platform as a Service to cloud users on pay-per-use bases. Over the past decades, researchers from different domains such as astronomy, physics, earth science, and bioinformatics have used scientific workflow applications to model many real-world problems in both paralleled and distributed computing environments. However, achieving efficient workflow scheduling is challenging. This is due to the large size of the task set that each workflow application generates. The complex dependencies between these workflows make it difficult to find an optimal solution to workflow scheduling problems within polynomial time. This paper analyzed workflows scheduling problems in cloud and grid computing environment through providing a comprehensive survey based on the state-of-the-art meta-heuristic algorithms. We analyzed the literature from four perspectives, including (i) existing meta-heuristics, (ii) scheduling efficiency, system performance, and execution budget, (iii) scheduling environment and (iv) quality of service performance metrics. Also, we have presented the research gaps and provided future directions for future investigation.
Journal Article