The Contemporary Affirmation of Taxonomy and Recent Literature on Workflow Scheduling and Management in Cloud Computing

The Cloud computing systemspreferred over the traditional forms of computing such as grid computing, utility computing, autonomic computing is attributed forits ease of access to computing, for its QoS preferences, SLA2019;s conformity, security and performance offered with minimal supervision. A cloud workflow schedule when designed efficiently achieves optimalre source sage, balance of workloads, deadline specific execution, cost control according to budget specifications, efficient consumption of energy etc. to meet the performance requirements of today2019; svast scientific and business requirements. The businesses requirements under recent technologies like pervasive computing are motivating the technology of cloud computing for further advancements. In this paper we discuss some of the important literature published on cloud workflow scheduling

Decentralized Resource Scheduling in Grid/Cloud Computing

In the Grid/Cloud environment, applications or services and resources belong to different organizations with different objectives. Entities in the Grid/Cloud are autonomous and self-interested; however, they are willing to share their resources and services to achieve their individual and collective goals. In such open environment, the scheduling decision is a challenge given the decentralized nature of the environment. Each entity has specific requirements and objectives that need to achieve. In this thesis, we review the Grid/Cloud computing technologies, environment characteristics and structure and indicate the challenges within the resource scheduling. We capture the Grid/Cloud scheduling model based on the complete requirement of the environment. We further create a mapping between the Grid/Cloud scheduling problem and the combinatorial allocation problem and propose an adequate economic-based optimization model based on the characteristic and the structure nature of the Grid/Cloud. By adequacy, we mean that a comprehensive view of required properties of the Grid/Cloud is captured. We utilize the captured properties and propose a bidding language that is expressive where entities have the ability to specify any set of preferences in the Grid/Cloud and simple as entities have the ability to express structured preferences directly. We propose a winner determination model and mechanism that utilizes the proposed bidding language and finds a scheduling solution. Our proposed approach integrates concepts and principles of mechanism design and classical scheduling theory. Furthermore, we argue that in such open environment privacy concerns by nature is part of the requirement in the Grid/Cloud. Hence, any scheduling decision within the Grid/Cloud computing environment is to incorporate the feasibility of privacy protection of an entity. Each entity has specific requirements in terms of scheduling and privacy preferences. We analyze the privacy problem in the Grid/Cloud computing environment and propose an economic based model and solution architecture that provides a scheduling solution given privacy concerns in the Grid/Cloud. Finally, as a demonstration of the applicability of the approach, we apply our solution by integrating with Globus toolkit (a well adopted tool to enable Grid/Cloud computing environment). We also, created simulation experimental results to capture the economic and time efficiency of the proposed solution

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Mechanism design for distributed task and resource allocation among self-interested agents in virtual organizations

The aggregate power of all resources on the Internet is enormous. The Internet can be viewed as a massive virtual organization that holds tremendous amounts of information and resources with different ownerships. However, little is known about how to run this organization efficiently. This dissertation studies the problems of distributed task and resource allocation among self-interested agents in virtual organizations. The developed solutions are not allocation mechanisms that can be imposed by a centralized designer, but decentralized interaction mechanisms that provide incentives to self-interested agents to behave cooperatively. These mechanisms also take computational tractability into consideration due to the inherent complexity of distributed task and resource allocation problems. Targeted allocation mechanisms can achieve global task allocation efficiency in a virtual organization and establish stable resource-sharing communities based on agentsâÃÂàown decisions about whether or not to behave cooperatively. This high level goal requires solving the following problems: synthetic task allocation, decentralized coalition formation and automated multiparty negotiation. For synthetic task allocation, in which each task needs to be accomplished by a virtual team composed of self-interested agents from different real organizations, my approach is to formalize the synthetic task allocation problem as an algorithmic mechanism design optimization problem. I have developed two approximation mechanisms that I prove are incentive compatible for a synthetic task allocation problem. This dissertation also develops a decentralized coalition formation mechanism, which is based on explicit negotiation among self-interested agents. Each agent makes its own decisions about whether or not to join a candidate coalition. The resulting coalitions are stable in the core in terms of coalition rationality. I have applied this mechanism to form resource sharing coalitions in computational grids and buyer coalitions in electronic markets. The developed negotiation mechanism in the decentralized coalition formation mechanism realizes automated multilateral negotiation among self-interested agents who have symmetric authority (i.e., no mediator exists and agents are peers). In combination, the decentralized allocation mechanisms presented in this dissertation lay a foundation for realizing automated resource management in open and scalable virtual organizations