Sharrukh Zaman

Bio: Sharrukh Zaman is an academic researcher from Wayne State University. The author has contributed to research in topics: Cloud computing & Combinatorial auction. The author has an hindex of 9, co-authored 11 publications receiving 833 citations.

A Combinatorial Auction-Based Mechanism for Dynamic VM Provisioning and Allocation in Clouds

Abstract: Cloud computing providers provision their resources into different types of virtual machine (VM) instances that are then allocated to the users for specific periods of time. The allocation of VM instances to users is usually determined through fixed-price allocation mechanisms that cannot guarantee an economically efficient allocation and the maximization of cloud provider's revenue. A better alternative would be to use combinatorial auction-based resource allocation mechanisms. This argument is supported by the economic theory; when the auction costs are low, as is the case in the context of cloud computing, auctions are especially efficient over the fixed-price markets because products are matched to customers having the highest valuation. The existing combinatorial auction-based VM allocation mechanisms do not take into account the user's demand when making provisioning decisions, that is, they assume that the VM instances are statically provisioned. We design an auction-based mechanism for dynamic VM provisioning and allocation that takes into account the user demand, when making provisioning decisions. We prove that our mechanism is truthful (i.e., a user maximizes its utility only by bidding its true valuation for the requested bundle of VMs). We evaluate the proposed mechanism by performing extensive simulation experiments using real workload traces. The experiments show that the proposed mechanism yields higher revenue for the cloud provider and improves the utilization of cloud resources.

Combinatorial Auction-Based Allocation of Virtual Machine Instances in Clouds

Abstract: The current cloud computing platforms allocate virtual machine instances to their users through fixed-price allocation mechanisms. We argue that combinatorial auction-based allocation mechanisms are especially efficient over the fixed-price mechanisms since the virtual machine instances are assigned to users having the highest valuation. We formulate the problem of virtual machine allocation in clouds as a combinatorial auction problem and propose two mechanisms to solve it. We perform extensive simulation experiments to compare the two proposed combinatorial auction-based mechanisms with the currently used fixed-price allocation mechanism. Our experiments reveal that the combinatorial auction-based mechanisms can significantly improve the allocation efficiency while generating higher revenue for the cloud providers.

A Distributed Algorithm for the Replica Placement Problem

Abstract: Caching and replication of popular data objects contribute significantly to the reduction of the network bandwidth usage and the overall access time to data. Our focus is to improve the efficiency of object replication within a given distributed replication group. Such a group consists of servers that dedicate certain amount of memory for replicating objects requested by their clients. The content replication problem we are solving is defined as follows: Given the request rates for the objects and the server capacities, find the replica allocation that minimizes the access time over all servers and objects. We design a distributed approximation algorithm that solves this problem and prove that it provides a 2-approximation solution. We also show that the communication and computational complexity of the algorithm is polynomial with respect to the number of servers, the number of objects, and the sum of the capacities of all servers. Finally, we perform simulation experiments to investigate the performance of our algorithm. The experiments show that our algorithm outperforms the best existing distributed algorithm that solves the replica placement problem.

Combinatorial Auction-Based Dynamic VM Provisioning and Allocation in Clouds

Abstract: Efficient Virtual Machine (VM) provisioning and allocation allows the cloud providers to effectively utilize their available resources and obtain higher profits. Existing combinatorial auction-based mechanisms assume that the VM instances are already provisioned, that is they assume static VM provisioning. A better solution would be to take into account the users' demand when provisioning VM instances. We design an auction-based mechanism for dynamic VM provisioning and allocation that takes into account the user demand for VMs when making VM provisioning decisions. We perform extensive simulation experiments using real workload traces and show that the proposed mechanism can improve the utilization, increase the efficiency of allocation, and yield higher revenue for the cloud provider.

Resource Management in Clouds: Survey and Research Challenges

Abstract: Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Abstract: A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon.

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

Abstract: Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.